diff --git a/.gitignore b/.gitignore index 3423c416a..9c124bdd2 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,8 @@ data/manifest.json data/docs_selected.jsonl .mypy_cache/ .venv -logs/ \ No newline at end of file +logs/ +results.tsv +run.log +notes.md +autoresearch-ref/ \ No newline at end of file diff --git a/autoresearch-ref b/autoresearch-ref new file mode 160000 index 000000000..32a1460f6 --- /dev/null +++ b/autoresearch-ref @@ -0,0 +1 @@ +Subproject commit 32a1460f626e28479d427c033ee485bf5f86875a diff --git a/modal_train.py b/modal_train.py new file mode 100644 index 000000000..36c3d678f --- /dev/null +++ b/modal_train.py @@ -0,0 +1,85 @@ +# modal launcher for parameter-golf autoresearch. +# +# usage: +# modal run modal_train.py +# +# custom env vars: +# modal run modal_train.py --env "ITERATIONS=5000,VAL_LOSS_EVERY=200" + +import modal + +app = modal.App("parameter-golf") + +# base image with deps + cached data + local train_gpt.py mounted +image = ( + modal.Image.debian_slim(python_version="3.11") + .pip_install( + "numpy", + "tqdm", + "torch==2.10", + "huggingface-hub", + "setuptools", + "typing-extensions==4.15.0", + "datasets", + "tiktoken", + "sentencepiece", + "zstandard", + ) + .apt_install("git") + .run_commands( + "git clone https://github.com/openai/parameter-golf.git /opt/parameter-golf", + "cd /opt/parameter-golf && python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 80", + ) + # mount local train_gpt.py so agent edits get picked up each run + .add_local_file("train_gpt.py", "/opt/parameter-golf/train_gpt.py") +) + + +@app.function( + image=image, + gpu="H100:8", + timeout=3600, +) +def train(env_overrides: dict[str, str] | None = None): + """8xh100 training""" + import os + import subprocess + + # try to install flash-attn at runtime (may timeout) + subprocess.run( + ["pip", "install", "flash-attn", "--no-build-isolation", "-q"], + capture_output=True, timeout=120, + ) + + os.chdir("/opt/parameter-golf") + + env = os.environ.copy() + env.update({ + "DATA_PATH": "./data/datasets/fineweb10B_sp1024", + "TOKENIZER_PATH": "./data/tokenizers/fineweb_1024_bpe.model", + "VOCAB_SIZE": "1024", + "RUN_ID": "modal_run", + }) + if env_overrides: + env.update(env_overrides) + + result = subprocess.run( + ["torchrun", "--standalone", "--nproc_per_node=8", "train_gpt.py"], + env=env, + ) + return result.returncode + + +@app.local_entrypoint() +def main( + env: str = "", +): + env_overrides = {} + if env: + for e in env.split(","): + k, v = e.split("=", 1) + env_overrides[k] = v + + print("launching 8xh100 training...") + rc = train.remote(env_overrides or None) + print(f"training finished with exit code: {rc}") diff --git a/program.md b/program.md new file mode 100644 index 000000000..8b44f96ab --- /dev/null +++ b/program.md @@ -0,0 +1,150 @@ +# Autoresearch for Parameter Golf + +Autonomous AI research agent for the OpenAI Parameter Golf challenge. + +## 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. `mar18`). The branch `autoresearch/` must not already exist. +2. **Create the branch**: `git checkout -b autoresearch/` from current main. +3. **Read the in-scope files**: + - `README.md` — Challenge rules + - `train_gpt.py` — The file you modify. Model, optimizer, training loop. +4. **Verify data exists**: Check that `./data/datasets/fineweb10B_sp1024/` and `./data/tokenizers/` exist. If not, tell the human to run `python3 data/cached_challenge_fineweb.py --variant sp1024 --train-shards 10` +5. **Initialize results.tsv**: Create with just the header row. +6. **Confirm and go**. + +Once you get confirmation, kick off the experimentation. + +## Experimentation + +Each experiment runs on 8xH100 via Modal. Launch it as: + +``` +modal run modal_train.py > run.log 2>&1 +``` + +The Modal script mounts your local `train_gpt.py`, so your edits are picked up each run automatically. + +**What you CAN do:** +- Modify `train_gpt.py` — everything is fair game: architecture, optimizer, hyperparameters, batch size, model shape, etc. + +**What you CANNOT do:** +- **NEVER push to GitHub. NEVER run `git push`. All work stays local.** +- Break the val_bpb evaluation correctness +- Install new packages beyond requirements.txt +- Exceed the 16MB artifact limit (code + int8 zlib-compressed model < 16,000,000 bytes) + +**The goal: get the lowest val_bpb.** Current SOTA is 1.2244. The artifact must stay under 16MB. + +**The first run**: Always establish the baseline first — run train_gpt.py as-is. + +## Output Format + +Extract results with: `grep "val_bpb\|final_int8_zlib_roundtrip\|model_params" run.log` + +If grep is empty, the run crashed or Modal failed. Run `tail -n 50 run.log` to read the error. + +## Reasoning + +Before EVERY experiment, you must think and write a reasoning block. No blind changes. + +``` +=== REASONING === +Hypothesis: [what you expect to happen and why] +Evidence: [what prior results, scaling laws, or theory supports this] +Risk: [what could go wrong — OOM, regression, artifact too large, etc.] +=== +``` + +After EVERY experiment, you must write an analysis block: + +``` +=== ANALYSIS === +Result: val_bpb=X.XXXX artifact=X.XMB (keep/discard/crash) +vs Expected: [better/worse/same than hypothesis predicted] +Why: [your best explanation for the result] +Lesson: [what this tells you about future experiments] +=== +``` + +These blocks are your research log. They compound — later experiments should reference lessons from earlier ones. If you find yourself repeating the same lesson, you're not learning from your results. + +## Logging + +Log every run to `results.tsv` (tab-separated). Header and 6 columns: + +``` +commit val_bpb artifact_mb status reasoning description +``` + +1. Git commit hash (short, 7 chars) +2. val_bpb (use 0.000000 for crashes) +3. Artifact size in MB (use 0.0 for crashes) +4. Status: `keep`, `discard`, or `crash` +5. One-line reasoning (the hypothesis, condensed) +6. Short description of the change + +Do not commit results.tsv — leave it untracked. + +Additionally, maintain a `notes.md` file (also untracked). This is your brain — your long-term memory that survives context compression. You MUST read it at the start of every loop iteration and update it after every experiment. Structure it as: + +```markdown +## Best Known Config +[current best val_bpb, commit hash, what config achieved it] + +## Dead Ends (do not revisit) +- [direction] — [why it failed] — [experiments that proved it] + +## What Works +- [direction] — [magnitude of improvement] — [experiments that proved it] + +## Ideas Queue (ranked by expected value) +1. [next thing to try and why] +2. ... + +## Experiment Log +### Experiment N: [description] +[paste your REASONING and ANALYSIS blocks here] +``` + +This file is what drives your decisions. If you're not reading it, you're flying blind. + +## Backtracking + +Not every path leads somewhere. Watch for these signals and respond: + +- **3+ consecutive discards in the same direction**: That direction is a dead end. Abandon it, note it in notes.md, move on to something completely different. +- **val_bpb regressed after a series of "keep" commits**: The accumulated changes interacted badly. Backtrack: + 1. Find the best commit hash from results.tsv + 2. `git reset --hard ` + 3. Log a row with `status=backtrack` in results.tsv + 4. Note in notes.md what went wrong and why + 5. Try a different approach from that known-good state +- **Stuck in a plateau (5+ experiments with <0.001 improvement)**: Step back. Re-read train_gpt.py from scratch. Look for something structural you've been overlooking. Consider a radical change (different architecture, different optimizer, etc.) + +## The Experiment Loop + +LOOP FOREVER: + +1. **Review (MANDATORY)**: You MUST read `results.tsv` and `notes.md` before every experiment. These files are your memory — they persist even if your context gets compressed. Run `cat results.tsv` and `cat notes.md` and use them to decide what to do next. Identify: current best val_bpb, what's been tried, what worked, what failed, what's in the ideas queue. +2. **Reason**: Write the REASONING block. No skipping this. Your hypothesis MUST reference specific lessons or results from the files you just read. +3. **Implement**: Modify `train_gpt.py`. +4. **Commit**: `git commit` the change. +5. **Run**: `modal run modal_train.py > run.log 2>&1` (redirect everything — do NOT flood context) +6. **Extract**: `grep "val_bpb\|final_int8_zlib_roundtrip\|model_params" run.log` +7. **Analyze**: Write the ANALYSIS block. No skipping this either. +8. **Log**: Record in results.tsv and append to notes.md. +9. **Decide**: + - val_bpb improved AND artifact < 16MB → **keep** the commit + - val_bpb worse or artifact too large → **discard**: `git reset --hard HEAD~1` + - crash → attempt trivial fix or discard and move on +10. **Check for backtracking signals** (see above). +11. **Loop**. + +**Crashes**: If it's a trivial fix (typo, missing import), fix and retry. If fundamentally broken, discard and move on. + +**Timeout**: If a run exceeds 15 minutes, kill it and treat as failure. + +**NEVER STOP**: Do not pause to ask the human if you should continue. The human might be asleep. You are autonomous. If you run out of ideas, re-read the code, re-analyze results.tsv for patterns, try combining near-misses, try radical changes. Consult notes.md for your ideas queue. The loop runs until the human interrupts you. diff --git a/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/README.md b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/README.md new file mode 100644 index 000000000..5a143ca78 --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/README.md @@ -0,0 +1,75 @@ +# Record: Order-Adaptive 9-gram Backoff + Distributed Prefill — val_bpb 0.4405 (3-seed mean) + +## Results + +| Seed | val_bpb | Artifact | Eval time | +|------|---------|----------|-----------| +| 42 | 0.4429 | 14,899,126 bytes | ~586s | +| 1337 | 0.4381 | 14,740,261 bytes | ~588s | +| 2024 | 0.4405 | 15,101,371 bytes | ~502s | +| **Mean** | **0.4405** | | | +| **Std** | **0.0024** | | | + +- Artifact: < 16,000,000 bytes (all seeds) +- Train: 600s on 8xH100 SXM +- Eval: < 600s (all seeds) + +## Method + +11-layer transformer (512d, 8/8 full MHA, XSA-all, LeakyReLU(0.5)², 3.5x MLP). +Order-adaptive entropy-gated 9-gram backoff cache with per-order entropy thresholds +and distributed cache prefill. Score-first, backward-looking, deterministic. + +### Architecture +- 11L, 512d, full MHA 8/8, MLP 3.5x (1792), LeakyReLU(0.5)² +- XSA on all 11 layers, partial RoPE 16/64 +- BigramHash(4096, 128d), SmearGate, VE128 on layers 9-10 +- Tied embeddings, logit softcap 30 +- EMA(0.997) + Tight SWA, Parallel Muon optimizer +- int5 per-row quantization + zstd-22 compression +- Early QAT (threshold 0.5) + +### Eval-time N-gram Cache +- Multi-order backoff, orders 2-9, 4M hash buckets per order +- Dual hash tables per order: context counts + full (context+target) counts +- Per-order entropy thresholds: {9: 2.6, 8: 2.8, 7: 3.0, 6: 3.2, 5: 3.5, 4: 3.8, 3: 4.2, 2: 4.5} +- Entropy-adaptive alpha: 0.05 + 0.55 * sigmoid(2.0 * (H - threshold)) +- Alpha range [0.05, 0.60]: low entropy = trust neural, high entropy = trust n-gram +- min_count=2, score-first (lookup then update per window) +- Distributed prefill: each rank pre-warms cache with all preceding token positions +- Sliding window eval with stride=32 + +### Key Insight +Distributed cache prefill is critical — without it, ranks 1-7 start with cold caches, +losing ~60% of n-gram effectiveness. Prefill makes distributed eval equivalent to +single-GPU sequential eval. Combined with 9-gram orders (capturing longer repeated +phrases) and per-order entropy gating (trusting higher orders at lower uncertainty), +this produces a -0.69 BPB gain over neural-only sliding window eval. + +## Legality + +- **Score-first n-gram cache**: Each window batch: (1) lookup cache for predictions, + (2) compute blended loss, (3) update cache with window tokens. Cache only uses + backward-looking tokens that have already been scored. No future data access. +- **Alpha depends on model entropy only**: The mixing weight uses the neural model's + output entropy, not the target token. No oracle/hindsight selection. +- **No TTT**: Test-time training is disabled (TTT_EPOCHS=0). +- **No GPTQ at eval time**: Quantization completes within the training budget. +- **No reordering**: Evaluation set processed in original sequential order. +- **Deterministic**: Given the same seed, produces identical results. + +## Acknowledgments + +Huge thanks to the incredible community: + +- @abaybektursun (PR #549) — base architecture + Legal TTT + Parallel Muon +- @deanbrr (PR #659, #779) — invented the n-gram eval cache, BackoffNgramMixer +- @Asukabot0 (PR #715, #727) — entropy-adaptive alpha formula +- @Robby955 (PR #796) — distributed cache prefill technique +- @hypery11 (PR #788, #795, #825) — order-adaptive entropy gating, 9-gram extension +- @newjordan (PR #753, #782) — multi-order backoff, per-order alpha scaling +- @travispchen (PR #798) — per-order entropy thresholds +- @gowtham0992 (PR #606) — int5 + QAT +- @signalrush (PR #414) — EMA training recipe +- @thwu1 (PR #180) — mixed quantization, BigramHash, SmearGate +- @raahilshah (PR #162) — int6 quantization foundation diff --git a/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/submission.json b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/submission.json new file mode 100644 index 000000000..d7163b1cf --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/submission.json @@ -0,0 +1,11 @@ +{ + "author": "sofiabod", + "github_id": "sofiabod", + "name": "Order-Adaptive 9-gram Backoff + Distributed Prefill", + "blurb": "9-gram backoff with per-order entropy thresholds and distributed cache prefill on 11L MHA transformer with int5 quantization", + "date": "2026-03-26", + "val_loss": 0.7437, + "val_bpb": 0.4405, + "bytes_total": 14899126, + "bytes_code": 86210 +} diff --git a/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_gpt.py b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_gpt.py new file mode 100644 index 000000000..4066f04c3 --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_gpt.py @@ -0,0 +1,1762 @@ +from __future__ import annotations +import copy +import glob +import io +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +_HAS_FA3 = False +try: + from flash_attn_interface import flash_attn_func as flash_attn_3_func + _HAS_FA3 = True +except ImportError: + try: + from flash_attn import flash_attn_func as flash_attn_3_func + _HAS_FA3 = True + except ImportError: + pass +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 1337)) + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 4000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 500)) + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 8)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.5)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + muon_beta2 = float(os.environ.get("MUON_BETA2", 0.95)) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) # tighter: collect more recent checkpoints + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 4096)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) # XSA on all layers (PR #825) + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.5)) + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: + a, b, c = (3.4445, -4.7750, 2.0315) + X = G.bfloat16() + X /= X.norm() + eps + transposed = G.size(0) > G.size(1) + if transposed: + X = X.T + for _ in range(steps): + A = X @ X.T + B = b * A + c * A @ A + X = a * X + B @ X + return X.T if transposed else X +class Muon(torch.optim.Optimizer): + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + distributed = dist.is_available() and dist.is_initialized() + world_size = dist.get_world_size() if distributed else 1 + rank = dist.get_rank() if distributed else 0 + for group in self.param_groups: + params = group["params"] + if not params: + continue + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + total_params = sum(int(p.numel()) for p in params) + updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16) + curr = 0 + for i, p in enumerate(params): + if i % world_size == rank and p.grad is not None: + g = p.grad + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + buf = state["momentum_buffer"] + buf.mul_(momentum).add_(g) + if nesterov: + g = g.add(buf, alpha=momentum) + g = zeropower_via_newtonschulz5(g, steps=backend_steps) + g *= max(1, g.size(0) / g.size(1)) ** 0.5 + updates_flat[curr : curr + p.numel()] = g.reshape(-1) + curr += p.numel() + if distributed: + dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) + wd = group.get("weight_decay", 0.0) + curr = 0 + for p in params: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) + p.add_(g, alpha=-lr) + curr += p.numel() + return loss +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("▁"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") + return tokens[: usable + 1] +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out +def load_data_shard(file: Path) -> Tensor: + header_bytes = 256 * np.dtype(" None: + self.file_idx = (self.file_idx + 1) % len(self.files) + self.tokens = load_data_shard(self.files[self.file_idx]) + self.pos = 0 + def take(self, n: int) -> Tensor: + chunks: list[Tensor] = [] + remaining = n + while remaining > 0: + avail = self.tokens.numel() - self.pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self.tokens[self.pos : self.pos + k]) + self.pos += k + remaining -= k + return chunks[0] if len(chunks) == 1 else torch.cat(chunks) +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + self.rank = rank + self.world_size = world_size + self.device = device + self.stream = TokenStream(pattern) + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + per_rank_span = local_tokens + 1 + chunk = self.stream.take(per_rank_span * self.world_size) + start = self.rank * per_rank_span + local = chunk[start : start + per_rank_span].to(dtype=torch.int64) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 15.0).clamp_min(1.0 / 15.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -15, 15) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + kv_dim = self.num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.proj._zero_init = True + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape (no repeat_interleave). + y: [B, T, H, D], v: [B, T, Hkv, D]. H must be divisible by Hkv.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) # [B, T, Hkv, group, D] + vn = F.normalize(v, dim=-1).unsqueeze(-2) # [B, T, Hkv, 1, D] — broadcast ready + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor: + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + if _HAS_FA3: + y = flash_attn_3_func(q, k, v, causal=True) + else: + # fallback to pytorch SDPA (q,k,v need to be [bsz, heads, seq, dim]) + q_t = q.transpose(1, 2) + k_t = k.transpose(1, 2) + v_t = v.transpose(1, 2) + y = F.scaled_dot_product_attention(q_t, k_t, v_t, is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads)) + y = y.transpose(1, 2) + if self.use_xsa: + y = self._xsa_efficient(y, v) + y = y.reshape(bsz, seqlen, dim) + return self.proj(y) +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev +class BigramHashEmbedding(nn.Module): + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + def bigram_hash(self, tokens: Tensor) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., 0] = mod + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(self.bigram_hash(token_ids)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers. + Each table maps vocab tokens to a low-dim embedding, projected to model_dim.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + hidden = int(mlp_mult * dim) + self.fc = CastedLinear(dim, hidden, bias=False) + self.proj = CastedLinear(hidden, dim, bias=False) + self.proj._zero_init = True + def forward(self, x: Tensor) -> Tensor: + # leaky_relu(0.5)^2 preserves negative gradient flow vs relu^2 + x = F.leaky_relu(self.fc(x), negative_slope=0.5) + return self.proj(x.square()) +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = MLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + def forward(self, x: Tensor, x0: Tensor, v_embed: Tensor | None = None) -> Tensor: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) # kv_dim for value projection + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + layer_idx=i, + ln_scale=ln_scale, + dtg=dtg, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() # keep empty for compat + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + num_layers = len(self.blocks) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + if ".proj." in name or name.endswith(".proj"): + with torch.no_grad(): + module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + """Get value embedding for a specific layer using shared table + per-layer scale.""" + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 32, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + """Sliding window evaluation: each token scored with maximum context.""" + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + base_model.eval() + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = token_count.item() / byte_count.item() + base_model.train() + return val_loss, bits_per_token * tokens_per_byte +class NgramCache: + """n-gram cache matching PR #753/#769/#779: two flat uint32 arrays per order + (ctx_counts, full_counts). hash context and full n-gram (context+target) separately.""" + PRIMES = [np.uint64(p) for p in [36313, 27191, 51647, 81929, 131071, 174763, 233017, 299993, 350377]] + + def __init__(self, max_order: int = 7, min_order: int = 2, num_buckets: int = 4194304, + min_count: int = 2, **kwargs): + self.max_order = max_order + self.min_order = min_order + self.num_buckets = num_buckets + self.min_count = min_count + self.mask = np.uint64(num_buckets - 1) + self.num_orders = max_order - min_order + 1 + # ~32MB per order (4M * 4 bytes * 2 arrays) = ~192MB for 6 orders + self.ctx_counts = [np.zeros(num_buckets, dtype=np.uint32) for _ in range(self.num_orders)] + self.full_counts = [np.zeros(num_buckets, dtype=np.uint32) for _ in range(self.num_orders)] + + def lookup(self, val_np: np.ndarray, start: int, end: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """score positions [start, end). returns (p_ngram, has_match, matched_order).""" + seg_len = end - start + p_ngram = np.zeros(seg_len, dtype=np.float64) + has_match = np.zeros(seg_len, dtype=np.bool_) + matched_order = np.zeros(seg_len, dtype=np.int32) + mask = self.mask + primes = self.PRIMES + # backoff: highest order first + for oi in range(self.num_orders - 1, -1, -1): + order = self.min_order + oi + cw = order - 1 + first_valid = max(cw, start) - start + n_pos = seg_len - first_valid + if n_pos <= 0: + continue + abs_s = start + first_valid + ctx_hash = np.zeros(n_pos, dtype=np.uint64) + for k in range(cw): + t = val_np[abs_s - cw + k:abs_s - cw + k + n_pos].astype(np.uint64) + ctx_hash ^= t * np.uint64(primes[k]) + ctx_key = (ctx_hash & mask).astype(np.int64) + targets = val_np[abs_s + 1:abs_s + 1 + n_pos].astype(np.uint64) + full_key = ((ctx_hash ^ (targets * np.uint64(primes[cw]))) & mask).astype(np.int64) + ctx_c = self.ctx_counts[oi][ctx_key] + full_c = self.full_counts[oi][full_key] + valid = (ctx_c >= self.min_count) & (full_c > 0) & ~has_match[first_valid:first_valid + n_pos] + if valid.any(): + idx = np.nonzero(valid)[0] + p_ngram[first_valid + idx] = np.minimum(full_c[idx], ctx_c[idx]).astype(np.float64) / ctx_c[idx].astype(np.float64) + has_match[first_valid + idx] = True + matched_order[first_valid + idx] = order + return p_ngram, has_match, matched_order + + def update(self, val_np: np.ndarray, start: int, end: int) -> None: + """update cache with tokens from [start, end).""" + seg_len = end - start + mask = self.mask + primes = self.PRIMES + for oi in range(self.num_orders): + order = self.min_order + oi + cw = order - 1 + first_valid = max(cw, start) - start + n_pos = seg_len - first_valid + if n_pos <= 0: + continue + abs_s = start + first_valid + ctx_hash = np.zeros(n_pos, dtype=np.uint64) + for k in range(cw): + t = val_np[abs_s - cw + k:abs_s - cw + k + n_pos].astype(np.uint64) + ctx_hash ^= t * np.uint64(primes[k]) + ctx_key = (ctx_hash & mask).astype(np.int64) + targets = val_np[abs_s + 1:abs_s + 1 + n_pos].astype(np.uint64) + full_key = ((ctx_hash ^ (targets * np.uint64(primes[cw]))) & mask).astype(np.int64) + np.add.at(self.ctx_counts[oi], ctx_key, 1) + np.add.at(self.full_counts[oi], full_key, 1) + + +def eval_val_ngram( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int, + stride: int, + batch_seqs: int = 32, + ngram_order: int = 7, + ngram_min_order: int = 2, + ngram_buckets: int = 4194304, + ngram_min_count: int = 2, + fixed_alpha: float = 0.2, + ent_base: float = 0.05, + ent_range: float = 0.55, + ent_scale: float = 2.0, + ent_thresh: float = 4.0, + log_fn=None, +) -> tuple[float, float]: + """sliding window eval with n-gram cache, matching PR #753/#769/#779. + score-first: for each window, compute neural logits, lookup cache, mix, then update.""" + total_tokens = val_tokens.numel() - 1 + seq_len = eval_seq_len + vocab_size = args.vocab_size + val_np = val_tokens[:total_tokens + 1].numpy() + adaptive = ent_range > 0 + + # distribute windows across ranks + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + + model.eval() + compiled_logits = torch.compile(model.forward_logits, dynamic=False, fullgraph=True) + cache = NgramCache(max_order=ngram_order, min_order=ngram_min_order, + num_buckets=ngram_buckets, min_count=ngram_min_count) + + # prefill: pre-warm cache with all tokens before this rank's first window (PR #796) + # this makes distributed eval equivalent to single-GPU sequential + if my_windows: + prefill_end = my_windows[0] + if prefill_end > 0: + chunk_sz = 65536 + for pf_start in range(0, prefill_end, chunk_sz): + pf_end = min(pf_start + chunk_sz, prefill_end) + cache.update(val_np, pf_start, pf_end) + if log_fn: + log_fn(f"ngram_prefill: warmed cache with {prefill_end} tokens for rank {rank}") + + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + loss_sum_neural = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + ngram_hits = 0 + ngram_total = 0 + base_bytes_cpu = base_bytes_lut.cpu() + has_space_cpu = has_leading_space_lut.cpu() + is_boundary_cpu = is_boundary_token_lut.cpu() + + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + logits_f = logits.float() + probs_all = torch.softmax(logits_f, dim=-1) + log_probs_all = torch.log_softmax(logits_f, dim=-1) + + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + seg_len = wlen - s + abs_start = ws + s + abs_end = ws + wlen + + # neural prob of target + seg_targets = y_batch[i, s:wlen] + model_p = probs_all[i, s:wlen].gather(1, seg_targets.unsqueeze(1)).squeeze(1).cpu().numpy().astype(np.float64) + seg_nll_neural = F.cross_entropy(logits_f[i, s:wlen], seg_targets, reduction='none').cpu().numpy().astype(np.float64) + + # n-gram: lookup THEN update (score-first) + p_ngram, has_match, matched_order = cache.lookup(val_np, abs_start, abs_end) + cache.update(val_np, abs_start, abs_end) + + # per-order entropy thresholds (PR #825) + ent_centers = {7: 3.0, 6: 3.2, 5: 3.5, 4: 3.8, 3: 4.2, 2: 4.5, 8: 2.8, 9: 2.6} + if adaptive: + seg_ent = (-(probs_all[i, s:wlen] * log_probs_all[i, s:wlen]).sum(dim=-1)).cpu().numpy() + # per-position alpha based on matched order's entropy center + alpha = np.full(seg_len, fixed_alpha, dtype=np.float64) + for pos_idx in range(seg_len): + if has_match[pos_idx]: + order = int(matched_order[pos_idx]) + center = ent_centers.get(order, ent_thresh) + sig = 1.0 / (1.0 + np.exp(-ent_scale * (seg_ent[pos_idx] - center))) + alpha[pos_idx] = ent_base + ent_range * sig + else: + alpha = np.full(seg_len, fixed_alpha, dtype=np.float64) + + # mix + blended_p = model_p.copy() + if has_match.any(): + m = has_match + blended_p[m] = (1.0 - alpha[m]) * model_p[m] + alpha[m] * p_ngram[m] + blended_p = np.maximum(blended_p, 1e-30) + seg_nll = -np.log(blended_p) + + loss_sum += float(seg_nll.sum()) + loss_sum_neural += float(seg_nll_neural.sum()) + token_count += float(seg_len) + ngram_hits += int(has_match.sum()) + ngram_total += seg_len + + # bytes + tgt_ids = seg_targets.cpu() + prev_ids = x_batch[i, s:wlen].cpu() + tb = base_bytes_cpu[tgt_ids].to(torch.float64) + tb += (has_space_cpu[tgt_ids] & ~is_boundary_cpu[prev_ids]).to(torch.float64) + byte_count += float(tb.sum()) + + if dist.is_available() and dist.is_initialized(): + for t in [loss_sum, loss_sum_neural, token_count, byte_count]: + dist.all_reduce(t, op=dist.ReduceOp.SUM) + + val_loss = (loss_sum / token_count).item() + val_loss_neural = (loss_sum_neural / token_count).item() + bpb = (val_loss / math.log(2.0)) * (token_count.item() / byte_count.item()) + bpb_neural = (val_loss_neural / math.log(2.0)) * (token_count.item() / byte_count.item()) + hit_rate = ngram_hits / max(ngram_total, 1) * 100 + if log_fn: + log_fn(f"neural_only_sw val_loss:{val_loss_neural:.4f} val_bpb:{bpb_neural:.4f}") + log_fn(f"ngram_hit_rate:{hit_rate:.1f}% ({ngram_hits}/{ngram_total})") + model.train() + return val_loss, bpb + + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" +def quantize_int6_per_row(t: Tensor, clip_range: int = 15) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" + continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + continue + if cat in int6_cats and t.ndim >= 1: + q, s = quantize_int6_per_row(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out +def main() -> None: + global zeropower_via_newtonschulz5 + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + if 8 % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral") + grad_accum_steps = 8 // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False) + enable_flash_sdp(True) + enable_mem_efficient_sdp(False) + enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True) -> None: + if not master_process: + return + if console: + print(msg) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0( + subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, + console=False, + ) + log0("=" * 100, console=False) + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError( + f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}" + ) + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, + num_layers=args.num_layers, + model_dim=args.model_dim, + num_heads=args.num_heads, + num_kv_heads=args.num_kv_heads, + mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, + tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, + rope_base=args.rope_base, + qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, + mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, + ln_scale=args.ln_scale, + dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, + ve_dim=args.ve_dim, + ve_layers=args.ve_layers, + ).to(device).bfloat16() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model + block_named_params = list(base_model.blocks.named_parameters()) + matrix_params = [ + p + for name, p in block_named_params + if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.mtp_num_heads > 0: + matrix_params.extend([p for p in base_model.mtp_heads.parameters() if p.ndim == 2]) + scalar_params = [ + p + for name, p in block_named_params + if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) + ] + if base_model.skip_weights.numel() > 0: + scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + matrix_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.ve_shared.proj is not None: + matrix_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: + scalar_params.append(s) + optimizer_tok = torch.optim.AdamW( + tok_params, + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + weight_decay=args.muon_wd, + ) + for group in optimizer_muon.param_groups: + group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW( + [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + optimizers.insert(1, optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") + log0( + f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " + f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " + f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" + ) + log0( + f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} " + f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} " + f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}" + ) + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + if distributed: + model.require_backward_grad_sync = True + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_decay = 0.997 + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + ) + log0( + f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0( + f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms " + f"step:{step}/{args.iterations}" + ) + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + if distributed: + model.require_backward_grad_sync = micro_step == grad_accum_steps - 1 + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + for group in optimizer_muon.param_groups: + group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + for opt in optimizers: + opt.step() + zero_grad_all() + # EMA update + with torch.no_grad(): + for name, t in base_model.state_dict().items(): + ema_state[name].mul_(ema_decay).add_(t.detach().float(), alpha=1.0 - ema_decay) + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu() + swa_count += 1 + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + # Apply EMA weights (better than SWA alone per PR#401) + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + # skip diagnostic eval to save eval-time budget + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: + log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"}) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) if _COMPRESSOR == "zstd" else zlib.compress(quant_raw, 9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") + log0(f"Total submission size int6+{_COMPRESSOR}: {quant_file_bytes + code_bytes} bytes") + log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() + with open("final_model.int6.ptz", "rb") as f: + quant_blob_disk = f.read() + quant_state = torch.load( + io.BytesIO(zstandard.ZstdDecompressor().decompress(quant_blob_disk) if _COMPRESSOR == "zstd" else zlib.decompress(quant_blob_disk)), + map_location="cpu", + ) + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, # must match training model + rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + # eval_model is used directly by n-gram eval (which compiles internally) + + # TTT: preeval (bulk train then score) or legal (score-first, chunk by chunk) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", 0)) + ttt_lr = float(os.environ.get("TTT_LR", 0.0005)) + ttt_mode = os.environ.get("TTT_MODE", "preeval") # "preeval" or "legal" + if ttt_epochs > 0 and ttt_mode == "preeval": + torch.cuda.synchronize() + t_ttt = time.perf_counter() + log0(f"ttt: starting {ttt_epochs} epochs, lr={ttt_lr}, cosine+perlayer") + # per-layer LR groups: 3x for MLP output projections, 0.5x for MLP input + proj_params, fc_params, other_params = [], [], [] + for name, p in eval_model.named_parameters(): + p.requires_grad_(True) + if "mlp.proj" in name: + proj_params.append(p) + elif "mlp.fc" in name: + fc_params.append(p) + else: + other_params.append(p) + ttt_opt = torch.optim.AdamW([ + {"params": proj_params, "lr": ttt_lr * 3.0}, + {"params": fc_params, "lr": ttt_lr * 0.5}, + {"params": other_params, "lr": ttt_lr}, + ], weight_decay=0.0) + total_val = val_tokens.numel() - 1 + ttt_batch = 32 + rank_tokens = total_val // world_size + rank_start = rank * rank_tokens + rank_end = rank_start + rank_tokens + steps_per_epoch = max(1, (rank_end - rank_start - args.train_seq_len) // (ttt_batch * args.train_seq_len)) + total_steps = ttt_epochs * steps_per_epoch + global_step = 0 + eval_model.train() + for ep in range(ttt_epochs): + ep_loss, ep_steps = 0.0, 0 + for bs in range(rank_start, rank_end - args.train_seq_len, ttt_batch * args.train_seq_len): + be = min(bs + ttt_batch * args.train_seq_len + 1, rank_end + 1) + local = val_tokens[bs:be].to(device=device, dtype=torch.int64) + n = (local.numel() - 1) // args.train_seq_len + if n == 0: + continue + x = local[:n * args.train_seq_len].reshape(n, args.train_seq_len) + y = local[1:n * args.train_seq_len + 1].reshape(n, args.train_seq_len) + # cosine LR schedule + progress = global_step / max(total_steps, 1) + cos_mul = 0.5 * (1.0 + math.cos(math.pi * progress)) + for g in ttt_opt.param_groups: + g["lr"] = g.get("initial_lr", g["lr"]) * cos_mul + if global_step == 0: + for g in ttt_opt.param_groups: + g["initial_lr"] = g["lr"] + ttt_opt.zero_grad() + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = eval_model(x, y) + loss.backward() + # sync gradients across ranks + if distributed: + for p in eval_model.parameters(): + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(eval_model.parameters(), 1.0) + ttt_opt.step() + ep_loss += loss.item() + ep_steps += 1 + global_step += 1 + if master_process and (ep + 1) % 5 == 0: + log0(f"ttt_epoch:{ep + 1}/{ttt_epochs} avg_loss:{ep_loss / max(ep_steps, 1):.4f}") + del ttt_opt + torch.cuda.empty_cache() + torch.cuda.synchronize() + log0(f"ttt: completed in {1000.0 * (time.perf_counter() - t_ttt):.0f}ms") + + # legal score-first TTT: score chunk, then train on scored tokens + if ttt_epochs > 0 and ttt_mode == "legal": + torch.cuda.synchronize(); t_ttt = time.perf_counter() + sl = effective_eval_seq_len; st = args.eval_stride if args.eval_stride > 0 else sl; scl = min(st, sl) + for p in eval_model.parameters(): p.requires_grad_(False) + nb = len(eval_model.blocks) if hasattr(eval_model, 'blocks') else 0 + tp = [] + for nm, p in eval_model.named_parameters(): + bi = next((i for i in range(nb) if f"blocks.{i}." in nm), -1) + if bi >= nb - 2 or any(k in nm for k in ("norm","scale","q_gain","lm_head","tok_emb","smear","bigram")): + p.requires_grad_(True); tp.append(p) + to = torch.optim.AdamW(tp, lr=ttt_lr * 0.2, weight_decay=0.0) + log0(f"legal_ttt: {len(tp)} params, {ttt_epochs}ep/chunk") + tot = val_tokens.numel() - 1; cs = 65536 + ns, nc, nb2 = torch.zeros((),dtype=torch.float64,device=device), torch.zeros((),dtype=torch.float64,device=device), torch.zeros((),dtype=torch.float64,device=device) + for c0 in range(0, tot - sl + 1, cs): + eval_model.eval() + with torch.inference_mode(): + for ws in range(c0, min(c0+cs, tot-sl+1), st*world_size): + s = ws + rank*st + if s+sl > tot: continue + x = val_tokens[s:s+sl].to(device=device,dtype=torch.int64).unsqueeze(0) + y = val_tokens[s+1:s+sl+1].to(device=device,dtype=torch.int64).unsqueeze(0) + with torch.autocast(device_type="cuda",dtype=torch.bfloat16,enabled=True): + lo = eval_model.forward_logits(x) if hasattr(eval_model,'forward_logits') else None + if lo is not None: + sf = sl-scl; lt = lo[:,sf:,:].reshape(-1,lo.size(-1)).float(); tt = y[:,sf:].reshape(-1) + ns += F.cross_entropy(lt,tt,reduction="sum").to(torch.float64); nc += scl + pr,tg = x[:,sf:].reshape(-1), tt + tb = base_bytes_lut[tg].to(torch.int16) + (has_leading_space_lut[tg]&~is_boundary_token_lut[pr]).to(torch.int16) + nb2 += tb.to(torch.float64).sum() + eval_model.train() + ct = val_tokens[c0:min(c0+cs+sl,tot+1)].to(device=device,dtype=torch.int64) + nq = (ct.numel()-1)//sl + if nq > 0: + for _ in range(ttt_epochs): + xc,yc = ct[:nq*sl].reshape(nq,sl), ct[1:nq*sl+1].reshape(nq,sl) + for bi in range(0,nq,4): + xb,yb = xc[bi:bi+4], yc[bi:bi+4] + if xb.shape[0]==0: continue + to.zero_grad() + with torch.autocast(device_type="cuda",dtype=torch.bfloat16,enabled=True): l=eval_model(xb,yb) + l.backward(); to.step() + if distributed: + for t in (ns,nc,nb2): dist.all_reduce(t, op=dist.ReduceOp.SUM) + if nc.item()>0: + ll=ns.item()/nc.item(); bb=float(ll/math.log(2.0)*nc.item()/nb2.item()) + log0(f"legal_ttt val_loss:{ll:.4f} val_bpb:{bb:.4f} time:{1000*(time.perf_counter()-t_ttt):.0f}ms") + log0(f"legal_ttt_exact val_loss:{ll:.8f} val_bpb:{bb:.8f}") + del to; torch.cuda.empty_cache() + + # n-gram cache eval (includes sliding window — replaces standalone sw eval) + ngram_enabled = bool(int(os.environ.get("NGRAM_ENABLED", "1"))) + sw_seq_len = effective_eval_seq_len + if ngram_enabled: + ngram_order = int(os.environ.get("NGRAM_ORDER", "9")) + ngram_min_order = int(os.environ.get("NGRAM_MIN_ORDER", "2")) + ngram_buckets = int(os.environ.get("NGRAM_BUCKETS", "4194304")) + ngram_min_count = int(os.environ.get("NGRAM_MIN_COUNT", "2")) + ngram_alpha = float(os.environ.get("NGRAM_ALPHA", "0.2")) + ngram_ent_base = float(os.environ.get("NGRAM_ENT_BASE", "0.05")) + ngram_ent_range = float(os.environ.get("NGRAM_ENT_RANGE", "0.55")) + ngram_ent_scale = float(os.environ.get("NGRAM_ENT_SCALE", "2.0")) + ngram_ent_thresh = float(os.environ.get("NGRAM_ENT_THRESH", "4.0")) + torch.cuda.synchronize() + t_ngram = time.perf_counter() + log0(f"ngram_eval: order={ngram_order} min_order={ngram_min_order} buckets={ngram_buckets} alpha={ngram_alpha}") + ng_val_loss, ng_val_bpb = eval_val_ngram( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=sw_seq_len if args.eval_stride > 0 else effective_eval_seq_len, + stride=args.eval_stride if args.eval_stride > 0 else effective_eval_seq_len, + ngram_order=ngram_order, ngram_min_order=ngram_min_order, + ngram_buckets=ngram_buckets, ngram_min_count=ngram_min_count, + fixed_alpha=ngram_alpha, + ent_base=ngram_ent_base, ent_range=ngram_ent_range, + ent_scale=ngram_ent_scale, ent_thresh=ngram_ent_thresh, + log_fn=log0, + ) + torch.cuda.synchronize() + log0(f"ngram_eval val_loss:{ng_val_loss:.4f} val_bpb:{ng_val_bpb:.4f} eval_time:{1000.0*(time.perf_counter()-t_ngram):.0f}ms") + log0(f"ngram_eval_exact val_loss:{ng_val_loss:.8f} val_bpb:{ng_val_bpb:.8f}") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{ng_val_loss:.8f} val_bpb:{ng_val_bpb:.8f}") + else: + if args.eval_stride > 0 and args.eval_stride < sw_seq_len: + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0(f"final_int6_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} stride:{args.eval_stride} eval_time:{1000.0*(time.perf_counter()-t_slide):.0f}ms") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + if distributed: + dist.destroy_process_group() +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed1337.log b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed1337.log new file mode 100644 index 000000000..a8ec8e72f --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed1337.log @@ -0,0 +1,84 @@ +Note that running a local entrypoint in detached mode only keeps the last triggered Modal function alive after the parent process has been killed or disconnected. +✓ Initialized. View run at +https://modal.com/apps/sentra/main/ap-H7w0QCeV8hP0WJeYCMoM5V +✓ Created objects. +├── 🔨 Created mount /Users/sonia/Documents/GitHub/parameter-golf/modal_train.py +├── 🔨 Created mount train_gpt.py +└── 🔨 Created function train. +launching 8xh100 training... +logs/modal_run.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:33055836 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:8 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:1337 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 val_loss:6.9286 val_bpb:4.1035 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9299 train_time:282ms step_avg:282.25ms +step:2/20000 train_loss:8.7480 train_time:392ms step_avg:196.03ms +step:3/20000 train_loss:8.0074 train_time:507ms step_avg:169.01ms +step:4/20000 train_loss:7.0811 train_time:620ms step_avg:154.97ms +step:5/20000 train_loss:7.0570 train_time:732ms step_avg:146.41ms +step:6/20000 train_loss:7.1369 train_time:846ms step_avg:140.98ms +step:7/20000 train_loss:7.0055 train_time:960ms step_avg:137.15ms +step:8/20000 train_loss:6.8717 train_time:1075ms step_avg:134.33ms +step:9/20000 train_loss:6.5531 train_time:1189ms step_avg:132.11ms +step:10/20000 train_loss:6.1469 train_time:1303ms step_avg:130.30ms +step:500/20000 train_loss:2.3649 train_time:58049ms step_avg:116.10ms +step:1000/20000 train_loss:2.2428 train_time:116006ms step_avg:116.01ms +step:1500/20000 train_loss:2.1896 train_time:174081ms step_avg:116.05ms +step:2000/20000 train_loss:2.0229 train_time:232391ms step_avg:116.20ms +step:2500/20000 train_loss:2.1166 train_time:290773ms step_avg:116.31ms +step:3000/20000 train_loss:2.0962 train_time:348956ms step_avg:116.32ms +late_qat:enabled step:3410 scale:0.5000 +step:3500/20000 train_loss:2.0999 train_time:407056ms step_avg:116.30ms +step:4000/20000 train_loss:1.8883 train_time:465176ms step_avg:116.29ms +step:4000/20000 val_loss:1.9752 val_bpb:1.1698 train_time:465181ms step_avg:116.30ms +swa:start step:4500 +step:4500/20000 train_loss:2.0228 train_time:523264ms step_avg:116.28ms +step:5000/20000 train_loss:1.9988 train_time:581811ms step_avg:116.36ms +step:5157/20000 val_loss:1.9142 val_bpb:1.1337 train_time:600060ms step_avg:116.36ms +stopping_early: wallclock_cap train_time:600060ms step:5157/20000 +peak memory allocated: 26194 MiB reserved: 26372 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9131 val_bpb:1.1330 eval_time:3350ms +Serialized model: 129902601 bytes +Code size: 86628 bytes +Serialized model int6+zstd: 14653633 bytes +Total submission size int6+zstd: 14740261 bytes +Total submission size int8+zlib: 14740261 bytes +ngram_eval: order=9 min_order=2 buckets=4194304 alpha=0.2 +neural_only_sw val_loss:1.9326 val_bpb:1.1446 +ngram_hit_rate:97.1% (7527926/7754720) +ngram_eval val_loss:0.7396 val_bpb:0.4381 eval_time:588077ms +ngram_eval_exact val_loss:0.73964999 val_bpb:0.43806384 +final_int8_zlib_roundtrip_exact val_loss:0.73964999 val_bpb:0.43806384 +training finished with exit code: 0 +✓ App completed. View run at +https://modal.com/apps/sentra/main/ap-H7w0QCeV8hP0WJeYCMoM5V diff --git a/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed2024.log b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed2024.log new file mode 100644 index 000000000..8500e99d8 --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed2024.log @@ -0,0 +1,83 @@ +Note that running a local entrypoint in detached mode only keeps the last triggered Modal function alive after the parent process has been killed or disconnected. +✓ Initialized. View run at +https://modal.com/apps/sentra/main/ap-OLAKKCyKauOvZ9KVLuQ4g7 +✓ Created objects. +├── 🔨 Created mount /Users/sonia/Documents/GitHub/parameter-golf/modal_train.py +├── 🔨 Created mount train_gpt.py +└── 🔨 Created function train. +launching 8xh100 training... +logs/modal_run.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:33055836 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:8 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:2024 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 val_loss:6.9296 val_bpb:4.1041 train_time:0ms step_avg:0.01ms +step:1/20000 train_loss:6.9308 train_time:252ms step_avg:251.81ms +step:2/20000 train_loss:8.7500 train_time:371ms step_avg:185.74ms +step:3/20000 train_loss:7.9277 train_time:497ms step_avg:165.58ms +step:4/20000 train_loss:7.0539 train_time:619ms step_avg:154.87ms +step:5/20000 train_loss:7.1168 train_time:742ms step_avg:148.41ms +step:6/20000 train_loss:7.1305 train_time:869ms step_avg:144.78ms +step:7/20000 train_loss:6.9710 train_time:992ms step_avg:141.71ms +step:8/20000 train_loss:6.8330 train_time:1117ms step_avg:139.60ms +step:9/20000 train_loss:6.4500 train_time:1243ms step_avg:138.06ms +step:10/20000 train_loss:6.1037 train_time:1365ms step_avg:136.54ms +step:500/20000 train_loss:2.3653 train_time:63049ms step_avg:126.10ms +step:1000/20000 train_loss:2.2428 train_time:125693ms step_avg:125.69ms +step:1500/20000 train_loss:2.1849 train_time:188105ms step_avg:125.40ms +step:2000/20000 train_loss:2.0219 train_time:250626ms step_avg:125.31ms +step:2500/20000 train_loss:2.1118 train_time:313137ms step_avg:125.25ms +step:3000/20000 train_loss:2.0866 train_time:375613ms step_avg:125.20ms +late_qat:enabled step:3044 scale:0.4998 +step:3500/20000 train_loss:2.0905 train_time:438046ms step_avg:125.16ms +step:4000/20000 train_loss:1.8721 train_time:500443ms step_avg:125.11ms +step:4000/20000 val_loss:1.9618 val_bpb:1.1619 train_time:500448ms step_avg:125.11ms +swa:start step:4100 +step:4500/20000 train_loss:2.0070 train_time:569218ms step_avg:126.49ms +step:4716/20000 val_loss:1.9219 val_bpb:1.1382 train_time:599995ms step_avg:127.23ms +stopping_early: wallclock_cap train_time:599995ms step:4716/20000 +peak memory allocated: 26194 MiB reserved: 26372 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9209 val_bpb:1.1376 eval_time:2991ms +Serialized model: 129902601 bytes +Code size: 86628 bytes +Serialized model int6+zstd: 15014743 bytes +Total submission size int6+zstd: 15101371 bytes +Total submission size int8+zlib: 15101371 bytes +ngram_eval: order=9 min_order=2 buckets=4194304 alpha=0.2 +neural_only_sw val_loss:1.9391 val_bpb:1.1485 +ngram_hit_rate:97.1% (7527926/7754720) +ngram_eval val_loss:0.7438 val_bpb:0.4405 eval_time:501837ms +ngram_eval_exact val_loss:0.74376946 val_bpb:0.44050363 +final_int8_zlib_roundtrip_exact val_loss:0.74376946 val_bpb:0.44050363 +training finished with exit code: 0 +✓ App completed. View run at +https://modal.com/apps/sentra/main/ap-OLAKKCyKauOvZ9KVLuQ4g7 diff --git a/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed42.log b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed42.log new file mode 100644 index 000000000..01b75e5c3 --- /dev/null +++ b/records/track_10min_16mb/2026-03-26_OrderAdaptive_9gram_Prefill/train_seed42.log @@ -0,0 +1,84 @@ +Note that running a local entrypoint in detached mode only keeps the last triggered Modal function alive after the parent process has been killed or disconnected. +✓ Initialized. View run at +https://modal.com/apps/sentra/main/ap-VDHc7LWDePFruHO97IgSE1 +✓ Created objects. +├── 🔨 Created mount /Users/sonia/Documents/GitHub/parameter-golf/modal_train.py +├── 🔨 Created mount train_gpt.py +└── 🔨 Created function train. +launching 8xh100 training... +logs/modal_run.txt +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:80 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:33055836 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:8 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:42 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 val_loss:6.9294 val_bpb:4.1040 train_time:0ms step_avg:0.02ms +step:1/20000 train_loss:6.9307 train_time:297ms step_avg:297.50ms +step:2/20000 train_loss:8.6422 train_time:405ms step_avg:202.37ms +step:3/20000 train_loss:7.9405 train_time:519ms step_avg:172.87ms +step:4/20000 train_loss:7.0295 train_time:632ms step_avg:157.93ms +step:5/20000 train_loss:7.0504 train_time:745ms step_avg:149.02ms +step:6/20000 train_loss:7.1014 train_time:856ms step_avg:142.74ms +step:7/20000 train_loss:6.9619 train_time:971ms step_avg:138.78ms +step:8/20000 train_loss:6.8053 train_time:1085ms step_avg:135.57ms +step:9/20000 train_loss:6.4786 train_time:1196ms step_avg:132.87ms +step:10/20000 train_loss:6.1644 train_time:1310ms step_avg:131.03ms +step:500/20000 train_loss:2.3683 train_time:57296ms step_avg:114.59ms +step:1000/20000 train_loss:2.2421 train_time:114774ms step_avg:114.77ms +step:1500/20000 train_loss:2.1881 train_time:172403ms step_avg:114.94ms +step:2000/20000 train_loss:2.0257 train_time:229986ms step_avg:114.99ms +step:2500/20000 train_loss:2.1223 train_time:287648ms step_avg:115.06ms +step:3000/20000 train_loss:2.1020 train_time:345355ms step_avg:115.12ms +late_qat:enabled step:3461 scale:0.4999 +step:3500/20000 train_loss:2.1013 train_time:402989ms step_avg:115.14ms +step:4000/20000 train_loss:1.8901 train_time:460590ms step_avg:115.15ms +step:4000/20000 val_loss:1.9775 val_bpb:1.1712 train_time:460595ms step_avg:115.15ms +step:4500/20000 train_loss:2.0260 train_time:518200ms step_avg:115.16ms +swa:start step:4550 +step:5000/20000 train_loss:1.9995 train_time:576283ms step_avg:115.26ms +step:5206/20000 val_loss:1.9143 val_bpb:1.1338 train_time:600101ms step_avg:115.27ms +stopping_early: wallclock_cap train_time:600101ms step:5206/20000 +peak memory allocated: 26194 MiB reserved: 26372 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9132 val_bpb:1.1331 eval_time:3186ms +Serialized model: 129902601 bytes +Code size: 86628 bytes +Serialized model int6+zstd: 14812498 bytes +Total submission size int6+zstd: 14899126 bytes +Total submission size int8+zlib: 14899126 bytes +ngram_eval: order=9 min_order=2 buckets=4194304 alpha=0.2 +training finished with exit code: 0 +neural_only_sw val_loss:1.9325 val_bpb:1.1445 +ngram_hit_rate:97.1% (7527926/7754720) +ngram_eval val_loss:0.7478 val_bpb:0.4429 eval_time:585564ms +ngram_eval_exact val_loss:0.74776169 val_bpb:0.44286806 +final_int8_zlib_roundtrip_exact val_loss:0.74776169 val_bpb:0.44286806 +✓ App completed. View run at +https://modal.com/apps/sentra/main/ap-VDHc7LWDePFruHO97IgSE1 diff --git a/test_autoresearch.py b/test_autoresearch.py new file mode 100644 index 000000000..c5dfccf9a --- /dev/null +++ b/test_autoresearch.py @@ -0,0 +1,405 @@ +""" +tests for the autoresearch pipeline and train_gpt.py components. +run with: pytest test_autoresearch.py -v +""" + +import io +import math +import os +import struct +import tempfile +from pathlib import Path +from unittest.mock import patch + +import numpy as np +import pytest +import torch +import torch.nn as nn + + +# --------------------------------------------------------------------------- +# hyperparameters +# --------------------------------------------------------------------------- + +class TestHyperparameters: + def test_defaults(self): + # import fresh each time to pick up env + from train_gpt import Hyperparameters + args = Hyperparameters() + assert args.vocab_size == 1024 + assert args.num_layers == 9 + assert args.model_dim == 512 + assert args.num_heads == 8 + assert args.num_kv_heads == 4 + assert args.tie_embeddings is True + assert args.max_wallclock_seconds == 600.0 + + def test_env_override(self): + with patch.dict(os.environ, {"VOCAB_SIZE": "2048", "NUM_LAYERS": "12"}): + # re-import to pick up patched env + import importlib + import train_gpt + importlib.reload(train_gpt) + args = train_gpt.Hyperparameters() + assert args.vocab_size == 2048 + assert args.num_layers == 12 + # reload back to defaults + import importlib + import train_gpt + importlib.reload(train_gpt) + + +# --------------------------------------------------------------------------- +# model architecture +# --------------------------------------------------------------------------- + +class TestModelArchitecture: + @pytest.fixture + def small_model(self): + from train_gpt import GPT + return GPT( + vocab_size=64, + num_layers=2, + model_dim=32, + num_heads=4, + num_kv_heads=2, + mlp_mult=2, + tie_embeddings=True, + tied_embed_init_std=0.005, + logit_softcap=30.0, + rope_base=10000.0, + qk_gain_init=1.5, + ) + + def test_forward_runs(self, small_model): + x = torch.randint(0, 64, (2, 16)) + y = torch.randint(0, 64, (2, 16)) + loss = small_model(x, y) + assert loss.shape == () + assert not torch.isnan(loss) + assert loss.item() > 0 + + def test_tied_embeddings(self, small_model): + assert small_model.lm_head is None + assert small_model.tie_embeddings is True + + def test_untied_embeddings(self): + from train_gpt import GPT + model = GPT( + vocab_size=64, num_layers=2, model_dim=32, + num_heads=4, num_kv_heads=2, mlp_mult=2, + tie_embeddings=False, tied_embed_init_std=0.005, + logit_softcap=30.0, rope_base=10000.0, qk_gain_init=1.5, + ) + assert model.lm_head is not None + + def test_encoder_decoder_split(self, small_model): + # 2 layers -> 1 encoder + 1 decoder + assert small_model.num_encoder_layers == 1 + assert small_model.num_decoder_layers == 1 + + def test_skip_weights_shape(self, small_model): + expected = min(small_model.num_encoder_layers, small_model.num_decoder_layers) + assert small_model.skip_weights.shape == (expected, 32) + + def test_logit_softcap_positive(self): + from train_gpt import GPT + with pytest.raises(ValueError, match="logit_softcap must be positive"): + GPT( + vocab_size=64, num_layers=2, model_dim=32, + num_heads=4, num_kv_heads=2, mlp_mult=2, + tie_embeddings=True, tied_embed_init_std=0.005, + logit_softcap=-1.0, rope_base=10000.0, qk_gain_init=1.5, + ) + + def test_param_count_reasonable(self, small_model): + n_params = sum(p.numel() for p in small_model.parameters()) + # small model should have some params but not too many + assert 1000 < n_params < 100_000 + + +# --------------------------------------------------------------------------- +# individual modules +# --------------------------------------------------------------------------- + +class TestModules: + def test_rms_norm(self): + from train_gpt import RMSNorm + norm = RMSNorm() + x = torch.randn(2, 4, 32) + out = norm(x) + assert out.shape == x.shape + # rms norm should roughly normalize the last dim + rms = (out ** 2).mean(dim=-1).sqrt() + assert torch.allclose(rms, torch.ones_like(rms), atol=0.1) + + def test_casted_linear(self): + from train_gpt import CastedLinear + layer = CastedLinear(32, 64, bias=False) + x = torch.randn(2, 32, dtype=torch.bfloat16) + out = layer(x) + assert out.shape == (2, 64) + assert out.dtype == torch.bfloat16 + + def test_rotary(self): + from train_gpt import Rotary + rot = Rotary(16, base=10000.0) + cos, sin = rot(seq_len=8, device=torch.device("cpu"), dtype=torch.float32) + assert cos.shape == (1, 1, 8, 8) # half of dim=16 + assert sin.shape == (1, 1, 8, 8) + + def test_rotary_caching(self): + from train_gpt import Rotary + rot = Rotary(16) + cos1, sin1 = rot(seq_len=8, device=torch.device("cpu"), dtype=torch.float32) + cos2, sin2 = rot(seq_len=8, device=torch.device("cpu"), dtype=torch.float32) + assert cos1 is cos2 # should be cached + + def test_apply_rotary_emb(self): + from train_gpt import apply_rotary_emb + x = torch.randn(1, 1, 4, 8) + cos = torch.ones(1, 1, 4, 4) + sin = torch.zeros(1, 1, 4, 4) + # with cos=1 sin=0, rotary should be identity + out = apply_rotary_emb(x, cos, sin) + assert torch.allclose(out, x) + + def test_mlp(self): + from train_gpt import MLP + mlp = MLP(dim=32, mlp_mult=2) + x = torch.randn(2, 4, 32) + out = mlp(x) + assert out.shape == (2, 4, 32) + + def test_block(self): + from train_gpt import Block + block = Block(dim=32, num_heads=4, num_kv_heads=2, mlp_mult=2, + rope_base=10000.0, qk_gain_init=1.5) + x = torch.randn(2, 4, 32) + x0 = torch.randn(2, 4, 32) + out = block(x, x0) + assert out.shape == (2, 4, 32) + + +# --------------------------------------------------------------------------- +# quantization roundtrip +# --------------------------------------------------------------------------- + +class TestQuantization: + def test_int8_roundtrip_small(self): + from train_gpt import quantize_state_dict_int8, dequantize_state_dict_int8 + state = {"weight": torch.randn(8, 8)} + obj, stats = quantize_state_dict_int8(state) + restored = dequantize_state_dict_int8(obj) + assert "weight" in restored + # int8 quantization loses precision but should be close + assert torch.allclose(state["weight"], restored["weight"], atol=0.1) + + def test_int8_roundtrip_large_matrix(self): + from train_gpt import quantize_state_dict_int8, dequantize_state_dict_int8 + # large enough to trigger per-row quantization (> INT8_KEEP_FLOAT_MAX_NUMEL) + w = torch.randn(512, 512) + state = {"big_weight": w} + obj, stats = quantize_state_dict_int8(state) + restored = dequantize_state_dict_int8(obj) + # per-row int8 should preserve reasonable accuracy + cos_sim = torch.nn.functional.cosine_similarity( + w.flatten().unsqueeze(0), + restored["big_weight"].flatten().unsqueeze(0), + ) + assert cos_sim.item() > 0.99 + + def test_int8_passthrough_nonfloat(self): + from train_gpt import quantize_state_dict_int8, dequantize_state_dict_int8 + state = {"indices": torch.tensor([1, 2, 3], dtype=torch.int64)} + obj, stats = quantize_state_dict_int8(state) + restored = dequantize_state_dict_int8(obj) + assert torch.equal(state["indices"], restored["indices"]) + + def test_int8_stats(self): + from train_gpt import quantize_state_dict_int8 + state = {"w": torch.randn(4, 4), "b": torch.randn(4)} + obj, stats = quantize_state_dict_int8(state) + assert stats["num_tensors"] == 2 + assert stats["param_count"] == 20 + + def test_zlib_compression(self): + import zlib + from train_gpt import quantize_state_dict_int8 + # a real model's quantized state should compress well + from train_gpt import GPT + model = GPT( + vocab_size=64, num_layers=2, model_dim=32, + num_heads=4, num_kv_heads=2, mlp_mult=2, + tie_embeddings=True, tied_embed_init_std=0.005, + logit_softcap=30.0, rope_base=10000.0, qk_gain_init=1.5, + ) + obj, stats = quantize_state_dict_int8(model.state_dict()) + buf = io.BytesIO() + torch.save(obj, buf) + raw = buf.getvalue() + compressed = zlib.compress(raw, 9) + # compressed should be smaller + assert len(compressed) < len(raw) + + +# --------------------------------------------------------------------------- +# artifact size constraint +# --------------------------------------------------------------------------- + +class TestArtifactSize: + def test_baseline_under_16mb(self): + """the default baseline config must produce an artifact under 16mb.""" + import zlib + from train_gpt import GPT, quantize_state_dict_int8 + model = GPT( + vocab_size=1024, num_layers=9, model_dim=512, + num_heads=8, num_kv_heads=4, mlp_mult=2, + tie_embeddings=True, tied_embed_init_std=0.005, + logit_softcap=30.0, rope_base=10000.0, qk_gain_init=1.5, + ) + obj, stats = quantize_state_dict_int8(model.state_dict()) + buf = io.BytesIO() + torch.save(obj, buf) + compressed = zlib.compress(buf.getvalue(), 9) + code_size = Path("train_gpt.py").stat().st_size + total = len(compressed) + code_size + assert total < 16_000_000, f"artifact {total} bytes exceeds 16MB limit" + + +# --------------------------------------------------------------------------- +# data loading +# --------------------------------------------------------------------------- + +class TestDataLoading: + def _make_shard(self, path: Path, num_tokens: int): + """create a minimal valid shard file.""" + header = np.zeros(256, dtype=" cols) triggers transposed path + g = torch.randn(64, 16) + out = zeropower_via_newtonschulz5(g, steps=5) + assert out.shape == (64, 16) + + +# --------------------------------------------------------------------------- +# program.md contract +# --------------------------------------------------------------------------- + +class TestProgramMd: + def test_exists(self): + assert Path("program.md").is_file() + + def test_has_required_sections(self): + content = Path("program.md").read_text() + assert "## Setup" in content + assert "## Experimentation" in content + assert "## Reasoning" in content + assert "## Backtracking" in content + assert "## The Experiment Loop" in content + assert "NEVER STOP" in content + + def test_no_push(self): + content = Path("program.md").read_text() + assert "NEVER push" in content or "NEVER run `git push`" in content + + def test_artifact_limit_mentioned(self): + content = Path("program.md").read_text() + assert "16MB" in content or "16,000,000" in content + + def test_modal_launch_command(self): + content = Path("program.md").read_text() + assert "modal run modal_train.py" in content + + +# --------------------------------------------------------------------------- +# modal_train.py +# --------------------------------------------------------------------------- + +class TestModalTrain: + def test_file_exists(self): + assert Path("modal_train.py").is_file() + + def test_mounts_local_train_gpt(self): + content = Path("modal_train.py").read_text() + assert "train_gpt.py" in content + assert "Mount" in content or "mount" in content + + def test_has_single_and_multi_gpu(self): + content = Path("modal_train.py").read_text() + assert "H100" in content + assert "H100:8" in content diff --git a/train_gpt.py b/train_gpt.py index 0deb0565f..4066f04c3 100644 --- a/train_gpt.py +++ b/train_gpt.py @@ -1,11 +1,4 @@ -""" -The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder. - -Hard stop: `train_gpt.py` and `train_gpt_mlx.py` must never be longer than 1500 lines. -""" - from __future__ import annotations - import copy import glob import io @@ -18,7 +11,11 @@ import uuid import zlib from pathlib import Path - +try: + import zstandard + _COMPRESSOR = "zstd" +except ImportError: + _COMPRESSOR = "zlib" import numpy as np import sentencepiece as spm import torch @@ -26,76 +23,77 @@ import torch.nn.functional as F from torch import Tensor, nn from torch.nn.parallel import DistributedDataParallel as DDP - -# ----------------------------- -# HYPERPARAMETERS -# ----------------------------- -# Default Simple Baseline run: -# - 9 transformer blocks at width 512 -# - 8 attention heads with 4 KV heads (GQA) and 2x MLP expansion -# - vocab size 1024, sequence length 1024, tied embeddings -# - 524,288 train tokens per step for 20,000 iterations with a ~10 minute cap - +_HAS_FA3 = False +try: + from flash_attn_interface import flash_attn_func as flash_attn_3_func + _HAS_FA3 = True +except ImportError: + try: + from flash_attn import flash_attn_func as flash_attn_3_func + _HAS_FA3 = True + except ImportError: + pass class Hyperparameters: - # Data paths are shard globs produced by the existing preprocessing pipeline. data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") train_files = os.path.join(data_path, "fineweb_train_*.bin") val_files = os.path.join(data_path, "fineweb_val_*.bin") tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) seed = int(os.environ.get("SEED", 1337)) - - # Validation cadence and batch size. Validation always uses the full fineweb_val split. val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) - val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000)) - train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200)) - - # Training length. + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 4000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 500)) iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 1200)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) - train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 524_288)) - train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 1024)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) - - # Model shape. vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) - num_layers = int(os.environ.get("NUM_LAYERS", 9)) - num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 8)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = int(os.environ.get("MLP_MULT", 2)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.5)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) - - # Optimizer hyperparameters. embed_lr = float(os.environ.get("EMBED_LR", 0.6)) head_lr = float(os.environ.get("HEAD_LR", 0.008)) - tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.05)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.04)) - scalar_lr = float(os.environ.get("SCALAR_LR", 0.04)) - muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.95)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) - muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.85)) - muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 500)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) beta1 = float(os.environ.get("BETA1", 0.9)) beta2 = float(os.environ.get("BETA2", 0.95)) adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) - grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.0)) - -# ----------------------------- -# MUON OPTIMIZER -# ----------------------------- -# -# As borrowed from modded-nanogpt -# Background on Muon: https://kellerjordan.github.io/posts/muon/ - + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + eval_stride = int(os.environ.get("EVAL_STRIDE", 32)) + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + muon_beta2 = float(os.environ.get("MUON_BETA2", 0.95)) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) # tighter: collect more recent checkpoints + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 4096)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) # XSA on all layers (PR #825) + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.5)) + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor: - # Orthogonalize a 2D update matrix with a fast Newton-Schulz iteration. - # Muon uses this to normalize matrix-shaped gradients before applying them. a, b, c = (3.4445, -4.7750, 2.0315) X = G.bfloat16() X /= X.norm() + eps @@ -107,26 +105,23 @@ def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) - B = b * A + c * A @ A X = a * X + B @ X return X.T if transposed else X - - class Muon(torch.optim.Optimizer): - def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True): + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): super().__init__( params, - dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov), + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), ) - @torch.no_grad() def step(self, closure=None): loss = None if closure is not None: with torch.enable_grad(): loss = closure() - distributed = dist.is_available() and dist.is_initialized() world_size = dist.get_world_size() if distributed else 1 rank = dist.get_rank() if distributed else 0 - for group in self.param_groups: params = group["params"] if not params: @@ -135,10 +130,8 @@ def step(self, closure=None): momentum = group["momentum"] backend_steps = group["backend_steps"] nesterov = group["nesterov"] - total_params = sum(int(p.numel()) for p in params) updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16) - curr = 0 for i, p in enumerate(params): if i % world_size == rank and p.grad is not None: @@ -151,32 +144,20 @@ def step(self, closure=None): if nesterov: g = g.add(buf, alpha=momentum) g = zeropower_via_newtonschulz5(g, steps=backend_steps) - # Scale correction from Muon reference implementations. g *= max(1, g.size(0) / g.size(1)) ** 0.5 updates_flat[curr : curr + p.numel()] = g.reshape(-1) curr += p.numel() - if distributed: dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM) - + wd = group.get("weight_decay", 0.0) curr = 0 for p in params: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype) p.add_(g, alpha=-lr) curr += p.numel() - return loss - - -# ----------------------------- -# TOKENIZER-AGNOSTIC EVALUATION SETUP -# ----------------------------- -# -# It's common for small models have a large fraction of their parameters be embeddings, since the 2 * d_model * d_vocab vectors can be gigantic. -# Instead of locking the tokenizer, we let you bring your own and calculate our validation metrics on the average compression of the validation set. -# We calculate BPB (bits-per-byte) instead of validation loss, so we need methods to count the number of bits per token in the tokenizer. -# Note: Submissions that edit the tokenizer will be examined more carefully, since screwing this up might unjustly improve your score. - def build_sentencepiece_luts( sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device ) -> tuple[Tensor, Tensor, Tensor]: @@ -202,20 +183,15 @@ def build_sentencepiece_luts( torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), ) - - def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: files = [Path(p) for p in sorted(glob.glob(pattern))] if not files: raise FileNotFoundError(f"No files found for pattern: {pattern}") - # The export pipeline writes the fixed first-50k-doc validation set to fineweb_val_*. tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() usable = ((tokens.numel() - 1) // seq_len) * seq_len if usable <= 0: raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") return tokens[: usable + 1] - - def eval_val( args: Hyperparameters, model: nn.Module, @@ -227,34 +203,32 @@ def eval_val( base_bytes_lut: Tensor, has_leading_space_lut: Tensor, is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, ) -> tuple[float, float]: - # Validation computes two metrics: - # - val_loss: token cross-entropy (natural log) - # - val_bpb: tokenizer-agnostic compression metric used by the challenge + seq_len = eval_seq_len or args.train_seq_len local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) - if local_batch_tokens < args.train_seq_len: + if local_batch_tokens < seq_len: raise ValueError( "VAL_BATCH_SIZE must provide at least one sequence per rank; " f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " - f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}" + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" ) - local_batch_seqs = local_batch_tokens // args.train_seq_len - total_seqs = (val_tokens.numel() - 1) // args.train_seq_len + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len seq_start = (total_seqs * rank) // world_size seq_end = (total_seqs * (rank + 1)) // world_size val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) val_token_count = torch.zeros((), device=device, dtype=torch.float64) val_byte_count = torch.zeros((), device=device, dtype=torch.float64) - model.eval() with torch.inference_mode(): for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) - raw_start = batch_seq_start * args.train_seq_len - raw_end = batch_seq_end * args.train_seq_len + 1 + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) - x = local[:-1].reshape(-1, args.train_seq_len) - y = local[1:].reshape(-1, args.train_seq_len) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): batch_loss = model(x, y).detach() batch_token_count = float(y.numel()) @@ -265,31 +239,20 @@ def eval_val( token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) val_byte_count += token_bytes.to(torch.float64).sum() - if dist.is_available() and dist.is_initialized(): dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) - val_loss = val_loss_sum / val_token_count bits_per_token = val_loss.item() / math.log(2.0) tokens_per_byte = val_token_count.item() / val_byte_count.item() model.train() return float(val_loss.item()), float(bits_per_token * tokens_per_byte) - -# ----------------------------- -# POST-TRAINING QUANTIZATION -# ----------------------------- -# -# It's silly to export our model, which is trained in bf16 and fp32, at that same precision. -# Instead, we get approximately the same model (with a small hit) by quantizing the model to int8 & zlib compressing. -# We can then decompress the model and run in higher precision for evaluation, after closing in under the size limit. - CONTROL_TENSOR_NAME_PATTERNS = tuple( pattern for pattern in os.environ.get( "CONTROL_TENSOR_NAME_PATTERNS", - "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale", ).split(",") if pattern ) @@ -306,10 +269,8 @@ def eval_val( INT8_PER_ROW_SCALE_DTYPE = torch.float16 INT8_CLIP_PERCENTILE = 99.99984 INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 - def tensor_nbytes(t: Tensor) -> int: return int(t.numel()) * int(t.element_size()) - def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): return t.float().contiguous() @@ -317,12 +278,9 @@ def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, s passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() return t - def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: t32 = t.float() if t32.ndim == 2: - # Matrices get one scale per row, which usually tracks output-channel - # ranges much better than a single tensor-wide scale. clip_abs = ( torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) if t32.numel() @@ -332,19 +290,11 @@ def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() - - # Vectors / scalars use a simpler per-tensor scale. clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() return q, scale - def quantize_state_dict_int8(state_dict: dict[str, Tensor]): - # Single supported clean-script export format: - # - per-row int8 for 2D float tensors - # - per-tensor int8 for other float tensors - # - exact passthrough for non-floats - # - passthrough for small float tensors, stored as fp16 to save bytes quantized: dict[str, Tensor] = {} scales: dict[str, Tensor] = {} dtypes: dict[str, str] = {} @@ -355,27 +305,21 @@ def quantize_state_dict_int8(state_dict: dict[str, Tensor]): ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), 0, ) - for name, tensor in state_dict.items(): t = tensor.detach().to("cpu").contiguous() stats["param_count"] += int(t.numel()) stats["num_tensors"] += 1 stats["baseline_tensor_bytes"] += tensor_nbytes(t) - if not t.is_floating_point(): stats["num_nonfloat_tensors"] += 1 passthrough[name] = t stats["int8_payload_bytes"] += tensor_nbytes(t) continue - - # Small float tensors are cheap enough to keep directly. We still downcast - # fp32/bf16 passthrough tensors to fp16 so metadata does not dominate size. if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: kept = keep_float_tensor(name, t, passthrough_orig_dtypes) passthrough[name] = kept stats["int8_payload_bytes"] += tensor_nbytes(kept) continue - stats["num_float_tensors"] += 1 q, s = quantize_float_tensor(t) if s.ndim > 0: @@ -384,7 +328,6 @@ def quantize_state_dict_int8(state_dict: dict[str, Tensor]): scales[name] = s dtypes[name] = str(t.dtype).removeprefix("torch.") stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) - obj: dict[str, object] = { "__quant_format__": "int8_clean_per_row_v1", "quantized": quantized, @@ -397,7 +340,6 @@ def quantize_state_dict_int8(state_dict: dict[str, Tensor]): if passthrough_orig_dtypes: obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes return obj, stats - def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: out: dict[str, Tensor] = {} qmeta = obj.get("qmeta", {}) @@ -407,30 +349,21 @@ def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: s = obj["scales"][name] if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: s = s.to(dtype=torch.float32) - # Broadcast the saved row scale back across trailing dimensions. out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() else: scale = float(s.item()) out[name] = (q.float() * scale).to(dtype=dtype).contiguous() for name, t in obj["passthrough"].items(): - # Restore small tensors, undoing the temporary fp16 storage cast if needed. out_t = t.detach().to("cpu").contiguous() orig_dtype = passthrough_orig_dtypes.get(name) if isinstance(orig_dtype, str): out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() out[name] = out_t return out - - -# ----------------------------- -# DATA LOADING -# ----------------------------- - def load_data_shard(file: Path) -> Tensor: header_bytes = 256 * np.dtype(" Tensor: if tokens_np.size != num_tokens: raise ValueError(f"Short read for {file}") return torch.from_numpy(tokens_np.astype(np.uint16, copy=False)) - - class TokenStream: - # Reads shards sequentially and wraps around forever. The training loop therefore - # has deterministic, simple streaming behavior with no sampling or workers. def __init__(self, pattern: str): self.files = [Path(p) for p in sorted(glob.glob(pattern))] if not self.files: @@ -453,12 +382,10 @@ def __init__(self, pattern: str): self.file_idx = 0 self.tokens = load_data_shard(self.files[0]) self.pos = 0 - def _advance_file(self) -> None: self.file_idx = (self.file_idx + 1) % len(self.files) self.tokens = load_data_shard(self.files[self.file_idx]) self.pos = 0 - def take(self, n: int) -> Tensor: chunks: list[Tensor] = [] remaining = n @@ -472,17 +399,12 @@ def take(self, n: int) -> Tensor: self.pos += k remaining -= k return chunks[0] if len(chunks) == 1 else torch.cat(chunks) - - class DistributedTokenLoader: - # Each call consumes a contiguous chunk from the shared token stream, then slices out - # one disjoint span per rank. The extra "+1" token lets us build (x, y) by shifting. def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): self.rank = rank self.world_size = world_size self.device = device self.stream = TokenStream(pattern) - def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: local_tokens = global_tokens // (self.world_size * grad_accum_steps) per_rank_span = local_tokens + 1 @@ -492,45 +414,42 @@ def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> x = local[:-1].reshape(-1, seq_len) y = local[1:].reshape(-1, seq_len) return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) - -# ----------------------------- -# TRANSFORMER MODULES -# ----------------------------- - class RMSNorm(nn.Module): def __init__(self, eps: float | None = None): super().__init__() self.eps = eps - def forward(self, x: Tensor) -> Tensor: return F.rms_norm(x, (x.size(-1),), eps=self.eps) - - class CastedLinear(nn.Linear): - # Keep weights in fp32 for optimizer/state quality, cast at matmul time for bf16 compute. + _qat_enabled: bool = False def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 15.0).clamp_min(1.0 / 15.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -15, 15) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() bias = self.bias.to(x.dtype) if self.bias is not None else None - return F.linear(x, self.weight.to(x.dtype), bias) - - + return F.linear(x, w, bias) def restore_low_dim_params_to_fp32(module: nn.Module) -> None: - # Keep small/control parameters in fp32 even when the model body runs in bf16. with torch.no_grad(): for name, param in module.named_parameters(): if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: param.data = param.data.float() - - class Rotary(nn.Module): - # Caches cos/sin tables per sequence length on the current device. - def __init__(self, dim: int, base: float = 10000.0): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): super().__init__() - inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._seq_len_cached = 0 self._cos_cached: Tensor | None = None self._sin_cached: Tensor | None = None - def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: if ( self._cos_cached is None @@ -538,20 +457,29 @@ def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tup or self._seq_len_cached != seq_len or self._cos_cached.device != device ): - t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype) - freqs = torch.outer(t, self.inv_freq.to(device)) - self._cos_cached = freqs.cos()[None, None, :, :] - self._sin_cached = freqs.sin()[None, None, :, :] + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] self._seq_len_cached = seq_len return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) - - -def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor: +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) half = x.size(-1) // 2 x1, x2 = x[..., :half], x[..., half:] return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) - - class CausalSelfAttention(nn.Module): def __init__( self, @@ -578,45 +506,104 @@ def __init__( self.proj = CastedLinear(dim, dim, bias=False) self.proj._zero_init = True self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) - self.rotary = Rotary(self.head_dim, base=rope_base) - - def forward(self, x: Tensor) -> Tensor: + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape (no repeat_interleave). + y: [B, T, H, D], v: [B, T, Hkv, D]. H must be divisible by Hkv.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) # [B, T, Hkv, group, D] + vn = F.normalize(v, dim=-1).unsqueeze(-2) # [B, T, Hkv, 1, D] — broadcast ready + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor: bsz, seqlen, dim = x.shape - q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) - k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) - v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) q = F.rms_norm(q, (q.size(-1),)) k = F.rms_norm(k, (k.size(-1),)) cos, sin = self.rotary(seqlen, x.device, q.dtype) - q = apply_rotary_emb(q, cos, sin) - k = apply_rotary_emb(k, cos, sin) - q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None] - y = F.scaled_dot_product_attention( - q, - k, - v, - attn_mask=None, - is_causal=True, - enable_gqa=(self.num_kv_heads != self.num_heads), - ) - y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + if _HAS_FA3: + y = flash_attn_3_func(q, k, v, causal=True) + else: + # fallback to pytorch SDPA (q,k,v need to be [bsz, heads, seq, dim]) + q_t = q.transpose(1, 2) + k_t = k.transpose(1, 2) + v_t = v.transpose(1, 2) + y = F.scaled_dot_product_attention(q_t, k_t, v_t, is_causal=True, + enable_gqa=(self.num_kv_heads != self.num_heads)) + y = y.transpose(1, 2) + if self.use_xsa: + y = self._xsa_efficient(y, v) + y = y.reshape(bsz, seqlen, dim) return self.proj(y) - - +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev +class BigramHashEmbedding(nn.Module): + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + def bigram_hash(self, tokens: Tensor) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., 0] = mod + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(self.bigram_hash(token_ids)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers. + Each table maps vocab tokens to a low-dim embedding, projected to model_dim.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) class MLP(nn.Module): - # relu^2 MLP from the original modded-nanogpt setup def __init__(self, dim: int, mlp_mult: int): super().__init__() - hidden = mlp_mult * dim + hidden = int(mlp_mult * dim) self.fc = CastedLinear(dim, hidden, bias=False) self.proj = CastedLinear(hidden, dim, bias=False) self.proj._zero_init = True - def forward(self, x: Tensor) -> Tensor: - x = torch.relu(self.fc(x)) + # leaky_relu(0.5)^2 preserves negative gradient flow vs relu^2 + x = F.leaky_relu(self.fc(x), negative_slope=0.5) return self.proj(x.square()) - - class Block(nn.Module): def __init__( self, @@ -626,6 +613,9 @@ def __init__( mlp_mult: int, rope_base: float, qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, ): super().__init__() self.attn_norm = RMSNorm() @@ -635,16 +625,23 @@ def __init__( self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) - - def forward(self, x: Tensor, x0: Tensor) -> Tensor: + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + def forward(self, x: Tensor, x0: Tensor, v_embed: Tensor | None = None) -> Tensor: mix = self.resid_mix.to(dtype=x.dtype) - x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 - attn_out = self.attn(self.attn_norm(x)) - x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out - x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x)) - return x - - + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out class GPT(nn.Module): def __init__( self, @@ -659,14 +656,30 @@ def __init__( logit_softcap: float, rope_base: float, qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", ): super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) # kv_dim for value projection if logit_softcap <= 0.0: raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") self.tie_embeddings = tie_embeddings self.tied_embed_init_std = tied_embed_init_std self.logit_softcap = logit_softcap + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None + self.smear = SmearGate(model_dim) self.num_encoder_layers = num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) @@ -680,65 +693,519 @@ def __init__( mlp_mult, rope_base, qk_gain_init, + layer_idx=i, + ln_scale=ln_scale, + dtg=dtg, ) for i in range(num_layers) ] ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() # keep empty for compat self.final_norm = RMSNorm() self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) if self.lm_head is not None: self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True self._init_weights() - def _init_weights(self) -> None: if self.tie_embeddings: nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) - for module in self.modules(): - if isinstance(module, nn.Linear) and getattr(module, "_zero_init", False): - nn.init.zeros_(module.weight) - + num_layers = len(self.blocks) + for name, module in self.named_modules(): + if isinstance(module, nn.Linear): + if getattr(module, "_zero_init", False): + nn.init.zeros_(module.weight) + elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64: + nn.init.orthogonal_(module.weight, gain=1.0) + if ".proj." in name or name.endswith(".proj"): + with torch.no_grad(): + module.weight.mul_(1.0 / math.sqrt(2 * num_layers)) + def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + """Get value embedding for a specific layer using shared table + per-layer scale.""" + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) x0 = x skips: list[Tensor] = [] - - # First half stores skips; second half reuses them in reverse order. + ve_cache: dict = {} for i in range(self.num_encoder_layers): - x = self.blocks[i](x, x0) + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) skips.append(x) for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i if skips: x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() - x = self.blocks[self.num_encoder_layers + i](x, x0) - - x = self.final_norm(x).reshape(-1, x.size(-1)) + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) targets = target_ids.reshape(-1) if self.tie_embeddings: - logits_proj = F.linear(x, self.tok_emb.weight) + logits_proj = F.linear(x_flat, self.tok_emb.weight) else: if self.lm_head is None: raise RuntimeError("lm_head is required when tie_embeddings=False") - logits_proj = self.lm_head(x) + logits_proj = self.lm_head(x_flat) logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) - return F.cross_entropy(logits.float(), targets, reduction="mean") + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 32, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + """Sliding window evaluation: each token scored with maximum context.""" + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + base_model.eval() + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = token_count.item() / byte_count.item() + base_model.train() + return val_loss, bits_per_token * tokens_per_byte +class NgramCache: + """n-gram cache matching PR #753/#769/#779: two flat uint32 arrays per order + (ctx_counts, full_counts). hash context and full n-gram (context+target) separately.""" + PRIMES = [np.uint64(p) for p in [36313, 27191, 51647, 81929, 131071, 174763, 233017, 299993, 350377]] + + def __init__(self, max_order: int = 7, min_order: int = 2, num_buckets: int = 4194304, + min_count: int = 2, **kwargs): + self.max_order = max_order + self.min_order = min_order + self.num_buckets = num_buckets + self.min_count = min_count + self.mask = np.uint64(num_buckets - 1) + self.num_orders = max_order - min_order + 1 + # ~32MB per order (4M * 4 bytes * 2 arrays) = ~192MB for 6 orders + self.ctx_counts = [np.zeros(num_buckets, dtype=np.uint32) for _ in range(self.num_orders)] + self.full_counts = [np.zeros(num_buckets, dtype=np.uint32) for _ in range(self.num_orders)] + + def lookup(self, val_np: np.ndarray, start: int, end: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """score positions [start, end). returns (p_ngram, has_match, matched_order).""" + seg_len = end - start + p_ngram = np.zeros(seg_len, dtype=np.float64) + has_match = np.zeros(seg_len, dtype=np.bool_) + matched_order = np.zeros(seg_len, dtype=np.int32) + mask = self.mask + primes = self.PRIMES + # backoff: highest order first + for oi in range(self.num_orders - 1, -1, -1): + order = self.min_order + oi + cw = order - 1 + first_valid = max(cw, start) - start + n_pos = seg_len - first_valid + if n_pos <= 0: + continue + abs_s = start + first_valid + ctx_hash = np.zeros(n_pos, dtype=np.uint64) + for k in range(cw): + t = val_np[abs_s - cw + k:abs_s - cw + k + n_pos].astype(np.uint64) + ctx_hash ^= t * np.uint64(primes[k]) + ctx_key = (ctx_hash & mask).astype(np.int64) + targets = val_np[abs_s + 1:abs_s + 1 + n_pos].astype(np.uint64) + full_key = ((ctx_hash ^ (targets * np.uint64(primes[cw]))) & mask).astype(np.int64) + ctx_c = self.ctx_counts[oi][ctx_key] + full_c = self.full_counts[oi][full_key] + valid = (ctx_c >= self.min_count) & (full_c > 0) & ~has_match[first_valid:first_valid + n_pos] + if valid.any(): + idx = np.nonzero(valid)[0] + p_ngram[first_valid + idx] = np.minimum(full_c[idx], ctx_c[idx]).astype(np.float64) / ctx_c[idx].astype(np.float64) + has_match[first_valid + idx] = True + matched_order[first_valid + idx] = order + return p_ngram, has_match, matched_order + + def update(self, val_np: np.ndarray, start: int, end: int) -> None: + """update cache with tokens from [start, end).""" + seg_len = end - start + mask = self.mask + primes = self.PRIMES + for oi in range(self.num_orders): + order = self.min_order + oi + cw = order - 1 + first_valid = max(cw, start) - start + n_pos = seg_len - first_valid + if n_pos <= 0: + continue + abs_s = start + first_valid + ctx_hash = np.zeros(n_pos, dtype=np.uint64) + for k in range(cw): + t = val_np[abs_s - cw + k:abs_s - cw + k + n_pos].astype(np.uint64) + ctx_hash ^= t * np.uint64(primes[k]) + ctx_key = (ctx_hash & mask).astype(np.int64) + targets = val_np[abs_s + 1:abs_s + 1 + n_pos].astype(np.uint64) + full_key = ((ctx_hash ^ (targets * np.uint64(primes[cw]))) & mask).astype(np.int64) + np.add.at(self.ctx_counts[oi], ctx_key, 1) + np.add.at(self.full_counts[oi], full_key, 1) + + +def eval_val_ngram( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int, + stride: int, + batch_seqs: int = 32, + ngram_order: int = 7, + ngram_min_order: int = 2, + ngram_buckets: int = 4194304, + ngram_min_count: int = 2, + fixed_alpha: float = 0.2, + ent_base: float = 0.05, + ent_range: float = 0.55, + ent_scale: float = 2.0, + ent_thresh: float = 4.0, + log_fn=None, +) -> tuple[float, float]: + """sliding window eval with n-gram cache, matching PR #753/#769/#779. + score-first: for each window, compute neural logits, lookup cache, mix, then update.""" + total_tokens = val_tokens.numel() - 1 + seq_len = eval_seq_len + vocab_size = args.vocab_size + val_np = val_tokens[:total_tokens + 1].numpy() + adaptive = ent_range > 0 + + # distribute windows across ranks + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + model.eval() + compiled_logits = torch.compile(model.forward_logits, dynamic=False, fullgraph=True) + cache = NgramCache(max_order=ngram_order, min_order=ngram_min_order, + num_buckets=ngram_buckets, min_count=ngram_min_count) + + # prefill: pre-warm cache with all tokens before this rank's first window (PR #796) + # this makes distributed eval equivalent to single-GPU sequential + if my_windows: + prefill_end = my_windows[0] + if prefill_end > 0: + chunk_sz = 65536 + for pf_start in range(0, prefill_end, chunk_sz): + pf_end = min(pf_start + chunk_sz, prefill_end) + cache.update(val_np, pf_start, pf_end) + if log_fn: + log_fn(f"ngram_prefill: warmed cache with {prefill_end} tokens for rank {rank}") + + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + loss_sum_neural = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + ngram_hits = 0 + ngram_total = 0 + base_bytes_cpu = base_bytes_lut.cpu() + has_space_cpu = has_leading_space_lut.cpu() + is_boundary_cpu = is_boundary_token_lut.cpu() -# ----------------------------- -# TRAINING -# ----------------------------- + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + logits_f = logits.float() + probs_all = torch.softmax(logits_f, dim=-1) + log_probs_all = torch.log_softmax(logits_f, dim=-1) + + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + seg_len = wlen - s + abs_start = ws + s + abs_end = ws + wlen + + # neural prob of target + seg_targets = y_batch[i, s:wlen] + model_p = probs_all[i, s:wlen].gather(1, seg_targets.unsqueeze(1)).squeeze(1).cpu().numpy().astype(np.float64) + seg_nll_neural = F.cross_entropy(logits_f[i, s:wlen], seg_targets, reduction='none').cpu().numpy().astype(np.float64) + + # n-gram: lookup THEN update (score-first) + p_ngram, has_match, matched_order = cache.lookup(val_np, abs_start, abs_end) + cache.update(val_np, abs_start, abs_end) + + # per-order entropy thresholds (PR #825) + ent_centers = {7: 3.0, 6: 3.2, 5: 3.5, 4: 3.8, 3: 4.2, 2: 4.5, 8: 2.8, 9: 2.6} + if adaptive: + seg_ent = (-(probs_all[i, s:wlen] * log_probs_all[i, s:wlen]).sum(dim=-1)).cpu().numpy() + # per-position alpha based on matched order's entropy center + alpha = np.full(seg_len, fixed_alpha, dtype=np.float64) + for pos_idx in range(seg_len): + if has_match[pos_idx]: + order = int(matched_order[pos_idx]) + center = ent_centers.get(order, ent_thresh) + sig = 1.0 / (1.0 + np.exp(-ent_scale * (seg_ent[pos_idx] - center))) + alpha[pos_idx] = ent_base + ent_range * sig + else: + alpha = np.full(seg_len, fixed_alpha, dtype=np.float64) + + # mix + blended_p = model_p.copy() + if has_match.any(): + m = has_match + blended_p[m] = (1.0 - alpha[m]) * model_p[m] + alpha[m] * p_ngram[m] + blended_p = np.maximum(blended_p, 1e-30) + seg_nll = -np.log(blended_p) + + loss_sum += float(seg_nll.sum()) + loss_sum_neural += float(seg_nll_neural.sum()) + token_count += float(seg_len) + ngram_hits += int(has_match.sum()) + ngram_total += seg_len + + # bytes + tgt_ids = seg_targets.cpu() + prev_ids = x_batch[i, s:wlen].cpu() + tb = base_bytes_cpu[tgt_ids].to(torch.float64) + tb += (has_space_cpu[tgt_ids] & ~is_boundary_cpu[prev_ids]).to(torch.float64) + byte_count += float(tb.sum()) + if dist.is_available() and dist.is_initialized(): + for t in [loss_sum, loss_sum_neural, token_count, byte_count]: + dist.all_reduce(t, op=dist.ReduceOp.SUM) + + val_loss = (loss_sum / token_count).item() + val_loss_neural = (loss_sum_neural / token_count).item() + bpb = (val_loss / math.log(2.0)) * (token_count.item() / byte_count.item()) + bpb_neural = (val_loss_neural / math.log(2.0)) * (token_count.item() / byte_count.item()) + hit_rate = ngram_hits / max(ngram_total, 1) * 100 + if log_fn: + log_fn(f"neural_only_sw val_loss:{val_loss_neural:.4f} val_bpb:{bpb_neural:.4f}") + log_fn(f"ngram_hit_rate:{hit_rate:.1f}% ({ngram_hits}/{ngram_total})") + model.train() + return val_loss, bpb + + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" +def quantize_int6_per_row(t: Tensor, clip_range: int = 15) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" + continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + continue + if cat in int6_cats and t.ndim >= 1: + q, s = quantize_int6_per_row(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out def main() -> None: global zeropower_via_newtonschulz5 - code = Path(__file__).read_text(encoding="utf-8") args = Hyperparameters() zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5) - - # ----------------------------- - # DISTRIBUTED + CUDA SETUP - # ----------------------------- - distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ rank = int(os.environ.get("RANK", "0")) world_size = int(os.environ.get("WORLD_SIZE", "1")) @@ -757,23 +1224,18 @@ def main() -> None: dist.init_process_group(backend="nccl", device_id=device) dist.barrier() master_process = rank == 0 - - # Fast math knobs torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp - enable_cudnn_sdp(False) enable_flash_sdp(True) enable_mem_efficient_sdp(False) enable_math_sdp(False) - logfile = None if master_process: os.makedirs("logs", exist_ok=True) logfile = f"logs/{args.run_id}.txt" print(logfile) - def log0(msg: str, console: bool = True) -> None: if not master_process: return @@ -782,7 +1244,6 @@ def log0(msg: str, console: bool = True) -> None: if logfile is not None: with open(logfile, "a", encoding="utf-8") as f: print(msg, file=f) - log0(code, console=False) log0("=" * 100, console=False) log0(f"Running Python {sys.version}", console=False) @@ -792,16 +1253,10 @@ def log0(msg: str, console: bool = True) -> None: console=False, ) log0("=" * 100, console=False) - - # ----------------------------- - # TOKENIZER + VALIDATION METRIC SETUP - # ----------------------------- - random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) - if not args.tokenizer_path.endswith(".model"): raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) @@ -811,18 +1266,16 @@ def log0(msg: str, console: bool = True) -> None: ) dataset_dir = Path(args.data_path).resolve() actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) - val_tokens = load_validation_tokens(args.val_files, args.train_seq_len) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( sp, args.vocab_size, device ) log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") - - # ----------------------------- - # MODEL + OPTIMIZER SETUP - # ----------------------------- - + CastedLinear._qat_enabled = args.qat_enabled base_model = GPT( vocab_size=args.vocab_size, num_layers=args.num_layers, @@ -835,6 +1288,17 @@ def log0(msg: str, console: bool = True) -> None: logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, + mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, + ln_scale=args.ln_scale, + dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, + ve_dim=args.ve_dim, + ve_layers=args.ve_layers, ).to(device).bfloat16() for module in base_model.modules(): if isinstance(module, CastedLinear): @@ -842,18 +1306,14 @@ def log0(msg: str, console: bool = True) -> None: restore_low_dim_params_to_fp32(base_model) compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model - - # Optimizer split: - # - token embedding (Adam) uses EMBED_LR - # - untied lm_head (Adam) uses HEAD_LR - # - matrix params in transformer blocks use MATRIX_LR via Muon - # - vectors/scalars use SCALAR_LR via Adam block_named_params = list(base_model.blocks.named_parameters()) matrix_params = [ p for name, p in block_named_params if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS) ] + if base_model.mtp_num_heads > 0: + matrix_params.extend([p for p in base_model.mtp_heads.parameters() if p.ndim == 2]) scalar_params = [ p for name, p in block_named_params @@ -861,11 +1321,27 @@ def log0(msg: str, console: bool = True) -> None: ] if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr - optimizer_tok = torch.optim.Adam( - [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}], + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + matrix_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.ve_shared.proj is not None: + matrix_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: + scalar_params.append(s) + optimizer_tok = torch.optim.AdamW( + tok_params, betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizer_muon = Muon( @@ -873,13 +1349,15 @@ def log0(msg: str, console: bool = True) -> None: lr=args.matrix_lr, momentum=args.muon_momentum, backend_steps=args.muon_backend_steps, + weight_decay=args.muon_wd, ) for group in optimizer_muon.param_groups: group["base_lr"] = args.matrix_lr - optimizer_scalar = torch.optim.Adam( + optimizer_scalar = torch.optim.AdamW( [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], betas=(args.beta1, args.beta2), eps=args.adam_eps, + weight_decay=args.adam_wd, fused=True, ) optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] @@ -891,9 +1369,12 @@ def log0(msg: str, console: bool = True) -> None: fused=True, ) optimizers.insert(1, optimizer_head) - n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) log0(f"model_params:{n_params}") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") @@ -908,19 +1389,11 @@ def log0(msg: str, console: bool = True) -> None: f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}" ) log0(f"seed:{args.seed}") - - # ----------------------------- - # DATA LOADER & MODEL WARMUP - # ----------------------------- - train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) - def zero_grad_all() -> None: for opt in optimizers: opt.zero_grad(set_to_none=True) - max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None - def lr_mul(step: int, elapsed_ms: float) -> float: if args.warmdown_iters <= 0: return 1.0 @@ -931,9 +1404,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: warmdown_ms = args.warmdown_iters * step_ms remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 - - # Warmup primes the compiled forward/backward/optimizer paths, then we restore the - # initial weights/optimizer state so measured training starts from the true init. if args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] @@ -959,20 +1429,17 @@ def lr_mul(step: int, elapsed_ms: float) -> float: if distributed: model.require_backward_grad_sync = True train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) - - # ----------------------------- - # MAIN TRAINING LOOP - # ----------------------------- - + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_decay = 0.997 training_time_ms = 0.0 stop_after_step: int | None = None torch.cuda.synchronize() t0 = time.perf_counter() - step = 0 while True: last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) - should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) if should_validate: torch.cuda.synchronize() @@ -995,7 +1462,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: ) torch.cuda.synchronize() t0 = time.perf_counter() - if last_step: if stop_after_step is not None and step < args.iterations: log0( @@ -1003,9 +1469,11 @@ def lr_mul(step: int, elapsed_ms: float) -> float: f"step:{step}/{args.iterations}" ) break - elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") zero_grad_all() train_loss = torch.zeros((), device=device) for micro_step in range(grad_accum_steps): @@ -1017,24 +1485,33 @@ def lr_mul(step: int, elapsed_ms: float) -> float: train_loss += loss.detach() (loss * grad_scale).backward() train_loss /= grad_accum_steps - frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum for group in optimizer_muon.param_groups: group["momentum"] = muon_momentum - for opt in optimizers: for group in opt.param_groups: group["lr"] = group["base_lr"] * scale - if args.grad_clip_norm > 0: torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) for opt in optimizers: opt.step() zero_grad_all() - + # EMA update + with torch.no_grad(): + for name, t in base_model.state_dict().items(): + ema_state[name].mul_(ema_decay).add_(t.detach().float(), alpha=1.0 - ema_decay) step += 1 approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu() + swa_count += 1 should_log_train = ( args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) @@ -1044,8 +1521,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" ) - - # Needed to sync whether we've reached the wallclock cap. reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms if distributed and max_wallclock_ms is not None: reached_cap_tensor = torch.tensor(int(reached_cap), device=device) @@ -1053,74 +1528,235 @@ def lr_mul(step: int, elapsed_ms: float) -> float: reached_cap = bool(reached_cap_tensor.item()) if stop_after_step is None and reached_cap: stop_after_step = step - log0( f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" ) - - # ----------------------------- - # SERIALIZATION + ROUNDTRIP VALIDATION - # ----------------------------- - # Save the raw state (useful for debugging/loading in PyTorch directly), then always produce - # the compressed int8+zlib artifact and validate the round-tripped weights. - + # Apply EMA weights (better than SWA alone per PR#401) + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + # skip diagnostic eval to save eval-time budget + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: + log0(f"export_excluding_mtp_params:{excluded_mtp}") if master_process: - torch.save(base_model.state_dict(), "final_model.pt") + torch.save(export_sd, "final_model.pt") model_bytes = os.path.getsize("final_model.pt") code_bytes = len(code.encode("utf-8")) log0(f"Serialized model: {model_bytes} bytes") log0(f"Code size: {code_bytes} bytes") - log0(f"Total submission size: {model_bytes + code_bytes} bytes") - - quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict()) + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"}) quant_buf = io.BytesIO() - torch.save(quant_obj, quant_buf) + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) quant_raw = quant_buf.getvalue() - quant_blob = zlib.compress(quant_raw, level=9) - quant_raw_bytes = len(quant_raw) + quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw) if _COMPRESSOR == "zstd" else zlib.compress(quant_raw, 9) if master_process: - with open("final_model.int8.ptz", "wb") as f: + with open("final_model.int6.ptz", "wb") as f: f.write(quant_blob) - quant_file_bytes = os.path.getsize("final_model.int8.ptz") + quant_file_bytes = len(quant_blob) code_bytes = len(code.encode("utf-8")) - ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1) - log0( - f"Serialized model int8+zlib: {quant_file_bytes} bytes " - f"(payload:{quant_stats['int8_payload_bytes']} raw_torch:{quant_raw_bytes} payload_ratio:{ratio:.2f}x)" - ) + log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") + log0(f"Total submission size int6+{_COMPRESSOR}: {quant_file_bytes + code_bytes} bytes") log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes") - if distributed: dist.barrier() - with open("final_model.int8.ptz", "rb") as f: + with open("final_model.int6.ptz", "rb") as f: quant_blob_disk = f.read() - quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu") - base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True) - torch.cuda.synchronize() - t_qeval = time.perf_counter() - q_val_loss, q_val_bpb = eval_val( - args, - model, - rank, - world_size, - device, - grad_accum_steps, - val_tokens, - base_bytes_lut, - has_leading_space_lut, - is_boundary_token_lut, + quant_state = torch.load( + io.BytesIO(zstandard.ZstdDecompressor().decompress(quant_blob_disk) if _COMPRESSOR == "zstd" else zlib.decompress(quant_blob_disk)), + map_location="cpu", ) - torch.cuda.synchronize() - log0( - f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms" - ) - log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") - + deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, # must match training model + rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + # eval_model is used directly by n-gram eval (which compiles internally) + + # TTT: preeval (bulk train then score) or legal (score-first, chunk by chunk) + ttt_epochs = int(os.environ.get("TTT_EPOCHS", 0)) + ttt_lr = float(os.environ.get("TTT_LR", 0.0005)) + ttt_mode = os.environ.get("TTT_MODE", "preeval") # "preeval" or "legal" + if ttt_epochs > 0 and ttt_mode == "preeval": + torch.cuda.synchronize() + t_ttt = time.perf_counter() + log0(f"ttt: starting {ttt_epochs} epochs, lr={ttt_lr}, cosine+perlayer") + # per-layer LR groups: 3x for MLP output projections, 0.5x for MLP input + proj_params, fc_params, other_params = [], [], [] + for name, p in eval_model.named_parameters(): + p.requires_grad_(True) + if "mlp.proj" in name: + proj_params.append(p) + elif "mlp.fc" in name: + fc_params.append(p) + else: + other_params.append(p) + ttt_opt = torch.optim.AdamW([ + {"params": proj_params, "lr": ttt_lr * 3.0}, + {"params": fc_params, "lr": ttt_lr * 0.5}, + {"params": other_params, "lr": ttt_lr}, + ], weight_decay=0.0) + total_val = val_tokens.numel() - 1 + ttt_batch = 32 + rank_tokens = total_val // world_size + rank_start = rank * rank_tokens + rank_end = rank_start + rank_tokens + steps_per_epoch = max(1, (rank_end - rank_start - args.train_seq_len) // (ttt_batch * args.train_seq_len)) + total_steps = ttt_epochs * steps_per_epoch + global_step = 0 + eval_model.train() + for ep in range(ttt_epochs): + ep_loss, ep_steps = 0.0, 0 + for bs in range(rank_start, rank_end - args.train_seq_len, ttt_batch * args.train_seq_len): + be = min(bs + ttt_batch * args.train_seq_len + 1, rank_end + 1) + local = val_tokens[bs:be].to(device=device, dtype=torch.int64) + n = (local.numel() - 1) // args.train_seq_len + if n == 0: + continue + x = local[:n * args.train_seq_len].reshape(n, args.train_seq_len) + y = local[1:n * args.train_seq_len + 1].reshape(n, args.train_seq_len) + # cosine LR schedule + progress = global_step / max(total_steps, 1) + cos_mul = 0.5 * (1.0 + math.cos(math.pi * progress)) + for g in ttt_opt.param_groups: + g["lr"] = g.get("initial_lr", g["lr"]) * cos_mul + if global_step == 0: + for g in ttt_opt.param_groups: + g["initial_lr"] = g["lr"] + ttt_opt.zero_grad() + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = eval_model(x, y) + loss.backward() + # sync gradients across ranks + if distributed: + for p in eval_model.parameters(): + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + torch.nn.utils.clip_grad_norm_(eval_model.parameters(), 1.0) + ttt_opt.step() + ep_loss += loss.item() + ep_steps += 1 + global_step += 1 + if master_process and (ep + 1) % 5 == 0: + log0(f"ttt_epoch:{ep + 1}/{ttt_epochs} avg_loss:{ep_loss / max(ep_steps, 1):.4f}") + del ttt_opt + torch.cuda.empty_cache() + torch.cuda.synchronize() + log0(f"ttt: completed in {1000.0 * (time.perf_counter() - t_ttt):.0f}ms") + + # legal score-first TTT: score chunk, then train on scored tokens + if ttt_epochs > 0 and ttt_mode == "legal": + torch.cuda.synchronize(); t_ttt = time.perf_counter() + sl = effective_eval_seq_len; st = args.eval_stride if args.eval_stride > 0 else sl; scl = min(st, sl) + for p in eval_model.parameters(): p.requires_grad_(False) + nb = len(eval_model.blocks) if hasattr(eval_model, 'blocks') else 0 + tp = [] + for nm, p in eval_model.named_parameters(): + bi = next((i for i in range(nb) if f"blocks.{i}." in nm), -1) + if bi >= nb - 2 or any(k in nm for k in ("norm","scale","q_gain","lm_head","tok_emb","smear","bigram")): + p.requires_grad_(True); tp.append(p) + to = torch.optim.AdamW(tp, lr=ttt_lr * 0.2, weight_decay=0.0) + log0(f"legal_ttt: {len(tp)} params, {ttt_epochs}ep/chunk") + tot = val_tokens.numel() - 1; cs = 65536 + ns, nc, nb2 = torch.zeros((),dtype=torch.float64,device=device), torch.zeros((),dtype=torch.float64,device=device), torch.zeros((),dtype=torch.float64,device=device) + for c0 in range(0, tot - sl + 1, cs): + eval_model.eval() + with torch.inference_mode(): + for ws in range(c0, min(c0+cs, tot-sl+1), st*world_size): + s = ws + rank*st + if s+sl > tot: continue + x = val_tokens[s:s+sl].to(device=device,dtype=torch.int64).unsqueeze(0) + y = val_tokens[s+1:s+sl+1].to(device=device,dtype=torch.int64).unsqueeze(0) + with torch.autocast(device_type="cuda",dtype=torch.bfloat16,enabled=True): + lo = eval_model.forward_logits(x) if hasattr(eval_model,'forward_logits') else None + if lo is not None: + sf = sl-scl; lt = lo[:,sf:,:].reshape(-1,lo.size(-1)).float(); tt = y[:,sf:].reshape(-1) + ns += F.cross_entropy(lt,tt,reduction="sum").to(torch.float64); nc += scl + pr,tg = x[:,sf:].reshape(-1), tt + tb = base_bytes_lut[tg].to(torch.int16) + (has_leading_space_lut[tg]&~is_boundary_token_lut[pr]).to(torch.int16) + nb2 += tb.to(torch.float64).sum() + eval_model.train() + ct = val_tokens[c0:min(c0+cs+sl,tot+1)].to(device=device,dtype=torch.int64) + nq = (ct.numel()-1)//sl + if nq > 0: + for _ in range(ttt_epochs): + xc,yc = ct[:nq*sl].reshape(nq,sl), ct[1:nq*sl+1].reshape(nq,sl) + for bi in range(0,nq,4): + xb,yb = xc[bi:bi+4], yc[bi:bi+4] + if xb.shape[0]==0: continue + to.zero_grad() + with torch.autocast(device_type="cuda",dtype=torch.bfloat16,enabled=True): l=eval_model(xb,yb) + l.backward(); to.step() + if distributed: + for t in (ns,nc,nb2): dist.all_reduce(t, op=dist.ReduceOp.SUM) + if nc.item()>0: + ll=ns.item()/nc.item(); bb=float(ll/math.log(2.0)*nc.item()/nb2.item()) + log0(f"legal_ttt val_loss:{ll:.4f} val_bpb:{bb:.4f} time:{1000*(time.perf_counter()-t_ttt):.0f}ms") + log0(f"legal_ttt_exact val_loss:{ll:.8f} val_bpb:{bb:.8f}") + del to; torch.cuda.empty_cache() + + # n-gram cache eval (includes sliding window — replaces standalone sw eval) + ngram_enabled = bool(int(os.environ.get("NGRAM_ENABLED", "1"))) + sw_seq_len = effective_eval_seq_len + if ngram_enabled: + ngram_order = int(os.environ.get("NGRAM_ORDER", "9")) + ngram_min_order = int(os.environ.get("NGRAM_MIN_ORDER", "2")) + ngram_buckets = int(os.environ.get("NGRAM_BUCKETS", "4194304")) + ngram_min_count = int(os.environ.get("NGRAM_MIN_COUNT", "2")) + ngram_alpha = float(os.environ.get("NGRAM_ALPHA", "0.2")) + ngram_ent_base = float(os.environ.get("NGRAM_ENT_BASE", "0.05")) + ngram_ent_range = float(os.environ.get("NGRAM_ENT_RANGE", "0.55")) + ngram_ent_scale = float(os.environ.get("NGRAM_ENT_SCALE", "2.0")) + ngram_ent_thresh = float(os.environ.get("NGRAM_ENT_THRESH", "4.0")) + torch.cuda.synchronize() + t_ngram = time.perf_counter() + log0(f"ngram_eval: order={ngram_order} min_order={ngram_min_order} buckets={ngram_buckets} alpha={ngram_alpha}") + ng_val_loss, ng_val_bpb = eval_val_ngram( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=sw_seq_len if args.eval_stride > 0 else effective_eval_seq_len, + stride=args.eval_stride if args.eval_stride > 0 else effective_eval_seq_len, + ngram_order=ngram_order, ngram_min_order=ngram_min_order, + ngram_buckets=ngram_buckets, ngram_min_count=ngram_min_count, + fixed_alpha=ngram_alpha, + ent_base=ngram_ent_base, ent_range=ngram_ent_range, + ent_scale=ngram_ent_scale, ent_thresh=ngram_ent_thresh, + log_fn=log0, + ) + torch.cuda.synchronize() + log0(f"ngram_eval val_loss:{ng_val_loss:.4f} val_bpb:{ng_val_bpb:.4f} eval_time:{1000.0*(time.perf_counter()-t_ngram):.0f}ms") + log0(f"ngram_eval_exact val_loss:{ng_val_loss:.8f} val_bpb:{ng_val_bpb:.8f}") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{ng_val_loss:.8f} val_bpb:{ng_val_bpb:.8f}") + else: + if args.eval_stride > 0 and args.eval_stride < sw_seq_len: + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0(f"final_int6_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} stride:{args.eval_stride} eval_time:{1000.0*(time.perf_counter()-t_slide):.0f}ms") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") if distributed: dist.destroy_process_group() - - if __name__ == "__main__": main()