diff --git a/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/README.md b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/README.md new file mode 100644 index 0000000000..3eb98cc32a --- /dev/null +++ b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/README.md @@ -0,0 +1,126 @@ +# SP8192 + LongCtx NoQV QK5.25 Prefix2750 + +This is a deliberately small follow-up candidate on PR #1953: + +- Base: PR #1953, `PR #1945 base + 2560 long-context + no_qv TTT mask + TTT LR 0.75 + QK_GAIN 5.25`. +- Only intended change: `PHASED_TTT_PREFIX_DOCS=2750` instead of `2500`. +- No tokenizer change, no PPM, no n-gram, no SLOT, no logit bias, no pre-quant validation adaptation. +- Artifact size is effectively unchanged; the risk is eval time, not bytes. + +## Why this change + +PR #1953 reports max eval time `513.1s`, leaving roughly `87s` under the 600s eval cap. Its lineage already shows that increasing phased-TTT prefix docs from earlier values to `2500` was useful. This candidate spends part of the remaining eval budget on a slightly larger TTT prefix (`2750`) while keeping every other mechanism unchanged. + +Single-seed testing shows this change is essentially neutral versus the #1953 seed 42 reference. It is included as a narrow phased-TTT prefix schedule experiment with the full seed 42 log. + +## Result + +| Run | Seed | Prefix docs | Final BPB | Eval time | Total bytes | +| --- | ---: | ---: | ---: | ---: | ---: | +| #1953 reference | 42 | 2500 | 1.05824720 | 430.0s | 15,988,861 | +| This experiment | 42 | 2750 | 1.05826976 | 495.0s | 15,978,173 | + +The longer prefix increases eval time by about 65s and lands within `0.00003 BPB` of the #1953 seed 42 reference. The full log is included as `train_seed42.log`. + +## Data + +This script uses the CaseOps SP8192 dataset. Do not use the ordinary `sp8192` FineWeb download. + +Expected layout after running `download_caseops_data.py`: + +```text +/workspace/caseops_data/datasets/ + tokenizers/fineweb_8192_bpe_lossless_caps_caseops_v1_reserved.model + datasets/fineweb10B_sp8192_lossless_caps_caseops_v1_reserved/ + fineweb_train_*.bin + fineweb_val_*.bin + fineweb_val_bytes_*.bin +``` + +Download and validate: + +```bash +python3 records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/download_caseops_data.py \ + --local-dir /workspace/caseops_data +``` + +## Dependencies + +Python packages are listed in `requirements.txt`. FlashAttention 3 and `lrzip` are required: + +```bash +apt-get update +apt-get install -y lrzip +pip3 install -r records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/requirements.txt +pip3 install --no-deps flash_attn_3 --find-links https://windreamer.github.io/flash-attention3-wheels/cu128_torch291/ +``` + +## Single-seed test + +```bash +RUN_ID=1953_prefix2750_seed42 \ +SEED=42 \ +DATA_DIR=/workspace/caseops_data/datasets \ +DATA_PATH=/workspace/caseops_data/datasets/datasets/fineweb10B_sp8192_lossless_caps_caseops_v1_reserved \ +TOKENIZER_PATH=/workspace/caseops_data/datasets/tokenizers/fineweb_8192_bpe_lossless_caps_caseops_v1_reserved.model \ +CASEOPS_ENABLED=1 \ +VOCAB_SIZE=8192 \ +ITERATIONS=20000 \ +MAX_WALLCLOCK_SECONDS=600 \ +VAL_LOSS_EVERY=0 \ +WARMDOWN_FRAC=0.85 \ +BETA2=0.99 \ +MUON_MOMENTUM=0.97 \ +MATRIX_LR=0.026 \ +MIN_LR=0.1 \ +EMBED_BITS=7 \ +MATRIX_CLIP_SIGMAS=12.85 \ +ATTN_CLIP_SIGMAS=13.0 \ +MLP_CLIP_SIGMAS=11.5 \ +EMBED_CLIP_SIGMAS=14.0 \ +GRAD_CLIP_NORM=0.3 \ +FUSED_CE_ENABLED=1 \ +SMEAR_GATE_ENABLED=1 \ +GATE_WINDOW=12 \ +SPARSE_ATTN_GATE_ENABLED=1 \ +SPARSE_ATTN_GATE_SCALE=0.5 \ +SPARSE_ATTN_GATE_INIT_STD=0.0 \ +GATED_ATTN_QUANT_GATE=1 \ +LQER_ENABLED=1 \ +LQER_RANK=4 \ +LQER_TOP_K=3 \ +LQER_GROUP_SIZE=64 \ +LQER_FACTOR_BITS=4 \ +LQER_ASYM_ENABLED=1 \ +LQER_ASYM_GROUP=64 \ +AWQ_LITE_ENABLED=1 \ +ASYM_LOGIT_RESCALE=1 \ +GPTQ_RESERVE_SECONDS=4.0 \ +GPTQ_CALIBRATION_BATCHES=16 \ +COMPRESSOR=pergroup \ +TTT_ENABLED=1 \ +PHASED_TTT_ENABLED=1 \ +PHASED_TTT_NUM_PHASES=3 \ +PHASED_TTT_PREFIX_DOCS=2750 \ +TTT_LORA_RANK=80 \ +TTT_MASK=no_qv \ +TTT_Q_LORA=0 \ +TTT_V_LORA=0 \ +TTT_LOCAL_LR_MULT=0.75 \ +TTT_BETA2=0.99 \ +TTT_WEIGHT_DECAY=0.5 \ +EVAL_SEQ_LEN=2560 \ +TTT_EVAL_SEQ_LEN=2560 \ +QK_GAIN_INIT=5.25 \ +NCCL_NET=Socket \ +torchrun --standalone --nproc_per_node=8 \ + records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/train_gpt.py +``` + +## Decision rule + +Compare seed 42 against PR #1953 seed 42: + +- PR #1953 seed 42 post-TTT: `1.05824720`. +- This experiment seed 42 post-TTT: `1.05826976`. +- This is a single-seed schedule experiment; the result is effectively tied with the #1953 seed 42 reference while using a larger phased-TTT prefix. diff --git a/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/download_caseops_data.py b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/download_caseops_data.py new file mode 100644 index 0000000000..83793e0a0d --- /dev/null +++ b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/download_caseops_data.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +from pathlib import Path + +from huggingface_hub import snapshot_download + + +DEFAULT_REPO_ID = "romeerp/parameter-golf-caseops-v1" +TOKENIZER = "datasets/tokenizers/fineweb_8192_bpe_lossless_caps_caseops_v1_reserved.model" +DATASET_DIR = "datasets/datasets/fineweb10B_sp8192_lossless_caps_caseops_v1_reserved" + + +def count_files(path: Path, pattern: str) -> int: + return len(list(path.glob(pattern))) + + +def validate(local_dir: Path, min_train_shards: int) -> None: + root = local_dir / "datasets" + tokenizer = root / "tokenizers" / "fineweb_8192_bpe_lossless_caps_caseops_v1_reserved.model" + dataset = root / "datasets" / "fineweb10B_sp8192_lossless_caps_caseops_v1_reserved" + missing = [p for p in (tokenizer, dataset) if not p.exists()] + if missing: + raise FileNotFoundError("Missing CaseOps files: " + ", ".join(str(p) for p in missing)) + + train = count_files(dataset, "fineweb_train_*.bin") + val = count_files(dataset, "fineweb_val_*.bin") + val_bytes = count_files(dataset, "fineweb_val_bytes_*.bin") + if train < min_train_shards: + raise RuntimeError(f"Expected at least {min_train_shards} train shards, found {train}") + if val == 0: + raise RuntimeError("No fineweb_val_*.bin shards found") + if val_bytes == 0: + raise RuntimeError("No fineweb_val_bytes_*.bin sidecar shards found") + print(f"CaseOps data ready: train_shards={train} val_shards={val} val_byte_shards={val_bytes}") + print(f"DATA_DIR={root}") + print(f"DATA_PATH={dataset}") + print(f"TOKENIZER_PATH={tokenizer}") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Download the CaseOps SP8192 dataset used by the #1953 lineage.") + parser.add_argument("--repo-id", default=DEFAULT_REPO_ID) + parser.add_argument("--local-dir", default="/workspace/caseops_data", type=Path) + parser.add_argument("--min-train-shards", default=80, type=int) + parser.add_argument("--validate-only", action="store_true") + args = parser.parse_args() + + if not args.validate_only: + snapshot_download( + repo_id=args.repo_id, + repo_type="dataset", + local_dir=str(args.local_dir), + allow_patterns=[ + TOKENIZER, + f"{DATASET_DIR}/fineweb_train_*.bin", + f"{DATASET_DIR}/fineweb_val_*.bin", + f"{DATASET_DIR}/fineweb_val_bytes_*.bin", + ], + ) + validate(args.local_dir, args.min_train_shards) + + +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/requirements.txt b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/requirements.txt new file mode 100644 index 0000000000..fbe6b214e9 --- /dev/null +++ b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/requirements.txt @@ -0,0 +1,7 @@ +--extra-index-url https://download.pytorch.org/whl/cu128 +torch==2.9.1+cu128 +sentencepiece +brotli +huggingface_hub +numpy +python-minifier diff --git a/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/submission.json b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/submission.json new file mode 100644 index 0000000000..f1c52c84e8 --- /dev/null +++ b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/submission.json @@ -0,0 +1,36 @@ +{ + "author": "someone114514", + "github_id": "someone114514", + "name": "SP8192 + LongCtx NoQV QK5.25 Prefix2750, seed42 1.05827 BPB", + "date": "2026-04-30", + "track": "10min_16mb", + "status": "single_seed_result", + "base_pr": 1953, + "base_val_bpb": 1.0585537, + "base_seed42_val_bpb": 1.0582472, + "candidate_change": { + "PHASED_TTT_PREFIX_DOCS": 2750, + "base_PHASED_TTT_PREFIX_DOCS": 2500, + "everything_else": "intended to match PR #1953" + }, + "val_bpb": 1.05826976, + "val_bpb_std": null, + "seeds": [42], + "seed_results": { + "42": { + "pre_quant_bpb": 1.06162201, + "quantized_bpb": 1.07001475, + "quantized_ttt_phased_bpb": 1.05826976, + "eval_time_s": 495.0, + "total_submission_bytes": 15978173 + } + }, + "compliance": { + "no_tokenizer_change": true, + "no_ppm": true, + "no_ngram": true, + "no_slot": true, + "no_logit_bias": true, + "score_before_update": "inherits PR #1953 phased TTT score-first implementation" + } +} diff --git a/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/train_gpt.py b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/train_gpt.py new file mode 100644 index 0000000000..b9226c6d53 --- /dev/null +++ b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/train_gpt.py @@ -0,0 +1,4064 @@ +import base64, collections, copy, fcntl, glob, io, lzma, math, os +from pathlib import Path +import random, re, subprocess, sys, time, uuid, numpy as np, sentencepiece as spm, torch, torch.distributed as dist, torch.nn.functional as F +from torch import Tensor, nn +from flash_attn_interface import ( + flash_attn_func as flash_attn_3_func, + flash_attn_varlen_func, +) +from concurrent.futures import ThreadPoolExecutor +import triton +import triton.language as tl +from triton.tools.tensor_descriptor import TensorDescriptor + + +# ===== Fused softcapped cross-entropy (Triton) — training-only path ===== +# Replaces the eager +# logits_softcap = softcap * tanh(logits / softcap) +# F.cross_entropy(logits_softcap.float(), targets, reduction="mean") +# sequence with a single fused kernel that reads logits_proj once, applies +# softcap in-register, and computes (LSE, loss) in one streaming pass. The +# backward kernel mirrors the forward so there's no stored softcapped logits. +# Numerically identical to the eager path up to fp32 accumulation differences. +_FUSED_CE_LIBRARY = "pgsubmission1draft7fusedce" +_FUSED_CE_BLOCK_SIZE = 1024 +_FUSED_CE_NUM_WARPS = 4 + + +@triton.jit +def _softcapped_ce_fwd_kernel( + logits_ptr, losses_ptr, lse_ptr, targets_ptr, + stride_logits_n, stride_logits_v, + n_rows, n_cols, softcap, + block_size: tl.constexpr, +): + row_idx = tl.program_id(0).to(tl.int64) + logits_row_ptr = logits_ptr + row_idx * stride_logits_n + max_val = -float("inf") + sum_exp = 0.0 + A = 2.0 * softcap + inv_C = 2.0 / softcap + for off in range(0, n_cols, block_size): + cols = off + tl.arange(0, block_size) + mask = cols < n_cols + val = tl.load( + logits_row_ptr + cols * stride_logits_v, + mask=mask, other=-float("inf"), + ).to(tl.float32) + z = A * tl.sigmoid(val * inv_C) + z = tl.where(mask, z, -float("inf")) + curr_max = tl.max(z, axis=0) + new_max = tl.maximum(max_val, curr_max) + sum_exp = sum_exp * tl.exp(max_val - new_max) + tl.sum(tl.exp(z - new_max), axis=0) + max_val = new_max + lse = max_val + tl.log(sum_exp) + tl.store(lse_ptr + row_idx, lse) + target = tl.load(targets_ptr + row_idx).to(tl.int32) + target_val = tl.load(logits_row_ptr + target * stride_logits_v).to(tl.float32) + target_z = A * tl.sigmoid(target_val * inv_C) + tl.store(losses_ptr + row_idx, lse - target_z) + + +@triton.jit +def _softcapped_ce_bwd_kernel( + grad_logits_ptr, grad_losses_ptr, lse_ptr, logits_ptr, targets_ptr, + stride_logits_n, stride_logits_v, + stride_grad_n, stride_grad_v, + n_rows, n_cols, softcap, + block_size: tl.constexpr, +): + row_idx = tl.program_id(0).to(tl.int64) + logits_row_ptr = logits_ptr + row_idx * stride_logits_n + grad_row_ptr = grad_logits_ptr + row_idx * stride_grad_n + lse = tl.load(lse_ptr + row_idx) + grad_loss = tl.load(grad_losses_ptr + row_idx).to(tl.float32) + target = tl.load(targets_ptr + row_idx).to(tl.int32) + A = 2.0 * softcap + inv_C = 2.0 / softcap + dz_dx_scale = A * inv_C + for off in range(0, n_cols, block_size): + cols = off + tl.arange(0, block_size) + mask = cols < n_cols + val = tl.load( + logits_row_ptr + cols * stride_logits_v, + mask=mask, other=0.0, + ).to(tl.float32) + sigmoid_u = tl.sigmoid(val * inv_C) + z = A * sigmoid_u + probs = tl.exp(z - lse) + grad_z = grad_loss * (probs - tl.where(cols == target, 1.0, 0.0)) + grad_x = grad_z * (dz_dx_scale * sigmoid_u * (1.0 - sigmoid_u)) + tl.store(grad_row_ptr + cols * stride_grad_v, grad_x, mask=mask) + + +def _validate_softcapped_ce_inputs( + logits: Tensor, targets: Tensor, softcap: float, +) -> tuple[Tensor, Tensor]: + if logits.ndim != 2: + raise ValueError(f"Expected logits.ndim=2, got {logits.ndim}") + if targets.ndim != 1: + raise ValueError(f"Expected targets.ndim=1, got {targets.ndim}") + if logits.shape[0] != targets.shape[0]: + raise ValueError( + f"Expected matching rows, got logits={tuple(logits.shape)} targets={tuple(targets.shape)}" + ) + if not logits.is_cuda or not targets.is_cuda: + raise ValueError("softcapped_cross_entropy requires CUDA tensors") + if softcap <= 0.0: + raise ValueError(f"softcap must be positive, got {softcap}") + if logits.dtype not in (torch.float16, torch.bfloat16, torch.float32): + raise ValueError(f"Unsupported logits dtype: {logits.dtype}") + logits = logits.contiguous() + targets = targets.contiguous() + if targets.dtype != torch.int64: + targets = targets.to(dtype=torch.int64) + return logits, targets + + +@torch.library.custom_op(f"{_FUSED_CE_LIBRARY}::softcapped_ce", mutates_args=()) +def softcapped_ce_op(logits: Tensor, targets: Tensor, softcap: float) -> tuple[Tensor, Tensor]: + logits, targets = _validate_softcapped_ce_inputs(logits, targets, float(softcap)) + n_rows, n_cols = logits.shape + losses = torch.empty((n_rows,), device=logits.device, dtype=torch.float32) + lse = torch.empty((n_rows,), device=logits.device, dtype=torch.float32) + _softcapped_ce_fwd_kernel[(n_rows,)]( + logits, losses, lse, targets, + logits.stride(0), logits.stride(1), + n_rows, n_cols, float(softcap), + block_size=_FUSED_CE_BLOCK_SIZE, num_warps=_FUSED_CE_NUM_WARPS, + ) + return losses, lse + + +@softcapped_ce_op.register_fake +def _(logits: Tensor, targets: Tensor, softcap: float): + if logits.ndim != 2 or targets.ndim != 1: + raise ValueError("softcapped_ce fake impl expects 2D logits and 1D targets") + if logits.shape[0] != targets.shape[0]: + raise ValueError( + f"Expected matching rows, got logits={tuple(logits.shape)} targets={tuple(targets.shape)}" + ) + n_rows = logits.shape[0] + return ( + logits.new_empty((n_rows,), dtype=torch.float32), + logits.new_empty((n_rows,), dtype=torch.float32), + ) + + +@torch.library.custom_op(f"{_FUSED_CE_LIBRARY}::softcapped_ce_backward", mutates_args=()) +def softcapped_ce_backward_op( + logits: Tensor, targets: Tensor, lse: Tensor, grad_losses: Tensor, softcap: float, +) -> Tensor: + logits, targets = _validate_softcapped_ce_inputs(logits, targets, float(softcap)) + lse = lse.contiguous() + grad_losses = grad_losses.contiguous().to(dtype=torch.float32) + if lse.ndim != 1 or grad_losses.ndim != 1: + raise ValueError("Expected 1D lse and grad_losses") + if lse.shape[0] != logits.shape[0] or grad_losses.shape[0] != logits.shape[0]: + raise ValueError( + f"Expected row-aligned lse/grad_losses, got logits={tuple(logits.shape)} " + f"lse={tuple(lse.shape)} grad_losses={tuple(grad_losses.shape)}" + ) + grad_logits = torch.empty_like(logits) + n_rows, n_cols = logits.shape + _softcapped_ce_bwd_kernel[(n_rows,)]( + grad_logits, grad_losses, lse, logits, targets, + logits.stride(0), logits.stride(1), + grad_logits.stride(0), grad_logits.stride(1), + n_rows, n_cols, float(softcap), + block_size=_FUSED_CE_BLOCK_SIZE, num_warps=_FUSED_CE_NUM_WARPS, + ) + return grad_logits + + +@softcapped_ce_backward_op.register_fake +def _(logits: Tensor, targets: Tensor, lse: Tensor, grad_losses: Tensor, softcap: float): + if logits.ndim != 2 or targets.ndim != 1 or lse.ndim != 1 or grad_losses.ndim != 1: + raise ValueError("softcapped_ce_backward fake impl expects 2D logits and 1D row tensors") + if ( + logits.shape[0] != targets.shape[0] + or logits.shape[0] != lse.shape[0] + or logits.shape[0] != grad_losses.shape[0] + ): + raise ValueError("softcapped_ce_backward fake impl expects row-aligned tensors") + return logits.new_empty(logits.shape) + + +def _softcapped_ce_setup_context( + ctx: torch.autograd.function.FunctionCtx, inputs, output, +) -> None: + logits, targets, softcap = inputs + _losses, lse = output + ctx.save_for_backward(logits, targets, lse) + ctx.softcap = float(softcap) + + +def _softcapped_ce_backward( + ctx: torch.autograd.function.FunctionCtx, grad_losses: Tensor, grad_lse: "Tensor | None", +): + del grad_lse + logits, targets, lse = ctx.saved_tensors + grad_logits = torch.ops.pgsubmission1draft7fusedce.softcapped_ce_backward( + logits, targets, lse, grad_losses, ctx.softcap + ) + return grad_logits, None, None + + +softcapped_ce_op.register_autograd( + _softcapped_ce_backward, setup_context=_softcapped_ce_setup_context, +) + + +def softcapped_cross_entropy( + logits: Tensor, targets: Tensor, softcap: float, reduction: str = "mean", +) -> Tensor: + losses, _lse = torch.ops.pgsubmission1draft7fusedce.softcapped_ce( + logits, targets, float(softcap) + ) + if reduction == "none": + return losses + if reduction == "sum": + return losses.sum() + if reduction == "mean": + return losses.mean() + raise ValueError(f"Unsupported reduction={reduction!r}") + + +class Hyperparameters: + data_dir = os.environ.get("DATA_DIR", "./data/") + seed = int(os.environ.get("SEED", 1337)) + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_frac = float(os.environ.get("WARMDOWN_FRAC", 0.75)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786432)) + # Fused softcapped CE (Triton). Training-only — forward_logits eval path still uses + # eager softcap+F.cross_entropy. Default ON since validated as at-worst neutral. + fused_ce_enabled = bool(int(os.environ.get("FUSED_CE_ENABLED", "1"))) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 500)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 6e2)) + val_batch_tokens = int(os.environ.get("VAL_BATCH_TOKENS", 524288)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 4000)) + vocab_size = int(os.environ.get("VOCAB_SIZE", 8192)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 4.0)) + skip_gates_enabled = bool(int(os.environ.get("SKIP_GATES_ENABLED", "1"))) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 3e1)) + rope_base = float(os.environ.get("ROPE_BASE", 1e4)) + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + rope_train_seq_len = int(os.environ.get("ROPE_TRAIN_SEQ_LEN", 2048)) + rope_yarn = bool(int(os.environ.get("ROPE_YARN", "0"))) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 5.0)) + num_loops = int(os.environ.get("NUM_LOOPS", 2)) + loop_start = int(os.environ.get("LOOP_START", 3)) + loop_end = int(os.environ.get("LOOP_END", 5)) + enable_looping_at = float(os.environ.get("ENABLE_LOOPING_AT", 0.35)) + parallel_start_layer = int(os.environ.get("PARALLEL_START_LAYER", 8)) + parallel_final_lane = os.environ.get("PARALLEL_FINAL_LANE", "mean") + min_lr = float(os.environ.get("MIN_LR", 0.0)) + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.026)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.02)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.97)) + 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)) + muon_row_normalize = bool(int(os.environ.get("MUON_ROW_NORMALIZE", "1"))) + 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-08)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + adam_wd = float(os.environ.get("ADAM_WD", 0.02)) + muon_wd = float(os.environ.get("MUON_WD", 0.095)) + embed_wd = float(os.environ.get("EMBED_WD", 0.085)) + ema_decay = float(os.environ.get("EMA_DECAY", 0.9965)) + ttt_enabled = bool(int(os.environ.get("TTT_ENABLED", "1"))) + ttt_lora_rank = int(os.environ.get("TTT_LORA_RANK", 96)) + ttt_lora_lr = float(os.environ.get("TTT_LORA_LR", 0.0001)) + ttt_local_lr_mult = float(os.environ.get("TTT_LOCAL_LR_MULT", 1.0)) + ttt_chunk_size = int(os.environ.get("TTT_CHUNK_SIZE", 48)) + ttt_eval_seq_len = int(os.environ.get("TTT_EVAL_SEQ_LEN", 2048)) + ttt_batch_size = int(os.environ.get("TTT_BATCH_SIZE", 64)) + ttt_grad_steps = int(os.environ.get("TTT_GRAD_STEPS", 1)) + # V19: PR #1886 (renqianluo) + sunnypatneedi research log 2026-04-28 found that + # the Triton fused-CE kernel's fp32-accumulation interacts with warm-start LoRA-A + # to destabilize seeds 314/1337 at TTT_WEIGHT_DECAY=1.0. Raising the default to + # 2.0 prevents seed collapse without measurably moving stable seeds. + ttt_weight_decay = float(os.environ.get("TTT_WEIGHT_DECAY", 2.0)) + ttt_beta1 = float(os.environ.get("TTT_BETA1", 0)) + ttt_beta2 = float(os.environ.get("TTT_BETA2", 0.999)) + ttt_mask = os.environ.get("TTT_MASK", "").strip().lower() + _ttt_q_default = "1" + _ttt_v_default = "1" + if ttt_mask in ("", "all", "baseline_all"): + pass + elif ttt_mask == "no_q": + _ttt_q_default = "0" + elif ttt_mask == "no_v": + _ttt_v_default = "0" + elif ttt_mask == "no_qv": + _ttt_q_default = "0" + _ttt_v_default = "0" + else: + raise ValueError(f"Unsupported TTT_MASK={ttt_mask!r}") + ttt_q_lora = bool(int(os.environ.get("TTT_Q_LORA", _ttt_q_default))) + ttt_k_lora = bool(int(os.environ.get("TTT_K_LORA", "1"))) + ttt_v_lora = bool(int(os.environ.get("TTT_V_LORA", _ttt_v_default))) + ttt_mlp_lora = bool(int(os.environ.get("TTT_MLP_LORA", "1"))) + ttt_o_lora = bool(int(os.environ.get("TTT_O_LORA", "1"))) + ttt_optimizer = os.environ.get("TTT_OPTIMIZER", "adam") + ttt_eval_batches = os.environ.get("TTT_EVAL_BATCHES", "") + val_doc_fraction = float(os.environ.get("VAL_DOC_FRACTION", 1.0)) + compressor = os.environ.get("COMPRESSOR", "brotli") + gptq_calibration_batches = int(os.environ.get("GPTQ_CALIBRATION_BATCHES", 16)) + gptq_reserve_seconds = float(os.environ.get("GPTQ_RESERVE_SECONDS", 4.0)) + phased_ttt_prefix_docs = int(os.environ.get("PHASED_TTT_PREFIX_DOCS", 2750)) + phased_ttt_num_phases = int(os.environ.get("PHASED_TTT_NUM_PHASES", 1)) + global_ttt_lr = float(os.environ.get("GLOBAL_TTT_LR", 0.001)) + global_ttt_momentum = float(os.environ.get("GLOBAL_TTT_MOMENTUM", 0.9)) + global_ttt_epochs = int(os.environ.get("GLOBAL_TTT_EPOCHS", 1)) + global_ttt_chunk_tokens = int(os.environ.get("GLOBAL_TTT_CHUNK_TOKENS", 32768)) + global_ttt_batch_seqs = int(os.environ.get("GLOBAL_TTT_BATCH_SEQS", 32)) + global_ttt_warmup_start_lr = float(os.environ.get("GLOBAL_TTT_WARMUP_START_LR", 0.0)) + global_ttt_warmup_chunks = int(os.environ.get("GLOBAL_TTT_WARMUP_CHUNKS", 0)) + global_ttt_grad_clip = float(os.environ.get("GLOBAL_TTT_GRAD_CLIP", 1.0)) + global_ttt_respect_doc_boundaries = bool(int(os.environ.get("GLOBAL_TTT_RESPECT_DOC_BOUNDARIES", "1"))) + matrix_bits = int(os.environ.get("MATRIX_BITS", 6)) + embed_bits = int(os.environ.get("EMBED_BITS", 8)) + matrix_clip_sigmas = float(os.environ.get("MATRIX_CLIP_SIGMAS", 12.85)) + embed_clip_sigmas = float(os.environ.get("EMBED_CLIP_SIGMAS", 2e1)) + mlp_clip_sigmas = float(os.environ.get("MLP_CLIP_SIGMAS", 10.0)) + attn_clip_sigmas = float(os.environ.get("ATTN_CLIP_SIGMAS", 13.0)) + # AttnOutGate (per-head multiplicative output gate, PR #1667 MarioPaerle). + # Zero-init weight: 2*sigmoid(0)=1 -> transparent at start. Source defaults to + # block input x ('proj'); 'q' uses raw Q projection output. + attn_out_gate_enabled = bool(int(os.environ.get("ATTN_OUT_GATE_ENABLED", "0"))) + attn_out_gate_src = os.environ.get("ATTN_OUT_GATE_SRC", "proj") + # SmearGate (input-dependent forward-1 token smear, modded-nanogpt @classiclarryd + # via PR #1667). x_t <- x_t + lam * sigmoid(W*x_t[:gate_window]) * x_{t-1}. + # lam=0 + W=0 -> transparent at init. + smear_gate_enabled = bool(int(os.environ.get("SMEAR_GATE_ENABLED", "0"))) + # Window: first GATE_WINDOW dims of the source feed the gate projection. + gate_window = int(os.environ.get("GATE_WINDOW", 12)) + # Gated Attention (Qwen, NeurIPS 2025 Best Paper, arXiv:2505.06708; + # qiuzh20/gated_attention). Per-head sigmoid gate on SDPA output, BEFORE + # out_proj. Gate input = full block input x (paper's headwise G1 variant + # driven from hidden_states). W_g shape (num_heads, dim), plain sigmoid. + # Near-zero init gives g~0.5 at step 0 (half attention output); per-block + # attn_scale (init 1.0) compensates during training. Name contains + # "attn_gate" so CONTROL_TENSOR_NAME_PATTERNS routes it to scalar AdamW. + gated_attn_enabled = bool(int(os.environ.get("GATED_ATTN_ENABLED", "0"))) + gated_attn_init_std = float(os.environ.get("GATED_ATTN_INIT_STD", 0.01)) + # Dedicated int8-per-row quantization for `attn_gate_w` tensors. These are + # small ((num_heads, dim) = (8, 512) = 4096 params) and bypass GPTQ via the + # numel<=65536 passthrough branch -> stored as fp16 (8 KB/layer, ~65 KB total + # compressed). int8-per-row cuts the raw tensor in half with negligible BPB + # impact: scales per head (8 values), symmetric quant over [-127, 127]. + # No Hessian needed (gate weights not in collect_hessians()). + gated_attn_quant_gate = bool(int(os.environ.get("GATED_ATTN_QUANT_GATE", "0"))) + # Sparse Attention Gate (modded-nanogpt-style). Keeps dense SDPA and only + # swaps the output-gate input to the first GATE_WINDOW residual dims. + # W_g: (num_heads, gate_window) = (8, 12) = 96 params/layer (~44K total), + # vs dense GatedAttn's (8, 512) = 4K/layer (~44K diff). Name "attn_gate_w" + # is shared so quant routing and int8 gate passthrough Just Work. Gate + # passthrough int8 still applies via GATED_ATTN_QUANT_GATE=1. + # Mutually exclusive with ATTN_OUT_GATE_ENABLED and GATED_ATTN_ENABLED. + sparse_attn_gate_enabled = bool(int(os.environ.get("SPARSE_ATTN_GATE_ENABLED", "0"))) + sparse_attn_gate_init_std = float(os.environ.get("SPARSE_ATTN_GATE_INIT_STD", 0.0)) + sparse_attn_gate_scale = float(os.environ.get("SPARSE_ATTN_GATE_SCALE", 1.0)) + # LQER asymmetric rank-k correction on top-K quant-error tensors (PR #1530 v2 port). + # Computes SVD of E = W_fp - W_quant, packs top-r A,B as INT2/INT4 (asym) or INTk (sym). + lqer_enabled = bool(int(os.environ.get("LQER_ENABLED", "1"))) + lqer_rank = int(os.environ.get("LQER_RANK", 4)) + lqer_top_k = int(os.environ.get("LQER_TOP_K", 3)) + lqer_factor_bits = int(os.environ.get("LQER_FACTOR_BITS", 4)) + lqer_asym_enabled = bool(int(os.environ.get("LQER_ASYM_ENABLED", "1"))) + lqer_asym_group = int(os.environ.get("LQER_ASYM_GROUP", "64")) + lqer_scope = os.environ.get("LQER_SCOPE", "all") + lqer_gain_select = bool(int(os.environ.get("LQER_GAIN_SELECT", "0"))) + awq_lite_enabled = bool(int(os.environ.get("AWQ_LITE_ENABLED", "0"))) + awq_lite_bits = int(os.environ.get("AWQ_LITE_BITS", "8")) + awq_lite_group_top_k = int(os.environ.get("AWQ_LITE_GROUP_TOP_K", "1")) + awq_lite_group_size = int(os.environ.get("AWQ_LITE_GROUP_SIZE", "64")) + 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")) + is_main_process = rank == 0 + grad_accum_steps = 8 // world_size + # CaseOps integration: optional override of dataset root + tokenizer path. + # When CASEOPS_ENABLED=1, the wrapper loads a per-token byte sidecar + # (fineweb_val_bytes_*.bin, identical shard layout to val_*.bin) and uses + # it as the canonical raw-byte budget for BPB accounting. The sidecar + # REPLACES the build_sentencepiece_luts byte-counting path entirely. + caseops_enabled = bool(int(os.environ.get("CASEOPS_ENABLED", "0"))) + _default_caseops_data = os.path.join( + data_dir, + "datasets", + "fineweb10B_sp8192_caseops", + "datasets", + "datasets", + "fineweb10B_sp8192_lossless_caps_caseops_v1_reserved", + ) + _default_caseops_tok = os.path.join( + data_dir, + "datasets", + "fineweb10B_sp8192_caseops", + "datasets", + "tokenizers", + "fineweb_8192_bpe_lossless_caps_caseops_v1_reserved.model", + ) + if caseops_enabled: + datasets_dir = os.environ.get("DATA_PATH", _default_caseops_data) + tokenizer_path = os.environ.get("TOKENIZER_PATH", _default_caseops_tok) + else: + datasets_dir = os.environ.get( + "DATA_PATH", + os.path.join(data_dir, "datasets", f"fineweb10B_sp{vocab_size}"), + ) + tokenizer_path = os.environ.get( + "TOKENIZER_PATH", + os.path.join(data_dir, "tokenizers", f"fineweb_{vocab_size}_bpe.model"), + ) + train_files = os.path.join(datasets_dir, "fineweb_train_*.bin") + val_files = os.path.join(datasets_dir, "fineweb_val_*.bin") + val_bytes_files = os.path.join(datasets_dir, "fineweb_val_bytes_*.bin") + artifact_dir = os.environ.get("ARTIFACT_DIR", "") + logfile = ( + os.path.join(artifact_dir, f"{run_id}.txt") + if artifact_dir + else f"logs/{run_id}.txt" + ) + model_path = ( + os.path.join(artifact_dir, "final_model.pt") + if artifact_dir + else "final_model.pt" + ) + quantized_model_path = ( + os.path.join(artifact_dir, "final_model.int6.ptz") + if artifact_dir + else "final_model.int6.ptz" + ) + + +_logger_hparams = None + + +def set_logging_hparams(h): + global _logger_hparams + _logger_hparams = h + + +def log(msg, console=True): + if _logger_hparams is None: + print(msg) + return + if _logger_hparams.is_main_process: + if console: + print(msg) + if _logger_hparams.logfile is not None: + with open(_logger_hparams.logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + + +class ValidationData: + def __init__(self, h, device): + self.sp = spm.SentencePieceProcessor(model_file=h.tokenizer_path) + if int(self.sp.vocab_size()) != h.vocab_size: + raise ValueError( + f"VOCAB_SIZE={h.vocab_size} does not match tokenizer vocab_size={int(self.sp.vocab_size())}" + ) + self.val_tokens = load_validation_tokens(h.val_files, h.eval_seq_len) + self.caseops_enabled = bool(getattr(h, "caseops_enabled", False)) + if self.caseops_enabled: + self.base_bytes_lut = None + self.has_leading_space_lut = None + self.is_boundary_token_lut = None + else: + ( + self.base_bytes_lut, + self.has_leading_space_lut, + self.is_boundary_token_lut, + ) = build_sentencepiece_luts(self.sp, h.vocab_size, device) + self.val_bytes = None + if self.caseops_enabled: + self.val_bytes = load_validation_byte_sidecar( + h.val_bytes_files, h.eval_seq_len, self.val_tokens.numel() + ) + + +def build_sentencepiece_luts(sp, vocab_size, device): + sp_vocab_size = int(sp.vocab_size()) + assert ( + sp.piece_to_id("▁") != sp.unk_id() + ), "Tokenizer must have '▁' (space) as its own token for correct BPB byte counting" + 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, seq_len): + # Filter out CaseOps byte sidecar shards which share the val_*.bin glob. + files = [ + Path(p) + for p in sorted(glob.glob(pattern)) + if "_bytes_" not in Path(p).name + ] + 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 load_validation_byte_sidecar(pattern, seq_len, expected_len): + """Load CaseOps per-token byte sidecar(s). Same shard layout as token shards + (256 int32 header + uint16 array). Each entry = canonical raw-text byte + budget for that token in the corresponding val shard. Returns a CPU + int16 tensor sliced to match expected_len (i.e. val_tokens length).""" + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No byte sidecar files for pattern: {pattern}") + shards = [load_data_shard(file) for file in files] + # load_data_shard returns uint16 — that's exactly what the sidecar stores. + bytes_full = torch.cat(shards).contiguous() + if bytes_full.numel() < expected_len: + raise ValueError( + f"Byte sidecar too short: {bytes_full.numel()} < val_tokens {expected_len}" + ) + return bytes_full[:expected_len].to(torch.int32) + + +def load_data_shard(file): + header_bytes = 256 * np.dtype(" 0: + pos = start + while pos < end: + seg_starts.append(pos) + pos += max_doc_len + else: + seg_starts.append(start) + boundaries = seg_starts + [total_len] + padded_len = get_next_multiple_of_n(len(boundaries), bucket_size) + cu = torch.full((padded_len,), total_len, dtype=torch.int32, device=device) + cu[: len(boundaries)] = torch.tensor(boundaries, dtype=torch.int32, device=device) + seg_ends = seg_starts[1:] + [total_len] + max_seqlen = max(end - start for start, end in zip(seg_starts, seg_ends)) + return cu, max_seqlen + +class DocumentPackingLoader: + _shard_pool = ThreadPoolExecutor(1) + + def __init__(self, h, device, cu_bucket_size=64): + self.rank = h.rank + self.world_size = h.world_size + self.device = device + self.cu_bucket_size = cu_bucket_size + self.max_seq_len = h.train_seq_len + all_files = [Path(p) for p in sorted(glob.glob(h.train_files))] + if not all_files: + raise FileNotFoundError(f"No files found for pattern: {h.train_files}") + self.files = all_files + self.file_iter = iter(self.files) + self._init_shard(load_data_shard(next(self.file_iter))) + self._next_shard = self._submit_next_shard() + self._batch_pool = ThreadPoolExecutor(1) + self._prefetch_queue = [] + + def _init_shard(self, tokens): + global BOS_ID + self.tokens = tokens + self.shard_size = tokens.numel() + if BOS_ID is None: + BOS_ID = 1 + self.bos_idx = ( + (tokens == BOS_ID).nonzero(as_tuple=True)[0].to(torch.int64).cpu().numpy() + ) + self.cursor = int(self.bos_idx[0]) + + def _submit_next_shard(self): + try: + path = next(self.file_iter) + return self._shard_pool.submit(load_data_shard, path) + except StopIteration: + return None + + def _advance_shard(self): + if self._next_shard is None: + self.file_iter = iter(self.files) + self._next_shard = self._shard_pool.submit( + load_data_shard, next(self.file_iter) + ) + self._init_shard(self._next_shard.result()) + self._next_shard = self._submit_next_shard() + + def _local_doc_starts(self, local_start, total_len): + lo = np.searchsorted(self.bos_idx, local_start, side="left") + hi = np.searchsorted(self.bos_idx, local_start + total_len, side="left") + return (self.bos_idx[lo:hi] - local_start).tolist() + + def _prepare_batch(self, num_tokens_local, max_seq_len): + per_rank_span = num_tokens_local + 1 + global_span = per_rank_span * self.world_size + while self.cursor + global_span > self.shard_size: + self._advance_shard() + local_start = self.cursor + self.rank * per_rank_span + buf = self.tokens[local_start : local_start + per_rank_span] + inputs = torch.empty(per_rank_span - 1, dtype=torch.int64, pin_memory=True) + targets = torch.empty(per_rank_span - 1, dtype=torch.int64, pin_memory=True) + inputs.copy_(buf[:-1]) + targets.copy_(buf[1:]) + starts = self._local_doc_starts(local_start, inputs.numel()) + cu_seqlens, max_seqlen = _build_cu_seqlens( + starts, inputs.numel(), inputs.device, max_seq_len, self.cu_bucket_size + ) + cu_seqlens = cu_seqlens.pin_memory() + self.cursor += global_span + return inputs, targets, cu_seqlens, max_seqlen + + def next_batch(self, global_tokens, grad_accum_steps): + num_tokens_local = global_tokens // (self.world_size * grad_accum_steps) + while len(self._prefetch_queue) < 2: + self._prefetch_queue.append( + self._batch_pool.submit(self._prepare_batch, num_tokens_local, self.max_seq_len)) + inputs, targets, cu_seqlens, max_seqlen = self._prefetch_queue.pop(0).result() + self._prefetch_queue.append( + self._batch_pool.submit(self._prepare_batch, num_tokens_local, self.max_seq_len)) + return ( + inputs[None].to(self.device, non_blocking=True), + targets[None].to(self.device, non_blocking=True), + cu_seqlens.to(self.device, non_blocking=True), + max_seqlen, + ) + + +class ShuffledSequenceLoader: + def __init__(self, h, device): + self.world_size = h.world_size + self.seq_len = h.train_seq_len + self.device = device + all_files = [Path(p) for p in sorted(glob.glob(h.train_files))] + if not all_files: + raise FileNotFoundError(f"No files found for pattern: {h.train_files}") + self.files = all_files[h.rank :: h.world_size] + self.rng = np.random.Generator(np.random.PCG64(h.rank)) + self.num_tokens = [_read_num_tokens(f) for f in self.files] + self.start_inds = [[] for _ in self.files] + for si in range(len(self.files)): + self._reset_shard(si) + + def _reset_shard(self, si): + max_phase = min( + self.seq_len - 1, max(0, self.num_tokens[si] - self.seq_len - 1) + ) + phase = int(self.rng.integers(max_phase + 1)) if max_phase > 0 else 0 + num_sequences = (self.num_tokens[si] - 1 - phase) // self.seq_len + sequence_order = self.rng.permutation(num_sequences) + self.start_inds[si] = (phase + sequence_order * self.seq_len).tolist() + + def next_batch(self, global_tokens, grad_accum_steps): + device_tokens = global_tokens // (self.world_size * grad_accum_steps) + device_batch_size = device_tokens // self.seq_len + remaining = np.array([len(s) for s in self.start_inds], dtype=np.float64) + x = torch.empty((device_batch_size, self.seq_len), dtype=torch.int64) + y = torch.empty((device_batch_size, self.seq_len), dtype=torch.int64) + for bi in range(device_batch_size): + total = remaining.sum() + if total <= 0: + for si in range(len(self.files)): + self._reset_shard(si) + remaining = np.array( + [len(s) for s in self.start_inds], dtype=np.float64 + ) + total = remaining.sum() + probs = remaining / total + si = int(self.rng.choice(len(self.files), p=probs)) + start_ind = self.start_inds[si].pop() + remaining[si] -= 1 + mm = _get_shard_memmap(self.files[si]) + window = torch.as_tensor( + np.array(mm[start_ind : start_ind + self.seq_len + 1], dtype=np.int64) + ) + x[bi] = window[:-1] + y[bi] = window[1:] + return x.to(self.device, non_blocking=True), y.to( + self.device, non_blocking=True + ) + + +class RMSNorm(nn.Module): + def __init__(self, eps=None): + super().__init__() + self.eps = eps + + def forward(self, x): + return F.rms_norm(x, (x.size(-1),), eps=self.eps) + + +class CastedLinear(nn.Linear): + def forward(self, x): + w = self.weight.to(x.dtype) + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + + +@triton.jit +def linear_leaky_relu_square_kernel( + a_desc, + b_desc, + c_desc, + aux_desc, + M, + N, + K, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, + NUM_SMS: tl.constexpr, + FORWARD: tl.constexpr, +): + dtype = tl.bfloat16 + start_pid = tl.program_id(axis=0) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + k_tiles = tl.cdiv(K, BLOCK_SIZE_K) + num_tiles = num_pid_m * num_pid_n + tile_id_c = start_pid - NUM_SMS + for tile_id in tl.range(start_pid, num_tiles, NUM_SMS, flatten=True): + pid_m = tile_id // num_pid_n + pid_n = tile_id % num_pid_n + offs_am = pid_m * BLOCK_SIZE_M + offs_bn = pid_n * BLOCK_SIZE_N + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for ki in range(k_tiles): + offs_k = ki * BLOCK_SIZE_K + a = a_desc.load([offs_am, offs_k]) + b = b_desc.load([offs_bn, offs_k]) + accumulator = tl.dot(a, b.T, accumulator) + tile_id_c += NUM_SMS + offs_am_c = offs_am + offs_bn_c = offs_bn + acc = tl.reshape(accumulator, (BLOCK_SIZE_M, 2, BLOCK_SIZE_N // 2)) + acc = tl.permute(acc, (0, 2, 1)) + acc0, acc1 = tl.split(acc) + c0 = acc0.to(dtype) + c1 = acc1.to(dtype) + if not FORWARD: + pre0 = aux_desc.load([offs_am_c, offs_bn_c]) + pre1 = aux_desc.load([offs_am_c, offs_bn_c + BLOCK_SIZE_N // 2]) + c0 = c0 * tl.where(pre0 > 0, 2.0 * pre0, 0.5 * pre0) + c1 = c1 * tl.where(pre1 > 0, 2.0 * pre1, 0.5 * pre1) + c_desc.store([offs_am_c, offs_bn_c], c0) + c_desc.store([offs_am_c, offs_bn_c + BLOCK_SIZE_N // 2], c1) + if FORWARD: + aux0 = tl.where(c0 > 0, c0, 0.5 * c0) + aux1 = tl.where(c1 > 0, c1, 0.5 * c1) + aux_desc.store([offs_am_c, offs_bn_c], aux0 * aux0) + aux_desc.store([offs_am_c, offs_bn_c + BLOCK_SIZE_N // 2], aux1 * aux1) + + +def linear_leaky_relu_square(a, b, aux=None): + M, K = a.shape + N, K2 = b.shape + assert K == K2 + c = torch.empty((M, N), device=a.device, dtype=a.dtype) + forward = aux is None + if aux is None: + aux = torch.empty((M, N), device=a.device, dtype=a.dtype) + num_sms = torch.cuda.get_device_properties(a.device).multi_processor_count + BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K = 256, 128, 64 + num_stages = 4 if forward else 3 + a_desc = TensorDescriptor.from_tensor(a, [BLOCK_SIZE_M, BLOCK_SIZE_K]) + b_desc = TensorDescriptor.from_tensor(b, [BLOCK_SIZE_N, BLOCK_SIZE_K]) + c_desc = TensorDescriptor.from_tensor(c, [BLOCK_SIZE_M, BLOCK_SIZE_N // 2]) + aux_desc = TensorDescriptor.from_tensor(aux, [BLOCK_SIZE_M, BLOCK_SIZE_N // 2]) + grid = lambda _meta: ( + min(num_sms, triton.cdiv(M, BLOCK_SIZE_M) * triton.cdiv(N, BLOCK_SIZE_N)), + ) + linear_leaky_relu_square_kernel[grid]( + a_desc, + b_desc, + c_desc, + aux_desc, + M, + N, + K, + BLOCK_SIZE_M=BLOCK_SIZE_M, + BLOCK_SIZE_N=BLOCK_SIZE_N, + BLOCK_SIZE_K=BLOCK_SIZE_K, + NUM_SMS=num_sms, + FORWARD=forward, + num_stages=num_stages, + num_warps=8, + ) + if forward: + return c, aux + return c + + +class FusedLinearLeakyReLUSquareFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, x, w1, w2): + x_flat = x.reshape(-1, x.shape[-1]) + pre, post = linear_leaky_relu_square(x_flat, w1) + out = F.linear(post, w2) + ctx.save_for_backward(x, w1, w2, pre, post) + return out.view(*x.shape[:-1], out.shape[-1]) + + @staticmethod + def backward(ctx, grad_output): + x, w1, w2, pre, post = ctx.saved_tensors + x_flat = x.reshape(-1, x.shape[-1]) + grad_output_flat = grad_output.reshape(-1, grad_output.shape[-1]) + dw2 = grad_output_flat.T @ post + dpre = linear_leaky_relu_square(grad_output_flat, w2.T.contiguous(), aux=pre) + dw1 = dpre.T @ x_flat + dx = dpre @ w1 + return dx.view_as(x), dw1, dw2 + + +FusedLeakyReLUSquareMLP = FusedLinearLeakyReLUSquareFunction.apply + + +class Rotary(nn.Module): + def __init__(self, dim, base=1e4, train_seq_len=1024, rope_dims=0, yarn=True): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.yarn = yarn + 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 = None + self._sin_cached = None + + def forward(self, seq_len, device, dtype): + 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 self.yarn and 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.float().to(device) + t = torch.arange(seq_len, device=device, dtype=torch.float32) + 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[:, :seq_len].to(dtype=dtype), self._sin_cached[:, :seq_len].to(dtype=dtype) + + +def apply_rotary_emb(x, cos, sin, rope_dims=0): + 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, num_heads, num_kv_heads, rope_base, qk_gain_init, train_seq_len, yarn=True, + attn_out_gate=False, attn_out_gate_src="proj", gate_window=12, + gated_attn=False, gated_attn_init_std=0.01, + sparse_attn_gate=False, sparse_attn_gate_init_std=0.0, sparse_attn_gate_scale=1.0, + ): + 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") + if int(attn_out_gate) + int(gated_attn) + int(sparse_attn_gate) > 1: + raise ValueError( + "attn_out_gate, gated_attn, and sparse_attn_gate are mutually exclusive" + ) + 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") + self.q_gain = nn.Parameter( + torch.full((num_heads,), qk_gain_init, dtype=torch.float32) + ) + self.rope_dims = 0 + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=train_seq_len, yarn=yarn) + self.use_xsa = False + # AttnOutGate (PR #1667 MarioPaerle): per-head multiplicative gate on attention + # output. CastedLinear so restore_fp32_params casts back to fp32 for GPTQ. + # _zero_init -> 2*sigmoid(0)=1 -> transparent at init. + self.attn_out_gate = attn_out_gate + self.attn_out_gate_src = attn_out_gate_src + self.gate_window = gate_window + if attn_out_gate: + self.attn_gate_proj = CastedLinear(gate_window, num_heads, bias=False) + self.attn_gate_proj._zero_init = True + # Gated Attention (arXiv:2505.06708, Qwen, NeurIPS 2025). Per-head sigmoid + # gate on SDPA output, BEFORE out_proj. Gate projection W_g: (num_heads, dim). + # Name "attn_gate_w" contains "attn_gate" substring so it matches + # CONTROL_TENSOR_NAME_PATTERNS and routes to the scalar AdamW group. + # fp32 Parameter -> restore_fp32_params path covers it via the ndim<2 OR + # name-pattern check (name matches "attn_gate"). Cast to x.dtype on use. + self.gated_attn = gated_attn + if gated_attn: + W = torch.empty(num_heads, dim, dtype=torch.float32) + nn.init.normal_(W, mean=0.0, std=gated_attn_init_std) + self.attn_gate_w = nn.Parameter(W) + # Sparse attention head-output gate (modded-nanogpt style). Keeps dense SDPA + # and only narrows the gate input to the first gate_window residual dims. + # W_g: (num_heads, gate_window). y_{t,h} <- sigmoid(scale * W_g_h @ x_t[:gate_window]) * y_{t,h}. + # Shares attn_gate_w name with dense GatedAttn so the quant routing + # (CONTROL_TENSOR_NAME_PATTERNS / attn_gate_w int8 passthrough) is unchanged. + self.sparse_attn_gate = sparse_attn_gate + self.sparse_attn_gate_scale = sparse_attn_gate_scale + if sparse_attn_gate: + W = torch.empty(num_heads, gate_window, dtype=torch.float32) + if sparse_attn_gate_init_std > 0: + nn.init.normal_(W, mean=0.0, std=sparse_attn_gate_init_std) + else: + nn.init.zeros_(W) + self.attn_gate_w = nn.Parameter(W) + + def _xsa_efficient(self, y, v): + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) + vn = F.normalize(v, dim=-1).unsqueeze(-2) + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + + def forward(self, x, q_w, k_w, v_w, out_w, cu_seqlens=None, max_seqlen=0): + bsz, seqlen, dim = x.shape + # q_raw kept around as a tap point for attn_out_gate_src='q' (post-projection, + # pre-reshape, pre-RoPE). + q_raw = F.linear(x, q_w.to(x.dtype)) + q = q_raw.reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = F.linear(x, k_w.to(x.dtype)).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = F.linear(x, v_w.to(x.dtype)).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 cu_seqlens is not None: + y = flash_attn_varlen_func( + q[0], + k[0], + v[0], + cu_seqlens_q=cu_seqlens, + cu_seqlens_k=cu_seqlens, + max_seqlen_q=max_seqlen, + max_seqlen_k=max_seqlen, + causal=True, + window_size=(-1, -1), + )[None] + else: + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + # AttnOutGate inlined (PR #1667). Inline + .contiguous() barrier so torch.compile + # fullgraph=True is happy (this avoids the @torch.compiler.disable trap that + # crashed gates v3). Per-head gate on (B,T,H,D) tensor: g shape [B,T,H], broadcast + # over D via [..., None]. zero-init weight -> 2*sigmoid(0)=1 -> transparent. + if self.attn_out_gate: + gate_src = q_raw if self.attn_out_gate_src == "q" else x + gate_in = gate_src[..., : self.gate_window].contiguous() + g = 2.0 * torch.sigmoid(self.attn_gate_proj(gate_in)) + y = y * g[..., None] + # Gated Attention (arXiv:2505.06708 G1). Inline + .contiguous() barrier so + # torch.compile fullgraph=True is happy. Per-head gate on (B,T,H,D): g shape + # [B,T,H], broadcast over D via [..., None]. Paper: g = sigmoid(x @ W_g.T) + # where W_g: (H, dim). .to(x.dtype) on fp32 param before broadcast with bf16. + if self.gated_attn: + x_c = x.contiguous() + g = torch.sigmoid(F.linear(x_c, self.attn_gate_w.to(x.dtype))) + y = y * g[..., None] + # Sparse head-output gate: narrower (gate_window) input, same shape g as GatedAttn. + if self.sparse_attn_gate: + gate_in = x[..., : self.gate_window].contiguous() + g = torch.sigmoid( + self.sparse_attn_gate_scale + * F.linear(gate_in, self.attn_gate_w.to(x.dtype)) + ) + y = y * g[..., None] + y = y.reshape(bsz, seqlen, dim) + self._last_proj_input = y.detach() if getattr(self, "_calib", False) else None + return F.linear(y, out_w.to(x.dtype)) + + +class MLP(nn.Module): + def __init__(self, dim, mlp_mult): + super().__init__() + self.use_fused = True + + def forward(self, x, up_w, down_w): + if self.training and self.use_fused: + return FusedLeakyReLUSquareMLP(x, up_w.to(x.dtype), down_w.to(x.dtype)) + hidden = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5).square() + self._last_down_input = hidden.detach() if getattr(self, "_calib", False) else None + return F.linear(hidden, down_w.to(x.dtype)) + + +class Block(nn.Module): + def __init__( + self, + dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + train_seq_len, + layer_idx=0, + ln_scale=False, + yarn=True, + attn_out_gate=False, + attn_out_gate_src="proj", + gate_window=12, + gated_attn=False, + gated_attn_init_std=0.01, + sparse_attn_gate=False, + sparse_attn_gate_init_std=0.0, + sparse_attn_gate_scale=1.0, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention( + dim, num_heads, num_kv_heads, rope_base, qk_gain_init, train_seq_len, yarn=yarn, + attn_out_gate=attn_out_gate, attn_out_gate_src=attn_out_gate_src, gate_window=gate_window, + gated_attn=gated_attn, gated_attn_init_std=gated_attn_init_std, + sparse_attn_gate=sparse_attn_gate, + sparse_attn_gate_init_std=sparse_attn_gate_init_std, + sparse_attn_gate_scale=sparse_attn_gate_scale, + ) + 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 + + def forward(self, x, x0, q_w, k_w, v_w, out_w, up_w, down_w, cu_seqlens=None, max_seqlen=0): + 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, + q_w, k_w, v_w, out_w, + cu_seqlens=cu_seqlens, + max_seqlen=max_seqlen, + ) + 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, up_w, down_w) + return x_out + +class GPT(nn.Module): + def __init__(self, h): + super().__init__() + if h.logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {h.logit_softcap}") + self.tie_embeddings = h.tie_embeddings + self.tied_embed_init_std = h.tied_embed_init_std + self.logit_softcap = h.logit_softcap + self.fused_ce_enabled = bool(h.fused_ce_enabled) + self.tok_emb = nn.Embedding(h.vocab_size, h.model_dim) + self.num_layers = h.num_layers + head_dim = h.model_dim // h.num_heads + kv_dim = h.num_kv_heads * head_dim + hidden_dim = int(h.mlp_mult * h.model_dim) + self.qo_bank = nn.Parameter(torch.empty(2 * h.num_layers, h.model_dim, h.model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * h.num_layers, kv_dim, h.model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(h.num_layers, hidden_dim, h.model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(h.num_layers, h.model_dim, hidden_dim)) + self.num_encoder_layers = h.num_layers // 2 + self.num_decoder_layers = h.num_layers - self.num_encoder_layers + self.blocks = nn.ModuleList( + [ + Block( + h.model_dim, + h.num_heads, + h.num_kv_heads, + h.mlp_mult, + h.rope_base, + h.qk_gain_init, + h.train_seq_len, + layer_idx=i, + ln_scale=h.ln_scale, + yarn=h.rope_yarn, + attn_out_gate=h.attn_out_gate_enabled, + attn_out_gate_src=h.attn_out_gate_src, + gate_window=h.gate_window, + gated_attn=h.gated_attn_enabled, + gated_attn_init_std=h.gated_attn_init_std, + sparse_attn_gate=h.sparse_attn_gate_enabled, + sparse_attn_gate_init_std=h.sparse_attn_gate_init_std, + sparse_attn_gate_scale=h.sparse_attn_gate_scale, + ) + for i in range(h.num_layers) + ] + ) + if h.rope_dims > 0: + head_dim = h.model_dim // h.num_heads + for block in self.blocks: + block.attn.rope_dims = h.rope_dims + block.attn.rotary = Rotary( + head_dim, + base=h.rope_base, + train_seq_len=h.train_seq_len, + rope_dims=h.rope_dims, + yarn=h.rope_yarn, + ) + self.final_norm = RMSNorm() + self.lm_head = ( + None + if h.tie_embeddings + else CastedLinear(h.model_dim, h.vocab_size, bias=False) + ) + if self.lm_head is not None: + self.lm_head._zero_init = True + if h.xsa_last_n > 0: + for i in range(max(0, h.num_layers - h.xsa_last_n), h.num_layers): + self.blocks[i].attn.use_xsa = True + self.looping_active = False + if h.num_loops > 0: + loop_seg = list(range(h.loop_start, h.loop_end + 1)) + all_indices = list(range(h.loop_start)) + for _ in range(h.num_loops + 1): + all_indices.extend(loop_seg) + all_indices.extend(range(h.loop_end + 1, h.num_layers)) + num_enc = len(all_indices) // 2 + self.encoder_indices = all_indices[:num_enc] + self.decoder_indices = all_indices[num_enc:] + else: + self.encoder_indices = list(range(self.num_encoder_layers)) + self.decoder_indices = list(range(self.num_encoder_layers, h.num_layers)) + self.num_skip_weights = min( + len(self.encoder_indices), len(self.decoder_indices) + ) + self.skip_weights = nn.Parameter( + torch.ones(self.num_skip_weights, h.model_dim, dtype=torch.float32) + ) + self.skip_gates = ( + nn.Parameter( + torch.zeros(self.num_skip_weights, h.model_dim, dtype=torch.float32) + ) + if h.skip_gates_enabled + else None + ) + self.parallel_start_layer = h.parallel_start_layer + self.parallel_final_lane = h.parallel_final_lane.lower() + self.parallel_post_lambdas = nn.Parameter( + torch.ones(h.num_layers, 2, 2, dtype=torch.float32) + ) + self.parallel_resid_lambdas = nn.Parameter( + torch.full((h.num_layers, 2), 1.1, dtype=torch.float32) + ) + # SmearGate (PR #1667 / modded-nanogpt @classiclarryd): + # x_t <- x_t + lam * sigmoid(W * x_t[:gate_window]) * x_{t-1}. + # Per-token forward-1 smear of the embedding lane. W zero-init + lam=0 -> + # transparent at init. Uses CastedLinear so restore_fp32_params handles dtype. + self.smear_gate_enabled = h.smear_gate_enabled + if self.smear_gate_enabled: + self.smear_window = h.gate_window + self.smear_gate = CastedLinear(self.smear_window, 1, bias=False) + self.smear_gate._zero_init = True + self.smear_lambda = nn.Parameter(torch.zeros(1, dtype=torch.float32)) + # V19: Asymmetric Logit Rescale (PR #1923 jorge-asenjo). + # Two learnable softcap scales applied on the EVAL path (forward_logits + + # forward_ttt). Init to logit_softcap so the layer is identity at step 0. + # Train path keeps the single fused softcap to preserve PR #1855 numerics. + self.asym_logit_enabled = bool(int(os.environ.get("ASYM_LOGIT_RESCALE", "0"))) + if self.asym_logit_enabled: + self.softcap_pos = nn.Parameter(torch.tensor(float(h.logit_softcap), dtype=torch.float32)) + self.softcap_neg = nn.Parameter(torch.tensor(float(h.logit_softcap), dtype=torch.float32)) + self._init_weights() + + def _init_weights(self): + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) + nn.init.zeros_(self.qo_bank.data[n + i]) + self.qo_bank.data[n + i].mul_(proj_scale) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) + for i in range(n): + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) + nn.init.zeros_(self.mlp_down_bank.data[i]) + self.mlp_down_bank.data[i].mul_(proj_scale) + 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) + + def _bank_weights(self, i): + n = self.num_layers + return ( + self.qo_bank[i], + self.kv_bank[i], + self.kv_bank[n + i], + self.qo_bank[n + i], + self.mlp_up_bank[i], + self.mlp_down_bank[i], + ) + + def _parallel_block( + self, block_idx, lane0, lane1, x0, + q_w, k_w, v_w, out_w, up_w, down_w, + cu_seqlens=None, max_seqlen=0, + ): + block = self.blocks[block_idx] + mix = block.resid_mix.to(dtype=lane0.dtype) + attn_read = mix[0][None, None, :] * lane0 + mix[1][None, None, :] * x0 + attn_out = block.attn( + block.attn_norm(attn_read) * block.ln_scale_factor, + q_w, k_w, v_w, out_w, + cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, + ) + attn_out = block.attn_scale.to(dtype=attn_out.dtype)[None, None, :] * attn_out + mlp_read = lane1 + mlp_out = block.mlp_scale.to(dtype=lane1.dtype)[None, None, :] * block.mlp( + block.mlp_norm(mlp_read) * block.ln_scale_factor, up_w, down_w + ) + attn_resid = self.parallel_resid_lambdas[block_idx, 0].to(dtype=lane0.dtype) + attn_post = self.parallel_post_lambdas[block_idx, 0].to(dtype=lane0.dtype) + mlp_resid = self.parallel_resid_lambdas[block_idx, 1].to(dtype=lane0.dtype) + mlp_post = self.parallel_post_lambdas[block_idx, 1].to(dtype=lane0.dtype) + lane0 = attn_resid * lane0 + attn_post[0] * attn_out + mlp_post[0] * mlp_out + lane1 = mlp_resid * lane1 + attn_post[1] * attn_out + mlp_post[1] * mlp_out + return lane0, lane1 + + def _final_parallel_hidden(self, lane0, lane1): + if self.parallel_final_lane == "mlp": + return lane1 + if self.parallel_final_lane == "attn": + return lane0 + return 0.5 * (lane0 + lane1) + + def _forward_hidden(self, input_ids, cu_seqlens=None, max_seqlen=0): + """Run the encoder/decoder stack to the final RMSNorm; returns pre-projection hidden. + Shared by eval (softcap+projection via forward_logits) and train (fused CE path).""" + x = self.tok_emb(input_ids) + # SmearGate (PR #1667). lam=0 + W=0 -> identity at init. + # Cross-doc leak fix: zero the prev-token smear at any position whose current token + # is BOS, so the BOS embedding starting doc N+1 in a packed stream is not + # contaminated by doc N's last token (audited issue on PR#1797 base). + if self.smear_gate_enabled: + sl = self.smear_lambda.to(dtype=x.dtype) + gate_in = x[:, 1:, : self.smear_window].contiguous() + g = sl * torch.sigmoid(self.smear_gate(gate_in)) + not_bos = (input_ids[:, 1:] != BOS_ID).to(x.dtype).unsqueeze(-1) + x = torch.cat([x[:, :1], x[:, 1:] + g * x[:, :-1] * not_bos], dim=1) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + skips = [] + enc_iter = ( + self.encoder_indices + if self.looping_active + else range(self.num_encoder_layers) + ) + dec_iter = ( + self.decoder_indices + if self.looping_active + else range( + self.num_encoder_layers, + self.num_encoder_layers + self.num_decoder_layers, + ) + ) + for i in enc_iter: + q_w, k_w, v_w, out_w, up_w, down_w = self._bank_weights(i) + x = self.blocks[i](x, x0, q_w, k_w, v_w, out_w, up_w, down_w, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen) + skips.append(x) + psl = self.parallel_start_layer + lane0 = None + lane1 = None + for skip_idx, i in enumerate(dec_iter): + q_w, k_w, v_w, out_w, up_w, down_w = self._bank_weights(i) + if i >= psl and psl > 0: + if lane0 is None: + lane0 = x + lane1 = x + if skip_idx < self.num_skip_weights and skips: + skip = skips.pop() + w = self.skip_weights[skip_idx].to(dtype=lane0.dtype)[None, None, :] + if self.skip_gates is not None: + g = torch.sigmoid(self.skip_gates[skip_idx].to(dtype=lane0.dtype))[None, None, :] + lane0 = torch.lerp(w * skip, lane0, g) + else: + lane0 = lane0 + w * skip + lane0, lane1 = self._parallel_block( + i, lane0, lane1, x0, q_w, k_w, v_w, out_w, up_w, down_w, + cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, + ) + else: + if skip_idx < self.num_skip_weights and skips: + scaled_skip = ( + self.skip_weights[skip_idx].to(dtype=x.dtype)[None, None, :] + * skips.pop() + ) + if self.skip_gates is not None: + g = torch.sigmoid(self.skip_gates[skip_idx].to(dtype=x.dtype))[None, None, :] + x = torch.lerp(scaled_skip, x, g) + else: + x = x + scaled_skip + x = self.blocks[i](x, x0, q_w, k_w, v_w, out_w, up_w, down_w, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen) + if lane0 is not None: + x = self._final_parallel_hidden(lane0, lane1) + x = self.final_norm(x) + return x + + def _project_logits(self, hidden): + if self.tie_embeddings: + return F.linear(hidden, self.tok_emb.weight) + return self.lm_head(hidden) + + def _apply_asym_softcap(self, logits): + # V19: Asymmetric softcap (PR #1923). Splits the logit_softcap scalar into + # learnable positive/negative branches. Score-first preserved: still a + # bounded, normalized post-projection nonlinearity feeding a standard + # softmax over the full vocab. + sp = self.softcap_pos.to(logits.dtype) + sn = self.softcap_neg.to(logits.dtype) + return torch.where(logits > 0, sp * torch.tanh(logits / sp), sn * torch.tanh(logits / sn)) + + def forward_logits(self, input_ids, cu_seqlens=None, max_seqlen=0): + hidden = self._forward_hidden(input_ids, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen) + logits_proj = self._project_logits(hidden) + if self.asym_logit_enabled: + return self._apply_asym_softcap(logits_proj) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + + def forward(self, input_ids, target_ids, cu_seqlens=None, max_seqlen=0): + hidden = self._forward_hidden(input_ids, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen) + logits_proj = self._project_logits(hidden) + flat_targets = target_ids.reshape(-1) + # Fused softcapped-CE kernel (training path only). Applies softcap inside the + # Triton kernel; takes pre-softcap logits_proj. Non-fused path matches stock + # PR-1736 numerics exactly (softcap in fp32, then F.cross_entropy on fp32). + if self.fused_ce_enabled: + return softcapped_cross_entropy( + logits_proj.reshape(-1, logits_proj.size(-1)), + flat_targets, + self.logit_softcap, + reduction="mean", + ) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + flat_targets, + reduction="mean", + ) + + def forward_ttt(self, input_ids, target_ids, lora): + x = self.tok_emb(input_ids) + # SmearGate on the TTT path — same inline compute as forward_logits. + # Cross-doc leak fix: see _forward_hidden comment. + if self.smear_gate_enabled: + sl = self.smear_lambda.to(dtype=x.dtype) + gate_in = x[:, 1:, : self.smear_window].contiguous() + g = sl * torch.sigmoid(self.smear_gate(gate_in)) + not_bos = (input_ids[:, 1:] != BOS_ID).to(x.dtype).unsqueeze(-1) + x = torch.cat([x[:, :1], x[:, 1:] + g * x[:, :-1] * not_bos], dim=1) + x = F.rms_norm(x, (x.size(-1),)) + x0 = x + skips = [] + enc_iter = ( + self.encoder_indices + if self.looping_active + else list(range(self.num_encoder_layers)) + ) + dec_iter = ( + self.decoder_indices + if self.looping_active + else list( + range( + self.num_encoder_layers, + self.num_encoder_layers + self.num_decoder_layers, + ) + ) + ) + slot = 0 + for i in enc_iter: + q_w, k_w, v_w, out_w, up_w, down_w = self._bank_weights(i) + x = self._block_with_lora(self.blocks[i], x, x0, lora, slot, q_w, k_w, v_w, out_w, up_w, down_w) + slot += 1 + skips.append(x) + psl = self.parallel_start_layer + lane0 = None + lane1 = None + for skip_idx, i in enumerate(dec_iter): + q_w, k_w, v_w, out_w, up_w, down_w = self._bank_weights(i) + if i >= psl and psl > 0: + if lane0 is None: + lane0 = x + lane1 = x + if skip_idx < self.num_skip_weights and skips: + skip = skips.pop() + w = self.skip_weights[skip_idx].to(dtype=lane0.dtype)[None, None, :] + if self.skip_gates is not None: + g = torch.sigmoid(self.skip_gates[skip_idx].to(dtype=lane0.dtype))[None, None, :] + lane0 = torch.lerp(w * skip, lane0, g) + else: + lane0 = lane0 + w * skip + lane0, lane1 = self._parallel_block_with_lora( + i, lane0, lane1, x0, lora, slot, + q_w, k_w, v_w, out_w, up_w, down_w, + ) + else: + if skip_idx < self.num_skip_weights and skips: + scaled_skip = ( + self.skip_weights[skip_idx].to(dtype=x.dtype)[None, None, :] + * skips.pop() + ) + if self.skip_gates is not None: + g = torch.sigmoid(self.skip_gates[skip_idx].to(dtype=x.dtype))[None, None, :] + x = torch.lerp(scaled_skip, x, g) + else: + x = x + scaled_skip + x = self._block_with_lora(self.blocks[i], x, x0, lora, slot, q_w, k_w, v_w, out_w, up_w, down_w) + slot += 1 + if lane0 is not None: + x = self._final_parallel_hidden(lane0, lane1) + x = self.final_norm(x) + if self.tie_embeddings: + logits = F.linear(x, self.tok_emb.weight) + else: + logits = self.lm_head(x) + logits = logits + lora.lm_head_lora(x) + # V19: same asymmetric softcap on the TTT eval path. + if self.asym_logit_enabled: + logits = self._apply_asym_softcap(logits) + else: + logits = self.logit_softcap * torch.tanh(logits / self.logit_softcap) + bsz, sl, V = logits.shape + return F.cross_entropy( + logits.float().reshape(-1, V), target_ids.reshape(-1), reduction="none" + ).reshape(bsz, sl) + + def _block_with_lora(self, block, x, x0, lora, slot, q_w, k_w, v_w, out_w, up_w, down_w): + mix = block.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + n = block.attn_norm(x_in) * block.ln_scale_factor + attn = block.attn + bsz, seqlen, dim = n.shape + # Keep raw Q for AttnOutGate src='q' (matches forward path semantics). + q_raw = F.linear(n, q_w.to(n.dtype)) + if lora.q_loras is not None: + q_raw = q_raw + lora.q_loras[slot](n) + q = q_raw.reshape(bsz, seqlen, attn.num_heads, attn.head_dim) + k = F.linear(n, k_w.to(n.dtype)) + if lora.k_loras is not None: + k = k + lora.k_loras[slot](n) + k = k.reshape(bsz, seqlen, attn.num_kv_heads, attn.head_dim) + v = F.linear(n, v_w.to(n.dtype)) + if lora.v_loras is not None: + v = v + lora.v_loras[slot](n) + v = v.reshape(bsz, seqlen, attn.num_kv_heads, attn.head_dim) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = attn.rotary(seqlen, n.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, attn.rope_dims) + k = apply_rotary_emb(k, cos, sin, attn.rope_dims) + q = q * attn.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if attn.use_xsa: + y = attn._xsa_efficient(y, v) + # AttnOutGate (TTT path) — inline + .contiguous() barrier, same as the eval path. + if attn.attn_out_gate: + gate_src = q_raw if attn.attn_out_gate_src == "q" else n + gate_in = gate_src[..., : attn.gate_window].contiguous() + g = 2.0 * torch.sigmoid(attn.attn_gate_proj(gate_in)) + y = y * g[..., None] + # Gated Attention (TTT path). Gate input is n (post-norm block input), same + # as eval path. .to(n.dtype) on fp32 param before bf16 broadcast. + if attn.gated_attn: + n_c = n.contiguous() + g = torch.sigmoid(F.linear(n_c, attn.attn_gate_w.to(n.dtype))) + y = y * g[..., None] + # Sparse attention head-output gate (TTT path) — must match the eval path in + # forward() exactly, else training (which applied the gate) and TTT eval (which + # skipped it) produce mismatched representations and catastrophic BPB regression. + if attn.sparse_attn_gate: + gate_in = n[..., : attn.gate_window].contiguous() + g = torch.sigmoid( + attn.sparse_attn_gate_scale + * F.linear(gate_in, attn.attn_gate_w.to(n.dtype)) + ) + y = y * g[..., None] + y = y.reshape(bsz, seqlen, dim) + attn_out = F.linear(y, out_w.to(n.dtype)) + if lora.o_loras is not None: + attn_out = attn_out + lora.o_loras[slot](n) + x_out = x_in + block.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + mlp_n = block.mlp_norm(x_out) * block.ln_scale_factor + mlp_out = block.mlp(mlp_n, up_w, down_w) + if lora.mlp_loras is not None: + mlp_out = mlp_out + lora.mlp_loras[slot](mlp_n) + x_out = x_out + block.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * mlp_out + return x_out + + def _parallel_block_with_lora( + self, block_idx, lane0, lane1, x0, lora, slot, + q_w, k_w, v_w, out_w, up_w, down_w, + ): + block = self.blocks[block_idx] + mix = block.resid_mix.to(dtype=lane0.dtype) + attn_read = mix[0][None, None, :] * lane0 + mix[1][None, None, :] * x0 + n = block.attn_norm(attn_read) * block.ln_scale_factor + attn = block.attn + bsz, seqlen, dim = n.shape + q_raw = F.linear(n, q_w.to(n.dtype)) + if lora.q_loras is not None: + q_raw = q_raw + lora.q_loras[slot](n) + q = q_raw.reshape(bsz, seqlen, attn.num_heads, attn.head_dim) + k = F.linear(n, k_w.to(n.dtype)) + if lora.k_loras is not None: + k = k + lora.k_loras[slot](n) + k = k.reshape(bsz, seqlen, attn.num_kv_heads, attn.head_dim) + v = F.linear(n, v_w.to(n.dtype)) + if lora.v_loras is not None: + v = v + lora.v_loras[slot](n) + v = v.reshape(bsz, seqlen, attn.num_kv_heads, attn.head_dim) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = attn.rotary(seqlen, n.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, attn.rope_dims) + k = apply_rotary_emb(k, cos, sin, attn.rope_dims) + q = q * attn.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if attn.use_xsa: + y = attn._xsa_efficient(y, v) + # AttnOutGate (TTT parallel path) — inline + .contiguous() barrier. + if attn.attn_out_gate: + gate_src = q_raw if attn.attn_out_gate_src == "q" else n + gate_in = gate_src[..., : attn.gate_window].contiguous() + g = 2.0 * torch.sigmoid(attn.attn_gate_proj(gate_in)) + y = y * g[..., None] + # Gated Attention (TTT parallel path). Gate input is n (post-norm block input). + if attn.gated_attn: + n_c = n.contiguous() + g = torch.sigmoid(F.linear(n_c, attn.attn_gate_w.to(n.dtype))) + y = y * g[..., None] + # Sparse attention head-output gate (TTT parallel path) — must match the + # eval path in forward() to keep train/eval semantics in sync. + if attn.sparse_attn_gate: + gate_in = n[..., : attn.gate_window].contiguous() + g = torch.sigmoid( + attn.sparse_attn_gate_scale + * F.linear(gate_in, attn.attn_gate_w.to(n.dtype)) + ) + y = y * g[..., None] + y = y.reshape(bsz, seqlen, dim) + attn_out = F.linear(y, out_w.to(n.dtype)) + if lora.o_loras is not None: + attn_out = attn_out + lora.o_loras[slot](n) + attn_out = block.attn_scale.to(dtype=attn_out.dtype)[None, None, :] * attn_out + mlp_read = lane1 + mlp_n = block.mlp_norm(mlp_read) * block.ln_scale_factor + mlp_out = block.mlp(mlp_n, up_w, down_w) + if lora.mlp_loras is not None: + mlp_out = mlp_out + lora.mlp_loras[slot](mlp_n) + mlp_out = block.mlp_scale.to(dtype=lane1.dtype)[None, None, :] * mlp_out + attn_resid = self.parallel_resid_lambdas[block_idx, 0].to(dtype=lane0.dtype) + attn_post = self.parallel_post_lambdas[block_idx, 0].to(dtype=lane0.dtype) + mlp_resid = self.parallel_resid_lambdas[block_idx, 1].to(dtype=lane0.dtype) + mlp_post = self.parallel_post_lambdas[block_idx, 1].to(dtype=lane0.dtype) + lane0 = attn_resid * lane0 + attn_post[0] * attn_out + mlp_post[0] * mlp_out + lane1 = mlp_resid * lane1 + attn_post[1] * attn_out + mlp_post[1] * mlp_out + return lane0, lane1 + + +class BatchedLinearLoRA(nn.Module): + # PR-1767: rank-scaled output (alpha/rank), like standard LoRA. Decouples + # effective magnitude from rank so changing rank does not change LR scale. + _ALPHA = float(os.environ.get("TTT_LORA_ALPHA", "144")) + # PR-1767: optionally keep A warm across per-doc resets (only B is zeroed). + # Accumulates useful feature directions across documents within a TTT phase. + _WARM_START_A = bool(int(os.environ.get("TTT_WARM_START_A", "1"))) + + def __init__(self, bsz, in_features, out_features, rank): + super().__init__() + self._bound = 1.0 / math.sqrt(in_features) + self._scale = self._ALPHA / rank + self.A = nn.Parameter( + torch.empty(bsz, rank, in_features).uniform_(-self._bound, self._bound) + ) + self.B = nn.Parameter(torch.zeros(bsz, out_features, rank)) + + def reset(self): + with torch.no_grad(): + if not self._WARM_START_A: + self.A.uniform_(-self._bound, self._bound) + self.B.zero_() + + def forward(self, x): + return ((x @ self.A.transpose(1, 2)) @ self.B.transpose(1, 2)) * self._scale + + +class BatchedTTTLoRA(nn.Module): + def __init__( + self, bsz, model, rank, + q_lora=True, k_lora=True, v_lora=True, mlp_lora=True, o_lora=True, + ): + super().__init__() + self.bsz = bsz + dim = model.qo_bank.shape[-1] + vocab = model.tok_emb.num_embeddings + if getattr(model, "looping_active", False): + num_slots = len(model.encoder_indices) + len(model.decoder_indices) + else: + num_slots = len(model.blocks) + kv_dim = model.blocks[0].attn.num_kv_heads * ( + dim // model.blocks[0].attn.num_heads + ) + embed_dim = model.tok_emb.embedding_dim + self.lm_head_lora = BatchedLinearLoRA(bsz, embed_dim, vocab, rank) + self.q_loras = ( + nn.ModuleList( + [BatchedLinearLoRA(bsz, dim, dim, rank) for _ in range(num_slots)] + ) + if q_lora + else None + ) + self.v_loras = ( + nn.ModuleList( + [BatchedLinearLoRA(bsz, dim, kv_dim, rank) for _ in range(num_slots)] + ) + if v_lora + else None + ) + self.k_loras = ( + nn.ModuleList( + [BatchedLinearLoRA(bsz, dim, kv_dim, rank) for _ in range(num_slots)] + ) + if k_lora + else None + ) + self.mlp_loras = ( + nn.ModuleList( + [BatchedLinearLoRA(bsz, dim, dim, rank) for _ in range(num_slots)] + ) + if mlp_lora + else None + ) + self.o_loras = ( + nn.ModuleList( + [BatchedLinearLoRA(bsz, dim, dim, rank) for _ in range(num_slots)] + ) + if o_lora + else None + ) + + def reset(self): + with torch.no_grad(): + self.lm_head_lora.reset() + for loras in [self.q_loras, self.v_loras, self.k_loras, + self.mlp_loras, self.o_loras]: + if loras is not None: + for lora in loras: + lora.reset() + + +# Polar Express per-iteration minimax Newton-Schulz coefficients (PR #1344). +# Replaces the fixed (3.4445, -4.775, 2.0315) coefficients of stock Muon. +# Applied at backend_steps=5 — taking more than 5 iterations from this list +# falls back to the final (converged) tuple via the slice guard below. +_PE_COEFFS = ( + (8.156554524902461, -22.48329292557795, 15.878769915207462), + (4.042929935166739, -2.808917465908714, 0.5000178451051316), + (3.8916678022926607, -2.772484153217685, 0.5060648178503393), + (3.285753657755655, -2.3681294933425376, 0.46449024233003106), + (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), +) + + +@torch.compile +def zeropower_via_newtonschulz5(G, steps=10, eps=1e-07): + was_2d = G.ndim == 2 + if was_2d: + G = G.unsqueeze(0) + X = G.bfloat16() + transposed = X.size(-2) > X.size(-1) + if transposed: + X = X.mT + X = X / (X.norm(dim=(-2, -1), keepdim=True) + eps) + coeffs = _PE_COEFFS[:steps] if steps <= len(_PE_COEFFS) else _PE_COEFFS + for a, b, c in coeffs: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + if transposed: + X = X.mT + if was_2d: + X = X.squeeze(0) + return X + + +class Muon(torch.optim.Optimizer): + def __init__( + self, + params, + lr, + momentum, + backend_steps, + nesterov=True, + weight_decay=0.0, + row_normalize=False, + ): + super().__init__( + params, + dict( + lr=lr, + momentum=momentum, + backend_steps=backend_steps, + nesterov=nesterov, + weight_decay=weight_decay, + row_normalize=row_normalize, + ), + ) + self._built = False + + def _build(self): + self._distributed = dist.is_available() and dist.is_initialized() + self._world_size = dist.get_world_size() if self._distributed else 1 + self._rank = dist.get_rank() if self._distributed else 0 + ws = self._world_size + self._bank_meta = [] + for group in self.param_groups: + for p in group["params"]: + B = p.shape[0] + padded_B = ((B + ws - 1) // ws) * ws + shard_B = padded_B // ws + tail = p.shape[1:] + dev = p.device + self._bank_meta.append({ + "p": p, + "B": B, + "padded_grad": torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + "shard": torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + "shard_mom": torch.zeros(shard_B, *tail, device=dev, dtype=torch.bfloat16), + "full_update": torch.zeros(padded_B, *tail, device=dev, dtype=torch.bfloat16), + "scale": max(1, p.shape[-2] / p.shape[-1]) ** 0.5, + }) + self._bank_meta.sort(key=lambda m: -m["p"].numel()) + self._built = True + + def launch_reduce_scatters(self): + if not self._built: + self._build() + if not self._distributed: + return + self._rs_futures = [] + for m in self._bank_meta: + p = m["p"] + if p.grad is None: + self._rs_futures.append(None) + continue + pg = m["padded_grad"] + pg[: m["B"]].copy_(p.grad) + fut = dist.reduce_scatter_tensor( + m["shard"], pg, op=dist.ReduceOp.AVG, async_op=True + ) + self._rs_futures.append(fut) + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + if not self._built: + self._build() + for group in self.param_groups: + lr = group["lr"] + momentum = group["momentum"] + backend_steps = group["backend_steps"] + nesterov = group["nesterov"] + wd = group.get("weight_decay", 0.0) + row_normalize = group.get("row_normalize", False) + prev_ag_handle = None + prev_m = None + sharded = self._distributed and hasattr(self, "_rs_futures") + for idx, m in enumerate(self._bank_meta): + p = m["p"] + if p.grad is None: + continue + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m["p"] + upd = prev_m["full_update"][: prev_m["B"]] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd, alpha=-lr * prev_m["scale"]) + if sharded and self._rs_futures[idx] is not None: + self._rs_futures[idx].wait() + g = m["shard"] + buf = m["shard_mom"] + else: + g = p.grad.bfloat16() + 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: + update = g.add(buf, alpha=momentum) + else: + update = buf + if row_normalize: + rn = update.float().norm(dim=-1, keepdim=True).clamp_min(1e-07) + update = update / rn.to(update.dtype) + update = zeropower_via_newtonschulz5(update, steps=backend_steps) + if sharded: + prev_ag_handle = dist.all_gather_into_tensor( + m["full_update"], update, async_op=True + ) + prev_m = m + else: + if wd > 0.0: + p.data.mul_(1.0 - lr * wd) + p.add_(update, alpha=-lr * m["scale"]) + if prev_ag_handle is not None: + prev_ag_handle.wait() + pp = prev_m["p"] + upd = prev_m["full_update"][: prev_m["B"]] + if wd > 0.0: + pp.data.mul_(1.0 - lr * wd) + pp.add_(upd, alpha=-lr * prev_m["scale"]) + if hasattr(self, "_rs_futures"): + del self._rs_futures + return loss + + +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,skip_gates,parallel_post_lambdas,parallel_resid_lambdas,attn_gate_proj,attn_gate_w,smear_gate,smear_lambda", + ).split(",") + if pattern +) + + +PACKED_REPLICATED_GRAD_MAX_NUMEL = 1 << 15 + + +class Optimizers: + def __init__(self, h, base_model): + matrix_params = [ + base_model.qo_bank, + base_model.kv_bank, + base_model.mlp_up_bank, + base_model.mlp_down_bank, + ] + block_named_params = list(base_model.blocks.named_parameters()) + 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) + if base_model.skip_gates is not None and base_model.skip_gates.numel() > 0: + scalar_params.append(base_model.skip_gates) + if base_model.parallel_post_lambdas is not None: + scalar_params.append(base_model.parallel_post_lambdas) + if base_model.parallel_resid_lambdas is not None: + scalar_params.append(base_model.parallel_resid_lambdas) + # SmearGate params live on GPT root (not in .blocks), so add them by hand. + # Both are tiny (gate_window scalars + 1 lambda). Optimized via scalar Adam. + if getattr(base_model, "smear_gate_enabled", False): + scalar_params.append(base_model.smear_gate.weight) + scalar_params.append(base_model.smear_lambda) + token_lr = h.tied_embed_lr if h.tie_embeddings else h.embed_lr + tok_params = [ + {"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr} + ] + self.optimizer_tok = torch.optim.AdamW( + tok_params, + betas=(h.beta1, h.beta2), + eps=h.adam_eps, + weight_decay=h.embed_wd, + fused=True, + ) + self.optimizer_muon = Muon( + matrix_params, + lr=h.matrix_lr, + momentum=h.muon_momentum, + backend_steps=h.muon_backend_steps, + weight_decay=h.muon_wd, + row_normalize=h.muon_row_normalize, + ) + for group in self.optimizer_muon.param_groups: + group["base_lr"] = h.matrix_lr + self.optimizer_scalar = torch.optim.AdamW( + [{"params": scalar_params, "lr": h.scalar_lr, "base_lr": h.scalar_lr}], + betas=(h.beta1, h.beta2), + eps=h.adam_eps, + weight_decay=h.adam_wd, + fused=True, + ) + self.optimizers = [ + self.optimizer_tok, + self.optimizer_muon, + self.optimizer_scalar, + ] + self.replicated_params = list(tok_params[0]["params"]) + self.replicated_params.extend(scalar_params) + self.replicated_large_params = [] + self.replicated_packed_params = [] + for p in self.replicated_params: + if p.numel() <= PACKED_REPLICATED_GRAD_MAX_NUMEL: + self.replicated_packed_params.append(p) + else: + self.replicated_large_params.append(p) + self._aux_stream = torch.cuda.Stream() + + def __iter__(self): + return iter(self.optimizers) + + def zero_grad_all(self): + for opt in self.optimizers: + opt.zero_grad(set_to_none=True) + + def _all_reduce_packed_grads(self): + grads_by_key = collections.defaultdict(list) + for p in self.replicated_packed_params: + if p.grad is not None: + grads_by_key[(p.grad.device, p.grad.dtype)].append(p.grad) + for grads in grads_by_key.values(): + flat = torch.empty( + sum(g.numel() for g in grads), + device=grads[0].device, + dtype=grads[0].dtype, + ) + offset = 0 + for g in grads: + n = g.numel() + flat[offset : offset + n].copy_(g.contiguous().view(-1)) + offset += n + dist.all_reduce(flat, op=dist.ReduceOp.AVG) + offset = 0 + for g in grads: + n = g.numel() + g.copy_(flat[offset : offset + n].view_as(g)) + offset += n + + def step(self, distributed=False): + self.optimizer_muon.launch_reduce_scatters() + if distributed: + reduce_handles = [ + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG, async_op=True) + for p in self.replicated_large_params + if p.grad is not None + ] + self._all_reduce_packed_grads() + for handle in reduce_handles: + handle.wait() + self._aux_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(self._aux_stream): + self.optimizer_tok.step() + self.optimizer_scalar.step() + self.optimizer_muon.step() + torch.cuda.current_stream().wait_stream(self._aux_stream) + self.zero_grad_all() + + +def restore_fp32_params(model): + for module in model.modules(): + if isinstance(module, CastedLinear): + module.float() + for name, param in model.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() + if hasattr(model, "qo_bank") and model.qo_bank is not None: + model.qo_bank.data = model.qo_bank.data.float() + model.kv_bank.data = model.kv_bank.data.float() + model.mlp_up_bank.data = model.mlp_up_bank.data.float() + model.mlp_down_bank.data = model.mlp_down_bank.data.float() + + +def collect_hessians(model, train_loader, h, device, n_calibration_batches=64): + hessians = {} + act_sumsq = {} + act_counts = {} + hooks = [] + for i, block in enumerate(model.blocks): + block.attn._calib = True + block.mlp._calib = True + block.mlp.use_fused = False + + def make_attn_hook(layer_idx): + def hook_fn(module, inp, out): + x = inp[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + x_sq = x.square().sum(dim=0) + x_count = x.shape[0] + for suffix in ["c_q", "c_k", "c_v"]: + name = f"blocks.{layer_idx}.attn.{suffix}.weight" + if name not in hessians: + hessians[name] = torch.zeros( + x.shape[1], x.shape[1], dtype=torch.float32, device=device + ) + hessians[name].addmm_(x.T, x) + if name not in act_sumsq: + act_sumsq[name] = torch.zeros( + x.shape[1], dtype=torch.float32, device=device + ) + act_counts[name] = 0 + act_sumsq[name] += x_sq + act_counts[name] += x_count + y = module._last_proj_input + if y is not None: + y = y.float() + if y.ndim == 3: + y = y.reshape(-1, y.shape[-1]) + name = f"blocks.{layer_idx}.attn.proj.weight" + if name not in hessians: + hessians[name] = torch.zeros( + y.shape[1], y.shape[1], dtype=torch.float32, device=device + ) + hessians[name].addmm_(y.T, y) + if name not in act_sumsq: + act_sumsq[name] = torch.zeros( + y.shape[1], dtype=torch.float32, device=device + ) + act_counts[name] = 0 + act_sumsq[name] += y.square().sum(dim=0) + act_counts[name] += y.shape[0] + return hook_fn + + def make_mlp_hook(layer_idx): + def hook_fn(module, inp, out): + x = inp[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + name = f"blocks.{layer_idx}.mlp.fc.weight" + if name not in hessians: + hessians[name] = torch.zeros( + x.shape[1], x.shape[1], dtype=torch.float32, device=device + ) + hessians[name].addmm_(x.T, x) + if name not in act_sumsq: + act_sumsq[name] = torch.zeros( + x.shape[1], dtype=torch.float32, device=device + ) + act_counts[name] = 0 + act_sumsq[name] += x.square().sum(dim=0) + act_counts[name] += x.shape[0] + h_act = module._last_down_input + if h_act is not None: + h_act = h_act.float() + if h_act.ndim == 3: + h_act = h_act.reshape(-1, h_act.shape[-1]) + name = f"blocks.{layer_idx}.mlp.proj.weight" + if name not in hessians: + hessians[name] = torch.zeros( + h_act.shape[1], h_act.shape[1], dtype=torch.float32, device=device + ) + hessians[name].addmm_(h_act.T, h_act) + if name not in act_sumsq: + act_sumsq[name] = torch.zeros( + h_act.shape[1], dtype=torch.float32, device=device + ) + act_counts[name] = 0 + act_sumsq[name] += h_act.square().sum(dim=0) + act_counts[name] += h_act.shape[0] + return hook_fn + + for i, block in enumerate(model.blocks): + hooks.append(block.attn.register_forward_hook(make_attn_hook(i))) + hooks.append(block.mlp.register_forward_hook(make_mlp_hook(i))) + + # Hessian hooks for embedding factorization projection layers + def make_linear_input_hook(weight_name): + def hook_fn(module, inp, out): + x = inp[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + if weight_name not in hessians: + hessians[weight_name] = torch.zeros( + x.shape[1], x.shape[1], dtype=torch.float32, device=device + ) + hessians[weight_name].addmm_(x.T, x) + return hook_fn + + if model.tie_embeddings: + hook_module = model.final_norm + + def make_output_hook(name): + def hook_fn(module, inp, out): + x = out.detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + if name not in hessians: + hessians[name] = torch.zeros( + x.shape[1], x.shape[1], dtype=torch.float32, device=device + ) + hessians[name].addmm_(x.T, x) + if name not in act_sumsq: + act_sumsq[name] = torch.zeros( + x.shape[1], dtype=torch.float32, device=device + ) + act_counts[name] = 0 + act_sumsq[name] += x.square().sum(dim=0) + act_counts[name] += x.shape[0] + return hook_fn + + hooks.append( + hook_module.register_forward_hook(make_output_hook("tok_emb.weight")) + ) + model.eval() + with torch.no_grad(): + for _ in range(n_calibration_batches): + x, _ = train_loader.next_batch(h.train_batch_tokens, h.grad_accum_steps) + model.forward_logits(x) + for hook in hooks: + hook.remove() + for i, block in enumerate(model.blocks): + block.attn._calib = False + block.mlp._calib = False + block.mlp.use_fused = True + for name in hessians: + hessians[name] = hessians[name].cpu() / n_calibration_batches + act_stats = {} + for name, sumsq in act_sumsq.items(): + count = max(act_counts.get(name, 0), 1) + act_stats[name] = (sumsq / count).sqrt().cpu() + return hessians, act_stats + + +def gptq_quantize_weight( + w, + H, + clip_sigmas=3.0, + clip_range=63, + block_size=128, + protect_groups=None, + group_size=None, + protect_clip_range=None, +): + W_orig = w.float().clone() + rows, cols = W_orig.shape + H = H.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * H.diag().mean() + H.diagonal().add_(damp) + perm = torch.argsort(H.diag(), descending=True) + invperm = torch.argsort(perm) + W_perm = W_orig[:, perm].clone() + W_perm[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.cholesky_inverse(torch.linalg.cholesky(H)) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + row_std = W_orig.std(dim=1) + s = (clip_sigmas * row_std / clip_range).clamp_min(1e-10).to(torch.float16) + sf = s.float() + protect_meta = None + protect_mask_perm = None + s_hi = None + sf_hi = None + if ( + protect_groups + and group_size is not None + and protect_clip_range is not None + and protect_clip_range > clip_range + ): + protect_mask = torch.zeros(cols, dtype=torch.bool) + starts = [] + for (start, end) in protect_groups: + if start < 0 or end > cols or end <= start: + continue + protect_mask[start:end] = True + starts.append(start) + if starts: + protect_mask_perm = protect_mask[perm] + s_hi = (clip_sigmas * row_std / protect_clip_range).clamp_min(1e-10).to( + torch.float16 + ) + sf_hi = s_hi.float() + protect_meta = { + "starts": torch.tensor(starts, dtype=torch.int16), + "size": int(group_size), + "s_hi": s_hi, + } + Q = torch.zeros(rows, cols, dtype=torch.int8) + W_work = W_perm.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + W_block = W_work[:, i1:i2].clone() + Hinv_block = Hinv[i1:i2, i1:i2] + Err = torch.zeros(rows, i2 - i1) + for j in range(i2 - i1): + w_col = W_block[:, j] + d = Hinv_block[j, j] + if protect_mask_perm is not None and bool(protect_mask_perm[i1 + j]): + q_col = torch.clamp( + torch.round(w_col / sf_hi), + -protect_clip_range, + protect_clip_range, + ) + w_recon = q_col.float() * sf_hi + else: + q_col = torch.clamp(torch.round(w_col / sf), -clip_range, clip_range) + w_recon = q_col.float() * sf + Q[:, i1 + j] = q_col.to(torch.int8) + err = (w_col - w_recon) / d + Err[:, j] = err + W_block[:, j:] -= err.unsqueeze(1) * Hinv_block[j, j:].unsqueeze(0) + if i2 < cols: + W_work[:, i2:] -= Err @ Hinv[i1:i2, i2:] + return Q[:, invperm], s, protect_meta + + +def _quantize_gate_int8_row(w): + # Symmetric int8-per-row quantization for small gate tensors. w shape + # (R, C) -> (R,) scales in fp16, int8 values in [-127, 127]. Single scale + # per row keeps accuracy high while halving storage vs fp16. + W = w.float().contiguous() + row_max = W.abs().amax(dim=1).clamp_min(1e-10) + s = (row_max / 127.0).to(torch.float16) + sf = s.float().view(-1, 1) + q = torch.clamp(torch.round(W / sf), -127, 127).to(torch.int8) + return q, s + + +def _lqer_pack(A, B, bits): + rng = 2 ** (bits - 1) - 1 + sA = (A.abs().amax(dim=1).clamp_min(1e-10) / rng).to(torch.float16) + sB = (B.abs().amax(dim=1).clamp_min(1e-10) / rng).to(torch.float16) + qA = torch.clamp(torch.round(A / sA.float().view(-1, 1)), -rng, rng).to(torch.int8) + qB = torch.clamp(torch.round(B / sB.float().view(-1, 1)), -rng, rng).to(torch.int8) + return qA, sA, qB, sB + + +def _lqer_pack_asym(A, B, g=64): + # A: INT2 per-matrix scalar (signed [-2,1], scale = |A|max/1.5). + sA = (A.abs().amax().clamp_min(1e-10) / 1.5).to(torch.float16) + qA = torch.clamp(torch.round(A / sA.float()), -2, 1).to(torch.int8) + # B: INT4 groupwise g over flattened B (signed [-8,7], per-group scale). + Bf = B.reshape(-1, g) + Bmax = Bf.abs().amax(dim=-1, keepdim=True).clamp_min(1e-10) + sB = (Bmax / 7.5).to(torch.float16).reshape(-1) + qB = torch.clamp(torch.round(Bf / sB.float().reshape(-1, 1)), -8, 7).to( + torch.int8 + ).reshape(B.shape) + return qA, sA, qB, sB + + +def _lqer_fit_quantized(E, h): + U, S, Vh = torch.linalg.svd(E, full_matrices=False) + r = min(h.lqer_rank, S.numel()) + if r <= 0: + return None + A = (U[:, :r] * S[:r]).contiguous() + B = Vh[:r, :].contiguous() + asym_on = bool(getattr(h, "lqer_asym_enabled", False)) + asym_g = int(getattr(h, "lqer_asym_group", 64)) + if asym_on and B.numel() % asym_g == 0: + qA, sA, qB, sB = _lqer_pack_asym(A, B, asym_g) + A_hat = qA.float() * float(sA) + g_sz = qB.numel() // sB.numel() + B_hat = (qB.reshape(-1, g_sz).float() * sB.float().view(-1, 1)).reshape( + qB.shape + ) + return { + "kind": "asym", + "qA": qA, + "sA": sA, + "qB": qB, + "sB": sB, + "delta": A_hat @ B_hat, + } + qA, sA, qB, sB = _lqer_pack(A, B, h.lqer_factor_bits) + A_hat = qA.float() * sA.float().view(-1, 1) + B_hat = qB.float() * sB.float().view(-1, 1) + return { + "kind": "sym", + "qA": qA, + "sA": sA, + "qB": qB, + "sB": sB, + "delta": A_hat @ B_hat, + } + + +def _awq_lite_group_candidates(w, act_rms, group_size): + cols = w.shape[1] + n_groups = cols // group_size + if n_groups <= 0: + return [] + weight_score = w.float().abs().mean(dim=0) + saliency = act_rms.float() * weight_score + cands = [] + for gi in range(n_groups): + start = gi * group_size + end = start + group_size + score = float(saliency[start:end].sum()) + cands.append((score, start, end)) + return cands + + +def gptq_mixed_quantize(state_dict, hessians, act_stats, h): + result = {} + meta = {} + quant_gate = bool(getattr(h, "gated_attn_quant_gate", False)) + lqer_on = bool(getattr(h, "lqer_enabled", False)) + awq_on = bool(getattr(h, "awq_lite_enabled", False)) + lqer_cands = {} + awq_selected = collections.defaultdict(list) + if awq_on: + awq_cands = [] + for (name, tensor) in state_dict.items(): + t = tensor.detach().cpu().contiguous() + if t.is_floating_point() and t.numel() > 65536 and name in act_stats: + bits = h.embed_bits if "tok_emb" in name else h.matrix_bits + if bits < h.awq_lite_bits: + for score, start, end in _awq_lite_group_candidates( + t, act_stats[name], h.awq_lite_group_size + ): + awq_cands.append((score, name, start, end)) + awq_cands.sort(key=lambda x: -x[0]) + for (_score, name, start, end) in awq_cands[: h.awq_lite_group_top_k]: + awq_selected[name].append((start, end)) + for (name, tensor) in state_dict.items(): + t = tensor.detach().cpu().contiguous() + # Dedicated int8-per-row path for attn_gate_w (bypasses both GPTQ and + # fp16 passthrough). Applied BEFORE the numel<=65536 passthrough check + # so the gate tensor is routed here instead of to fp16. + if ( + quant_gate + and t.is_floating_point() + and t.ndim == 2 + and name.endswith(".attn_gate_w") + # Dense GatedAttn: (num_heads, dim) = (8, 512) = 4096. + # Sparse gate: (num_heads, gate_window) = (8, 12) = 96. + # Both need int8-per-row routing; the 1024 lower bound in stock + # PR-1736 presumed dense-only. Widen to catch both. + and 32 <= t.numel() <= 8192 + ): + gq, gs = _quantize_gate_int8_row(t) + result[name + ".gq"] = gq + result[name + ".gs"] = gs + meta[name] = "gate_int8_row" + continue + 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 (float16)" + continue + if "tok_emb" in name: + cs = h.embed_clip_sigmas + elif ".mlp." in name: + cs = h.mlp_clip_sigmas + elif ".attn." in name: + cs = h.attn_clip_sigmas + else: + cs = h.matrix_clip_sigmas + bits = h.embed_bits if "tok_emb" in name else h.matrix_bits + clip_range = 2 ** (bits - 1) - 1 + q, s, protect_meta = gptq_quantize_weight( + t, + hessians[name], + clip_sigmas=cs, + clip_range=clip_range, + protect_groups=awq_selected.get(name), + group_size=h.awq_lite_group_size if name in awq_selected else None, + protect_clip_range=(2 ** (h.awq_lite_bits - 1) - 1) + if name in awq_selected + else None, + ) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = f"gptq (int{bits})" + W_q = q.float() * s.float().view(-1, 1) + if protect_meta is not None: + result[name + ".awqg_start"] = protect_meta["starts"] + result[name + ".awqg_s_hi"] = protect_meta["s_hi"] + result[name + ".awqg_size"] = torch.tensor( + protect_meta["size"], dtype=torch.int16 + ) + meta[name] = meta[name] + f"+awqgrpint{h.awq_lite_bits}" + gsz = protect_meta["size"] + for start in protect_meta["starts"].tolist(): + W_q[:, start : start + gsz] = ( + q[:, start : start + gsz].float() + * protect_meta["s_hi"].float().view(-1, 1) + ) + if lqer_on: + # LQER is fit on top of the fully realized GPTQ base, which already + # includes any higher-precision AWQ-protected groups. + scope = str(getattr(h, "lqer_scope", "all")).lower() + scope_ok = ( + scope == "all" + or (scope == "mlp" and ".mlp." in name) + or (scope == "attn" and ".attn." in name) + or (scope == "embed" and "tok_emb" in name) + ) + if scope_ok: + E = t.float() - W_q + err_norm = float(E.norm()) + if err_norm > 0: + lqer_cands[name] = (E, err_norm) + if lqer_on and lqer_cands: + if bool(getattr(h, "lqer_gain_select", False)): + scored = [] + for (name, (E, base_err)) in lqer_cands.items(): + fit = _lqer_fit_quantized(E, h) + if fit is None: + continue + new_err = float((E - fit["delta"]).norm()) + gain = base_err - new_err + if gain > 0: + scored.append((gain, name, fit)) + scored.sort(key=lambda x: -x[0]) + for (_gain, name, fit) in scored[: h.lqer_top_k]: + if fit["kind"] == "asym": + result[name + ".lqA_a"] = fit["qA"] + result[name + ".lqAs_a"] = fit["sA"] + result[name + ".lqB_a"] = fit["qB"] + result[name + ".lqBs_a"] = fit["sB"] + meta[name] = meta[name] + "+lqer_asym" + else: + result[name + ".lqA"] = fit["qA"] + result[name + ".lqAs"] = fit["sA"] + result[name + ".lqB"] = fit["qB"] + result[name + ".lqBs"] = fit["sB"] + meta[name] = meta[name] + "+lqer" + else: + top = sorted(lqer_cands.items(), key=lambda kv: -kv[1][1])[: h.lqer_top_k] + asym_on = bool(getattr(h, "lqer_asym_enabled", False)) + asym_g = int(getattr(h, "lqer_asym_group", 64)) + for (name, (E, _)) in top: + U, S, Vh = torch.linalg.svd(E, full_matrices=False) + r = min(h.lqer_rank, S.numel()) + A = (U[:, :r] * S[:r]).contiguous() + B = Vh[:r, :].contiguous() + if asym_on and B.numel() % asym_g == 0: + qA, sA, qB, sB = _lqer_pack_asym(A, B, asym_g) + result[name + ".lqA_a"] = qA + result[name + ".lqAs_a"] = sA + result[name + ".lqB_a"] = qB + result[name + ".lqBs_a"] = sB + meta[name] = meta[name] + "+lqer_asym" + else: + qA, sA, qB, sB = _lqer_pack(A, B, h.lqer_factor_bits) + result[name + ".lqA"] = qA + result[name + ".lqAs"] = sA + result[name + ".lqB"] = qB + result[name + ".lqBs"] = sB + meta[name] = meta[name] + "+lqer" + categories = collections.defaultdict(set) + for (name, cat) in meta.items(): + short = re.sub("\\.\\d+$", "", re.sub("blocks\\.\\d+", "blocks", name)) + categories[cat].add(short) + log("Quantized weights:") + for cat in sorted(categories): + log(f" {cat}: {', '.join(sorted(categories[cat]))}") + return result, meta + +def dequantize_mixed(result, meta, template_sd): + out = {} + for (name, orig) in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if "passthrough" in info: + 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 + if info == "gate_int8_row": + gq = result[name + ".gq"] + gs = result[name + ".gs"] + out[name] = (gq.float() * gs.float().view(-1, 1)).to(orig_dtype) + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + W = q.float() * s.float().view(q.shape[0], *[1] * (q.ndim - 1)) + else: + W = q.float() * float(s.item()) + if "awqgrpint" in info: + starts = result[name + ".awqg_start"].tolist() + s_hi = result[name + ".awqg_s_hi"].float() + gsz = int(result[name + ".awqg_size"].item()) + for start in starts: + W[:, start : start + gsz] = ( + q[:, start : start + gsz].float() * s_hi.view(-1, 1) + ) + if "lqer_asym" in info: + qA_t = result[name + ".lqA_a"] + sA_t = result[name + ".lqAs_a"] + qB_t = result[name + ".lqB_a"] + sB_t = result[name + ".lqBs_a"] + qA = qA_t.float() * float(sA_t) + g_sz = qB_t.numel() // sB_t.numel() + qB = (qB_t.reshape(-1, g_sz).float() * sB_t.float().view(-1, 1)).reshape( + qB_t.shape + ) + W = W + qA @ qB + elif "lqer" in info: + qA = result[name + ".lqA"].float() * result[name + ".lqAs"].float().view(-1, 1) + qB = result[name + ".lqB"].float() * result[name + ".lqBs"].float().view(-1, 1) + W = W + qA @ qB + out[name] = W.to(orig_dtype) + return out + + +_BSHF_MAGIC = b"BSHF" + + +# ── Per-group lrzip compression (ported from PR#1586 via PR#1667/1729) ──────── + +_GROUP_ORDER = [ + "_tok_emb.weight.q", + "attn.c_k.weight.q", "attn.c_q.weight.q", + "attn.c_v.weight.q", "attn.proj.weight.q", + "mlp.fc.weight.q", "mlp.proj.weight.q", +] +_SIMSORT_KEYS = {"_tok_emb.weight.q", "attn.c_q.weight.q", "mlp.fc.weight.q"} +_PACK_MAGIC = b"PGRP" + + +def _similarity_sort_l1(matrix): + import numpy as _np + n = matrix.shape[0] + used = _np.zeros(n, dtype=bool) + order = [0] + used[0] = True + cur = matrix[0].astype(_np.float32) + for _ in range(n - 1): + dists = _np.sum(_np.abs(matrix[~used].astype(_np.float32) - cur), axis=1) + unused = _np.where(~used)[0] + best = unused[_np.argmin(dists)] + order.append(best) + used[best] = True + cur = matrix[best].astype(_np.float32) + return _np.array(order, dtype=_np.uint16) + + +def _lrzip_compress(data, tmpdir, label): + inp = os.path.join(tmpdir, f"{label}.bin") + out = f"{inp}.lrz" + with open(inp, "wb") as f: + f.write(data) + subprocess.run(["lrzip", "-z", "-L", "9", "-o", out, inp], capture_output=True, check=True) + with open(out, "rb") as f: + result = f.read() + os.remove(inp); os.remove(out) + return result + + +def _lrzip_decompress(data, tmpdir, label): + inp = os.path.join(tmpdir, f"{label}.lrz") + out = os.path.join(tmpdir, f"{label}.bin") + with open(inp, "wb") as f: + f.write(data) + subprocess.run(["lrzip", "-d", "-f", "-o", out, inp], capture_output=True, check=True) + with open(out, "rb") as f: + result = f.read() + os.remove(inp); os.remove(out) + return result + + +def _pack_streams(streams): + import struct + n = len(streams) + hdr = _PACK_MAGIC + struct.pack("= 2 + docs.append((start, end - start)) + return docs + + +def _build_ttt_global_batches(doc_entries, h, ascending=False): + batch_size = h.ttt_batch_size + global_doc_entries = sorted(doc_entries, key=lambda x: x[1][1]) + global_batches = [ + global_doc_entries[i : i + batch_size] + for i in range(0, len(global_doc_entries), batch_size) + ] + indexed = list(enumerate(global_batches)) + if not ascending: + indexed.sort(key=lambda ib: -max(dl for _, (_, dl) in ib[1])) + return indexed + + +def _init_batch_counter(path): + with open(path, "wb") as f: + f.write((0).to_bytes(4, "little")) + + +def _claim_next_batch(counter_path, queue_len): + try: + with open(counter_path, "r+b") as f: + fcntl.flock(f, fcntl.LOCK_EX) + idx = int.from_bytes(f.read(4), "little") + f.seek(0) + f.write((idx + 1).to_bytes(4, "little")) + f.flush() + except FileNotFoundError: + return queue_len + return idx + + +def _compute_chunk_window(ci, pred_len, num_chunks, chunk_size, eval_seq_len): + chunk_end = pred_len if ci == num_chunks - 1 else (ci + 1) * chunk_size + win_start = max(0, chunk_end - eval_seq_len) + win_len = chunk_end - win_start + chunk_start = ci * chunk_size + chunk_offset = chunk_start - win_start + chunk_len = chunk_end - chunk_start + return win_start, win_len, chunk_offset, chunk_len + + +def _accumulate_bpb( + ptl, + x, + y, + chunk_offsets, + chunk_lens, + pos_idx, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + loss_sum, + byte_sum, + token_count, + y_bytes=None, +): + pos = pos_idx[: x.size(1)].unsqueeze(0) + mask = ( + (chunk_lens.unsqueeze(1) > 0) + & (pos >= chunk_offsets.unsqueeze(1)) + & (pos < (chunk_offsets + chunk_lens).unsqueeze(1)) + ) + mask_f64 = mask.to(torch.float64) + if y_bytes is not None: + tok_bytes = y_bytes.to(torch.float64) + else: + tok_bytes = base_bytes_lut[y].to(torch.float64) + tok_bytes += (has_leading_space_lut[y] & ~is_boundary_token_lut[x]).to( + torch.float64 + ) + loss_sum += (ptl.to(torch.float64) * mask_f64).sum() + byte_sum += (tok_bytes * mask_f64).sum() + token_count += chunk_lens.to(torch.float64).sum() + + +def _loss_bpb_from_sums(loss_sum, token_count, byte_sum): + val_loss = (loss_sum / token_count).item() + val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_sum.item()) + return val_loss, val_bpb + + +def _add_to_counter(path, delta): + try: + with open(path, "r+b") as f: + fcntl.flock(f, fcntl.LOCK_EX) + cur = int.from_bytes(f.read(8), "little", signed=True) + cur += int(delta) + f.seek(0) + f.write(int(cur).to_bytes(8, "little", signed=True)) + f.flush() + return cur + except FileNotFoundError: + return int(delta) + + +def _init_int64_counter(path): + with open(path, "wb") as f: + f.write((0).to_bytes(8, "little", signed=True)) + + +def _select_ttt_doc_entries(docs, h): + doc_entries = list(enumerate(docs)) + if h.val_doc_fraction < 1.0: + sample_n = max(1, int(round(len(docs) * h.val_doc_fraction))) + sampled_indices = sorted( + random.Random(h.seed).sample(range(len(docs)), sample_n) + ) + return [(i, docs[i]) for i in sampled_indices] + return doc_entries + + +def train_val_ttt_global_sgd_distributed(h, device, val_data, base_model, val_tokens, batch_seqs=None): + global BOS_ID + if BOS_ID is None: + BOS_ID = 1 + base_model.eval() + seq_len = h.eval_seq_len + total_tokens = val_tokens.numel() - 1 + ttt_chunk = h.global_ttt_chunk_tokens + batch_seqs = h.global_ttt_batch_seqs if batch_seqs is None else batch_seqs + num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk + ttt_params = [p for p in base_model.parameters()] + for p in ttt_params: + p.requires_grad_(True) + optimizer = torch.optim.SGD( + ttt_params, lr=h.global_ttt_lr, momentum=h.global_ttt_momentum + ) + t_start = time.perf_counter() + for ci in range(num_chunks): + chunk_start = ci * ttt_chunk + chunk_end = min((ci + 1) * ttt_chunk, total_tokens) + is_last_chunk = ci == num_chunks - 1 + if is_last_chunk or h.global_ttt_epochs <= 0: + continue + base_model.train() + chunk_seqs = (chunk_end - chunk_start) // seq_len + if chunk_seqs <= 0: + continue + warmup_chunks = max(0, min(h.global_ttt_warmup_chunks, num_chunks - 1)) + if warmup_chunks > 0 and ci < warmup_chunks: + warmup_denom = max(warmup_chunks - 1, 1) + warmup_t = ci / warmup_denom + lr_now = ( + h.global_ttt_warmup_start_lr + + (h.global_ttt_lr - h.global_ttt_warmup_start_lr) * warmup_t + ) + else: + decay_steps = max(num_chunks - 1 - warmup_chunks, 1) + decay_ci = max(ci - warmup_chunks, 0) + lr_now = h.global_ttt_lr * 0.5 * ( + 1.0 + math.cos(math.pi * decay_ci / decay_steps) + ) + for pg in optimizer.param_groups: + pg["lr"] = lr_now + my_seq_s = chunk_seqs * h.rank // h.world_size + my_seq_e = chunk_seqs * (h.rank + 1) // h.world_size + my_chunk_seqs = my_seq_e - my_seq_s + for _ in range(h.global_ttt_epochs): + for bs in range(0, my_chunk_seqs, batch_seqs): + be = min(bs + batch_seqs, my_chunk_seqs) + actual_bs = my_seq_s + bs + start_tok = chunk_start + actual_bs * seq_len + end_tok = chunk_start + (my_seq_s + be) * seq_len + 1 + if end_tok > val_tokens.numel(): + continue + local = val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64) + x_flat = local[:-1] + y_flat = local[1:] + optimizer.zero_grad(set_to_none=True) + with torch.enable_grad(): + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + if h.global_ttt_respect_doc_boundaries: + bos_pos = (x_flat == BOS_ID).nonzero(as_tuple=True)[0].tolist() + cu_seqlens, max_seqlen = _build_cu_seqlens( + bos_pos, x_flat.numel(), x_flat.device, h.eval_seq_len, 64 + ) + loss = base_model( + x_flat[None], + y_flat[None], + cu_seqlens=cu_seqlens, + max_seqlen=max_seqlen, + ) + else: + x = x_flat.reshape(-1, seq_len) + y = y_flat.reshape(-1, seq_len) + loss = base_model(x, y) + loss.backward() + if dist.is_available() and dist.is_initialized(): + for p in ttt_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.SUM) + p.grad.mul_(1.0 / h.world_size) + if h.global_ttt_grad_clip > 0: + torch.nn.utils.clip_grad_norm_(ttt_params, h.global_ttt_grad_clip) + optimizer.step() + base_model.eval() + if h.rank == 0: + elapsed = time.perf_counter() - t_start + log( + f"tttg: c{ci+1}/{num_chunks} lr:{lr_now:.6f} t:{elapsed:.1f}s" + ) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.eval() + + +def eval_val_ttt_phased(h, base_model, device, val_data, forward_ttt_train): + global BOS_ID + if BOS_ID is None: + BOS_ID = 1 + base_model.eval() + for p in base_model.parameters(): + p.requires_grad_(False) + all_tokens = val_data.val_tokens + all_tokens_idx = all_tokens.to(torch.int32) + docs = _find_docs(all_tokens) + doc_entries = _select_ttt_doc_entries(docs, h) + prefix_doc_limit = max(0, min(len(doc_entries), int(h.phased_ttt_prefix_docs))) + num_phases = max(1, int(h.phased_ttt_num_phases)) + phase_boundaries = [] + for pi in range(num_phases): + boundary = prefix_doc_limit * (pi + 1) // num_phases + phase_boundaries.append(boundary) + current_phase = 0 + current_phase_boundary = phase_boundaries[0] + log( + "ttt_phased:" + f" total_docs:{len(doc_entries)} prefix_docs:{prefix_doc_limit} " + f"suffix_docs:{len(doc_entries) - prefix_doc_limit}" + f" num_phases:{num_phases} boundaries:{phase_boundaries}" + ) + chunk_size, eval_seq_len = h.ttt_chunk_size, h.ttt_eval_seq_len + eval_batch_set = None + if h.ttt_eval_batches: + eval_batch_set = set(int(x) for x in h.ttt_eval_batches.split(",") if x.strip()) + use_ascending = eval_batch_set is not None + global_batches_sorted = _build_ttt_global_batches( + doc_entries, h, ascending=use_ascending + ) + queue_len = len(global_batches_sorted) + counter_path = f"/tmp/ttt_counter_{h.run_id}" + prefix_counter_path = f"/tmp/ttt_prefix_counter_{h.run_id}" + pause_flag_path = f"/tmp/ttt_pause_flag_{h.run_id}" + if h.rank == 0: + _init_batch_counter(counter_path) + _init_int64_counter(prefix_counter_path) + try: + os.remove(pause_flag_path) + except FileNotFoundError: + pass + if dist.is_available() and dist.is_initialized(): + path_list = [counter_path, prefix_counter_path, pause_flag_path] + dist.broadcast_object_list(path_list, src=0) + counter_path, prefix_counter_path, pause_flag_path = path_list + dist.barrier() + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + byte_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + t_start = time.perf_counter() + reusable_lora = BatchedTTTLoRA( + h.ttt_batch_size, base_model, h.ttt_lora_rank, + q_lora=h.ttt_q_lora, k_lora=h.ttt_k_lora, v_lora=h.ttt_v_lora, + mlp_lora=h.ttt_mlp_lora, o_lora=h.ttt_o_lora, + ).to(device) + + def _build_opt(lora): + local_lr = h.ttt_lora_lr * h.ttt_local_lr_mult + if h.ttt_optimizer == "sgd": + return torch.optim.SGD( + lora.parameters(), lr=local_lr, + momentum=h.ttt_beta1, weight_decay=h.ttt_weight_decay, + ) + return torch.optim.AdamW( + lora.parameters(), lr=local_lr, + betas=(h.ttt_beta1, h.ttt_beta2), + eps=1e-10, weight_decay=h.ttt_weight_decay, fused=True, + ) + + reusable_opt = _build_opt(reusable_lora) + local_scored_docs = [] + global_ttt_done = prefix_doc_limit == 0 + try: + while True: + queue_idx = _claim_next_batch(counter_path, queue_len) + if queue_idx >= queue_len: + break + orig_batch_idx, batch_entries = global_batches_sorted[queue_idx] + batch = [doc for _, doc in batch_entries] + bsz = len(batch) + prev_loss = loss_sum.item() + prev_bytes = byte_sum.item() + prev_tokens = token_count.item() + if bsz == reusable_lora.bsz: + reusable_lora.reset() + for s in reusable_opt.state.values(): + for k, v in s.items(): + if isinstance(v, torch.Tensor): + v.zero_() + elif k == "step": + s[k] = 0 + cur_lora = reusable_lora + cur_opt = reusable_opt + else: + cur_lora = BatchedTTTLoRA( + bsz, base_model, h.ttt_lora_rank, + q_lora=h.ttt_q_lora, k_lora=h.ttt_k_lora, v_lora=h.ttt_v_lora, + mlp_lora=h.ttt_mlp_lora, o_lora=h.ttt_o_lora, + ).to(device) + cur_opt = _build_opt(cur_lora) + pred_lens = [doc_len - 1 for _, doc_len in batch] + num_chunks = [(pl + chunk_size - 1) // chunk_size for pl in pred_lens] + max_nc = max(num_chunks) + num_chunks_t = torch.tensor(num_chunks, dtype=torch.int64, device=device) + for ci in range(max_nc): + active = [ci < nc for nc in num_chunks] + needs_train = any(ci < nc - 1 for nc in num_chunks) + tok_starts = torch.zeros(bsz, dtype=torch.int64) + tok_wls = torch.zeros(bsz, dtype=torch.int64) + chunk_offsets_cpu = torch.zeros(bsz, dtype=torch.int64) + chunk_lens_cpu = torch.zeros(bsz, dtype=torch.int64) + for b in range(bsz): + if not active[b]: + continue + doc_start, doc_len = batch[b] + win_start, win_len, chunk_offset, chunk_len = _compute_chunk_window( + ci, pred_lens[b], num_chunks[b], chunk_size, eval_seq_len + ) + tok_starts[b] = doc_start + win_start + tok_wls[b] = win_len + chunk_offsets_cpu[b] = chunk_offset + chunk_lens_cpu[b] = chunk_len + _, context_size, chunk_offset, _ = _compute_chunk_window( + ci, (ci + 1) * chunk_size, ci + 1, chunk_size, eval_seq_len + ) + col_idx = torch.arange(context_size + 1) + idx = tok_starts.unsqueeze(1) + col_idx.unsqueeze(0) + idx.clamp_(max=all_tokens.numel() - 1) + gathered_gpu = all_tokens_idx[idx].to( + device=device, dtype=torch.int64, non_blocking=True + ) + valid = (col_idx[:context_size].unsqueeze(0) < tok_wls.unsqueeze(1)).to( + device, non_blocking=True + ) + chunk_offsets = chunk_offsets_cpu.to(device, non_blocking=True) + chunk_lens = chunk_lens_cpu.to(device, non_blocking=True) + x = torch.where(valid, gathered_gpu[:, :context_size], 0) + y = torch.where(valid, gathered_gpu[:, 1 : context_size + 1], 0) + ctx_pos = torch.arange(context_size, device=device, dtype=torch.int64) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + per_tok_loss = forward_ttt_train(x, y, lora=cur_lora) + # CaseOps sidecar-driven byte budget. Mirror the index pattern + # used to build y from all_tokens: y[b, j] corresponds to the + # token at global position tok_starts[b] + 1 + j (when valid). + y_bytes_arg = None + if val_data.caseops_enabled and val_data.val_bytes is not None: + y_idx = ( + tok_starts.unsqueeze(1) + + 1 + + col_idx[:context_size].unsqueeze(0) + ) + y_idx = y_idx.clamp_(max=val_data.val_bytes.numel() - 1) + y_bytes_arg = val_data.val_bytes[y_idx].to( + device=device, dtype=torch.int32, non_blocking=True + ) + # Mirror the `valid` masking used for y so out-of-range tokens + # contribute zero bytes (matches y=0 substitution above). + y_bytes_arg = torch.where( + valid, y_bytes_arg, torch.zeros_like(y_bytes_arg) + ) + with torch.no_grad(): + _accumulate_bpb( + per_tok_loss, + x, + y, + chunk_offsets, + chunk_lens, + ctx_pos, + val_data.base_bytes_lut, + val_data.has_leading_space_lut, + val_data.is_boundary_token_lut, + loss_sum, + byte_sum, + token_count, + y_bytes=y_bytes_arg, + ) + if needs_train: + activate_chunk_mask = (num_chunks_t - 1 > ci).float() + for gi in range(h.ttt_grad_steps): + if gi > 0: + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + per_tok_loss = forward_ttt_train(x, y, lora=cur_lora) + per_doc = per_tok_loss[ + :, chunk_offset : chunk_offset + chunk_size + ].mean(dim=-1) + cur_opt.zero_grad(set_to_none=True) + (per_doc * activate_chunk_mask).sum().backward() + cur_opt.step() + else: + del per_tok_loss + batch_num = orig_batch_idx + 1 + doc_lens = [dl for _, dl in batch] + should_report = batch_num in eval_batch_set if eval_batch_set is not None else True + if should_report: + cur_tokens = token_count.item() + cur_loss_val = loss_sum.item() + cur_bytes_val = byte_sum.item() + dt = cur_tokens - prev_tokens + db = cur_bytes_val - prev_bytes + if dt > 0 and db > 0: + b_loss = (cur_loss_val - prev_loss) / dt + b_bpb = b_loss / math.log(2.0) * (dt / db) + else: + b_loss = b_bpb = 0.0 + r_loss = cur_loss_val / max(cur_tokens, 1) + r_bpb = r_loss / math.log(2.0) * (cur_tokens / max(cur_bytes_val, 1)) + elapsed = time.perf_counter() - t_start + log( + f"ttp: b{batch_num}/{queue_len} bl:{b_loss:.4f} bb:{b_bpb:.4f} " + f"rl:{r_loss:.4f} rb:{r_bpb:.4f} dl:{min(doc_lens)}-{max(doc_lens)} " + f"gd:{int(global_ttt_done)}" + ) + if not global_ttt_done: + local_scored_docs.extend( + (orig_batch_idx, pos, doc_start, doc_len) + for pos, (doc_start, doc_len) in enumerate(batch) + ) + prefix_done = _add_to_counter(prefix_counter_path, len(batch_entries)) + if prefix_done >= current_phase_boundary: + try: + with open(pause_flag_path, "x"): + pass + except FileExistsError: + pass + should_pause = os.path.exists(pause_flag_path) + if should_pause: + if dist.is_available() and dist.is_initialized(): + dist.barrier() + gathered_scored_docs = [None] * h.world_size + if dist.is_available() and dist.is_initialized(): + dist.all_gather_object(gathered_scored_docs, local_scored_docs) + else: + gathered_scored_docs = [local_scored_docs] + scored_docs_for_global = [] + for rank_docs in gathered_scored_docs: + if rank_docs: + scored_docs_for_global.extend(rank_docs) + scored_docs_for_global.sort(key=lambda x: (x[0], x[1])) + scored_docs_for_global = scored_docs_for_global[:current_phase_boundary] + scored_token_chunks = [ + val_data.val_tokens[doc_start : doc_start + doc_len] + for _, _, doc_start, doc_len in scored_docs_for_global + ] + if scored_token_chunks: + global_ttt_tokens = torch.cat(scored_token_chunks) + else: + global_ttt_tokens = val_data.val_tokens[:0] + if h.rank == 0: + prefix_done = 0 + try: + with open(prefix_counter_path, "rb") as f: + prefix_done = int.from_bytes( + f.read(8), "little", signed=True + ) + except FileNotFoundError: + pass + log( + f"ttpp: phase:{current_phase + 1}/{num_phases} pd:{prefix_done} " + f"gd:{len(scored_docs_for_global)} " + f"t:{time.perf_counter() - t_start:.1f}s" + ) + train_val_ttt_global_sgd_distributed( + h, device, val_data, base_model, global_ttt_tokens + ) + for p in base_model.parameters(): + p.requires_grad_(False) + reusable_lora = BatchedTTTLoRA( + h.ttt_batch_size, base_model, h.ttt_lora_rank, + q_lora=h.ttt_q_lora, k_lora=h.ttt_k_lora, v_lora=h.ttt_v_lora, + mlp_lora=h.ttt_mlp_lora, o_lora=h.ttt_o_lora, + ).to(device) + reusable_opt = _build_opt(reusable_lora) + current_phase += 1 + if current_phase >= num_phases: + global_ttt_done = True + else: + current_phase_boundary = phase_boundaries[current_phase] + if h.rank == 0: + try: + os.remove(pause_flag_path) + except FileNotFoundError: + pass + if dist.is_available() and dist.is_initialized(): + dist.barrier() + if h.rank == 0: + log(f"ttpr: phase:{current_phase}/{num_phases} t:{time.perf_counter() - t_start:.1f}s") + del cur_lora, cur_opt + finally: + pass + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + for p in base_model.parameters(): + p.requires_grad_(True) + base_model.train() + return _loss_bpb_from_sums(loss_sum, token_count, byte_sum) + + +def timed_eval(label, fn, *args, **kwargs): + torch.cuda.synchronize() + t0 = time.perf_counter() + val_loss, val_bpb = fn(*args, **kwargs) + torch.cuda.synchronize() + elapsed_ms = 1e3 * (time.perf_counter() - t0) + log( + f"{label} val_loss:{val_loss:.8f} val_bpb:{val_bpb:.8f} eval_time:{elapsed_ms:.0f}ms" + ) + return val_loss, val_bpb + + +def train_model(h, device, val_data): + base_model = GPT(h).to(device).bfloat16() + restore_fp32_params(base_model) + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + compiled_forward_logits = torch.compile( + base_model.forward_logits, dynamic=False, fullgraph=True + ) + model = compiled_model + log(f"model_params:{sum(p.numel()for p in base_model.parameters())}") + optimizers = Optimizers(h, base_model) + train_loader = DocumentPackingLoader(h, device) + max_wallclock_ms = ( + 1e3 * h.max_wallclock_seconds if h.max_wallclock_seconds > 0 else None + ) + if max_wallclock_ms is not None: + max_wallclock_ms -= h.gptq_reserve_seconds * 1e3 + log( + f"gptq:reserving {h.gptq_reserve_seconds:.0f}s, effective={max_wallclock_ms:.0f}ms" + ) + + def training_frac(step, elapsed_ms): + if max_wallclock_ms is None: + return step / max(h.iterations, 1) + return elapsed_ms / max(max_wallclock_ms, 1e-09) + + def lr_mul(frac): + if h.warmdown_frac <= 0: + return 1.0 + if frac >= 1.0 - h.warmdown_frac: + return max((1.0 - frac) / h.warmdown_frac, h.min_lr) + return 1.0 + + _clip_params = [p for p in base_model.parameters() if p.requires_grad] + def step_fn(step, lr_scale): + train_loss = torch.zeros((), device=device) + for micro_step in range(h.grad_accum_steps): + x, y, cu_seqlens, _max_seqlen = train_loader.next_batch( + h.train_batch_tokens, h.grad_accum_steps + ) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y, cu_seqlens=cu_seqlens, max_seqlen=h.train_seq_len) + train_loss += loss.detach() + (loss / h.grad_accum_steps).backward() + train_loss /= h.grad_accum_steps + if step <= h.muon_momentum_warmup_steps: + + frac = ( + + min(step / h.muon_momentum_warmup_steps, 1.0) + + if h.muon_momentum_warmup_steps > 0 + + else 1.0 + + ) + + muon_momentum = ( + + 1 - frac + + ) * h.muon_momentum_warmup_start + frac * h.muon_momentum + + for group in optimizers.optimizer_muon.param_groups: + + group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * lr_scale + if h.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(_clip_params, h.grad_clip_norm) + optimizers.step(distributed=h.distributed) + return train_loss + + if h.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() + num_tokens_local = h.train_batch_tokens // h.world_size + for blk in base_model.blocks: + blk.attn.rotary(num_tokens_local, device, torch.bfloat16) + cu_bucket_size = train_loader.cu_bucket_size + warmup_cu_buckets = tuple(cu_bucket_size * i for i in range(1, 5)) + warmup_cu_iters = 3 + x, y, cu_seqlens, _ = train_loader.next_batch( + h.train_batch_tokens, h.grad_accum_steps + ) + log(f"warmup_cu_buckets:{','.join(str(b) for b in warmup_cu_buckets)} iters_each:{warmup_cu_iters}") + def _run_cu_bucket_warmup(): + for bucket_len in warmup_cu_buckets: + boundaries = list(range(0, x.size(1), max(h.train_seq_len, 1))) + if boundaries[-1] != x.size(1): + boundaries.append(x.size(1)) + cu = torch.full((bucket_len,), x.size(1), dtype=torch.int32, device=device) + cu[: len(boundaries)] = torch.tensor(boundaries, dtype=torch.int32, device=device) + for _ in range(warmup_cu_iters): + optimizers.zero_grad_all() + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + wloss = model(x, y, cu_seqlens=cu, max_seqlen=h.train_seq_len) + (wloss / h.grad_accum_steps).backward() + optimizers.zero_grad_all() + _run_cu_bucket_warmup() + if h.num_loops > 0: + base_model.looping_active = True + _run_cu_bucket_warmup() + base_model.looping_active = False + for warmup_step in range(h.warmup_steps): + step_fn(warmup_step, 1.0) + if ( + warmup_step <= 5 + or (warmup_step + 1) % 10 == 0 + or warmup_step + 1 == h.warmup_steps + ): + log(f"warmup_step: {warmup_step+1}/{h.warmup_steps}") + if h.num_loops > 0: + base_model.looping_active = True + log( + f"loop_warmup:enabled encoder:{base_model.encoder_indices} decoder:{base_model.decoder_indices}" + ) + for warmup_step in range(h.warmup_steps): + step_fn(warmup_step, 1.0) + if ( + warmup_step <= 5 + or (warmup_step + 1) % 10 == 0 + or warmup_step + 1 == h.warmup_steps + ): + log(f"loop_warmup_step: {warmup_step+1}/{h.warmup_steps}") + base_model.looping_active = False + 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) + optimizers.zero_grad_all() + train_loader = DocumentPackingLoader(h, device) + _live_state = base_model.state_dict(keep_vars=True) + ema_state = { + name: t.detach().float().clone() + for (name, t) in _live_state.items() + } + _ema_pairs = [(ema_state[name], t) for (name, t) in _live_state.items()] + ema_decay = h.ema_decay + training_time_ms = 0.0 + forced_stop_step = int(os.environ.get("FORCE_STOP_STEP", "0")) + stop_after_step = forced_stop_step if forced_stop_step > 0 else None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = ( + step == h.iterations + or stop_after_step is not None + and step >= stop_after_step + ) + should_validate = ( + last_step or h.val_loss_every > 0 and step % h.val_loss_every == 0 + ) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1e3 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + h, device, val_data, model, compiled_forward_logits + ) + log( + f"{step}/{h.iterations} val_loss: {val_loss:.4f} val_bpb: {val_bpb:.4f}" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < h.iterations: + log( + f"stopping_early: wallclock_cap train_time: {training_time_ms:.0f}ms step: {step}/{h.iterations}" + ) + break + elapsed_ms = training_time_ms + 1e3 * (time.perf_counter() - t0) + frac = training_frac(step, elapsed_ms) + scale = lr_mul(frac) + if ( + h.num_loops > 0 + and not base_model.looping_active + and frac >= h.enable_looping_at + ): + base_model.looping_active = True + log( + f"layer_loop:enabled step:{step} frac:{frac:.3f} encoder:{base_model.encoder_indices} decoder:{base_model.decoder_indices}" + ) + train_loss = step_fn(step, scale) + with torch.no_grad(): + for ema_t, t in _ema_pairs: + ema_t.mul_(ema_decay).add_(t.detach(), alpha=1.0 - ema_decay) + step += 1 + approx_training_time_ms = training_time_ms + 1e3 * (time.perf_counter() - t0) + should_log_train = h.train_log_every > 0 and ( + step <= 5 or step % h.train_log_every == 0 or stop_after_step is not None + ) + if should_log_train: + tok_per_sec = step * h.train_batch_tokens / (approx_training_time_ms / 1e3) + log( + f"{step}/{h.iterations} train_loss: {train_loss.item():.4f} train_time: {approx_training_time_ms/60000:.1f}m tok/s: {tok_per_sec:.0f}" + ) + reached_cap = ( + forced_stop_step <= 0 + and max_wallclock_ms is not None + and approx_training_time_ms >= max_wallclock_ms + ) + if h.distributed and forced_stop_step <= 0 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 + log( + f"peak memory allocated: {torch.cuda.max_memory_allocated()//1024//1024} MiB reserved: {torch.cuda.max_memory_reserved()//1024//1024} MiB" + ) + log("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) + return base_model, compiled_model, compiled_forward_logits + + +def train_and_eval(h, device): + global BOS_ID + random.seed(h.seed) + np.random.seed(h.seed) + torch.manual_seed(h.seed) + torch.cuda.manual_seed_all(h.seed) + if h.artifact_dir and h.is_main_process: + os.makedirs(h.artifact_dir, exist_ok=True) + val_data = ValidationData(h, device) + log( + f"train_shards: {len(list(Path(h.datasets_dir).resolve().glob('fineweb_train_*.bin')))}" + ) + log(f"val_tokens: {val_data.val_tokens.numel()-1}") + # TTT_EVAL_ONLY: skip training + GPTQ, jump straight to TTT eval on a + # pre-existing quantized artifact. Used to test TTT-only improvements + # (e.g., PR-1767's alpha/warm-start/WD) without retraining. + ttt_eval_only = os.environ.get("TTT_EVAL_ONLY", "0") == "1" + quantize_only = os.environ.get("QUANTIZE_ONLY", "0") == "1" + if ttt_eval_only: + log("TTT_EVAL_ONLY=1 — skipping training + GPTQ, loading saved artifact for TTT eval") + log(f"ttt_lora_alpha: {BatchedLinearLoRA._ALPHA}") + log(f"ttt_warm_start_a: {BatchedLinearLoRA._WARM_START_A}") + log(f"ttt_weight_decay: {h.ttt_weight_decay}") + elif quantize_only: + log("QUANTIZE_ONLY=1 — skipping training, loading saved full-precision checkpoint") + log(f"quantize_only checkpoint: {h.model_path}") + if BOS_ID is None: + BOS_ID = 1 + base_model = GPT(h).to(device).bfloat16() + state = torch.load(h.model_path, map_location="cpu") + base_model.load_state_dict(state, strict=True) + del state + serialize(h, base_model, Path(__file__).read_text(encoding="utf-8")) + if h.distributed: + dist.barrier() + else: + base_model, compiled_model, compiled_forward_logits = train_model( + h, device, val_data + ) + torch._dynamo.reset() + timed_eval( + "diagnostic pre-quantization post-ema", + eval_val, + h, + device, + val_data, + compiled_model, + compiled_forward_logits, + ) + if os.environ.get("PREQUANT_ONLY", "0") == "1": + log("PREQUANT_ONLY=1 — skipping serialize/GPTQ/post-quant eval/TTT") + return + serialize(h, base_model, Path(__file__).read_text(encoding="utf-8")) + if h.distributed: + dist.barrier() + eval_model = deserialize(h, device) + if h.num_loops > 0: + eval_model.looping_active = True + if not ttt_eval_only: + compiled_model = torch.compile(eval_model, dynamic=False, fullgraph=True) + compiled_forward_logits = torch.compile( + eval_model.forward_logits, dynamic=False, fullgraph=True + ) + timed_eval( + "diagnostic quantized", + eval_val, + h, + device, + val_data, + compiled_model, + compiled_forward_logits, + ) + del eval_model + if h.ttt_enabled: + if not ttt_eval_only: + del compiled_model + if ttt_eval_only: + del eval_model + torch._dynamo.reset() + torch.cuda.empty_cache() + ttt_model = deserialize(h, device) + if h.num_loops > 0: + ttt_model.looping_active = True + for p in ttt_model.parameters(): + p.requires_grad_(False) + + if h.rope_yarn: + _yarn_seqlen = h.train_batch_tokens // h.grad_accum_steps + for block in ttt_model.blocks: + block.attn.rotary(_yarn_seqlen, device, torch.bfloat16) + else: + for block in ttt_model.blocks: + block.attn.rotary._cos_cached = None + block.attn.rotary._sin_cached = None + block.attn.rotary._seq_len_cached = 0 + block.attn.rotary(h.ttt_eval_seq_len, device, torch.bfloat16) + + def _fwd_ttt_inner(input_ids, target_ids, lora): + return ttt_model.forward_ttt(input_ids, target_ids, lora=lora) + + _fwd_ttt_compiled_inner = None + + def _fwd_ttt(input_ids, target_ids, lora): + nonlocal _fwd_ttt_compiled_inner + if _fwd_ttt_compiled_inner is None: + _fwd_ttt_compiled_inner = torch.compile(_fwd_ttt_inner, dynamic=True) + return _fwd_ttt_compiled_inner(input_ids, target_ids, lora=lora) + + fwd_ttt_compiled = _fwd_ttt + log(f"ttt_lora:warming up compile (random tokens, no val data)") + if BOS_ID is None: + BOS_ID = 1 + t_warmup = time.perf_counter() + warmup_bszes = [h.ttt_batch_size] + for bsz in warmup_bszes: + wl = BatchedTTTLoRA( + bsz, ttt_model, h.ttt_lora_rank, + q_lora=h.ttt_q_lora, k_lora=h.ttt_k_lora, v_lora=h.ttt_v_lora, + mlp_lora=h.ttt_mlp_lora, o_lora=h.ttt_o_lora, + ).to(device) + wo = torch.optim.AdamW( + wl.parameters(), + lr=h.ttt_lora_lr * h.ttt_local_lr_mult, + betas=(h.ttt_beta1, h.ttt_beta2), + eps=1e-10, + weight_decay=h.ttt_weight_decay, + fused=True, + ) + for ctx_len in (h.ttt_chunk_size, h.ttt_eval_seq_len): + xw = torch.randint(0, h.vocab_size, (bsz, ctx_len), device=device, dtype=torch.int64) + yw = torch.randint(0, h.vocab_size, (bsz, ctx_len), device=device, dtype=torch.int64) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + ptl = fwd_ttt_compiled(xw, yw, lora=wl) + ptl[:, : min(h.ttt_chunk_size, ctx_len)].mean(dim=-1).sum().backward() + wo.step() + wo.zero_grad(set_to_none=True) + del wl, wo + torch.cuda.empty_cache() + compile_elapsed = time.perf_counter() - t_warmup + log(f"ttt_lora:compile warmup done ({compile_elapsed:.1f}s)") + log("\nbeginning TTT eval timer") + torch.cuda.synchronize() + t_ttt = time.perf_counter() + ttt_val_loss, ttt_val_bpb = eval_val_ttt_phased( + h, ttt_model, device, val_data, forward_ttt_train=fwd_ttt_compiled + ) + torch.cuda.synchronize() + ttt_eval_elapsed = time.perf_counter() - t_ttt + log( + "quantized_ttt_phased " + f"val_loss:{ttt_val_loss:.8f} val_bpb:{ttt_val_bpb:.8f} " + f"eval_time:{1e3*ttt_eval_elapsed:.0f}ms" + ) + log(f"total_eval_time:{ttt_eval_elapsed:.1f}s") + del ttt_model + + +def main(): + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + 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" + ) + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + torch.set_float32_matmul_precision("high") + 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) + torch._dynamo.config.optimize_ddp = False + torch._dynamo.config.cache_size_limit = 64 + h = Hyperparameters() + set_logging_hparams(h) + if h.is_main_process: + os.makedirs(h.artifact_dir if h.artifact_dir else "logs", exist_ok=True) + log(100 * "=", console=False) + log("Hyperparameters:", console=True) + for (k, v) in sorted(vars(type(h)).items()): + if not k.startswith("_"): + log(f" {k}: {v}", console=True) + log("=" * 100, console=False) + log("Source code:", console=False) + log("=" * 100, console=False) + with open(__file__, "r", encoding="utf-8") as _src: + log(_src.read(), console=False) + log("=" * 100, console=False) + log(f"Running Python {sys.version}", console=False) + log(f"Running PyTorch {torch.__version__}", console=False) + log("=" * 100, console=False) + train_and_eval(h, device) + if distributed: + dist.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/train_seed42.log b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/train_seed42.log new file mode 100644 index 0000000000..9b4db157e4 --- /dev/null +++ b/records/track_10min_16mb/2026-04-30_LongCtx_NoQV_QK525_Prefix2750/train_seed42.log @@ -0,0 +1,991 @@ +W0430 05:39:35.236000 2384 torch/distributed/run.py:803] +W0430 05:39:35.236000 2384 torch/distributed/run.py:803] ***************************************** +W0430 05:39:35.236000 2384 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0430 05:39:35.236000 2384 torch/distributed/run.py:803] ***************************************** +Hyperparameters: + adam_eps: 1e-08 + adam_wd: 0.02 + artifact_dir: + attn_clip_sigmas: 13.0 + attn_out_gate_enabled: False + attn_out_gate_src: proj + awq_lite_bits: 8 + awq_lite_enabled: True + awq_lite_group_size: 64 + awq_lite_group_top_k: 1 + beta1: 0.9 + beta2: 0.99 + caseops_enabled: True + compressor: pergroup + data_dir: ./data/ + datasets_dir: /workspace/caseops_data/datasets/datasets/fineweb10B_sp8192_lossless_caps_caseops_v1_reserved + distributed: True + ema_decay: 0.9965 + embed_bits: 7 + embed_clip_sigmas: 14.0 + embed_lr: 0.6 + embed_wd: 0.085 + enable_looping_at: 0.35 + eval_seq_len: 2560 + eval_stride: 64 + fused_ce_enabled: True + gate_window: 12 + gated_attn_enabled: False + gated_attn_init_std: 0.01 + gated_attn_quant_gate: True + global_ttt_batch_seqs: 32 + global_ttt_chunk_tokens: 32768 + global_ttt_epochs: 1 + global_ttt_grad_clip: 1.0 + global_ttt_lr: 0.001 + global_ttt_momentum: 0.9 + global_ttt_respect_doc_boundaries: True + global_ttt_warmup_chunks: 0 + global_ttt_warmup_start_lr: 0.0 + gptq_calibration_batches: 16 + gptq_reserve_seconds: 4.0 + grad_accum_steps: 1 + grad_clip_norm: 0.3 + is_main_process: True + iterations: 20000 + ln_scale: True + local_rank: 0 + logfile: logs/prefix2750_seed42.txt + logit_softcap: 30.0 + loop_end: 5 + loop_start: 3 + lqer_asym_enabled: True + lqer_asym_group: 64 + lqer_enabled: True + lqer_factor_bits: 4 + lqer_gain_select: False + lqer_rank: 4 + lqer_scope: all + lqer_top_k: 3 + matrix_bits: 6 + matrix_clip_sigmas: 12.85 + matrix_lr: 0.026 + max_wallclock_seconds: 600.0 + min_lr: 0.1 + mlp_clip_sigmas: 11.5 + mlp_mult: 4.0 + model_dim: 512 + model_path: final_model.pt + muon_backend_steps: 5 + muon_momentum: 0.97 + muon_momentum_warmup_start: 0.92 + muon_momentum_warmup_steps: 1500 + muon_row_normalize: True + muon_wd: 0.095 + num_heads: 8 + num_kv_heads: 4 + num_layers: 11 + num_loops: 2 + parallel_final_lane: mean + parallel_start_layer: 8 + phased_ttt_num_phases: 3 + phased_ttt_prefix_docs: 2750 + qk_gain_init: 5.25 + quantized_model_path: final_model.int6.ptz + rank: 0 + rope_base: 10000.0 + rope_dims: 16 + rope_train_seq_len: 2048 + rope_yarn: False + run_id: prefix2750_seed42 + scalar_lr: 0.02 + seed: 42 + skip_gates_enabled: True + smear_gate_enabled: True + sparse_attn_gate_enabled: True + sparse_attn_gate_init_std: 0.0 + sparse_attn_gate_scale: 0.5 + tie_embeddings: True + tied_embed_init_std: 0.005 + tied_embed_lr: 0.03 + tokenizer_path: /workspace/caseops_data/datasets/tokenizers/fineweb_8192_bpe_lossless_caps_caseops_v1_reserved.model + train_batch_tokens: 786432 + train_files: /workspace/caseops_data/datasets/datasets/fineweb10B_sp8192_lossless_caps_caseops_v1_reserved/fineweb_train_*.bin + train_log_every: 500 + train_seq_len: 2048 + ttt_batch_size: 64 + ttt_beta1: 0.0 + ttt_beta2: 0.99 + ttt_chunk_size: 48 + ttt_enabled: True + ttt_eval_batches: + ttt_eval_seq_len: 2560 + ttt_grad_steps: 1 + ttt_k_lora: True + ttt_local_lr_mult: 0.75 + ttt_lora_lr: 0.0001 + ttt_lora_rank: 80 + ttt_mask: no_qv + ttt_mlp_lora: True + ttt_o_lora: True + ttt_optimizer: adam + ttt_q_lora: False + ttt_v_lora: False + ttt_weight_decay: 0.5 + val_batch_tokens: 524288 + val_bytes_files: /workspace/caseops_data/datasets/datasets/fineweb10B_sp8192_lossless_caps_caseops_v1_reserved/fineweb_val_bytes_*.bin + val_doc_fraction: 1.0 + val_files: /workspace/caseops_data/datasets/datasets/fineweb10B_sp8192_lossless_caps_caseops_v1_reserved/fineweb_val_*.bin + val_loss_every: 0 + vocab_size: 8192 + warmdown_frac: 0.85 + warmup_steps: 20 + world_size: 8 + xsa_last_n: 11 +train_shards: 80 +val_tokens: 47851520 +model_params:35945673 +gptq:reserving 4s, effective=596000ms +warmup_cu_buckets:64,128,192,256 iters_each:3 +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: 10/20 +warmup_step: 20/20 +loop_warmup:enabled encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10] +loop_warmup_step: 1/20 +loop_warmup_step: 2/20 +loop_warmup_step: 3/20 +loop_warmup_step: 4/20 +loop_warmup_step: 5/20 +loop_warmup_step: 6/20 +loop_warmup_step: 10/20 +loop_warmup_step: 20/20 +1/20000 train_loss: 9.0087 train_time: 0.0m tok/s: 15547692 +2/20000 train_loss: 12.8321 train_time: 0.0m tok/s: 11636237 +3/20000 train_loss: 10.2131 train_time: 0.0m tok/s: 10288997 +4/20000 train_loss: 8.6912 train_time: 0.0m tok/s: 9753434 +5/20000 train_loss: 7.9433 train_time: 0.0m tok/s: 9438258 +500/20000 train_loss: 2.5615 train_time: 0.8m tok/s: 8237080 +1000/20000 train_loss: 2.7985 train_time: 1.6m tok/s: 8188635 +1500/20000 train_loss: 2.6166 train_time: 2.4m tok/s: 8174697 +2000/20000 train_loss: 2.6526 train_time: 3.2m tok/s: 8169661 +layer_loop:enabled step:2166 frac:0.350 encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10] +2500/20000 train_loss: 2.5385 train_time: 4.3m tok/s: 7689716 +3000/20000 train_loss: 2.5543 train_time: 5.4m tok/s: 7236758 +3500/20000 train_loss: 2.5558 train_time: 6.6m tok/s: 6944794 +4000/20000 train_loss: 2.4025 train_time: 7.8m tok/s: 6734630 +4500/20000 train_loss: 2.2746 train_time: 9.0m tok/s: 6585085 +4917/20000 val_loss: 2.3488 val_bpb: 1.0732 +stopping_early: wallclock_cap train_time: 596041ms step: 4917/20000 +peak memory allocated: 41720 MiB reserved: 47014 MiB +ema:applying EMA weights +diagnostic pre-quantization post-ema val_loss:2.32342496 val_bpb:1.06162201 eval_time:9526ms +Serialized model: 135418111 bytes +Code size (uncompressed): 171669 bytes +Code size (compressed): 34142 bytes +GPTQ:collecting Hessians from calibration data... +GPTQ:collected 67 Hessians in 4.1s +Quantized weights: + gate_int8_row: blocks.attn.attn_gate_w + gptq (int6): blocks.attn.c_k.weight, blocks.attn.c_q.weight, blocks.attn.c_v.weight, blocks.attn.proj.weight, blocks.mlp.fc.weight, blocks.mlp.proj.weight + gptq (int6)+lqer_asym: blocks.mlp.fc.weight + gptq (int7)+awqgrpint8+lqer_asym: tok_emb.weight + passthrough (float16): blocks.attn.q_gain, blocks.attn_scale, blocks.mlp_scale, blocks.resid_mix, parallel_post_lambdas, parallel_resid_lambdas, skip_gates, skip_weights, smear_gate.weight, smear_lambda, softcap_neg, softcap_pos +Serialize: per-group lrzip compression... +Serialize: per-group compression done in 120.3s +Serialized model quantized+pergroup: 15944031 bytes +Total submission size quantized+pergroup: 15978173 bytes +Deserialize: per-group lrzip decompression... +Deserialize: decompression done in 21.1s +diagnostic quantized val_loss:2.34179299 val_bpb:1.07001475 eval_time:81171ms +Deserialize: per-group lrzip decompression... +Deserialize: decompression done in 20.8s +ttt_lora:warming up compile (random tokens, no val data) + +ttt_lora:compile warmup done (148.6s) + +beginning TTT eval timer +ttt_phased: total_docs:50000 prefix_docs:2750 suffix_docs:47250 num_phases:3 boundaries:[916, 1833, 2750] +ttp: b780/782 bl:2.2334 bb:1.0759 rl:2.2334 rb:1.0759 dl:13091-17244 gd:0 +ttp: b764/782 bl:2.2871 bb:1.0713 rl:2.2456 rb:1.0748 dl:4284-4392 gd:0 +ttpp: phase:1/3 pd:1360 gd:916 t:234.9s +tttg: c1/142 lr:0.001000 t:1.9s +tttg: c2/142 lr:0.001000 t:2.0s +tttg: c3/142 lr:0.001000 t:2.2s +tttg: c4/142 lr:0.000999 t:2.2s +tttg: c5/142 lr:0.000998 t:2.3s +tttg: c6/142 lr:0.000997 t:2.4s +tttg: c7/142 lr:0.000996 t:2.5s +tttg: c8/142 lr:0.000994 t:2.6s 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lr:0.000953 t:2.7s +tttg: c34/231 lr:0.000950 t:2.7s +tttg: c35/231 lr:0.000947 t:2.8s +tttg: c36/231 lr:0.000944 t:2.9s +tttg: c37/231 lr:0.000941 t:3.0s +tttg: c38/231 lr:0.000937 t:3.1s +tttg: c39/231 lr:0.000934 t:3.2s +tttg: c40/231 lr:0.000931 t:3.2s +tttg: c41/231 lr:0.000927 t:3.3s +tttg: c42/231 lr:0.000924 t:3.4s +tttg: c43/231 lr:0.000920 t:3.5s +tttg: c44/231 lr:0.000916 t:3.6s +tttg: c45/231 lr:0.000912 t:3.6s +tttg: c46/231 lr:0.000908 t:3.7s +tttg: c47/231 lr:0.000905 t:3.8s +tttg: c48/231 lr:0.000900 t:3.9s +tttg: c49/231 lr:0.000896 t:4.0s +tttg: c50/231 lr:0.000892 t:4.0s +tttg: c51/231 lr:0.000888 t:4.1s +tttg: c52/231 lr:0.000884 t:4.2s +tttg: c53/231 lr:0.000879 t:4.3s +tttg: c54/231 lr:0.000875 t:4.4s +tttg: c55/231 lr:0.000870 t:4.4s +tttg: c56/231 lr:0.000865 t:4.5s +tttg: c57/231 lr:0.000861 t:4.6s +tttg: c58/231 lr:0.000856 t:4.7s +tttg: c59/231 lr:0.000851 t:4.8s +tttg: c60/231 lr:0.000846 t:4.8s +tttg: c61/231 lr:0.000841 t:4.9s +tttg: c62/231 lr:0.000836 t:5.0s +tttg: c63/231 lr:0.000831 t:5.1s +tttg: c64/231 lr:0.000826 t:5.2s +tttg: c65/231 lr:0.000821 t:5.3s +tttg: c66/231 lr:0.000816 t:5.3s +tttg: c67/231 lr:0.000810 t:5.4s +tttg: c68/231 lr:0.000805 t:5.5s +tttg: c69/231 lr:0.000799 t:5.6s +tttg: c70/231 lr:0.000794 t:5.7s +tttg: c71/231 lr:0.000788 t:5.7s +tttg: c72/231 lr:0.000783 t:5.8s +tttg: c73/231 lr:0.000777 t:5.9s +tttg: c74/231 lr:0.000771 t:6.0s +tttg: c75/231 lr:0.000766 t:6.1s +tttg: c76/231 lr:0.000760 t:6.1s +tttg: c77/231 lr:0.000754 t:6.2s +tttg: c78/231 lr:0.000748 t:6.3s +tttg: c79/231 lr:0.000742 t:6.4s +tttg: c80/231 lr:0.000736 t:6.5s +tttg: c81/231 lr:0.000730 t:6.5s +tttg: c82/231 lr:0.000724 t:6.6s +tttg: c83/231 lr:0.000718 t:6.7s +tttg: c84/231 lr:0.000712 t:6.8s +tttg: c85/231 lr:0.000705 t:6.9s +tttg: c86/231 lr:0.000699 t:6.9s +tttg: c87/231 lr:0.000693 t:7.0s +tttg: c88/231 lr:0.000687 t:7.1s +tttg: c89/231 lr:0.000680 t:7.2s +tttg: c90/231 lr:0.000674 t:7.3s +tttg: c91/231 lr:0.000667 t:7.4s +tttg: c92/231 lr:0.000661 t:7.4s +tttg: c93/231 lr:0.000655 t:7.5s +tttg: c94/231 lr:0.000648 t:7.6s +tttg: c95/231 lr:0.000641 t:7.7s +tttg: c96/231 lr:0.000635 t:7.8s +tttg: c97/231 lr:0.000628 t:7.8s +tttg: c98/231 lr:0.000622 t:7.9s +tttg: c99/231 lr:0.000615 t:8.0s +tttg: c100/231 lr:0.000608 t:8.1s +tttg: c101/231 lr:0.000602 t:8.2s +tttg: c102/231 lr:0.000595 t:8.3s +tttg: c103/231 lr:0.000588 t:8.3s +tttg: c104/231 lr:0.000582 t:8.4s +tttg: c105/231 lr:0.000575 t:8.5s +tttg: c106/231 lr:0.000568 t:8.6s +tttg: c107/231 lr:0.000561 t:8.7s +tttg: c108/231 lr:0.000555 t:8.7s +tttg: c109/231 lr:0.000548 t:8.8s +tttg: c110/231 lr:0.000541 t:8.9s +tttg: c111/231 lr:0.000534 t:9.0s +tttg: c112/231 lr:0.000527 t:9.1s +tttg: c113/231 lr:0.000520 t:9.1s +tttg: c114/231 lr:0.000514 t:9.2s +tttg: c115/231 lr:0.000507 t:9.3s +tttg: c116/231 lr:0.000500 t:9.4s +tttg: c117/231 lr:0.000493 t:9.5s +tttg: c118/231 lr:0.000486 t:9.5s +tttg: c119/231 lr:0.000480 t:9.6s +tttg: c120/231 lr:0.000473 t:9.7s 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t:12.0s +tttg: c149/231 lr:0.000282 t:12.1s +tttg: c150/231 lr:0.000276 t:12.2s +tttg: c151/231 lr:0.000270 t:12.2s +tttg: c152/231 lr:0.000264 t:12.3s +tttg: c153/231 lr:0.000258 t:12.4s +tttg: c154/231 lr:0.000252 t:12.5s +tttg: c155/231 lr:0.000246 t:12.5s +tttg: c156/231 lr:0.000240 t:12.6s +tttg: c157/231 lr:0.000234 t:12.7s +tttg: c158/231 lr:0.000229 t:12.8s +tttg: c159/231 lr:0.000223 t:12.9s +tttg: c160/231 lr:0.000217 t:12.9s +tttg: c161/231 lr:0.000212 t:13.0s +tttg: c162/231 lr:0.000206 t:13.1s +tttg: c163/231 lr:0.000201 t:13.2s +tttg: c164/231 lr:0.000195 t:13.3s +tttg: c165/231 lr:0.000190 t:13.3s +tttg: c166/231 lr:0.000184 t:13.4s +tttg: c167/231 lr:0.000179 t:13.5s +tttg: c168/231 lr:0.000174 t:13.6s +tttg: c169/231 lr:0.000169 t:13.7s +tttg: c170/231 lr:0.000164 t:13.8s +tttg: c171/231 lr:0.000159 t:13.8s +tttg: c172/231 lr:0.000154 t:13.9s +tttg: c173/231 lr:0.000149 t:14.0s +tttg: c174/231 lr:0.000144 t:14.1s +tttg: c175/231 lr:0.000139 t:14.2s +tttg: c176/231 lr:0.000135 t:14.2s +tttg: c177/231 lr:0.000130 t:14.3s +tttg: c178/231 lr:0.000125 t:14.4s +tttg: c179/231 lr:0.000121 t:14.5s +tttg: c180/231 lr:0.000116 t:14.6s +tttg: c181/231 lr:0.000112 t:14.6s +tttg: c182/231 lr:0.000108 t:14.7s +tttg: c183/231 lr:0.000104 t:14.8s +tttg: c184/231 lr:0.000100 t:14.9s +tttg: c185/231 lr:0.000095 t:14.9s +tttg: c186/231 lr:0.000092 t:15.0s +tttg: c187/231 lr:0.000088 t:15.1s +tttg: c188/231 lr:0.000084 t:15.2s +tttg: c189/231 lr:0.000080 t:15.3s +tttg: c190/231 lr:0.000076 t:15.4s +tttg: c191/231 lr:0.000073 t:15.4s +tttg: c192/231 lr:0.000069 t:15.5s +tttg: c193/231 lr:0.000066 t:15.6s +tttg: c194/231 lr:0.000063 t:15.7s +tttg: c195/231 lr:0.000059 t:15.8s +tttg: c196/231 lr:0.000056 t:15.8s +tttg: c197/231 lr:0.000053 t:15.9s +tttg: c198/231 lr:0.000050 t:16.0s +tttg: c199/231 lr:0.000047 t:16.1s +tttg: c200/231 lr:0.000044 t:16.2s +tttg: c201/231 lr:0.000041 t:16.2s +tttg: c202/231 lr:0.000039 t:16.3s +tttg: c203/231 lr:0.000036 t:16.4s +tttg: c204/231 lr:0.000034 t:16.5s +tttg: c205/231 lr:0.000031 t:16.6s +tttg: c206/231 lr:0.000029 t:16.6s +tttg: c207/231 lr:0.000027 t:16.7s +tttg: c208/231 lr:0.000024 t:16.8s +tttg: c209/231 lr:0.000022 t:16.9s +tttg: c210/231 lr:0.000020 t:17.0s +tttg: c211/231 lr:0.000019 t:17.0s +tttg: c212/231 lr:0.000017 t:17.1s +tttg: c213/231 lr:0.000015 t:17.2s +tttg: c214/231 lr:0.000013 t:17.3s +tttg: c215/231 lr:0.000012 t:17.4s +tttg: c216/231 lr:0.000010 t:17.4s +tttg: c217/231 lr:0.000009 t:17.5s +tttg: c218/231 lr:0.000008 t:17.6s +tttg: c219/231 lr:0.000007 t:17.7s +tttg: c220/231 lr:0.000006 t:17.8s +tttg: c221/231 lr:0.000005 t:17.8s +tttg: c222/231 lr:0.000004 t:17.9s +tttg: c223/231 lr:0.000003 t:18.0s +tttg: c224/231 lr:0.000002 t:18.1s +tttg: c225/231 lr:0.000002 t:18.2s +tttg: c226/231 lr:0.000001 t:18.2s +tttg: c227/231 lr:0.000001 t:18.3s +tttg: c228/231 lr:0.000000 t:18.4s +tttg: c229/231 lr:0.000000 t:18.5s +tttg: c230/231 lr:0.000000 t:18.6s +ttpr: phase:2/3 t:372.9s +ttp: b739/782 bl:2.2882 bb:1.0209 rl:2.2633 rb:1.0605 dl:2619-2652 gd:0 +ttp: b736/782 bl:2.2373 bb:1.0541 rl:2.2613 rb:1.0600 dl:2526-2550 gd:0 +ttpp: phase:3/3 pd:3216 gd:2750 t:386.6s +tttg: c1/307 lr:0.001000 t:0.1s +tttg: c2/307 lr:0.001000 t:0.2s +tttg: c3/307 lr:0.001000 t:0.2s +tttg: c4/307 lr:0.001000 t:0.3s +tttg: c5/307 lr:0.001000 t:0.4s +tttg: c6/307 lr:0.000999 t:0.5s +tttg: c7/307 lr:0.000999 t:0.5s +tttg: c8/307 lr:0.000999 t:0.6s +tttg: c9/307 lr:0.000998 t:0.7s +tttg: c10/307 lr:0.000998 t:0.8s +tttg: c11/307 lr:0.000997 t:0.9s +tttg: c12/307 lr:0.000997 t:0.9s +tttg: c13/307 lr:0.000996 t:1.0s +tttg: c14/307 lr:0.000996 t:1.1s +tttg: c15/307 lr:0.000995 t:1.2s +tttg: c16/307 lr:0.000994 t:1.3s +tttg: c17/307 lr:0.000993 t:1.3s +tttg: c18/307 lr:0.000992 t:1.4s +tttg: c19/307 lr:0.000991 t:1.5s +tttg: c20/307 lr:0.000991 t:1.6s +tttg: c21/307 lr:0.000989 t:1.6s +tttg: c22/307 lr:0.000988 t:1.7s +tttg: c23/307 lr:0.000987 t:1.8s +tttg: c24/307 lr:0.000986 t:1.9s +tttg: c25/307 lr:0.000985 t:2.0s +tttg: c26/307 lr:0.000984 t:2.1s +tttg: c27/307 lr:0.000982 t:2.1s +tttg: c28/307 lr:0.000981 t:2.2s +tttg: c29/307 lr:0.000979 t:2.3s +tttg: c30/307 lr:0.000978 t:2.4s +tttg: c31/307 lr:0.000976 t:2.5s +tttg: c32/307 lr:0.000975 t:2.5s +tttg: c33/307 lr:0.000973 t:2.6s +tttg: c34/307 lr:0.000972 t:2.7s +tttg: c35/307 lr:0.000970 t:2.8s +tttg: c36/307 lr:0.000968 t:2.9s +tttg: c37/307 lr:0.000966 t:2.9s +tttg: c38/307 lr:0.000964 t:3.0s +tttg: c39/307 lr:0.000962 t:3.1s +tttg: c40/307 lr:0.000960 t:3.2s +tttg: c41/307 lr:0.000958 t:3.3s +tttg: c42/307 lr:0.000956 t:3.3s +tttg: c43/307 lr:0.000954 t:3.4s +tttg: c44/307 lr:0.000952 t:3.5s +tttg: c45/307 lr:0.000950 t:3.6s +tttg: c46/307 lr:0.000948 t:3.7s +tttg: c47/307 lr:0.000945 t:3.7s +tttg: c48/307 lr:0.000943 t:3.8s +tttg: c49/307 lr:0.000941 t:3.9s +tttg: c50/307 lr:0.000938 t:4.0s +tttg: c51/307 lr:0.000936 t:4.1s +tttg: c52/307 lr:0.000933 t:4.1s +tttg: c53/307 lr:0.000930 t:4.2s +tttg: c54/307 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bb:1.2301 rl:2.3084 rb:1.0572 dl:169-169 gd:1 +ttp: b57/782 bl:2.4514 bb:1.1544 rl:2.3087 rb:1.0574 dl:160-161 gd:1 +ttp: b49/782 bl:2.4513 bb:1.1656 rl:2.3089 rb:1.0576 dl:152-153 gd:1 +ttp: b42/782 bl:2.4681 bb:1.2018 rl:2.3091 rb:1.0578 dl:145-146 gd:1 +ttp: b34/782 bl:2.6249 bb:1.2017 rl:2.3096 rb:1.0580 dl:137-138 gd:1 +ttp: b26/782 bl:2.5815 bb:1.2850 rl:2.3100 rb:1.0583 dl:129-130 gd:1 +ttp: b18/782 bl:2.6390 bb:1.2033 rl:2.3104 rb:1.0584 dl:119-121 gd:1 +ttp: b10/782 bl:2.6123 bb:1.1704 rl:2.3107 rb:1.0586 dl:107-109 gd:1 +ttp: b2/782 bl:2.8103 bb:1.2352 rl:2.3111 rb:1.0587 dl:83-89 gd:1 +quantized_ttt_phased val_loss:2.31588651 val_bpb:1.05826976 eval_time:495019ms +total_eval_time:495.0s \ No newline at end of file