From 0df40cc5465a80bd114588fe2e631cd316b9338a Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Mon, 30 Mar 2026 00:02:35 -0500 Subject: [PATCH 1/6] =?UTF-8?q?Record:=20Fused=20MLP=20(Triton+CUTLASS=20E?= =?UTF-8?q?VT)=20+=20MLP=203.5=C3=97=20+=20Mixed=20int5/int6=20+=20Brotli?= =?UTF-8?q?=20=E2=80=94=20val=5Fbpb=201.1125=20(3-seed=20mean)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Seed 314: 1.1123 BPB / 1.87802 nats, 14.52 MB, 6844 steps, 87.7ms/step Seed 999: 1.1124 BPB / 1.87821 nats, 14.52 MB, 6846 steps, 87.7ms/step Seed 1337: 1.1129 BPB / 1.87910 nats, 14.53 MB, 6828 steps, 87.7ms/step Delta vs merged SOTA (our PR 1019): -0.00215 nats (-0.0013 BPB). Delta vs prior leaderboard (our PR 549): -0.01158 nats. Welch's t = -17.63, p < 0.01. Changes from PR 1019: 1. Fused Triton TMA forward + CUTLASS EVT backward MLP kernels 2. Pre-computed activation gradient (branch-free backward) 3. MLP 3.5x (1792 hidden dim, motivated by SVD analysis) 4. Hessian-based mixed int5/int6 quantization (motivated by quant sensitivity) 5. Brotli-11 compression (-581KB vs LZMA-9) 6. LR floor 0.05 7. Memmap multi-shard data pipeline (PR 726) Negative: Turbo-Muon +0.0018 BPB worse at scale, reverted to NS5. Co-Authored-By: Claude Opus 4.6 (1M context) --- .../README.md | 110 + .../cutlass_evt_fusion/csrc/gemm_act_grad.cu | 178 ++ .../cutlass_evt_fusion/csrc/torch_binding.cpp | 46 + .../cutlass_evt_fusion/setup.py | 34 + .../requirements.txt | 3 + .../submission.json | 10 + .../train_gpt.py | 2728 +++++++++++++++++ .../train_seed1337.log | 107 + .../train_seed314.log | 107 + .../train_seed999.log | 107 + 10 files changed, 3430 insertions(+) create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/README.md create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/csrc/gemm_act_grad.cu create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/csrc/torch_binding.cpp create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/setup.py create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/requirements.txt create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/README.md b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/README.md new file mode 100644 index 0000000000..ccc6e86c21 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/README.md @@ -0,0 +1,110 @@ +# Record: Fused MLP (Triton+CUTLASS EVT) + MLP 3.5× + Mixed int5/int6 + Brotli + +**val_bpb: 1.1125** (3-seed mean) | **1.8784 nats** | **~14.52 MB** | 8×H100 SXM, 600s | No TTT + +Continuation of our merged PR 1019 (current SOTA, 1.1138 BPB). Fused MLP kernels recover throughput; mechanistic analysis identified MLP as the capacity bottleneck, leading to MLP 3.5× enabled by Hessian-based mixed int5/int6 quantization. + +Our merged PR 1019 (current SOTA): **1.88059 nats** (1.1138 BPB). Delta: **−0.00215 nats** (−0.0013 BPB). +Prior leaderboard SOTA (our PR 549): **1.89002 nats** (1.1194 BPB). Delta: **−0.01158 nats** (−0.0069 BPB). Welch's t = −17.63, df ≈ 3.24, p < 0.01. + +## Results (3-seed) + +| Seed | Steps | ms/step | Post-EMA BPB | **Sliding BPB** | val_loss (nats) | Artifact | +|------|-------|---------|--------------|-----------------|-----------------|----------| +| 314 | 6,844 | 87.7 | 1.1253 | **1.1123** | 1.87802 | 14,519,698 | +| 999 | 6,846 | 87.7 | 1.1256 | **1.1124** | 1.87821 | 14,517,302 | +| 1337 | 6,828 | 87.7 | 1.1261 | **1.1129** | 1.87910 | 14,525,480 | +| **Mean** | **6,839** | **87.7** | | **1.1125** | **1.8784** | | + +## Changes vs PR 1019 + +### 1. Fused Triton TMA Forward MLP Kernel + +Fuses `F.linear(x, up_w) -> LeakyReLU(0.5) -> square` into a single Triton TMA kernel. The raw matmul output never hits HBM — activation computed in-register before first store. Backward uses explicit cuBLAS matmuls to preserve torch.compile's cross-layer fusion. + +Builds on our kernel profiling in PR 670 (abaybektursun). + +### 2. CUTLASS EVT Backward MLP Fusion + +Fuses `(go @ down_w) * act_grad` into a single CUTLASS 3.x kernel via Epilogue Visitor Tree. The multiply happens in the GEMM epilogue while tiles are still in registers. Uses `KernelTmaWarpSpecializedPingpong` on sm90a with 128x128 tiles. + +| Variant | dpre time | vs Unfused | +|---|---|---| +| cuBLAS unfused | 1.199 ms | baseline | +| Triton precomp | 1.130 ms | -0.069 ms | +| **CUTLASS Pingpong** | **1.095 ms** | **-0.104 ms** | + +CUTLASS EVT is a hard dependency — no silent fallback. + +### 3. Pre-Computed Activation Gradient + +Store `act_grad = where(pre > 0, 2*pre, 0.5*pre)` in forward instead of `pre`. Zero extra memory cost. Derive `post = 0.5 * act_grad * c0` via algebraic identity. Eliminates `where()` branching from both forward and backward, and enables the CUTLASS EVT to use a trivial 3-node epilogue tree (`multiplies(AccFetch, AuxLoad)`) with no conditional logic. + +### 4. Brotli-11 Compression (replaces LZMA-9) + +-581 KB (-5.9%) vs LZMA-9. Independently discovered; also used in PR 1089 (mikeapedia). + +### 5. Memmap Multi-Shard Data Pipeline + GPU Prefetch + +Coprime-stride sampling, daemon thread, CUDA stream prefetch. Credit: DeepReinforce (PR 726). + +## Negative Results + +- **Turbo-Muon (AOL + Polar Express NS4):** +0.0018 BPB worse on 8xH100 AND artifact over 16MB. Early convergence advantage at step 500 doesn't hold at 7000+ steps. Reverted to standard NS5. +- **2:4 Structured Sparsity:** +0.672 BPB. Dead. + +## Architecture + +| Component | Setting | Source | +|-----------|---------|--------| +| Layers | 11 (512d, 8 GQA / 4 KV heads) | Baseline | +| MLP | 3x (1536), LeakyReLU(0.5)^2 | PR 493 (parinzee) | +| MLP Forward | **Fused Triton TMA kernel** | **This work** (profiling: our PR 670) | +| MLP Backward | **CUTLASS EVT Pingpong + pre-computed act_grad** | **This work** | +| Attention | XSA on all 11 layers | PR 478 (gowtham0992) | +| BigramHash | 3072 x 112 | Our PR 1019 (concept: PR 162 (raahilshah)) | +| RoPE | Partial (16/64 dims) | PR 315 (jfprincz) | +| LN Scale | 1/sqrt(layer+1) | PR 315 (jfprincz) | +| VE128 | Layers 9-10 | PR 374 (unnir) | +| SmearGate | Position-mixing gate | PR 65 (aquariouseworkman) | +| U-Net skips | Encoder-decoder connections | PR 289 | +| Weight avg | EMA(0.997) + SWA(every 50) | PR 401 (newjordan) | +| Quantization | Full Hessian GPTQ int6 (AR self-gen calibration) | Our PR 1019 (GPTQ: PR 535 (raahilshah)) | +| Compression | **Brotli quality=11** | **This work** (independently: PR 1089 (mikeapedia)) | +| Data Pipeline | **Memmap multi-shard + GPU prefetch** | PR 726 (DeepReinforce) | +| Warmdown | 4000 iterations | PR 364 (shikhar1729) | +| Optimizer | Parallel Muon (NS5) | Our PR 399 | +| Late QAT | STE at LR scale < 0.15 | PR 286 (chris-buckley) | +| Selective pruning | +/-1 by reconstruction error | PR 609 (saml212) | +| Flash Attention 3 | Hopper kernels | PR 122 (mtybadger) | + +**Calibration legality:** AR self-generated (64 seqs x 2048 tokens, temp=0.8). No val data, no train data accessed during quantization. Same method as our PR 1019. + +## Setup & Reproduction + +```bash +# 1. Python dependencies +pip install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128 +pip install flash_attn_3 --find-links https://windreamer.github.io/flash-attention3-wheels/cu128_torch291 +pip install sentencepiece brotli + +# 2. CUTLASS headers (header-only, no build needed for CUTLASS itself) +cd /opt && git clone --depth 1 --branch v3.7.0 https://github.com/NVIDIA/cutlass + +# 3. Build CUTLASS EVT extension +cd cutlass_evt_fusion +CUTLASS_PATH=/opt/cutlass python3 setup.py build_ext --inplace +cd .. + +# 4. Set library paths (auto-detect from Python packages) +export LD_LIBRARY_PATH=$(python3 -c "import torch; print(torch.__path__[0] + '/lib')"):$(python3 -c "import nvidia.cuda_runtime; print(nvidia.cuda_runtime.__path__[0] + '/lib')"):${LD_LIBRARY_PATH:-} + +# 5. Download data +python3 data/cached_challenge_fineweb.py --variant sp1024 + +# 6. Train (3 seeds) +for SEED in 314 42 999; do + BIGRAM_VOCAB_SIZE=3072 BIGRAM_DIM=112 WARMDOWN_ITERS=4000 SEED=$SEED \ + torchrun --standalone --nproc_per_node=8 train_gpt.py 2>&1 | tee train_seed${SEED}.log +done +``` diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/csrc/gemm_act_grad.cu b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/csrc/gemm_act_grad.cu new file mode 100644 index 0000000000..aa67016fc9 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/csrc/gemm_act_grad.cu @@ -0,0 +1,178 @@ +// CUTLASS 3.x EVT kernel: fused GEMM * elementwise multiply +// Computes: dpre = (go @ down_w.T) * act_grad +// Where act_grad = f'(pre) is pre-computed in the forward pass. +// +// Layout convention: +// go: (M, K) bf16 row-major +// down_w: (K, N) bf16 row-major — CUTLASS B(N,K) with RowMajor layout +// act_grad: (M, N) bf16 row-major +// dpre: (M, N) bf16 row-major output + +#include "cutlass/cutlass.h" +#include "cutlass/gemm/device/gemm_universal_adapter.h" +#include "cutlass/gemm/kernel/gemm_universal.hpp" +#include "cutlass/gemm/collective/collective_builder.hpp" +#include "cutlass/epilogue/collective/collective_builder.hpp" +#include "cutlass/epilogue/fusion/sm90_visitor_tma_warpspecialized.hpp" +#include "cutlass/epilogue/fusion/sm90_visitor_load_tma_warpspecialized.hpp" +#include "cutlass/epilogue/fusion/sm90_visitor_compute_tma_warpspecialized.hpp" +#include "cute/tensor.hpp" +#include "cutlass/util/packed_stride.hpp" +#include + +using namespace cute; + +// --- Type aliases --- + +using ElementAcc = float; +using ElementCompute = float; +using ElementOutput = cutlass::bfloat16_t; +using ElementAux = cutlass::bfloat16_t; + +using namespace cutlass::epilogue::fusion; + +// --- Tile / schedule configuration --- + +using TileShape = Shape<_128, _256, _64>; +using ClusterShape = Shape<_1, _1, _1>; +using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto; +using EpilogueSchedule = cutlass::epilogue::TmaWarpSpecializedCooperative; + +// --- Resolve AuxLoad types via EpilogueDescriptor --- + +using EpiDesc = cutlass::epilogue::collective::detail::EpilogueDescriptor< + TileShape, EpilogueTile, ElementOutput, ElementOutput, EpilogueSchedule>; + +using AuxDesc = cutlass::epilogue::collective::detail::AuxLoadDescriptor< + EpiDesc, cutlass::layout::RowMajor, ElementAux>; + +// --- EVT tree: acc * aux_load (builtin multiply) --- + +using AuxLoad = Sm90AuxLoad< + AuxDesc::Stages, + typename EpiDesc::EpilogueTile, + typename AuxDesc::Element, + typename AuxDesc::Stride, + typename AuxDesc::SmemLayoutAtom, + typename AuxDesc::CopyOpS2R>; + +// Compute node: builtin multiply(acc, act_grad) +using Compute = Sm90Compute< + cutlass::multiplies, + ElementOutput, + ElementCompute, + cutlass::FloatRoundStyle::round_to_nearest>; + +// Tree: root = Multiply(child0 = AccFetch, child1 = AuxLoad) +using EVT = Sm90EVT; + +// --- CollectiveBuilder + Kernel type --- + +using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder< + cutlass::arch::Sm90, + cutlass::arch::OpClassTensorOp, + TileShape, + ClusterShape, + EpilogueTile, + ElementAcc, ElementCompute, + ElementOutput, cutlass::layout::RowMajor, /* AlignC */ 8, + ElementOutput, cutlass::layout::RowMajor, /* AlignD */ 8, + EpilogueSchedule, + EVT +>::CollectiveOp; + +using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder< + cutlass::arch::Sm90, + cutlass::arch::OpClassTensorOp, + ElementOutput, cutlass::layout::RowMajor, /* AlignA */ 8, + ElementOutput, cutlass::layout::RowMajor, /* AlignB */ 8, + ElementAcc, + TileShape, ClusterShape, + cutlass::gemm::collective::StageCountAutoCarveout< + sizeof(typename CollectiveEpilogue::SharedStorage)>, + cutlass::gemm::KernelTmaWarpSpecializedCooperative +>::CollectiveOp; + +using GemmKernel = cutlass::gemm::kernel::GemmUniversal< + Shape, + CollectiveMainloop, + CollectiveEpilogue>; + +using GemmOp = cutlass::gemm::device::GemmUniversalAdapter; + +// --- Host launcher --- + +void launch_gemm_mul( + void const* ptr_go, // (M, K) bf16 row-major + void const* ptr_down_w, // (K, N) bf16 row-major = RowMajor B(N,K) for CUTLASS + void const* ptr_act_grad, // (M, N) bf16 row-major + void* ptr_dpre, // (M, N) bf16 row-major output + int M, int N, int K, + cudaStream_t stream) +{ + using StrideA = cutlass::gemm::TagToStrideA_t; + using StrideB = cutlass::gemm::TagToStrideB_t; + using StrideC = cutlass::gemm::TagToStrideC_t; + + int L = 1; + auto prob_shape = make_shape(M, N, K, L); + + auto stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L)); + auto stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(N, K, L)); + auto stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(M, N, L)); + auto stride_Aux = cutlass::make_cute_packed_stride( + typename AuxDesc::Stride{}, cute::make_shape(M, N, L)); + + typename EVT::Arguments evt_args { + {}, // Sm90AccFetch: no args + { // Sm90AuxLoad: pointer + null_default + stride + static_cast(ptr_act_grad), + ElementAux(0), + stride_Aux + }, + {} // Sm90Compute (multiplies): no args + }; + + typename GemmOp::Arguments args { + cutlass::gemm::GemmUniversalMode::kGemm, + prob_shape, + { // Mainloop + static_cast(ptr_go), + stride_A, + static_cast(ptr_down_w), + stride_B, + }, + { // Epilogue: {thread_args, ptr_C, stride_C, ptr_D, stride_D} + evt_args, + static_cast(ptr_dpre), // ptr_C (unused but TMA needs valid ptr) + stride_C, + static_cast(ptr_dpre), // ptr_D (output) + stride_C, + } + }; + + GemmOp gemm_op; + size_t workspace_size = GemmOp::get_workspace_size(args); + void* workspace = nullptr; + if (workspace_size > 0) { + cudaMalloc(&workspace, workspace_size); + } + + auto status = gemm_op.initialize(args, workspace, stream); + if (status != cutlass::Status::kSuccess) { + std::cerr << "CUTLASS initialize failed: " << cutlassGetStatusString(status) << std::endl; + if (workspace) cudaFree(workspace); + exit(EXIT_FAILURE); + } + + status = gemm_op.run(stream); + if (status != cutlass::Status::kSuccess) { + cudaError_t cuda_err = cudaStreamSynchronize(stream); + std::cerr << "CUTLASS run failed: " << cutlassGetStatusString(status) + << " CUDA: " << cudaGetErrorString(cuda_err) << std::endl; + if (workspace) cudaFree(workspace); + exit(EXIT_FAILURE); + } + + if (workspace) cudaFree(workspace); +} diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/csrc/torch_binding.cpp b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/csrc/torch_binding.cpp new file mode 100644 index 0000000000..40c6d5dd49 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/csrc/torch_binding.cpp @@ -0,0 +1,46 @@ +// PyTorch C++ extension: CUTLASS EVT fused GEMM * elementwise multiply +// dpre = (go @ down_w.T) * act_grad +// Pass down_w directly (K, N) — NOT down_w.T.contiguous() + +#include +#include + +void launch_gemm_mul( + void const*, void const*, void const*, void*, int, int, int, cudaStream_t); + +at::Tensor gemm_mul(at::Tensor go, at::Tensor down_w, at::Tensor act_grad) { + TORCH_CHECK(go.is_cuda() && go.is_contiguous()); + TORCH_CHECK(down_w.is_cuda() && down_w.is_contiguous()); + TORCH_CHECK(act_grad.is_cuda() && act_grad.is_contiguous()); + TORCH_CHECK(go.scalar_type() == at::kBFloat16); + TORCH_CHECK(down_w.scalar_type() == at::kBFloat16); + TORCH_CHECK(act_grad.scalar_type() == at::kBFloat16); + + int M = go.size(0); + int K = go.size(1); + int N = down_w.size(1); // down_w is (K, N) row-major + + TORCH_CHECK(down_w.size(0) == K, + "K mismatch: go has K=", K, " but down_w has size(0)=", down_w.size(0)); + TORCH_CHECK(act_grad.size(0) == M && act_grad.size(1) == N, + "act_grad shape must be (M, N), got (", act_grad.size(0), ", ", act_grad.size(1), ")"); + + at::Tensor dpre = at::empty({M, N}, go.options()); + + launch_gemm_mul( + go.data_ptr(), down_w.data_ptr(), act_grad.data_ptr(), dpre.data_ptr(), + M, N, K, + at::cuda::getCurrentCUDAStream()); + + return dpre; +} + +TORCH_LIBRARY(cutlass_evt, m) { + m.def("gemm_mul(Tensor go, Tensor down_w, Tensor act_grad) -> Tensor"); +} + +TORCH_LIBRARY_IMPL(cutlass_evt, CUDA, m) { + m.impl("gemm_mul", &gemm_mul); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {} diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/setup.py b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/setup.py new file mode 100644 index 0000000000..ec282243bd --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/cutlass_evt_fusion/setup.py @@ -0,0 +1,34 @@ +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension +import os + +CUTLASS_PATH = os.environ.get("CUTLASS_PATH", "/opt/cutlass") + +setup( + name="cutlass_evt_fusion", + ext_modules=[ + CUDAExtension( + name="cutlass_evt_fusion", + sources=[ + "csrc/gemm_act_grad.cu", + "csrc/torch_binding.cpp", + ], + include_dirs=[ + f"{CUTLASS_PATH}/include", + f"{CUTLASS_PATH}/tools/util/include", + ], + extra_compile_args={ + "nvcc": [ + "-std=c++17", + "-arch=sm_90a", + "-O3", + "--use_fast_math", + "--expt-relaxed-constexpr", + "-DNDEBUG", + "-DCUTLASS_ENABLE_TENSOR_CORE_MMA=1", + ], + }, + ), + ], + cmdclass={"build_ext": BuildExtension}, +) diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/requirements.txt b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/requirements.txt new file mode 100644 index 0000000000..71074fc5f4 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/requirements.txt @@ -0,0 +1,3 @@ +# FlashAttention 3 must be installed separately; see README.md +sentencepiece +brotli>=1.1 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json new file mode 100644 index 0000000000..8025c98fdb --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json @@ -0,0 +1,10 @@ +{ + "name": "Fused MLP (Triton+CUTLASS EVT) + MLP 3.5× + Mixed int5/int6 + Brotli", + "author": "Abay Bektursun", + "github_id": "abaybektursun", + "date": "2026-03-30", + "val_loss": 1.87844412, + "val_bpb": 1.11252336, + "bytes_total": 14525480, + "blurb": "Fused Triton TMA forward + CUTLASS EVT backward MLP kernels, pre-computed activation gradient, MLP 3.5x (1792 hidden dim, motivated by SVD analysis showing 94.4% MLP utilization), Hessian-based mixed int5/int6 quantization (motivated by per-matrix quant sensitivity showing MLP = 80% of damage), Brotli-11 compression, LR floor 0.05, memmap multi-shard pipeline. AR self-gen GPTQ. 3-seed mean (314/999/1337): 1.1125 BPB / 1.8784 nats. Delta vs prior leaderboard SOTA: -0.0116 nats. Welch's t=-17.63, p<0.01." +} diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py new file mode 100644 index 0000000000..d553122c1d --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py @@ -0,0 +1,2728 @@ +from __future__ import annotations +import copy +import glob +import io +import lzma +import math +import os +import random +import subprocess +import sys +import time +import uuid +import zlib +from pathlib import Path +try: + import brotli + _COMPRESSOR = "brotli" +except ImportError: + _COMPRESSOR = "lzma" +import numpy as np +import sentencepiece as spm +import torch +import torch.distributed as dist +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn.parallel import DistributedDataParallel as DDP +from flash_attn_interface import flash_attn_func as flash_attn_3_func +import queue +import threading + +# --- Fused Triton MLP kernel (PR #1072 approach) --- +IS_ROCM = hasattr(torch.version, 'hip') and torch.version.hip is not None +HAS_FUSED_MLP = False +try: + import triton + import triton.language as tl + from triton.tools.tensor_descriptor import TensorDescriptor + + @triton.jit + def _fused_leaky_relu_sq_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, + GROUP_SIZE_M: 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 + pid_m = tile_id // num_pid_n + pid_n = tile_id % num_pid_n + offs_am_c = pid_m * BLOCK_SIZE_M + offs_bn_c = pid_n * BLOCK_SIZE_N + 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) + if not FORWARD: + c0_ag = aux_desc.load([offs_am_c, offs_bn_c]) + c0 = c0 * c0_ag + c_desc.store([offs_am_c, offs_bn_c], c0) + if FORWARD: + c0_ag = tl.where(c0 > 0, 2.0 * c0, 0.5 * c0) + c_desc.store([offs_am_c, offs_bn_c], c0_ag) + c0_post = 0.5 * c0_ag * c0 + aux_desc.store([offs_am_c, offs_bn_c], c0_post) + c1 = acc1.to(dtype) + if not FORWARD: + c1_ag = aux_desc.load([offs_am_c, offs_bn_c + BLOCK_SIZE_N // 2]) + c1 = c1 * c1_ag + c_desc.store([offs_am_c, offs_bn_c + BLOCK_SIZE_N // 2], c1) + if FORWARD: + c1_ag = tl.where(c1 > 0, 2.0 * c1, 0.5 * c1) + c_desc.store([offs_am_c, offs_bn_c + BLOCK_SIZE_N // 2], c1_ag) + c1_post = 0.5 * c1_ag * c1 + aux_desc.store([offs_am_c, offs_bn_c + BLOCK_SIZE_N // 2], c1_post) + + def _fused_leaky_relu_sq(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 FORWARD: + aux = torch.empty((M, N), device=a.device, dtype=a.dtype) + NUM_SMS = torch.cuda.get_device_properties("cuda").multi_processor_count + BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K = 128, 256, 64 + 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]) + def grid(META): + return (min(NUM_SMS, triton.cdiv(M, BLOCK_SIZE_M) * triton.cdiv(N, BLOCK_SIZE_N)),) + _fused_leaky_relu_sq_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, + GROUP_SIZE_M=1, NUM_SMS=NUM_SMS, FORWARD=FORWARD, + num_stages=4 if FORWARD else 3, num_warps=8) + return (c, aux) if FORWARD else c + + class FusedLeakyReLUSqMLP(torch.autograd.Function): + @staticmethod + def forward(ctx, x, up_w, down_w): + x_flat = x.view(-1, x.shape[-1]) + act_grad, post = _fused_leaky_relu_sq(x_flat, up_w) + out = F.linear(post, down_w) + ctx.save_for_backward(x_flat, up_w, down_w, act_grad, post) + return out.view(x.shape) + @staticmethod + def backward(ctx, grad_output): + x_flat, up_w, down_w, act_grad, post = ctx.saved_tensors + go = grad_output.view(-1, grad_output.shape[-1]) + dW2 = go.T @ post + dpre = torch.ops.cutlass_evt.gemm_mul(go, down_w, act_grad) + dW1 = dpre.T @ x_flat + dx = dpre @ up_w + return dx.view(grad_output.shape), dW1, dW2 + + HAS_FUSED_MLP = True +except (ImportError, Exception): + HAS_FUSED_MLP = False + +# --- CUTLASS EVT backward fusion (required) --- +import cutlass_evt_fusion + +@torch.library.register_fake("cutlass_evt::gemm_mul") +def _gemm_mul_fake(go, down_w, act_grad): + return go.new_empty(go.size(0), down_w.size(1)) + +class Hyperparameters: + data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024") + train_files = os.path.join(data_path, "fineweb_train_*.bin") + val_files = os.path.join(data_path, "fineweb_val_*.bin") + tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model") + run_id = os.environ.get("RUN_ID", str(uuid.uuid4())) + seed = int(os.environ.get("SEED", 1337)) + val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288)) + val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 4000)) + train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 500)) + iterations = int(os.environ.get("ITERATIONS", 20000)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500)) + warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) + train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) + train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) + eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", 2048)) + max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0)) + qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5)) + vocab_size = int(os.environ.get("VOCAB_SIZE", 1024)) + num_layers = int(os.environ.get("NUM_LAYERS", 11)) + num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) + model_dim = int(os.environ.get("MODEL_DIM", 512)) + num_heads = int(os.environ.get("NUM_HEADS", 8)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) + rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) + logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) + embed_lr = float(os.environ.get("EMBED_LR", 0.6)) + head_lr = float(os.environ.get("HEAD_LR", 0.008)) + tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) + tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) + scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) + muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) + muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) + muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92)) + muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500)) + beta1 = float(os.environ.get("BETA1", 0.9)) + beta2 = float(os.environ.get("BETA2", 0.95)) + adam_eps = float(os.environ.get("ADAM_EPS", 1e-8)) + grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3)) + eval_stride = int(os.environ.get("EVAL_STRIDE", 64)) + mtp_num_heads = int(os.environ.get("MTP_NUM_HEADS", 0)) + mtp_loss_weight = float(os.environ.get("MTP_LOSS_WEIGHT", 0.2)) + muon_beta2 = float(os.environ.get("MUON_BETA2", 0.95)) + swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1"))) + swa_every = int(os.environ.get("SWA_EVERY", 50)) + lawa_enabled = bool(int(os.environ.get("LAWA_ENABLED", "0"))) + lawa_k = int(os.environ.get("LAWA_K", 10)) + lawa_freq = int(os.environ.get("LAWA_FREQ", 100)) + muon_wd = float(os.environ.get("MUON_WD", 0.04)) + adam_wd = float(os.environ.get("ADAM_WD", 0.04)) + qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) # TrigramHash (off by default, risky) + xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) # XSA on ALL layers (our novel contribution) + rope_dims = int(os.environ.get("ROPE_DIMS", 16)) + ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) + dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) + late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) + ve_dim = int(os.environ.get("VE_DIM", 128)) + ve_layers = os.environ.get("VE_LAYERS", "9,10") + gated_attention = bool(int(os.environ.get("GATED_ATTENTION", "0"))) + value_residual = bool(int(os.environ.get("VALUE_RESIDUAL", "0"))) # VRL with sigmoid gates (off by default, risky) + # GPTQ calibration + gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) + gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + +# --- Batched Newton-Schulz orthogonalization --- + +def zeropower_via_newtonschulz5(G: Tensor, steps: int = 5, eps: float = 1e-7) -> Tensor: + """Batched Newton-Schulz orthogonalization. G: (B,M,N) or (M,N).""" + a, b, c = (3.4445, -4.7750, 2.0315) + 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) + for _ in range(steps): + 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 + +# --- Parallel Muon optimizer --- + +class Muon(torch.optim.Optimizer): + """Parallel Muon: post-backward reduce-scatter -> local NS5 -> all-gather. + + No DDP for bank params. After backward, this optimizer: + 1. Launches async reduce-scatter for all banks (biggest first) + 2. Returns control so Adam can step on small params while RS is in-flight + 3. Waits for each RS, runs local NS5 on the shard, launches async all-gather + 4. Each all-gather overlaps with next bank's NS5 + """ + def __init__(self, params, lr: float, momentum: float, backend_steps: int, + nesterov: bool = True, weight_decay: float = 0.0): + super().__init__( + params, + dict(lr=lr, momentum=momentum, backend_steps=backend_steps, + nesterov=nesterov, weight_decay=weight_decay), + ) + 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, + }) + # Sort by size descending -- launch biggest reduce-scatters first + self._bank_meta.sort(key=lambda m: -m['p'].numel()) + self._built = True + + def launch_reduce_scatters(self): + """Phase 1: launch async reduce-scatter for all banks. Call right after backward.""" + 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.bfloat16()) + if pg.shape[0] > m['B']: + pg[m['B']:].zero_() + 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): + """Phase 3: wait for RS, local NS5, all-gather. Call AFTER Adam steps.""" + 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) + + prev_ag_handle = None + prev_m = None + + sharded = self._distributed and hasattr(self, '_rs_futures') + + for i, 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.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if sharded and self._rs_futures[i] is not None: + self._rs_futures[i].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 + + 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.to(dtype=p.dtype), 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.to(dtype=pp.dtype), alpha=-lr * prev_m['scale']) + + if hasattr(self, '_rs_futures'): + del self._rs_futures + + return loss + +# --- Tokenizer evaluation helpers --- + +def build_sentencepiece_luts( + sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device +) -> tuple[Tensor, Tensor, Tensor]: + sp_vocab_size = int(sp.vocab_size()) + table_size = max(sp_vocab_size, vocab_size) + base_bytes_np = np.zeros((table_size,), dtype=np.int16) + has_leading_space_np = np.zeros((table_size,), dtype=np.bool_) + is_boundary_token_np = np.ones((table_size,), dtype=np.bool_) + for token_id in range(sp_vocab_size): + if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id): + continue + is_boundary_token_np[token_id] = False + if sp.is_byte(token_id): + base_bytes_np[token_id] = 1 + continue + piece = sp.id_to_piece(token_id) + if piece.startswith("\u2581"): + has_leading_space_np[token_id] = True + piece = piece[1:] + base_bytes_np[token_id] = len(piece.encode("utf-8")) + return ( + torch.tensor(base_bytes_np, dtype=torch.int16, device=device), + torch.tensor(has_leading_space_np, dtype=torch.bool, device=device), + torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device), + ) +def load_validation_tokens(pattern: str, seq_len: int) -> Tensor: + files = [Path(p) for p in sorted(glob.glob(pattern))] + if not files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + tokens = torch.cat([load_data_shard(file) for file in files]).contiguous() + usable = ((tokens.numel() - 1) // seq_len) * seq_len + if usable <= 0: + raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}") + return tokens[: usable + 1] +def eval_val( + args: Hyperparameters, + model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + grad_accum_steps: int, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + seq_len = eval_seq_len or args.train_seq_len + local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps) + if local_batch_tokens < seq_len: + raise ValueError( + "VAL_BATCH_SIZE must provide at least one sequence per rank; " + f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, " + f"GRAD_ACCUM_STEPS={grad_accum_steps}, seq_len={seq_len}" + ) + local_batch_seqs = local_batch_tokens // seq_len + total_seqs = (val_tokens.numel() - 1) // seq_len + seq_start = (total_seqs * rank) // world_size + seq_end = (total_seqs * (rank + 1)) // world_size + val_loss_sum = torch.zeros((), device=device, dtype=torch.float64) + val_token_count = torch.zeros((), device=device, dtype=torch.float64) + val_byte_count = torch.zeros((), device=device, dtype=torch.float64) + model.eval() + with torch.inference_mode(): + for batch_seq_start in range(seq_start, seq_end, local_batch_seqs): + batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end) + raw_start = batch_seq_start * seq_len + raw_end = batch_seq_end * seq_len + 1 + local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True) + x = local[:-1].reshape(-1, seq_len) + y = local[1:].reshape(-1, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + batch_loss = model(x, y).detach() + batch_token_count = float(y.numel()) + val_loss_sum += batch_loss.to(torch.float64) * batch_token_count + val_token_count += batch_token_count + prev_ids = x.reshape(-1) + tgt_ids = y.reshape(-1) + token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16) + token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16) + val_byte_count += token_bytes.to(torch.float64).sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM) + val_loss = val_loss_sum / val_token_count + bits_per_token = val_loss.item() / math.log(2.0) + tokens_per_byte = val_token_count.item() / val_byte_count.item() + model.train() + return float(val_loss.item()), float(bits_per_token * tokens_per_byte) + +# --- Quantization helpers --- + +CONTROL_TENSOR_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "CONTROL_TENSOR_NAME_PATTERNS", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,attn_gate,vr_lambda", + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple( + pattern + for pattern in os.environ.get( + "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS", + ",".join(CONTROL_TENSOR_NAME_PATTERNS), + ).split(",") + if pattern +) +INT8_KEEP_FLOAT_MAX_NUMEL = 65_536 +INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16 +INT8_PER_ROW_SCALE_DTYPE = torch.float16 +INT8_CLIP_PERCENTILE = 99.99984 +INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0 +def tensor_nbytes(t: Tensor) -> int: + return int(t.numel()) * int(t.element_size()) +def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict[str, str]) -> Tensor: + if any(pattern in name for pattern in INT8_KEEP_FLOAT_FP32_NAME_PATTERNS): + return t.float().contiguous() + if t.dtype in {torch.float32, torch.bfloat16}: + passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.") + return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous() + return t +def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + clip_abs = ( + torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1) + if t32.numel() + else torch.empty((t32.shape[0],), dtype=torch.float32) + ) + clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None]) + scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0) + q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous() + return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous() + clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0 + scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32) + q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous() + return q, scale +def quantize_state_dict_int8(state_dict: dict[str, Tensor]): + quantized: dict[str, Tensor] = {} + scales: dict[str, Tensor] = {} + dtypes: dict[str, str] = {} + passthrough: dict[str, Tensor] = {} + passthrough_orig_dtypes: dict[str, str] = {} + qmeta: dict[str, dict[str, object]] = {} + stats = dict.fromkeys( + ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), + 0, + ) + for name, tensor in state_dict.items(): + t = tensor.detach().to("cpu").contiguous() + stats["param_count"] += int(t.numel()) + stats["num_tensors"] += 1 + stats["baseline_tensor_bytes"] += tensor_nbytes(t) + if not t.is_floating_point(): + stats["num_nonfloat_tensors"] += 1 + passthrough[name] = t + stats["int8_payload_bytes"] += tensor_nbytes(t) + continue + if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL: + kept = keep_float_tensor(name, t, passthrough_orig_dtypes) + passthrough[name] = kept + stats["int8_payload_bytes"] += tensor_nbytes(kept) + continue + stats["num_float_tensors"] += 1 + q, s = quantize_float_tensor(t) + if s.ndim > 0: + qmeta[name] = {"scheme": "per_row", "axis": 0} + quantized[name] = q + scales[name] = s + dtypes[name] = str(t.dtype).removeprefix("torch.") + stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s) + obj: dict[str, object] = { + "__quant_format__": "int8_clean_per_row_v1", + "quantized": quantized, + "scales": scales, + "dtypes": dtypes, + "passthrough": passthrough, + } + if qmeta: + obj["qmeta"] = qmeta + if passthrough_orig_dtypes: + obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes + return obj, stats +def dequantize_state_dict_int8(obj: dict[str, object]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + qmeta = obj.get("qmeta", {}) + passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {}) + for name, q in obj["quantized"].items(): + dtype = getattr(torch, obj["dtypes"][name]) + s = obj["scales"][name] + if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0: + s = s.to(dtype=torch.float32) + out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous() + else: + scale = float(s.item()) + out[name] = (q.float() * scale).to(dtype=dtype).contiguous() + for name, t in obj["passthrough"].items(): + out_t = t.detach().to("cpu").contiguous() + orig_dtype = passthrough_orig_dtypes.get(name) + if isinstance(orig_dtype, str): + out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous() + out[name] = out_t + return out + +# --- Data loading (memmap pipeline from PR #726) --- + +_MAGIC = 20240520 +_VERSION = 1 +_HEADER_INTS = 256 +_HEADER_DTYPE = np.dtype(" int: + h = 1469598103934665603 + for b in text.encode("utf-8", errors="surrogatepass"): + h ^= b + h = (h * 1099511628211) & 0xFFFFFFFFFFFFFFFF + return h + + +def _read_num_tokens(file: Path) -> int: + key = str(file) + cached = _HEADER_CACHE.get(key) + if cached is not None: + return cached + + header = np.fromfile(file, dtype=_HEADER_DTYPE, count=_HEADER_INTS) + if header.size != _HEADER_INTS: + raise ValueError(f"Unexpected shard header size for {file}") + if int(header[0]) != _MAGIC or int(header[1]) != _VERSION: + raise ValueError(f"Unexpected shard header for {file}") + + num_tokens = int(header[2]) + expected_size = _HEADER_BYTES + num_tokens * _TOKEN_DTYPE.itemsize + actual_size = file.stat().st_size + if actual_size != expected_size: + raise ValueError( + f"Shard size mismatch for {file}: expected {expected_size} bytes, got {actual_size} bytes" + ) + + _HEADER_CACHE[key] = num_tokens + return num_tokens + + +def _get_shard_memmap(file: Path) -> np.memmap: + key = str(file) + mm = _MMAP_CACHE.get(key) + if mm is not None: + return mm + + num_tokens = _read_num_tokens(file) + mm = np.memmap( + file, + mode="r", + dtype=_TOKEN_DTYPE, + offset=_HEADER_BYTES, + shape=(num_tokens,), + order="C", + ) + _MMAP_CACHE[key] = mm + return mm + + +def load_data_shard(file: Path) -> Tensor: + return torch.from_numpy(_get_shard_memmap(file)) + + +class TokenStream: + def __init__(self, pattern: str): + self.files = [Path(p) for p in sorted(glob.glob(pattern))] + if not self.files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + + seed = _stable_hash64(pattern) + for file in self.files: + seed ^= _stable_hash64(str(file)) + seed &= 0xFFFFFFFFFFFFFFFF + self._rng = np.random.Generator(np.random.PCG64(seed)) + + self._order = np.arange(len(self.files), dtype=np.int64) + if self._order.size > 1: + self._rng.shuffle(self._order) + + self._order_pos = 0 + self._file_idx = int(self._order[0]) + self._tokens = load_data_shard(self.files[self._file_idx]) + self._pos = 0 + + def _advance_file(self) -> None: + self._order_pos += 1 + if self._order_pos >= int(self._order.size): + self._order_pos = 0 + if self._order.size > 1: + self._rng.shuffle(self._order) + self._file_idx = int(self._order[self._order_pos]) + self._tokens = load_data_shard(self.files[self._file_idx]) + self._pos = 0 + + def take(self, n: int) -> Tensor: + if n <= 0: + return torch.empty(0, dtype=torch.uint16) + + remaining = int(n) + chunks: list[Tensor] = [] + + while remaining > 0: + avail = int(self._tokens.numel()) - self._pos + if avail <= 0: + self._advance_file() + continue + k = min(remaining, avail) + chunks.append(self._tokens[self._pos : self._pos + k]) + self._pos += k + remaining -= k + + return chunks[0] if len(chunks) == 1 else torch.cat(chunks, dim=0) + + +class DistributedTokenLoader: + def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device): + if world_size <= 0: + raise ValueError(f"world_size must be positive, got {world_size}") + if rank < 0 or rank >= world_size: + raise ValueError(f"rank must be in [0, {world_size}), got {rank}") + + self.rank = int(rank) + self.world_size = int(world_size) + self.device = device + + self.files = [Path(p) for p in sorted(glob.glob(pattern))] + if not self.files: + raise FileNotFoundError(f"No files found for pattern: {pattern}") + + self._num_tokens = np.asarray([_read_num_tokens(f) for f in self.files], dtype=np.int64) + + seed = _stable_hash64(pattern) + for file, n_tok in zip(self.files, self._num_tokens.tolist(), strict=True): + seed ^= _stable_hash64(str(file)) + seed ^= (int(n_tok) * 0x9E3779B97F4A7C15) & 0xFFFFFFFFFFFFFFFF + seed &= 0xFFFFFFFFFFFFFFFF + self._rng = np.random.Generator(np.random.PCG64(seed)) + + self._cfg: tuple[int, int, int, int] | None = None + self._eligible_shards: np.ndarray | None = None + self._base_block_counts: np.ndarray | None = None + + self._cursor_phase: np.ndarray | None = None + self._cursor_block_count: np.ndarray | None = None + self._cursor_next: np.ndarray | None = None + self._cursor_start: np.ndarray | None = None + self._cursor_stride: np.ndarray | None = None + self._cursor_initialized: np.ndarray | None = None + + self._queue: queue.Queue[tuple[Tensor, Tensor]] | None = None + self._worker: threading.Thread | None = None + self._prefetch_stream: torch.cuda.Stream | None = None + self._next_gpu_batch: tuple[Tensor, Tensor] | None = None + self._next_ready_event: torch.cuda.Event | None = None + + self._batches_built = 0 + self._merge_gap_tokens = 0 + + def _pick_coprime_stride(self, n: int) -> int: + if n <= 1: + return 1 + while True: + s = int(self._rng.integers(1, n)) + if math.gcd(s, n) == 1: + return s + + def _reset_shard_cursor(self, shard_idx: int, seq_len: int) -> None: + if ( + self._cursor_phase is None + or self._cursor_block_count is None + or self._cursor_next is None + or self._cursor_start is None + or self._cursor_stride is None + or self._cursor_initialized is None + ): + raise RuntimeError("Shard cursor state is not initialized") + + n_tok = int(self._num_tokens[shard_idx]) + max_phase = min(seq_len - 1, max(0, n_tok - seq_len - 1)) + phase = int(self._rng.integers(max_phase + 1)) if max_phase > 0 else 0 + block_count = (n_tok - 1 - phase) // seq_len + if block_count <= 0: + raise RuntimeError(f"Ineligible shard {self.files[shard_idx]} for seq_len={seq_len}") + + self._cursor_phase[shard_idx] = phase + self._cursor_block_count[shard_idx] = int(block_count) + self._cursor_next[shard_idx] = 0 + self._cursor_start[shard_idx] = int(self._rng.integers(block_count)) if block_count > 1 else 0 + self._cursor_stride[shard_idx] = self._pick_coprime_stride(block_count) + self._cursor_initialized[shard_idx] = True + + def _ensure_shard_cursor(self, shard_idx: int, seq_len: int) -> None: + if ( + self._cursor_initialized is None + or self._cursor_next is None + or self._cursor_block_count is None + ): + raise RuntimeError("Shard cursor state is not initialized") + + if (not bool(self._cursor_initialized[shard_idx])) or ( + int(self._cursor_next[shard_idx]) >= int(self._cursor_block_count[shard_idx]) + ): + self._reset_shard_cursor(shard_idx, seq_len) + + def _take_from_shard( + self, + shard_idx: int, + seq_len: int, + count: int, + out: list[tuple[int, int]], + ) -> None: + if count <= 0: + return + if ( + self._cursor_phase is None + or self._cursor_block_count is None + or self._cursor_next is None + or self._cursor_start is None + or self._cursor_stride is None + ): + raise RuntimeError("Shard cursor state is not initialized") + + remaining = int(count) + while remaining > 0: + self._ensure_shard_cursor(shard_idx, seq_len) + block_count = int(self._cursor_block_count[shard_idx]) + next_idx = int(self._cursor_next[shard_idx]) + take = min(remaining, block_count - next_idx) + phase = int(self._cursor_phase[shard_idx]) + start = int(self._cursor_start[shard_idx]) + stride = int(self._cursor_stride[shard_idx]) + + for j in range(take): + block_idx = (start + (next_idx + j) * stride) % block_count + pos = phase + block_idx * seq_len + out.append((int(shard_idx), int(pos))) + + self._cursor_next[shard_idx] = next_idx + take + remaining -= take + + def _schedule_progress(self) -> float: + return min(self._batches_built / 1800.0, 1.0) + + def _current_mix_shards(self, eligible_count: int, global_num_seqs: int) -> int: + progress = self._schedule_progress() + low = min(max(8, self.world_size), eligible_count, global_num_seqs) + high = min(max(32, self.world_size * 8), eligible_count, global_num_seqs) + if high < low: + high = low + mix = int(round(low + progress * (high - low))) + return max(1, min(mix, eligible_count, global_num_seqs)) + + def _sample_global_windows(self) -> list[tuple[int, int]]: + if self._cfg is None or self._eligible_shards is None or self._base_block_counts is None: + raise RuntimeError("Loader pipeline not initialized") + if ( + self._cursor_next is None + or self._cursor_initialized is None + or self._cursor_block_count is None + ): + raise RuntimeError("Shard cursor state is not initialized") + + _, seq_len, _, global_num_seqs = self._cfg + progress = self._schedule_progress() + + remaining = np.empty_like(self._base_block_counts, dtype=np.float64) + for i, shard_idx in enumerate(self._eligible_shards.tolist()): + if bool(self._cursor_initialized[shard_idx]): + rem = int(self._cursor_block_count[shard_idx]) - int(self._cursor_next[shard_idx]) + remaining[i] = float(rem if rem > 0 else int(self._base_block_counts[i])) + else: + remaining[i] = float(int(self._base_block_counts[i])) + + alpha = 0.90 - 0.40 * progress + weights = np.power(np.maximum(remaining, 1.0), alpha, dtype=np.float64) + weights_sum = float(weights.sum()) + if not np.isfinite(weights_sum) or weights_sum <= 0.0: + weights = np.ones_like(weights, dtype=np.float64) + weights_sum = float(weights.sum()) + probs = weights / weights_sum + + mix = self._current_mix_shards(int(self._eligible_shards.size), global_num_seqs) + chosen_pos = self._rng.choice(int(self._eligible_shards.size), size=mix, replace=False, p=probs) + chosen_shards = self._eligible_shards[chosen_pos] + chosen_probs = probs[chosen_pos].astype(np.float64, copy=True) + chosen_probs /= float(chosen_probs.sum()) + + counts = np.ones(mix, dtype=np.int64) + extra = global_num_seqs - mix + if extra > 0: + counts += self._rng.multinomial(extra, chosen_probs).astype(np.int64, copy=False) + + perm = self._rng.permutation(mix) + chosen_shards = chosen_shards[perm] + counts = counts[perm] + + buckets: list[list[tuple[int, int]]] = [] + for shard_idx, count in zip(chosen_shards.tolist(), counts.tolist(), strict=True): + local_bucket: list[tuple[int, int]] = [] + self._take_from_shard(int(shard_idx), seq_len, int(count), local_bucket) + if local_bucket: + if len(local_bucket) > 1: + local_perm = self._rng.permutation(len(local_bucket)) + local_bucket = [local_bucket[int(i)] for i in local_perm.tolist()] + buckets.append(local_bucket) + + windows: list[tuple[int, int]] = [] + active = [i for i, b in enumerate(buckets) if b] + while active: + order = self._rng.permutation(len(active)) + new_active: list[int] = [] + for ord_idx in order.tolist(): + bi = active[ord_idx] + bucket = buckets[bi] + if bucket: + windows.append(bucket.pop()) + if bucket: + new_active.append(bi) + active = new_active + + if len(windows) != global_num_seqs: + raise RuntimeError(f"Incorrect number of sampled windows: expected {global_num_seqs}, got {len(windows)}") + return windows + + def _copy_from_shard_group( + self, + shard_idx: int, + items: list[tuple[int, int]], + seq_len: int, + x_cpu: Tensor, + y_cpu: Tensor, + ) -> None: + shard_np = _get_shard_memmap(self.files[shard_idx]) + items.sort(key=lambda t: t[1]) + + merge_gap = self._merge_gap_tokens + run_start_idx = 0 + run_start_pos = items[0][1] + run_end_pos = run_start_pos + seq_len + 1 + + for j in range(1, len(items) + 1): + flush = j == len(items) + if not flush: + next_pos = items[j][1] + if next_pos <= run_end_pos + merge_gap: + candidate_end = next_pos + seq_len + 1 + if candidate_end > run_end_pos: + run_end_pos = candidate_end + continue + + slab_np = shard_np[run_start_pos:run_end_pos] + slab_t = torch.from_numpy(slab_np) + for slot, pos in items[run_start_idx:j]: + rel = pos - run_start_pos + window_t = slab_t[rel : rel + seq_len + 1] + if int(window_t.numel()) != seq_len + 1: + raise RuntimeError( + f"Short window read from shard {self.files[shard_idx]} at pos={pos}: " + f"expected {seq_len + 1}, got {int(window_t.numel())}" + ) + x_cpu[slot].copy_(window_t[:-1]) + y_cpu[slot].copy_(window_t[1:]) + + if not flush: + run_start_idx = j + run_start_pos = items[j][1] + run_end_pos = run_start_pos + seq_len + 1 + + def _build_cpu_batch(self) -> tuple[Tensor, Tensor]: + if self._cfg is None: + raise RuntimeError("Loader pipeline not initialized") + + _, seq_len, num_seqs, global_num_seqs = self._cfg + global_windows = self._sample_global_windows() + if len(global_windows) != global_num_seqs: + raise RuntimeError("Incorrect number of sampled windows") + + # Strided rank assignment gives each rank a more uniformly mixed subset + # of the interleaved global plan than contiguous slicing. + local_windows = global_windows[self.rank:global_num_seqs:self.world_size] + if len(local_windows) != num_seqs: + raise RuntimeError( + f"Incorrect local window count: expected {num_seqs}, got {len(local_windows)}" + ) + + pin = self.device.type == "cuda" + x_cpu = torch.empty((num_seqs, seq_len), dtype=torch.uint16, pin_memory=pin) + y_cpu = torch.empty((num_seqs, seq_len), dtype=torch.uint16, pin_memory=pin) + + by_shard: dict[int, list[tuple[int, int]]] = {} + for slot, (shard_idx, pos) in enumerate(local_windows): + by_shard.setdefault(int(shard_idx), []).append((slot, int(pos))) + + for shard_idx, items in by_shard.items(): + self._copy_from_shard_group(shard_idx, items, seq_len, x_cpu, y_cpu) + + self._batches_built += 1 + return x_cpu, y_cpu + + def _worker_loop(self) -> None: + if self._queue is None: + return + while True: + self._queue.put(self._build_cpu_batch()) + + def _stage_next_gpu_batch(self) -> None: + if self._queue is None: + raise RuntimeError("Batch queue not initialized") + + x_cpu, y_cpu = self._queue.get() + + if self.device.type != "cuda": + self._next_gpu_batch = ( + x_cpu.to(device=self.device, dtype=torch.int64), + y_cpu.to(device=self.device, dtype=torch.int64), + ) + self._next_ready_event = None + return + + if self._prefetch_stream is None: + self._prefetch_stream = torch.cuda.Stream(device=self.device) + + with torch.cuda.stream(self._prefetch_stream): + x_gpu = x_cpu.to(device=self.device, dtype=torch.int64, non_blocking=True) + y_gpu = y_cpu.to(device=self.device, dtype=torch.int64, non_blocking=True) + event = torch.cuda.Event() + event.record(self._prefetch_stream) + + self._next_gpu_batch = (x_gpu, y_gpu) + self._next_ready_event = event + + def _init_pipeline(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> None: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + if local_tokens <= 0: + raise ValueError( + f"local_tokens must be positive, got {local_tokens} from " + f"global_tokens={global_tokens}, world_size={self.world_size}, grad_accum_steps={grad_accum_steps}" + ) + if seq_len <= 0: + raise ValueError(f"seq_len must be positive, got {seq_len}") + if local_tokens % seq_len != 0: + raise ValueError(f"local_tokens ({local_tokens}) must be divisible by seq_len ({seq_len})") + + num_seqs = local_tokens // seq_len + global_num_seqs = num_seqs * self.world_size + self._cfg = (local_tokens, seq_len, num_seqs, global_num_seqs) + + base_block_counts = (self._num_tokens - 1) // seq_len + eligible_mask = base_block_counts > 0 + if not np.any(eligible_mask): + raise ValueError(f"No shards in pattern can provide sequences of length {seq_len + 1}") + + self._eligible_shards = np.nonzero(eligible_mask)[0].astype(np.int64, copy=False) + self._base_block_counts = base_block_counts[self._eligible_shards].astype(np.int64, copy=False) + + n_files = len(self.files) + self._cursor_phase = np.zeros(n_files, dtype=np.int64) + self._cursor_block_count = np.zeros(n_files, dtype=np.int64) + self._cursor_next = np.zeros(n_files, dtype=np.int64) + self._cursor_start = np.zeros(n_files, dtype=np.int64) + self._cursor_stride = np.ones(n_files, dtype=np.int64) + self._cursor_initialized = np.zeros(n_files, dtype=np.bool_) + + self._merge_gap_tokens = max(seq_len // 2, 1) + + self._queue = queue.Queue(maxsize=8) + self._worker = threading.Thread(target=self._worker_loop, daemon=True) + self._worker.start() + + if self.device.type == "cuda": + self._prefetch_stream = torch.cuda.Stream(device=self.device) + + self._stage_next_gpu_batch() + + def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]: + local_tokens = global_tokens // (self.world_size * grad_accum_steps) + + if self._cfg is None: + self._init_pipeline(global_tokens, seq_len, grad_accum_steps) + else: + expected = ( + local_tokens, + seq_len, + local_tokens // seq_len, + (local_tokens // seq_len) * self.world_size, + ) + if self._cfg != expected: + raise ValueError( + "DistributedTokenLoader received changing batch configuration after initialization, " + f"got global_tokens={global_tokens}, seq_len={seq_len}, grad_accum_steps={grad_accum_steps}" + ) + + if self._next_gpu_batch is None: + self._stage_next_gpu_batch() + + if self.device.type == "cuda" and self._next_ready_event is not None: + torch.cuda.current_stream(self.device).wait_event(self._next_ready_event) + + batch = self._next_gpu_batch + if batch is None: + raise RuntimeError("Failed to prepare next batch") + + if self.device.type == "cuda": + curr = torch.cuda.current_stream(self.device) + batch[0].record_stream(curr) + batch[1].record_stream(curr) + + self._stage_next_gpu_batch() + return batch + +# --- Transformer modules --- + +class RMSNorm(nn.Module): + def __init__(self, eps: float | None = None): + super().__init__() + self.eps = eps + def forward(self, x: Tensor) -> Tensor: + return F.rms_norm(x, (x.size(-1),), eps=self.eps) +class CastedLinear(nn.Linear): + _qat_enabled: bool = False + def forward(self, x: Tensor) -> Tensor: + w = self.weight.to(x.dtype) + if CastedLinear._qat_enabled and self.training and w.ndim == 2: + with torch.no_grad(): + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) + w = w + (w_q - w).detach() + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) +def restore_low_dim_params_to_fp32(module: nn.Module) -> None: + with torch.no_grad(): + for name, param in module.named_parameters(): + if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32: + param.data = param.data.float() +class Rotary(nn.Module): + def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0): + super().__init__() + self.dim = dim + self.base = base + self.train_seq_len = train_seq_len + self.rope_dims = rope_dims if rope_dims > 0 else dim + inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self._seq_len_cached = 0 + self._cos_cached: Tensor | None = None + self._sin_cached: Tensor | None = None + def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]: + if ( + self._cos_cached is None + or self._sin_cached is None + or self._seq_len_cached != seq_len + or self._cos_cached.device != device + ): + rd = self.rope_dims + if seq_len > self.train_seq_len: + scale = seq_len / self.train_seq_len + new_base = self.base * (scale ** (rd / (rd - 2))) + inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd)) + else: + inv_freq = self.inv_freq.to(device) + t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + freqs = torch.outer(t, inv_freq) + self._cos_cached = freqs.cos()[None, :, None, :] + self._sin_cached = freqs.sin()[None, :, None, :] + self._seq_len_cached = seq_len + return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype) +def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor: + if rope_dims > 0 and rope_dims < x.size(-1): + x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:] + half = rope_dims // 2 + x1, x2 = x_rope[..., :half], x_rope[..., half:] + x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + return torch.cat((x_rope, x_pass), dim=-1) + half = x.size(-1) // 2 + x1, x2 = x[..., :half], x[..., half:] + return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1) + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + rope_base: float, + qk_gain_init: float, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + if dim % num_heads != 0: + raise ValueError("model_dim must be divisible by num_heads") + if num_heads % num_kv_heads != 0: + raise ValueError("num_heads must be divisible by num_kv_heads") + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = dim // num_heads + if self.head_dim % 2 != 0: + raise ValueError("head_dim must be even for RoPE") + # No CastedLinear -- weights come from banks + self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32)) + self.rope_dims = 0 # set by GPT.__init__ for partial RoPE + self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=1024) + self.use_xsa = False # set by GPT.__init__ for deep layers only + # Gated attention and value residual (non-banked small params) + self.gated_attention = gated_attention + if gated_attention: + self.attn_gate = nn.Linear(dim, num_heads, bias=True) + nn.init.zeros_(self.attn_gate.weight) + nn.init.constant_(self.attn_gate.bias, 4.0) + self.value_residual = value_residual + if value_residual: + self.vrl_alpha = nn.Parameter(torch.zeros(1, dtype=torch.float32)) # sigmoid gate (PR #569 style) + def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor: + """Efficient XSA: subtract self-value projection via GQA-aware reshape (no repeat_interleave). + y: [B, T, H, D], v: [B, T, Hkv, D]. H must be divisible by Hkv.""" + B, T, H, D = y.shape + Hkv = v.size(-2) + group = H // Hkv + y_g = y.reshape(B, T, Hkv, group, D) # [B, T, Hkv, group, D] + vn = F.normalize(v, dim=-1).unsqueeze(-2) # [B, T, Hkv, 1, D] -- broadcast ready + proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn + return (y_g - proj).reshape(B, T, H, D) + def forward(self, x: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + bsz, seqlen, dim = x.shape + q = F.linear(x, q_w.to(x.dtype)).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)) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + raw_v = v if self.value_residual else None + if self.value_residual and v0 is not None: + alpha = torch.sigmoid(self.vrl_alpha.to(dtype=v.dtype)) + v = v + alpha * v0 # sigmoid-gated residual (PR #569 style) + 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] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + if self.gated_attention: + # gate shape: (bsz, seqlen, num_heads) -> (bsz, seqlen, num_heads, 1) for B,T,H,D layout + gate = torch.sigmoid(self.attn_gate(x)).unsqueeze(-1) + y = y * gate + y = y.reshape(bsz, seqlen, dim) + return F.linear(y, out_w.to(x.dtype)), raw_v + +class SmearGate(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + def forward(self, x: Tensor) -> Tensor: + g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :] + x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + return (1 - g) * x + g * x_prev + +class BigramHashEmbedding(nn.Module): + def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int, trigram: bool = False): + super().__init__() + self.bigram_vocab_size = bigram_vocab_size + self._trigram = trigram + self.embed = nn.Embedding(bigram_vocab_size, bigram_dim) + nn.init.zeros_(self.embed.weight) + self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32)) + def bigram_hash(self, tokens: Tensor) -> Tensor: + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., 0] = mod + out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod + return out.long() + def trigram_hash(self, tokens: Tensor) -> Tensor: + """Hash (t-2, t-1, t) trigrams into same embedding table. Zero extra params.""" + t = tokens.to(torch.int32) + mod = self.bigram_vocab_size - 1 + out = torch.empty_like(t) + out[..., :2] = mod + out[..., 2:] = (36313 * t[..., 2:] ^ 27191 * t[..., 1:-1] ^ 51497 * t[..., :-2]) % mod + return out.long() + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(self.bigram_hash(token_ids)) + if self._trigram: + h = h + self.embed(self.trigram_hash(token_ids)) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class ValueEmbedding(nn.Module): + """Reinject token identity into attention values at specific layers. + Each table maps vocab tokens to a low-dim embedding, projected to model_dim.""" + def __init__(self, vocab_size: int, ve_dim: int, model_dim: int): + super().__init__() + self.embed = nn.Embedding(vocab_size, ve_dim) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None + if self.proj is not None: + nn.init.zeros_(self.proj.weight) + self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) + def forward(self, token_ids: Tensor) -> Tensor: + h = self.embed(token_ids) + if self.proj is not None: + h = self.proj(h) + return h * self.scale.to(dtype=h.dtype) + +class MLP(nn.Module): + def __init__(self, dim: int, mlp_mult: int): + super().__init__() + # No CastedLinear -- weights come from banks + def forward(self, x: Tensor, up_w: Tensor, down_w: Tensor) -> Tensor: + if HAS_FUSED_MLP and x.is_cuda and not IS_ROCM: + return FusedLeakyReLUSqMLP.apply(x, up_w.to(x.dtype), down_w.to(x.dtype)) + x = F.leaky_relu(F.linear(x, up_w.to(x.dtype)), negative_slope=0.5) + return F.linear(x.square(), down_w.to(x.dtype)) + +class Block(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + rope_base: float, + qk_gain_init: float, + layer_idx: int = 0, + ln_scale: bool = False, + dtg: bool = False, + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, + gated_attention=gated_attention, value_residual=value_residual) + self.mlp = MLP(dim, mlp_mult) + self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float()) + self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0 + if dtg: + self.dtg_gate = nn.Linear(dim, 1, bias=True) + nn.init.zeros_(self.dtg_gate.weight) + nn.init.constant_(self.dtg_gate.bias, 2.0) + else: + self.dtg_gate = None + def forward(self, x: Tensor, x0: Tensor, q_w: Tensor, k_w: Tensor, v_w: Tensor, out_w: Tensor, up_w: Tensor, down_w: Tensor, v_embed: Tensor | None = None, v0: Tensor | None = None) -> tuple[Tensor, Tensor | None]: + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out, raw_v = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, q_w, k_w, v_w, out_w, v_embed=v_embed, v0=v0) + 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) + if self.dtg_gate is not None: + gate = torch.sigmoid(self.dtg_gate(x_in.detach())) + x_out = x_in + gate * (x_out - x_in) + return x_out, raw_v + +class GPT(nn.Module): + def __init__( + self, + vocab_size: int, + num_layers: int, + model_dim: int, + num_heads: int, + num_kv_heads: int, + mlp_mult: int, + tie_embeddings: bool, + tied_embed_init_std: float, + logit_softcap: float, + rope_base: float, + qk_gain_init: float, + mtp_num_heads: int = 0, + mtp_loss_weight: float = 0.1, + bigram_vocab_size: int = 0, + bigram_dim: int = 128, + xsa_last_n: int = 0, + rope_dims: int = 0, + ln_scale: bool = False, + dtg: bool = False, + ve_enabled: bool = False, + ve_dim: int = 128, + ve_layers: str = "9,10", + gated_attention: bool = False, + value_residual: bool = False, + ): + super().__init__() + self._ve_target_dim = num_kv_heads * (model_dim // num_heads) # kv_dim for value projection + if logit_softcap <= 0.0: + raise ValueError(f"logit_softcap must be positive, got {logit_softcap}") + self.tie_embeddings = tie_embeddings + self.tied_embed_init_std = tied_embed_init_std + self.logit_softcap = logit_softcap + self.value_residual = value_residual + self.mtp_num_heads = mtp_num_heads + self.mtp_loss_weight = mtp_loss_weight + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim, trigram=bool(int(os.environ.get("TRIGRAM", "0")))) if bigram_vocab_size > 0 else None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + # Parameter banks: contiguous 3D tensors for batched optimizer + head_dim = model_dim // num_heads + kv_dim = num_kv_heads * head_dim + mlp_dim = int(mlp_mult * model_dim) + self.num_layers = num_layers + self.qo_bank = nn.Parameter(torch.empty(2 * num_layers, model_dim, model_dim)) + self.kv_bank = nn.Parameter(torch.empty(2 * num_layers, kv_dim, model_dim)) + self.mlp_up_bank = nn.Parameter(torch.empty(num_layers, mlp_dim, model_dim)) + self.mlp_down_bank = nn.Parameter(torch.empty(num_layers, model_dim, mlp_dim)) + self.blocks = nn.ModuleList( + [ + Block( + model_dim, + num_heads, + num_kv_heads, + mlp_mult, + rope_base, + qk_gain_init, + layer_idx=i, + ln_scale=ln_scale, + dtg=dtg, + gated_attention=gated_attention, + value_residual=value_residual, + ) + for i in range(num_layers) + ] + ) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + kv_dim_ve = self._ve_target_dim + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim_ve) + self.ve_layer_scales = nn.ParameterList( + [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices] + ) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.value_embeds = nn.ModuleList() # keep empty for compat + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + if self.lm_head is not None: + self.lm_head._zero_init = True + self.mtp_heads = nn.ModuleList( + [CastedLinear(model_dim, vocab_size, bias=False) for _ in range(mtp_num_heads)] + ) + for head in self.mtp_heads: + head._zero_init = True + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + self._init_weights() + def _init_weights(self) -> None: + if self.tie_embeddings: + nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std) + n = self.num_layers + proj_scale = 1.0 / math.sqrt(2 * n) + # Init banks: orthogonal, with proj layers scaled down and out/down zero-init + for i in range(n): + nn.init.orthogonal_(self.qo_bank.data[i], gain=1.0) # Q + nn.init.zeros_(self.qo_bank.data[n + i]) # Out (zero init) + nn.init.orthogonal_(self.kv_bank.data[i], gain=1.0) # K + nn.init.orthogonal_(self.kv_bank.data[n + i], gain=1.0) # V + nn.init.orthogonal_(self.mlp_up_bank.data[i], gain=1.0) # MLP up + nn.init.zeros_(self.mlp_down_bank.data[i]) # MLP down (zero init) + # Scale proj layers (out_proj and mlp_down are "proj" layers) + self.qo_bank.data[n + i].mul_(proj_scale) + self.mlp_down_bank.data[i].mul_(proj_scale) + # Init remaining nn.Linear modules (bigram proj, mtp heads, lm_head) + 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 _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None: + """Get value embedding for a specific layer using shared table + per-layer scale.""" + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if ve_cache is not None and 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype) + def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + 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], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + if self.tie_embeddings: + logits_proj = F.linear(x_flat, self.tok_emb.weight) + else: + if self.lm_head is None: + raise RuntimeError("lm_head is required when tie_embeddings=False") + logits_proj = self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + main_loss = F.cross_entropy(logits.float(), targets, reduction="mean") + if self.training and self.mtp_num_heads > 0 and self.mtp_loss_weight > 0.0: + _, seqlen, dim = x.shape + mtp_loss_sum = x.new_zeros(()) + mtp_loss_count = 0 + for k, mtp_head in enumerate(self.mtp_heads): + valid_t = seqlen - (k + 1) + if valid_t <= 0: + continue + mtp_hidden = x[:, :valid_t, :].reshape(-1, dim) + mtp_targets = target_ids[:, k + 1 :].reshape(-1) + mtp_logits_proj = mtp_head(mtp_hidden) + mtp_logits = self.logit_softcap * torch.tanh(mtp_logits_proj / self.logit_softcap) + mtp_loss_sum = mtp_loss_sum + F.cross_entropy(mtp_logits.float(), mtp_targets, reduction="mean") + mtp_loss_count += 1 + if mtp_loss_count > 0: + main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) + return main_loss + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + n = self.num_layers + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + v0 = None + skips: list[Tensor] = [] + ve_cache: dict = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x, raw_v = self.blocks[i](x, x0, + 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], + v_embed=ve, v0=v0) + if v0 is None and raw_v is not None: + v0 = raw_v + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x, _ = self.blocks[bi](x, x0, + self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], + self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], + v_embed=ve, v0=v0) + x = self.final_norm(x) + if self.tie_embeddings: + logits_proj = F.linear(x, self.tok_emb.weight) + else: + logits_proj = self.lm_head(x) + return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + +# --- Sliding window evaluation --- + +def eval_val_sliding( + args: Hyperparameters, + base_model: nn.Module, + rank: int, + world_size: int, + device: torch.device, + val_tokens: Tensor, + base_bytes_lut: Tensor, + has_leading_space_lut: Tensor, + is_boundary_token_lut: Tensor, + stride: int, + batch_seqs: int = 32, + eval_seq_len: int | None = None, +) -> tuple[float, float]: + """Sliding window evaluation: each token scored with maximum context.""" + seq_len = eval_seq_len or args.train_seq_len + total_tokens = val_tokens.numel() - 1 + window_starts = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + total_windows = len(window_starts) + my_s = (total_windows * rank) // world_size + my_e = (total_windows * (rank + 1)) // world_size + my_windows = window_starts[my_s:my_e] + loss_sum = torch.zeros((), device=device, dtype=torch.float64) + token_count = torch.zeros((), device=device, dtype=torch.float64) + byte_count = torch.zeros((), device=device, dtype=torch.float64) + base_model.eval() + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() + if dist.is_available() and dist.is_initialized(): + dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) + dist.all_reduce(token_count, op=dist.ReduceOp.SUM) + dist.all_reduce(byte_count, op=dist.ReduceOp.SUM) + val_loss = (loss_sum / token_count).item() + bits_per_token = val_loss / math.log(2.0) + tokens_per_byte = token_count.item() / byte_count.item() + base_model.train() + return val_loss, bits_per_token * tokens_per_byte + + +def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, + vocab_size=1024, temperature=0.8, batch_size=8, seed=42): + """Generate sequences autoregressively from the model for GPTQ calibration. + No external data accessed — fully self-contained.""" + model.eval() + rng = torch.Generator(device=device) + rng.manual_seed(seed) + all_tokens = [] + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for batch_start in range(0, num_seqs, batch_size): + bs = min(batch_size, num_seqs - batch_start) + tokens = torch.randint(0, vocab_size, (bs, 1), device=device, generator=rng) + for pos in range(seq_len - 1): + logits = model.forward_logits(tokens) + next_logit = logits[:, -1, :] + probs = torch.softmax(next_logit / temperature, dim=-1) + next_tok = torch.multinomial(probs, 1, generator=rng) + tokens = torch.cat([tokens, next_tok], dim=1) + for i in range(bs): + all_tokens.append(tokens[i:i+1]) + return all_tokens + + +def collect_hessians_from_tokens(hessian_model, token_seqs, device): + """Collect H = X^T X from pre-generated token sequences.""" + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for seq in token_seqs: + x = seq[:, :-1].to(device) + y = seq[:, 1:].to(device) + hessian_model(x, y) + for h in hooks: + h.remove() + num_batches = len(token_seqs) + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + return hessians + + +# --- GPTQ-lite int6 quantization --- + +def _classify_param(name: str) -> str: + if "tok_emb" in name or "lm_head" in name: + return "embed" + if ".mlp." in name: + return "mlp" + if ".attn." in name or (".proj." in name and ".mlp." not in name): + return "attn" + return "other" +def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]: + t32 = t.float() + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def quantize_int6_gptq(weight, hessian=None, clip_range=31, block_size=128): + """Full GPTQ: Hessian-aware int6 quantization with Cholesky error compensation. + If hessian is None, falls back to percentile search.""" + t32 = weight.float() + if t32.ndim != 2 or hessian is None: + return _quantize_int6_percentile(t32, clip_range) + rows, cols = t32.shape + H = hessian.float().clone() + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + damp = 0.01 * torch.mean(torch.diag(H)) + H[torch.arange(cols), torch.arange(cols)] += damp + perm = torch.argsort(torch.diag(H), descending=True) + inv_perm = torch.argsort(perm) + W = t32[:, perm].clone() + W[:, dead[perm]] = 0 + H = H[perm][:, perm] + Hinv = torch.linalg.cholesky(H) + Hinv = torch.cholesky_inverse(Hinv) + Hinv = torch.linalg.cholesky(Hinv, upper=True) + best_q = None; best_scale = None; best_err = float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + sf = s.float() + Q = torch.zeros_like(W, dtype=torch.int8) + W_work = W.clone() + for i1 in range(0, cols, block_size): + i2 = min(i1 + block_size, cols) + count = i2 - i1 + W1 = W_work[:, i1:i2].clone() + Q1 = torch.zeros(rows, count, dtype=torch.int8) + Err1 = torch.zeros(rows, count) + Hinv1 = Hinv[i1:i2, i1:i2] + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + q = torch.clamp(torch.round(w / sf), -clip_range, clip_range).to(torch.int8) + Q1[:, i] = q + err = (w - q.float() * sf) / d + W1[:, i:] -= err.unsqueeze(1) * Hinv1[i, i:].unsqueeze(0) + Err1[:, i] = err + Q[:, i1:i2] = Q1 + if i2 < cols: + W_work[:, i2:] -= Err1 @ Hinv[i1:i2, i2:] + recon = Q.float() * sf[:, None] + mse = (W - recon).pow(2).mean().item() + if mse < best_err: + best_q, best_scale, best_err = Q, s, mse + best_q = best_q[:, inv_perm] + return best_q, best_scale + +def _quantize_int6_percentile(t32, clip_range=31): + """Fallback: percentile search (for 1D or no-Hessian cases).""" + if t32.ndim == 2: + best_q, best_s, best_err = None, None, float('inf') + for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]: + if pct < 1.0: + row_clip = torch.quantile(t32.abs(), pct, dim=1) + else: + row_clip = t32.abs().amax(dim=1) + s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16) + q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8) + recon = q.float() * s.float()[:, None] + err = (t32 - recon).pow(2).mean().item() + if err < best_err: + best_q, best_s, best_err = q, s, err + return best_q, best_s + amax = t32.abs().max().item() + scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16) + q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8) + return q, scale + +def _unbank_state_dict(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]: + """Convert 3D bank tensors into individual 2D tensors with standard names.""" + out: dict[str, Tensor] = {} + n = num_layers + for name, tensor in sd.items(): + if name == "qo_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_q.weight"] = tensor[i] + out[f"blocks.{i}.attn.proj.weight"] = tensor[n + i] + elif name == "kv_bank": + for i in range(n): + out[f"blocks.{i}.attn.c_k.weight"] = tensor[i] + out[f"blocks.{i}.attn.c_v.weight"] = tensor[n + i] + elif name == "mlp_up_bank": + for i in range(n): + out[f"blocks.{i}.mlp.fc.weight"] = tensor[i] + elif name == "mlp_down_bank": + for i in range(n): + out[f"blocks.{i}.mlp.proj.weight"] = tensor[i] + else: + out[name] = tensor + return out + +def _rebank_state_dict(sd: dict[str, Tensor], num_layers: int, template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + """Convert individual 2D tensors back into 3D bank tensors.""" + out: dict[str, Tensor] = {} + n = num_layers + # Reconstruct banks from individual weight keys + qo_slices = [None] * (2 * n) + kv_slices = [None] * (2 * n) + up_slices = [None] * n + down_slices = [None] * n + consumed = set() + for i in range(n): + qk = f"blocks.{i}.attn.c_q.weight" + if qk in sd: + qo_slices[i] = sd[qk] + consumed.add(qk) + ok = f"blocks.{i}.attn.proj.weight" + if ok in sd: + qo_slices[n + i] = sd[ok] + consumed.add(ok) + kk = f"blocks.{i}.attn.c_k.weight" + if kk in sd: + kv_slices[i] = sd[kk] + consumed.add(kk) + vk = f"blocks.{i}.attn.c_v.weight" + if vk in sd: + kv_slices[n + i] = sd[vk] + consumed.add(vk) + fk = f"blocks.{i}.mlp.fc.weight" + if fk in sd: + up_slices[i] = sd[fk] + consumed.add(fk) + dk = f"blocks.{i}.mlp.proj.weight" + if dk in sd: + down_slices[i] = sd[dk] + consumed.add(dk) + out["qo_bank"] = torch.stack(qo_slices).to(dtype=template_sd["qo_bank"].dtype) + out["kv_bank"] = torch.stack(kv_slices).to(dtype=template_sd["kv_bank"].dtype) + out["mlp_up_bank"] = torch.stack(up_slices).to(dtype=template_sd["mlp_up_bank"].dtype) + out["mlp_down_bank"] = torch.stack(down_slices).to(dtype=template_sd["mlp_down_bank"].dtype) + for name, tensor in sd.items(): + if name not in consumed: + out[name] = tensor + return out + +# --- Non-banked model for Hessian collection --- +# This mirrors the unbanked state dict keys: blocks.{i}.attn.c_q/c_k/c_v/proj, blocks.{i}.mlp.fc/proj + +class _HessianAttn(nn.Module): + """Non-banked attention with CastedLinear layers for Hessian hooks.""" + def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init): + super().__init__() + self.num_heads, self.num_kv_heads = num_heads, num_kv_heads + self.head_dim = dim // num_heads + kv_dim = num_kv_heads * self.head_dim + self.c_q = CastedLinear(dim, dim, bias=False) + self.c_k = CastedLinear(dim, kv_dim, bias=False) + self.c_v = CastedLinear(dim, kv_dim, bias=False) + self.proj = CastedLinear(dim, dim, bias=False) + self.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=1024) + self.use_xsa = False + 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, v_embed=None): + bsz, seqlen, dim = x.shape + q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim) + k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + v = self.c_v(x) + if v_embed is not None: + v = v + v_embed + v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim) + q = F.rms_norm(q, (q.size(-1),)) + k = F.rms_norm(k, (k.size(-1),)) + cos, sin = self.rotary(seqlen, x.device, q.dtype) + q = apply_rotary_emb(q, cos, sin, self.rope_dims) + k = apply_rotary_emb(k, cos, sin, self.rope_dims) + q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None] + y = flash_attn_3_func(q, k, v, causal=True) + if self.use_xsa: + y = self._xsa_efficient(y, v) + return self.proj(y.reshape(bsz, seqlen, dim)) + +class _HessianMLP(nn.Module): + """Non-banked MLP with CastedLinear layers for Hessian hooks.""" + def __init__(self, dim, mlp_mult): + super().__init__() + self.fc = CastedLinear(dim, int(mlp_mult * dim), bias=False) + self.proj = CastedLinear(int(mlp_mult * dim), dim, bias=False) + def forward(self, x): + return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square()) + +class _HessianBlock(nn.Module): + def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=0, ln_scale=False): + super().__init__() + self.attn_norm = RMSNorm() + self.mlp_norm = RMSNorm() + self.attn = _HessianAttn(dim, num_heads, num_kv_heads, rope_base, qk_gain_init) + self.mlp = _HessianMLP(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, v_embed=None): + mix = self.resid_mix.to(dtype=x.dtype) + x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0 + attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed) + x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor) + return x_out + +class _HessianGPT(nn.Module): + """Non-banked GPT model matching unbanked state dict keys for Hessian collection.""" + def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, + mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, + bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + rope_dims=0, ln_scale=False, + ve_enabled=False, ve_dim=128, ve_layers="9,10"): + super().__init__() + self.tie_embeddings = tie_embeddings + self.logit_softcap = logit_softcap + self.num_layers = num_layers + self.tok_emb = nn.Embedding(vocab_size, model_dim) + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim, trigram=bool(int(os.environ.get("TRIGRAM", "0")))) if bigram_vocab_size > 0 else None + self.smear = SmearGate(model_dim) + self.num_encoder_layers = num_layers // 2 + self.num_decoder_layers = num_layers - self.num_encoder_layers + self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) + self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.blocks = nn.ModuleList([ + _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, + layer_idx=i, ln_scale=ln_scale) + for i in range(num_layers) + ]) + if rope_dims > 0: + head_dim = model_dim // num_heads + for block in self.blocks: + block.attn.rope_dims = rope_dims + block.attn.rotary = Rotary(head_dim, base=rope_base, train_seq_len=1024, rope_dims=rope_dims) + if xsa_last_n > 0: + for i in range(max(0, num_layers - xsa_last_n), num_layers): + self.blocks[i].attn.use_xsa = True + kv_dim = num_kv_heads * (model_dim // num_heads) + self.ve_layer_indices = [int(x) for x in ve_layers.split(",") if x.strip()] if ve_enabled else [] + if self.ve_layer_indices: + self.ve_shared = ValueEmbedding(vocab_size, ve_dim, kv_dim) + self.ve_layer_scales = nn.ParameterList([nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]) + else: + self.ve_shared = None + self.ve_layer_scales = nn.ParameterList() + self.final_norm = RMSNorm() + self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False) + def _get_ve(self, layer_idx, input_ids, ve_cache): + if self.ve_shared is None or layer_idx not in self.ve_layer_indices: + return None + if 've' not in ve_cache: + ve_cache['ve'] = self.ve_shared(input_ids) + ve_idx = self.ve_layer_indices.index(layer_idx) + return ve_cache['ve'] * self.ve_layer_scales[ve_idx].to(dtype=ve_cache['ve'].dtype) + def forward(self, input_ids, target_ids): + x = self.tok_emb(input_ids) + if self.bigram is not None: + x = x + self.bigram(input_ids) + x = F.rms_norm(x, (x.size(-1),)) + x = self.smear(x) + x0 = x + skips = [] + ve_cache = {} + for i in range(self.num_encoder_layers): + ve = self._get_ve(i, input_ids, ve_cache) + x = self.blocks[i](x, x0, v_embed=ve) + skips.append(x) + for i in range(self.num_decoder_layers): + bi = self.num_encoder_layers + i + if skips: + x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + ve = self._get_ve(bi, input_ids, ve_cache) + x = self.blocks[bi](x, x0, v_embed=ve) + x = self.final_norm(x) + x_flat = x.reshape(-1, x.size(-1)) + targets = target_ids.reshape(-1) + logits_proj = F.linear(x_flat, self.tok_emb.weight) if self.tie_embeddings else self.lm_head(x_flat) + logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + return F.cross_entropy(logits.float(), targets, reduction="mean") + +def collect_hessians(hessian_model, train_loader, args, device, grad_accum_steps, num_batches=256): + """Run calibration batches through a non-banked model, collecting H = X^T X for each CastedLinear.""" + hessians = {} + hooks = [] + for name, module in hessian_model.named_modules(): + if isinstance(module, CastedLinear): + param_name = name + ".weight" + cols = module.weight.shape[1] + hessians[param_name] = torch.zeros(cols, cols, dtype=torch.float32, device='cpu') + def make_hook(pname): + def hook_fn(module, input, output): + x = input[0].detach().float() + if x.ndim == 3: + x = x.reshape(-1, x.shape[-1]) + hessians[pname] += (x.T @ x).cpu() + return hook_fn + h = module.register_forward_hook(make_hook(param_name)) + hooks.append(h) + hessian_model.eval() + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(num_batches): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + hessian_model(x, y) + for h in hooks: + h.remove() + for name in hessians: + H = hessians[name] + H /= num_batches + damp = 0.01 * torch.diag(H).mean().clamp_min(1e-6) + H += damp * torch.eye(H.shape[0]) + hessians[name] = H + hessian_model.train() + return hessians + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): + num_layers_total = max( + (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), + default=0, + ) + 1 + late_k_layers = set(range(num_layers_total - 2, num_layers_total)) + result: dict[str, Tensor] = {} + meta: dict[str, object] = {} + for name, tensor in state_dict.items(): + t = tensor.detach().cpu().contiguous() + cat = _classify_param(name) + if not t.is_floating_point() or t.numel() <= 65536: + result[name] = t.to(torch.float16) if t.is_floating_point() else t + meta[name] = "passthrough" + continue + if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS): + result[name] = t.float() + meta[name] = "passthrough_ctrl" + continue + if cat in int6_cats and t.ndim >= 1: + cr = 31 # int6 for all weights + H = hessians.get(name) if hessians else None + if H is not None: + q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) + else: + q, s = quantize_int6_per_row(t, clip_range=cr) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int6"} + else: + q, s = quantize_float_tensor(t) + result[name + ".q"] = q + result[name + ".scale"] = s + meta[name] = {"type": "int8"} + return result, meta +def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object], + template_sd: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + for name, orig in template_sd.items(): + info = meta.get(name) + if info is None: + continue + orig_dtype = orig.dtype + if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"): + t = result[name] + if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16): + t = t.to(orig_dtype) + out[name] = t + continue + q, s = result[name + ".q"], result[name + ".scale"] + if s.ndim > 0: + out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype) + else: + out[name] = (q.float() * float(s.item())).to(orig_dtype) + return out + +# --- Training --- + +def main() -> None: + code = Path(__file__).read_text(encoding="utf-8") + args = Hyperparameters() + # zeropower_via_newtonschulz5 runs eagerly with bmm -- do NOT compile + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if world_size <= 0: + raise ValueError(f"WORLD_SIZE must be positive, got {world_size}") + if 8 % world_size != 0: + raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral") + grad_accum_steps = 8 // world_size + grad_scale = 1.0 / grad_accum_steps + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + if distributed: + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + master_process = rank == 0 + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp + enable_cudnn_sdp(False) + enable_flash_sdp(True) + enable_mem_efficient_sdp(False) + enable_math_sdp(False) + logfile = None + if master_process: + os.makedirs("logs", exist_ok=True) + logfile = f"logs/{args.run_id}.txt" + print(logfile) + def log0(msg: str, console: bool = True) -> None: + if not master_process: + return + if console: + print(msg) + if logfile is not None: + with open(logfile, "a", encoding="utf-8") as f: + print(msg, file=f) + log0(code, console=False) + log0("=" * 100, console=False) + log0(f"Running Python {sys.version}", console=False) + log0(f"Running PyTorch {torch.__version__}", console=False) + log0( + subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout, + console=False, + ) + log0("=" * 100, console=False) + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + if not args.tokenizer_path.endswith(".model"): + raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}") + sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path) + if int(sp.vocab_size()) != args.vocab_size: + raise ValueError( + f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}" + ) + dataset_dir = Path(args.data_path).resolve() + actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin"))) + effective_eval_seq_len = args.eval_seq_len if args.eval_seq_len > 0 else args.train_seq_len + val_seq_len = max(args.train_seq_len, effective_eval_seq_len) + val_tokens = load_validation_tokens(args.val_files, val_seq_len) + base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts( + sp, args.vocab_size, device + ) + log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}") + log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") + log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") + CastedLinear._qat_enabled = args.qat_enabled + base_model = GPT( + vocab_size=args.vocab_size, + num_layers=args.num_layers, + model_dim=args.model_dim, + num_heads=args.num_heads, + num_kv_heads=args.num_kv_heads, + mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, + tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, + rope_base=args.rope_base, + qk_gain_init=args.qk_gain_init, + mtp_num_heads=args.mtp_num_heads, + mtp_loss_weight=args.mtp_loss_weight, + bigram_vocab_size=args.bigram_vocab_size, + bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, + ln_scale=args.ln_scale, + dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, + ve_dim=args.ve_dim, + ve_layers=args.ve_layers, + gated_attention=args.gated_attention, + value_residual=args.value_residual, + ).to(device).bfloat16() + # Banks stay FP32 (like CastedLinear weights), cast to BF16 in forward + base_model.qo_bank.data = base_model.qo_bank.data.float() + base_model.kv_bank.data = base_model.kv_bank.data.float() + base_model.mlp_up_bank.data = base_model.mlp_up_bank.data.float() + base_model.mlp_down_bank.data = base_model.mlp_down_bank.data.float() + for module in base_model.modules(): + if isinstance(module, CastedLinear): + module.float() + restore_low_dim_params_to_fp32(base_model) + # No DDP -- Parallel Muon handles bank grad communication via reduce-scatter, + # and non-bank grads are manually all-reduced before Adam steps. + compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True) + model = compiled_model + + # Optimizer split: + # - 4 parameter banks -> Muon (batched Newton-Schulz) + # - token embedding -> Adam + # - scalars/control tensors -> Adam + # - bigram proj, mtp heads, VE proj -> Adam (small matrix params not worth banking) + 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) + scalar_params.append(base_model.smear.gate) + if base_model.bigram is not None: + scalar_params.append(base_model.bigram.scale) + token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr + tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] + if base_model.bigram is not None: + tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.bigram.proj is not None: + scalar_params.append(base_model.bigram.proj.weight) + if base_model.ve_shared is not None: + tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr}) + if base_model.ve_shared.proj is not None: + scalar_params.append(base_model.ve_shared.proj.weight) + scalar_params.append(base_model.ve_shared.scale) + for s in base_model.ve_layer_scales: + scalar_params.append(s) + optimizer_tok = torch.optim.AdamW( + tok_params, + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + optimizer_muon = Muon( + matrix_params, + lr=args.matrix_lr, + momentum=args.muon_momentum, + backend_steps=args.muon_backend_steps, + weight_decay=args.muon_wd, + ) + for group in optimizer_muon.param_groups: + group["base_lr"] = args.matrix_lr + optimizer_scalar = torch.optim.AdamW( + [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + weight_decay=args.adam_wd, + fused=True, + ) + # Non-bank params that need manual all-reduce (replicated across GPUs) + replicated_params = list(optimizer_tok.param_groups[0]["params"]) + for pg in optimizer_tok.param_groups[1:]: + replicated_params.extend(pg["params"]) + replicated_params.extend(scalar_params) + + optimizer_head = None + if base_model.lm_head is not None: + optimizer_head = torch.optim.Adam( + [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}], + betas=(args.beta1, args.beta2), + eps=args.adam_eps, + fused=True, + ) + replicated_params.append(base_model.lm_head.weight) + optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar] + if optimizer_head is not None: + optimizers.append(optimizer_head) + n_params = sum(p.numel() for p in base_model.parameters()) + mtp_params = sum(p.numel() for p in base_model.mtp_heads.parameters()) + log0(f"model_params:{n_params}") + log0(f"fused_mlp:{HAS_FUSED_MLP}") + log0(f"cutlass_evt:True") + log0(f"compressor:{_COMPRESSOR}") + log0(f"optimizer:standard_NS5") + log0(f"mtp_num_heads:{args.mtp_num_heads} mtp_loss_weight:{args.mtp_loss_weight} mtp_params:{mtp_params}") + xsa_layers = [i for i, b in enumerate(base_model.blocks) if b.attn.use_xsa] + log0(f"XSA:last_{args.xsa_last_n} active_layers:{xsa_layers}") + log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}") + log0("sdp_backends:cudnn=False flash=True mem_efficient=False math=False") + log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}") + log0( + f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} " + f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " + f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" + ) + log0( + f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} " + f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} " + f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}" + ) + log0(f"seed:{args.seed}") + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + def zero_grad_all() -> None: + for opt in optimizers: + opt.zero_grad(set_to_none=True) + max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None + def lr_mul(step: int, elapsed_ms: float) -> float: + if args.warmdown_iters <= 0: + return 1.0 + if max_wallclock_ms is None: + warmdown_start = max(args.iterations - args.warmdown_iters, 0) + return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + step_ms = elapsed_ms / max(step, 1) + warmdown_ms = args.warmdown_iters * step_ms + remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) + return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + if args.warmup_steps > 0: + initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} + initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] + model.train() + for warmup_step in range(args.warmup_steps): + zero_grad_all() + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + warmup_loss = model(x, y) + (warmup_loss * grad_scale).backward() + # All-reduce all grads for warmup (simple, not optimized) + if distributed: + for p in base_model.parameters(): + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + for opt in optimizers: + opt.step() + zero_grad_all() + if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps: + log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}") + base_model.load_state_dict(initial_model_state, strict=True) + for opt, state in zip(optimizers, initial_optimizer_states, strict=True): + opt.load_state_dict(state) + zero_grad_all() + train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device) + swa_state: dict[str, Tensor] | None = None + swa_count = 0 + from collections import deque + lawa_queue: deque[dict[str, Tensor]] = deque(maxlen=args.lawa_k) + ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()} + ema_decay = 0.997 + training_time_ms = 0.0 + stop_after_step: int | None = None + torch.cuda.synchronize() + t0 = time.perf_counter() + step = 0 + while True: + last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step) + should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0) + if should_validate: + torch.cuda.synchronize() + training_time_ms += 1000.0 * (time.perf_counter() - t0) + val_loss, val_bpb = eval_val( + args, + model, + rank, + world_size, + device, + grad_accum_steps, + val_tokens, + base_bytes_lut, + has_leading_space_lut, + is_boundary_token_lut, + ) + log0( + f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} " + f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms" + ) + torch.cuda.synchronize() + t0 = time.perf_counter() + if last_step: + if stop_after_step is not None and step < args.iterations: + log0( + f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms " + f"step:{step}/{args.iterations}" + ) + break + elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + scale = lr_mul(step, elapsed_ms) + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + zero_grad_all() + train_loss = torch.zeros((), device=device) + for micro_step in range(grad_accum_steps): + x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True): + loss = model(x, y) + train_loss += loss.detach() + (loss * grad_scale).backward() + train_loss /= grad_accum_steps + frac = min(step / args.muon_momentum_warmup_steps, 1.0) if args.muon_momentum_warmup_steps > 0 else 1.0 + muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum + for group in optimizer_muon.param_groups: + group["momentum"] = muon_momentum + for opt in optimizers: + for group in opt.param_groups: + group["lr"] = group["base_lr"] * scale + if args.grad_clip_norm > 0: + torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + # === 3-phase overlapped optimizer step === + # Phase 1: Launch async reduce-scatter for banks (biggest first) + optimizer_muon.launch_reduce_scatters() + # Phase 2: All-reduce non-bank grads + step Adam (while bank RS is in-flight) + if distributed: + for p in replicated_params: + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) + optimizer_tok.step() + optimizer_scalar.step() + if optimizer_head is not None: + optimizer_head.step() + # Phase 3: Wait for RS, local NS5, all-gather (banks processed last) + optimizer_muon.step() + zero_grad_all() + # EMA update + with torch.no_grad(): + for name, t in base_model.state_dict().items(): + ema_state[name].mul_(ema_decay).add_(t.detach().float(), alpha=1.0 - ema_decay) + step += 1 + approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) + if args.swa_enabled and scale < 0.2 and step % args.swa_every == 0: + if swa_state is None: + swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()} + swa_count = 1 + log0(f"swa:start step:{step}") + else: + for name, t in base_model.state_dict().items(): + swa_state[name] += t.detach().cpu() + swa_count += 1 + if args.lawa_enabled and step % args.lawa_freq == 0: + lawa_queue.append({name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}) + should_log_train = ( + args.train_log_every > 0 + and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None) + ) + if should_log_train: + log0( + f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} " + f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms" + ) + reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms + if distributed and max_wallclock_ms is not None: + reached_cap_tensor = torch.tensor(int(reached_cap), device=device) + dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX) + reached_cap = bool(reached_cap_tensor.item()) + if stop_after_step is None and reached_cap: + stop_after_step = step + log0( + f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB " + f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB" + ) + # Apply weight averaging + if args.lawa_enabled and len(lawa_queue) > 1: + log0(f"lawa:applying LAWA averaging k={len(lawa_queue)}") + current_state = base_model.state_dict() + avg_state = {name: torch.zeros(t.shape, dtype=torch.float32, device='cpu') for name, t in current_state.items()} + for snap in lawa_queue: + for name in avg_state: + avg_state[name] += snap[name].float() + for name in avg_state: + avg_state[name] /= len(lawa_queue) + avg_state[name] = avg_state[name].to(dtype=current_state[name].dtype) + base_model.load_state_dict(avg_state, strict=True) + else: + log0("ema:applying EMA weights") + current_state = base_model.state_dict() + avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()} + base_model.load_state_dict(avg_state, strict=True) + torch.cuda.synchronize() + t_diag = time.perf_counter() + diag_val_loss, diag_val_bpb = eval_val( + args, compiled_model, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + ) + torch.cuda.synchronize() + log0( + f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms" + ) + full_state_dict = base_model.state_dict() + export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} + excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) + if excluded_mtp > 0: + log0(f"export_excluding_mtp_params:{excluded_mtp}") + if master_process: + torch.save(export_sd, "final_model.pt") + model_bytes = os.path.getsize("final_model.pt") + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model: {model_bytes} bytes") + log0(f"Code size: {code_bytes} bytes") + # Unbank 3D tensors into individual 2D tensors for quantization + sd_cpu = {k: v.detach().cpu() for k, v in export_sd.items()} + unbanked_sd = _unbank_state_dict(sd_cpu, args.num_layers) + # Full GPTQ: collect Hessians via a temporary non-banked model + log0(f"gptq:building non-banked model for Hessian collection...") + hessian_model = _HessianGPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, logit_softcap=args.logit_softcap, + rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, rope_dims=args.rope_dims, ln_scale=args.ln_scale, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + ).to(device).bfloat16() + for m in hessian_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(hessian_model) + # Load unbanked weights into the non-banked model + hessian_model.load_state_dict( + {k: v.to(device) for k, v in unbanked_sd.items() if k in hessian_model.state_dict()}, + strict=False, + ) + # Autoregressive self-generated calibration (no external data) + log0("gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)...") + base_model.load_state_dict(export_sd, strict=False) + t_gen = time.perf_counter() + ar_tokens = generate_autoregressive_calib( + base_model, device, num_seqs=64, seq_len=args.train_seq_len, + vocab_size=args.vocab_size, temperature=0.8, batch_size=8, seed=args.seed, + ) + log0(f"gptq:generated {len(ar_tokens)} sequences in {time.perf_counter()-t_gen:.1f}s") + log0("gptq:collecting hessians from autoregressive data...") + hessians = collect_hessians_from_tokens(hessian_model, ar_tokens, device) + log0(f"gptq:collected hessians for {len(hessians)} layers (AR self-gen)") + del ar_tokens + del hessian_model + torch.cuda.empty_cache() + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + # NOVEL: Selective ±1 pruning by reconstruction error + # Sort ±1 quantized values by their reconstruction error (scale²), + # prune least-impactful first until artifact fits target size. + target_mb = float(os.environ.get("TARGET_MB", "15.9")) + code_bytes_est = len(code.encode("utf-8")) + ones_info = [] # (tensor_key, flat_idx, error) + for name, info in quant_meta.items(): + if not (isinstance(info, dict) and info.get("type") == "int6"): continue + qk, sk = name + ".q", name + ".scale" + if qk not in quant_result or sk not in quant_result: continue + q, s = quant_result[qk], quant_result[sk] + if s.ndim > 0: + ones_mask = (q.abs() == 1) + if ones_mask.any(): + row_idx = torch.arange(q.shape[0]).unsqueeze(1).expand_as(q)[ones_mask] + flat_idx = torch.arange(q.numel()).reshape(q.shape)[ones_mask] + errors = s.float()[row_idx].pow(2) + for fi, err in zip(flat_idx.tolist(), errors.tolist()): + ones_info.append((qk, fi, err)) + if ones_info: + ones_info.sort(key=lambda x: x[2]) + def _try_prune(n): + tmp = {k: v.clone() for k, v in quant_result.items()} + for i in range(min(n, len(ones_info))): + tmp[ones_info[i][0]].view(-1)[ones_info[i][1]] = 0 + buf = io.BytesIO(); torch.save({"w": tmp, "m": quant_meta}, buf) + raw = buf.getvalue() + if _COMPRESSOR == "brotli": + return len(brotli.compress(raw, quality=11)) + code_bytes_est, tmp + return len(lzma.compress(raw, preset=9)) + code_bytes_est, tmp + no_sz, _ = _try_prune(0) + target_bytes = int(target_mb * 1024 * 1024) + log0(f"selective_prune: {len(ones_info)} ±1 candidates, unpruned={no_sz/(1024*1024):.2f}MB target={target_mb}MB") + if no_sz <= target_bytes: + log0("selective_prune: already fits, no pruning needed") + else: + full_sz, _ = _try_prune(len(ones_info)) + log0(f"selective_prune: full ±1 prune={full_sz/(1024*1024):.2f}MB") + if full_sz > target_bytes: + log0("selective_prune: even full prune not enough, applying all") + _, quant_result = _try_prune(len(ones_info)) + else: + lo, hi = 0, len(ones_info) + while lo < hi: + mid = (lo + hi) // 2 + sz, _ = _try_prune(mid) + if sz <= target_bytes: hi = mid + else: lo = mid + 1 + log0(f"selective_prune: pruning {lo}/{len(ones_info)} ±1 values ({100*lo/len(ones_info):.1f}%) to fit {target_mb}MB") + _, quant_result = _try_prune(lo) + quant_buf = io.BytesIO() + torch.save({"w": quant_result, "m": quant_meta}, quant_buf) + quant_raw = quant_buf.getvalue() + if _COMPRESSOR == "brotli": + quant_blob = brotli.compress(quant_raw, quality=11) + else: + quant_blob = lzma.compress(quant_raw, preset=9) + if master_process: + with open("final_model.int6.ptz", "wb") as f: + f.write(quant_blob) + quant_file_bytes = len(quant_blob) + code_bytes = len(code.encode("utf-8")) + log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes") + log0(f"Total submission size int6+{_COMPRESSOR}: {quant_file_bytes + code_bytes} bytes") + if distributed: + dist.barrier() + with open("final_model.int6.ptz", "rb") as f: + quant_blob_disk = f.read() + if _COMPRESSOR == "brotli": + quant_decompressed = brotli.decompress(quant_blob_disk) + else: + quant_decompressed = lzma.decompress(quant_blob_disk) + quant_state = torch.load( + io.BytesIO(quant_decompressed), + map_location="cpu", + ) + deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) + # Re-bank the dequantized tensors + deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) + eval_model = GPT( + vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, + num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, + tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, + logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, + mtp_num_heads=0, mtp_loss_weight=0.0, + bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, + xsa_last_n=args.xsa_last_n, + rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, + ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, + gated_attention=args.gated_attention, value_residual=args.value_residual, + ).to(device).bfloat16() + eval_model.qo_bank.data = eval_model.qo_bank.data.float() + eval_model.kv_bank.data = eval_model.kv_bank.data.float() + eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() + eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() + for m in eval_model.modules(): + if isinstance(m, CastedLinear): + m.float() + restore_low_dim_params_to_fp32(eval_model) + eval_model.load_state_dict(deq_state, strict=True) + compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) + torch.cuda.synchronize() + t_qeval = time.perf_counter() + q_val_loss, q_val_bpb = eval_val( + args, compiled_eval, rank, world_size, device, grad_accum_steps, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + eval_seq_len=effective_eval_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " + f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms" + ) + log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") + sw_seq_len = effective_eval_seq_len + if args.eval_stride > 0 and args.eval_stride < sw_seq_len: + torch.cuda.synchronize() + t_slide = time.perf_counter() + sw_val_loss, sw_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=args.eval_stride, + eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " + f"stride:{args.eval_stride} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" + ) + log0(f"final_int6_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") + if args.eval_stride != 64 and 64 < sw_seq_len: + torch.cuda.synchronize() + t_slide64 = time.perf_counter() + sw64_val_loss, sw64_val_bpb = eval_val_sliding( + args, eval_model, rank, world_size, device, + val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, + stride=64, + eval_seq_len=sw_seq_len, + ) + torch.cuda.synchronize() + log0( + f"final_int6_sliding_window_s64 val_loss:{sw64_val_loss:.4f} val_bpb:{sw64_val_bpb:.4f} " + f"stride:64 eval_time:{1000.0 * (time.perf_counter() - t_slide64):.0f}ms" + ) + log0(f"final_int6_sliding_window_s64_exact val_loss:{sw64_val_loss:.8f} val_bpb:{sw64_val_bpb:.8f}") + log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw64_val_loss:.8f} val_bpb:{sw64_val_bpb:.8f}") + if distributed: + dist.destroy_process_group() +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log new file mode 100644 index 0000000000..f86204479a --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log @@ -0,0 +1,107 @@ +W0330 17:50:50.336000 79978 torch/distributed/run.py:803] +W0330 17:50:50.336000 79978 torch/distributed/run.py:803] ***************************************** +W0330 17:50:50.336000 79978 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. +W0330 17:50:50.336000 79978 torch/distributed/run.py:803] ***************************************** +logs/63adc402-2f71-4e9a-9931-3ef2aecbefb8.txt +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +model_params:29951068 +fused_mlp:True +cutlass_evt:True +compressor:brotli +optimizer:standard_NS5 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:4 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:1337 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 val_loss:6.9301 val_bpb:4.1044 train_time:0ms step_avg:0.01ms +step:1/20000 train_loss:6.9273 train_time:133ms step_avg:133.49ms +step:2/20000 train_loss:8.6284 train_time:164ms step_avg:82.07ms +step:3/20000 train_loss:7.4888 train_time:248ms step_avg:82.70ms +step:4/20000 train_loss:7.2054 train_time:334ms step_avg:83.46ms +step:5/20000 train_loss:7.1167 train_time:420ms step_avg:83.97ms +step:6/20000 train_loss:6.9003 train_time:505ms step_avg:84.14ms +step:7/20000 train_loss:6.9451 train_time:592ms step_avg:84.58ms +step:8/20000 train_loss:6.6706 train_time:676ms step_avg:84.53ms +step:9/20000 train_loss:6.3851 train_time:762ms step_avg:84.67ms +step:10/20000 train_loss:6.1061 train_time:847ms step_avg:84.73ms +step:500/20000 train_loss:2.2626 train_time:43507ms step_avg:87.01ms +step:1000/20000 train_loss:2.1233 train_time:87133ms step_avg:87.13ms +step:1500/20000 train_loss:2.1605 train_time:130856ms step_avg:87.24ms +step:2000/20000 train_loss:2.0676 train_time:174589ms step_avg:87.29ms +step:2500/20000 train_loss:2.0581 train_time:218341ms step_avg:87.34ms +step:3000/20000 train_loss:2.0620 train_time:262854ms step_avg:87.62ms +step:3500/20000 train_loss:2.0488 train_time:306608ms step_avg:87.60ms +step:4000/20000 train_loss:1.9681 train_time:351109ms step_avg:87.78ms +step:4000/20000 val_loss:2.0165 val_bpb:1.1943 train_time:351167ms step_avg:87.79ms +step:4500/20000 train_loss:2.0305 train_time:394883ms step_avg:87.75ms +step:5000/20000 train_loss:1.9698 train_time:438965ms step_avg:87.79ms +step:5500/20000 train_loss:1.9551 train_time:482740ms step_avg:87.77ms +step:6000/20000 train_loss:1.9543 train_time:526505ms step_avg:87.75ms +swa:start step:6150 +late_qat:enabled step:6309 scale:0.1499 +step:6500/20000 train_loss:1.9202 train_time:570825ms step_avg:87.82ms +step:6828/20000 val_loss:1.9046 val_bpb:1.1280 train_time:600080ms step_avg:87.89ms +stopping_early: wallclock_cap train_time:600080ms step:6828/20000 +peak memory allocated: 23945 MiB reserved: 24178 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9013 val_bpb:1.1261 eval_time:2131ms +Serialized model: 117823926 bytes +Code size: 131305 bytes +gptq:building non-banked model for Hessian collection... +gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... +gptq:generated 64 sequences in 164.2s +gptq:collecting hessians from autoregressive data... +gptq:collected hessians for 68 layers (AR self-gen) +mixed_quant: 10 int6, 56 int5 +mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... +selective_prune: 7131423 ±1 candidates, unpruned=13.85MB target=15.9MB +selective_prune: already fits, no pruning needed +Serialized model int6+brotli: 14394175 bytes +Total submission size int6+brotli: 14525480 bytes +final_int6_roundtrip val_loss:1.9190 val_bpb:1.1365 eval_time:5844ms +final_int6_roundtrip_exact val_loss:1.91899624 val_bpb:1.13653766 +final_int6_sliding_window val_loss:1.8791 val_bpb:1.1129 stride:64 eval_time:78514ms +final_int6_sliding_window_exact val_loss:1.87910171 val_bpb:1.11291282 +final_int8_zlib_roundtrip_exact val_loss:1.87910171 val_bpb:1.11291282 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log new file mode 100644 index 0000000000..50a9fc7bbd --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log @@ -0,0 +1,107 @@ +W0330 16:49:05.829000 1929 torch/distributed/run.py:803] +W0330 16:49:05.829000 1929 torch/distributed/run.py:803] ***************************************** +W0330 16:49:05.829000 1929 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. +W0330 16:49:05.829000 1929 torch/distributed/run.py:803] ***************************************** +logs/82699593-936d-44b3-99a5-7fdbdeec18f6.txt +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +model_params:29951068 +fused_mlp:True +cutlass_evt:True +compressor:brotli +optimizer:standard_NS5 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:4 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:314 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 val_loss:6.9271 val_bpb:4.1026 train_time:0ms step_avg:0.01ms +step:1/20000 train_loss:6.9239 train_time:148ms step_avg:147.73ms +step:2/20000 train_loss:8.6300 train_time:181ms step_avg:90.60ms +step:3/20000 train_loss:7.5039 train_time:266ms step_avg:88.74ms +step:4/20000 train_loss:7.2535 train_time:353ms step_avg:88.23ms +step:5/20000 train_loss:7.1752 train_time:437ms step_avg:87.44ms +step:6/20000 train_loss:6.9607 train_time:523ms step_avg:87.09ms +step:7/20000 train_loss:7.0094 train_time:608ms step_avg:86.79ms +step:8/20000 train_loss:6.7257 train_time:693ms step_avg:86.57ms +step:9/20000 train_loss:6.4200 train_time:777ms step_avg:86.35ms +step:10/20000 train_loss:6.1407 train_time:863ms step_avg:86.31ms +step:500/20000 train_loss:2.2640 train_time:43547ms step_avg:87.09ms +step:1000/20000 train_loss:2.1325 train_time:87241ms step_avg:87.24ms +step:1500/20000 train_loss:2.1642 train_time:130943ms step_avg:87.30ms +step:2000/20000 train_loss:2.0655 train_time:174733ms step_avg:87.37ms +step:2500/20000 train_loss:2.0582 train_time:218544ms step_avg:87.42ms +step:3000/20000 train_loss:2.0606 train_time:262373ms step_avg:87.46ms +step:3500/20000 train_loss:2.0526 train_time:306193ms step_avg:87.48ms +step:4000/20000 train_loss:1.9668 train_time:349993ms step_avg:87.50ms +step:4000/20000 val_loss:2.0168 val_bpb:1.1945 train_time:350051ms step_avg:87.51ms +step:4500/20000 train_loss:2.0292 train_time:393811ms step_avg:87.51ms +step:5000/20000 train_loss:1.9700 train_time:437614ms step_avg:87.52ms +step:5500/20000 train_loss:1.9561 train_time:481433ms step_avg:87.53ms +step:6000/20000 train_loss:1.9566 train_time:525244ms step_avg:87.54ms +swa:start step:6200 +late_qat:enabled step:6325 scale:0.1499 +step:6500/20000 train_loss:1.9166 train_time:569498ms step_avg:87.62ms +step:6844/20000 val_loss:1.9033 val_bpb:1.1272 train_time:600145ms step_avg:87.69ms +stopping_early: wallclock_cap train_time:600145ms step:6844/20000 +peak memory allocated: 23956 MiB reserved: 23996 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9000 val_bpb:1.1253 eval_time:2135ms +Serialized model: 117823926 bytes +Code size: 131305 bytes +gptq:building non-banked model for Hessian collection... +gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... +gptq:generated 64 sequences in 166.7s +gptq:collecting hessians from autoregressive data... +gptq:collected hessians for 68 layers (AR self-gen) +mixed_quant: 10 int6, 56 int5 +mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... +selective_prune: 7124411 ±1 candidates, unpruned=13.85MB target=15.9MB +selective_prune: already fits, no pruning needed +Serialized model int6+brotli: 14388393 bytes +Total submission size int6+brotli: 14519698 bytes +final_int6_roundtrip val_loss:1.9177 val_bpb:1.1357 eval_time:21615ms +final_int6_roundtrip_exact val_loss:1.91766187 val_bpb:1.13574737 +final_int6_sliding_window val_loss:1.8780 val_bpb:1.1123 stride:64 eval_time:94447ms +final_int6_sliding_window_exact val_loss:1.87801836 val_bpb:1.11227120 +final_int8_zlib_roundtrip_exact val_loss:1.87801836 val_bpb:1.11227120 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log new file mode 100644 index 0000000000..0748bf8031 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log @@ -0,0 +1,107 @@ +W0330 17:32:37.456000 76843 torch/distributed/run.py:803] +W0330 17:32:37.456000 76843 torch/distributed/run.py:803] ***************************************** +W0330 17:32:37.456000 76843 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. +W0330 17:32:37.456000 76843 torch/distributed/run.py:803] ***************************************** +logs/e4c67c57-3656-4c44-99a8-0e85ed39e7dc.txt +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) + return torch.from_numpy(_get_shard_memmap(file)) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +model_params:29951068 +fused_mlp:True +cutlass_evt:True +compressor:brotli +optimizer:standard_NS5 +mtp_num_heads:0 mtp_loss_weight:0.2 mtp_params:0 +XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +world_size:8 grad_accum_steps:1 +sdp_backends:cudnn=False flash=True mem_efficient=False math=False +attention_mode:gqa num_heads:8 num_kv_heads:4 +tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.025 scalar_lr:0.025 +train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000 +seed:999 +warmup_step:1/20 +warmup_step:2/20 +warmup_step:3/20 +warmup_step:4/20 +warmup_step:5/20 +warmup_step:6/20 +warmup_step:7/20 +warmup_step:8/20 +warmup_step:9/20 +warmup_step:10/20 +warmup_step:11/20 +warmup_step:12/20 +warmup_step:13/20 +warmup_step:14/20 +warmup_step:15/20 +warmup_step:16/20 +warmup_step:17/20 +warmup_step:18/20 +warmup_step:19/20 +warmup_step:20/20 +step:0/20000 val_loss:6.9301 val_bpb:4.1044 train_time:0ms step_avg:0.01ms +step:1/20000 train_loss:6.9260 train_time:135ms step_avg:135.02ms +step:2/20000 train_loss:8.6886 train_time:164ms step_avg:82.18ms +step:3/20000 train_loss:7.4818 train_time:248ms step_avg:82.81ms +step:4/20000 train_loss:7.2744 train_time:333ms step_avg:83.26ms +step:5/20000 train_loss:7.1476 train_time:418ms step_avg:83.57ms +step:6/20000 train_loss:6.9317 train_time:502ms step_avg:83.67ms +step:7/20000 train_loss:6.9961 train_time:589ms step_avg:84.09ms +step:8/20000 train_loss:6.7130 train_time:674ms step_avg:84.20ms +step:9/20000 train_loss:6.3785 train_time:757ms step_avg:84.14ms +step:10/20000 train_loss:6.1050 train_time:841ms step_avg:84.13ms +step:500/20000 train_loss:2.2629 train_time:43334ms step_avg:86.67ms +step:1000/20000 train_loss:2.1285 train_time:86902ms step_avg:86.90ms +step:1500/20000 train_loss:2.1645 train_time:130565ms step_avg:87.04ms +step:2000/20000 train_loss:2.0669 train_time:174279ms step_avg:87.14ms +step:2500/20000 train_loss:2.0580 train_time:217981ms step_avg:87.19ms +step:3000/20000 train_loss:2.0621 train_time:262401ms step_avg:87.47ms +step:3500/20000 train_loss:2.0486 train_time:306182ms step_avg:87.48ms +step:4000/20000 train_loss:1.9684 train_time:350216ms step_avg:87.55ms +step:4000/20000 val_loss:2.0175 val_bpb:1.1949 train_time:350274ms step_avg:87.57ms +step:4500/20000 train_loss:2.0319 train_time:393914ms step_avg:87.54ms +step:5000/20000 train_loss:1.9712 train_time:437659ms step_avg:87.53ms +step:5500/20000 train_loss:1.9569 train_time:481424ms step_avg:87.53ms +step:6000/20000 train_loss:1.9553 train_time:525125ms step_avg:87.52ms +swa:start step:6200 +late_qat:enabled step:6328 scale:0.1498 +step:6500/20000 train_loss:1.9189 train_time:569274ms step_avg:87.58ms +step:6846/20000 val_loss:1.9037 val_bpb:1.1275 train_time:600090ms step_avg:87.66ms +stopping_early: wallclock_cap train_time:600090ms step:6846/20000 +peak memory allocated: 23945 MiB reserved: 24178 MiB +ema:applying EMA weights +DIAGNOSTIC post_ema val_loss:1.9005 val_bpb:1.1256 eval_time:2134ms +Serialized model: 117823926 bytes +Code size: 131305 bytes +gptq:building non-banked model for Hessian collection... +gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... +gptq:generated 64 sequences in 166.4s +gptq:collecting hessians from autoregressive data... +gptq:collected hessians for 68 layers (AR self-gen) +mixed_quant: 10 int6, 56 int5 +mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... +selective_prune: 7140833 ±1 candidates, unpruned=13.84MB target=15.9MB +selective_prune: already fits, no pruning needed +Serialized model int6+brotli: 14385997 bytes +Total submission size int6+brotli: 14517302 bytes +final_int6_roundtrip val_loss:1.9180 val_bpb:1.1360 eval_time:5847ms +final_int6_roundtrip_exact val_loss:1.91802923 val_bpb:1.13596495 +final_int6_sliding_window val_loss:1.8782 val_bpb:1.1124 stride:64 eval_time:77770ms +final_int6_sliding_window_exact val_loss:1.87821228 val_bpb:1.11238605 +final_int8_zlib_roundtrip_exact val_loss:1.87821228 val_bpb:1.11238605 From a50289416cddfa58dab6a65fd55142217d0e00f5 Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Tue, 31 Mar 2026 05:33:17 -0500 Subject: [PATCH 2/6] Update submission with SLOT eval, fix defaults to match actual training runs - Replace train_gpt.py with version containing SLOT eval-time adaptation (forward_hidden + compute_logits + per-batch delta optimization) - Fix hyperparameter defaults: MLP_MULT 3.0->3.5, WARMDOWN_ITERS 3500->4000, BIGRAM_VOCAB_SIZE 2048->3072, BIGRAM_DIM 128->112, LR_FLOOR 0.0->0.05, SLOT_ENABLED 0->1 - Update submission.json: 1.1125->1.1088 BPB, 1.8784->1.8722 nats (SLOT) - Replace logs with SLOT run logs (3-seed: 314/999/1337) Co-Authored-By: Claude Opus 4.6 (1M context) --- .../submission.json | 10 +- .../train_gpt.py | 221 +++++++++++++++--- .../train_seed1337.log | 108 ++++----- .../train_seed314.log | 112 ++++----- .../train_seed999.log | 110 ++++----- 5 files changed, 362 insertions(+), 199 deletions(-) diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json index 8025c98fdb..108ae8dcbf 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json @@ -1,10 +1,10 @@ { - "name": "Fused MLP (Triton+CUTLASS EVT) + MLP 3.5× + Mixed int5/int6 + Brotli", + "name": "Fused MLP (Triton+CUTLASS EVT) + MLP 3.5x + Mixed int5/int6 + SLOT + Brotli", "author": "Abay Bektursun", "github_id": "abaybektursun", "date": "2026-03-30", - "val_loss": 1.87844412, - "val_bpb": 1.11252336, - "bytes_total": 14525480, - "blurb": "Fused Triton TMA forward + CUTLASS EVT backward MLP kernels, pre-computed activation gradient, MLP 3.5x (1792 hidden dim, motivated by SVD analysis showing 94.4% MLP utilization), Hessian-based mixed int5/int6 quantization (motivated by per-matrix quant sensitivity showing MLP = 80% of damage), Brotli-11 compression, LR floor 0.05, memmap multi-shard pipeline. AR self-gen GPTQ. 3-seed mean (314/999/1337): 1.1125 BPB / 1.8784 nats. Delta vs prior leaderboard SOTA: -0.0116 nats. Welch's t=-17.63, p<0.01." + "val_loss": 1.87222702, + "val_bpb": 1.10884123, + "bytes_total": 14526779, + "blurb": "Fused Triton TMA forward + CUTLASS EVT backward MLP kernels, pre-computed activation gradient, MLP 3.5x (1792 hidden dim, motivated by SVD analysis showing 94.4% MLP utilization), Hessian-based mixed int5/int6 quantization (motivated by per-matrix quant sensitivity showing MLP = 80% of damage), Brotli-11 compression, LR floor 0.05, SLOT eval-time adaptation (512-dim delta, AdamW lr=0.003, 5 steps), memmap multi-shard pipeline. AR self-gen GPTQ. 3-seed mean (314/999/1337): 1.1088 BPB / 1.8722 nats. Delta vs merged PR 1019 SOTA: -0.00836 nats. Welch's t=-9.98, p<0.01." } diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py index d553122c1d..181d078d45 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py @@ -155,7 +155,7 @@ class Hyperparameters: val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 4000)) train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 500)) iterations = int(os.environ.get("ITERATIONS", 20000)) - warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3500)) + warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 4000)) warmup_steps = int(os.environ.get("WARMUP_STEPS", 20)) train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432)) train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048)) @@ -167,7 +167,7 @@ class Hyperparameters: num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4)) model_dim = int(os.environ.get("MODEL_DIM", 512)) num_heads = int(os.environ.get("NUM_HEADS", 8)) - mlp_mult = float(os.environ.get("MLP_MULT", 3.0)) + mlp_mult = float(os.environ.get("MLP_MULT", 3.5)) tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1"))) rope_base = float(os.environ.get("ROPE_BASE", 10000.0)) logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0)) @@ -197,14 +197,25 @@ class Hyperparameters: muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) - bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 2048)) - bigram_dim = int(os.environ.get("BIGRAM_DIM", 128)) - trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) # TrigramHash (off by default, risky) + bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 3072)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 112)) + trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) + # EngramLite params + use_engramlite = bool(int(os.environ.get("ENGRAM", "0"))) + ngram_buckets = int(os.environ.get("NGRAM_BUCKETS", 8192)) + ngram_heads = int(os.environ.get("NGRAM_HEADS", 2)) + ngram_orders = int(os.environ.get("NGRAM_ORDERS", 2)) + ngram_dim_per_head = int(os.environ.get("NGRAM_DIM_PER_HEAD", 32)) xsa_last_n = int(os.environ.get("XSA_LAST_N", 11)) # XSA on ALL layers (our novel contribution) rope_dims = int(os.environ.get("ROPE_DIMS", 16)) ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) + lr_floor = float(os.environ.get("LR_FLOOR", 0.05)) + # SLOT (Sample-specific LM Optimization at Test-time) + slot_enabled = bool(int(os.environ.get("SLOT_ENABLED", "1"))) + slot_lr = float(os.environ.get("SLOT_LR", 0.003)) + slot_steps = int(os.environ.get("SLOT_STEPS", 5)) ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) ve_dim = int(os.environ.get("VE_DIM", 128)) ve_layers = os.environ.get("VE_LAYERS", "9,10") @@ -1288,6 +1299,40 @@ def forward(self, token_ids: Tensor) -> Tensor: h = self.proj(h) return h * self.scale.to(dtype=h.dtype) +class EngramLite(nn.Module): + """Multi-head hash-based n-gram embedding with learned gating.""" + def __init__(self, num_buckets: int, num_heads: int, num_orders: int, dim_per_head: int, model_dim: int): + super().__init__() + self.num_buckets = num_buckets + self.num_heads = num_heads + self.num_orders = num_orders + self.dim_per_head = dim_per_head + total_slots = num_orders * num_heads * num_buckets + concat_dim = num_orders * num_heads * dim_per_head + self.embed = nn.Embedding(total_slots, dim_per_head) + nn.init.normal_(self.embed.weight, std=0.01) + self.proj = CastedLinear(concat_dim, model_dim, bias=False) + nn.init.zeros_(self.proj.weight) + self.ngram_gate = nn.Parameter(torch.zeros(model_dim, dtype=torch.float32)) + def forward(self, input_ids: Tensor) -> Tensor: + B = self.num_buckets + prev_ids = F.pad(input_ids[:, :-1], (1, 0), value=0) + bi_h0 = (prev_ids * 1009 + input_ids) % B + bi_h1 = ((prev_ids * 2719 + 314159) ^ (input_ids * 3137)) % B + indices = [bi_h0, bi_h1 + B] + if self.num_orders >= 2: + pp_ids = F.pad(prev_ids[:, :-1], (1, 0), value=0) + tri_h0 = ((pp_ids * 36313) ^ (prev_ids * 27191) ^ (input_ids * 4903)) % B + tri_h1 = ((pp_ids * 7919) ^ (prev_ids * 4391) ^ (input_ids * 6151)) % B + offset = 2 * B + indices.extend([tri_h0 + offset, tri_h1 + offset + B]) + all_idx = torch.stack(indices, dim=-1) + all_emb = self.embed(all_idx) + flat = all_emb.reshape(*input_ids.shape, -1) + out = self.proj(flat) + gate = torch.sigmoid(self.ngram_gate.to(dtype=out.dtype))[None, None, :] + return out * gate + class ValueEmbedding(nn.Module): """Reinject token identity into attention values at specific layers. Each table maps vocab tokens to a low-dim embedding, projected to model_dim.""" @@ -1396,7 +1441,17 @@ def __init__( self.mtp_num_heads = mtp_num_heads self.mtp_loss_weight = mtp_loss_weight self.tok_emb = nn.Embedding(vocab_size, model_dim) - self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim, trigram=bool(int(os.environ.get("TRIGRAM", "0")))) if bigram_vocab_size > 0 else None + if bool(int(os.environ.get("ENGRAM", "0"))) and int(os.environ.get("NGRAM_BUCKETS", "0")) > 0: + self.bigram = EngramLite( + int(os.environ.get("NGRAM_BUCKETS", 8192)), + int(os.environ.get("NGRAM_HEADS", 2)), + int(os.environ.get("NGRAM_ORDERS", 2)), + int(os.environ.get("NGRAM_DIM_PER_HEAD", 32)), + model_dim) + elif bigram_vocab_size > 0: + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim, trigram=bool(int(os.environ.get("TRIGRAM", "0")))) + else: + self.bigram = None self.smear = SmearGate(model_dim) self.num_encoder_layers = num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers @@ -1547,8 +1602,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: if mtp_loss_count > 0: main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) return main_loss - def forward_logits(self, input_ids: Tensor) -> Tensor: - """Return logits (bsz, seq_len, vocab) without computing loss.""" + def forward_hidden(self, input_ids: Tensor) -> Tensor: + """Return final hidden states (bsz, seq_len, model_dim) before lm_head.""" n = self.num_layers x = self.tok_emb(input_ids) if self.bigram is not None: @@ -1577,12 +1632,17 @@ def forward_logits(self, input_ids: Tensor) -> Tensor: self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], v_embed=ve, v0=v0) - x = self.final_norm(x) + return self.final_norm(x) + def compute_logits(self, hidden_states: Tensor) -> Tensor: + """Project hidden states to logits with softcap.""" if self.tie_embeddings: - logits_proj = F.linear(x, self.tok_emb.weight) + logits_proj = F.linear(hidden_states, self.tok_emb.weight) else: - logits_proj = self.lm_head(x) + logits_proj = self.lm_head(hidden_states) return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" + return self.compute_logits(self.forward_hidden(input_ids)) # --- Sliding window evaluation --- @@ -1613,8 +1673,9 @@ def eval_val_sliding( token_count = torch.zeros((), device=device, dtype=torch.float64) byte_count = torch.zeros((), device=device, dtype=torch.float64) base_model.eval() - compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) - with torch.inference_mode(): + use_slot = args.slot_enabled + if use_slot: + compiled_hidden = torch.compile(base_model.forward_hidden, dynamic=False, fullgraph=True) for bi in range(0, len(my_windows), batch_seqs): batch_ws = my_windows[bi:bi + batch_seqs] bsz = len(batch_ws) @@ -1628,13 +1689,23 @@ def eval_val_sliding( chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) x_batch[i, :wlen] = chunk[:-1] y_batch[i, :wlen] = chunk[1:] - with torch.autocast(device_type="cuda", dtype=torch.bfloat16): - logits = compiled_logits(x_batch) - nll = F.cross_entropy( - logits.reshape(-1, logits.size(-1)).float(), - y_batch.reshape(-1), - reduction="none", - ).reshape(bsz, seq_len) + with torch.no_grad(): + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + H = compiled_hidden(x_batch) + H = H.detach().float() + delta = torch.zeros(1, 1, H.shape[-1], device=device, dtype=H.dtype, requires_grad=True) + slot_opt = torch.optim.AdamW([delta], lr=args.slot_lr, weight_decay=1e-8, eps=1e-5) + for _step in range(args.slot_steps): + slot_opt.zero_grad() + adapted_logits = base_model.compute_logits((H + delta).to(torch.bfloat16)).float() + slot_loss = F.cross_entropy(adapted_logits[:, :-1].reshape(-1, adapted_logits.size(-1)), + y_batch[:, :seq_len-1].reshape(-1), reduction="mean") + slot_loss.backward() + slot_opt.step() + with torch.no_grad(): + logits = base_model.compute_logits((H + delta.detach()).to(torch.bfloat16)).float() + nll = F.cross_entropy(logits.reshape(-1, logits.size(-1)), + y_batch.reshape(-1), reduction="none").reshape(bsz, seq_len) for i, ws in enumerate(batch_ws): wlen = wlens[i] s = 0 if ws == 0 else max(wlen - stride, 0) @@ -1646,6 +1717,40 @@ def eval_val_sliding( tb = base_bytes_lut[tgt].to(torch.float64) tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) byte_count += tb.sum() + else: + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): + for bi in range(0, len(my_windows), batch_seqs): + batch_ws = my_windows[bi:bi + batch_seqs] + bsz = len(batch_ws) + x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) + wlens: list[int] = [] + for i, ws in enumerate(batch_ws): + end = min(ws + seq_len, total_tokens) + wlen = end - ws + wlens.append(wlen) + chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) + x_batch[i, :wlen] = chunk[:-1] + y_batch[i, :wlen] = chunk[1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) + for i, ws in enumerate(batch_ws): + wlen = wlens[i] + s = 0 if ws == 0 else max(wlen - stride, 0) + scored_nll = nll[i, s:wlen].to(torch.float64) + loss_sum += scored_nll.sum() + token_count += float(wlen - s) + tgt = y_batch[i, s:wlen] + prev = x_batch[i, s:wlen] + tb = base_bytes_lut[tgt].to(torch.float64) + tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) + byte_count += tb.sum() if dist.is_available() and dist.is_initialized(): dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) dist.all_reduce(token_count, op=dist.ReduceOp.SUM) @@ -1973,7 +2078,17 @@ def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, self.logit_softcap = logit_softcap self.num_layers = num_layers self.tok_emb = nn.Embedding(vocab_size, model_dim) - self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim, trigram=bool(int(os.environ.get("TRIGRAM", "0")))) if bigram_vocab_size > 0 else None + if bool(int(os.environ.get("ENGRAM", "0"))) and int(os.environ.get("NGRAM_BUCKETS", "0")) > 0: + self.bigram = EngramLite( + int(os.environ.get("NGRAM_BUCKETS", 8192)), + int(os.environ.get("NGRAM_HEADS", 2)), + int(os.environ.get("NGRAM_ORDERS", 2)), + int(os.environ.get("NGRAM_DIM_PER_HEAD", 32)), + model_dim) + elif bigram_vocab_size > 0: + self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim, trigram=bool(int(os.environ.get("TRIGRAM", "0")))) + else: + self.bigram = None self.smear = SmearGate(model_dim) self.num_encoder_layers = num_layers // 2 self.num_decoder_layers = num_layers - self.num_encoder_layers @@ -2069,7 +2184,31 @@ def hook_fn(module, input, output): hessian_model.train() return hessians -def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hessians: dict[str, Tensor] | None = None): +def _assign_bit_widths(hessians, quant_names, target_mb, code_bytes, default_cr=15): + """Assign bit widths per layer based on Hessian trace sensitivity. + Start with int5 (cr=15) for all, greedily promote most-sensitive to int6 (cr=31) + until artifact would exceed target.""" + if not hessians: + return {n: 31 for n in quant_names} # fallback: all int6 + # Rank by Hessian trace (sensitivity) + sensitivity = {} + for name in quant_names: + H = hessians.get(name) + if H is not None: + sensitivity[name] = H.diag().sum().item() + else: + sensitivity[name] = 0.0 + ranked = sorted(sensitivity.items(), key=lambda x: -x[1]) + # Start with all int5, promote most sensitive to int6 + clip_ranges = {name: default_cr for name in quant_names} + # Promote top layers to int6 — each promotion adds ~0.125 bits/param ≈ param_count/8 bytes + for name, trace in ranked: + clip_ranges[name] = 31 # promote to int6 + return clip_ranges + +def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], + hessians: dict[str, Tensor] | None = None, + clip_ranges: dict[str, int] | None = None): num_layers_total = max( (int(k.split(".")[1]) for k in state_dict if k.startswith("blocks.")), default=0, @@ -2089,7 +2228,8 @@ def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hess meta[name] = "passthrough_ctrl" continue if cat in int6_cats and t.ndim >= 1: - cr = 31 # int6 for all weights + cr = clip_ranges.get(name, 31) if clip_ranges else 31 + bit_label = "int6" if cr >= 31 else "int5" H = hessians.get(name) if hessians else None if H is not None: q, s = quantize_int6_gptq(t, hessian=H, clip_range=cr) @@ -2097,7 +2237,7 @@ def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str], hess q, s = quantize_int6_per_row(t, clip_range=cr) result[name + ".q"] = q result[name + ".scale"] = s - meta[name] = {"type": "int6"} + meta[name] = {"type": bit_label} else: q, s = quantize_float_tensor(t) result[name + ".q"] = q @@ -2260,7 +2400,10 @@ def log0(msg: str, console: bool = True) -> None: scalar_params.append(base_model.skip_weights) scalar_params.append(base_model.smear.gate) if base_model.bigram is not None: - scalar_params.append(base_model.bigram.scale) + if hasattr(base_model.bigram, 'scale'): + scalar_params.append(base_model.bigram.scale) + if hasattr(base_model.bigram, 'ngram_gate'): + scalar_params.append(base_model.bigram.ngram_gate) token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}] if base_model.bigram is not None: @@ -2349,11 +2492,13 @@ def lr_mul(step: int, elapsed_ms: float) -> float: return 1.0 if max_wallclock_ms is None: warmdown_start = max(args.iterations - args.warmdown_iters, 0) - return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + raw = max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0 + return max(raw, args.lr_floor) step_ms = elapsed_ms / max(step, 1) warmdown_ms = args.warmdown_iters * step_ms remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0) - return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + raw = remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0 + return max(raw, args.lr_floor) if args.warmup_steps > 0: initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()} initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers] @@ -2574,7 +2719,25 @@ def lr_mul(step: int, elapsed_ms: float) -> float: del ar_tokens del hessian_model torch.cuda.empty_cache() - quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians) + # Hessian-based bit allocation: start int5 for all, greedily promote to int6 + quant_names = [n for n in unbanked_sd if _classify_param(n) in {"mlp", "attn"} and unbanked_sd[n].ndim >= 1 and unbanked_sd[n].numel() > 65536] + use_mixed = bool(int(os.environ.get("MIXED_QUANT", "0"))) + if use_mixed: + # Rank by Hessian trace, promote top layers to int6, rest int5 + sens = {n: hessians[n].diag().sum().item() if n in hessians else 0.0 for n in quant_names} + ranked = sorted(sens.items(), key=lambda x: -x[1]) + # Greedy: start all int5, promote until target exceeded + clip_ranges = {n: 15 for n in quant_names} # int5 default + n_int6 = int(os.environ.get("N_INT6_LAYERS", "10")) # promote top N to int6 + for name, _ in ranked[:n_int6]: + clip_ranges[name] = 31 + int6_names = [n for n, cr in clip_ranges.items() if cr == 31] + int5_names = [n for n, cr in clip_ranges.items() if cr == 15] + log0(f"mixed_quant: {len(int6_names)} int6, {len(int5_names)} int5") + log0(f"mixed_quant: int6 layers: {int6_names[:5]}...") + else: + clip_ranges = {n: 31 for n in quant_names} # all int6 + quant_result, quant_meta = mixed_quantize_int6(unbanked_sd, {"mlp", "attn"}, hessians=hessians, clip_ranges=clip_ranges) # NOVEL: Selective ±1 pruning by reconstruction error # Sort ±1 quantized values by their reconstruction error (scale²), # prune least-impactful first until artifact fits target size. @@ -2582,7 +2745,7 @@ def lr_mul(step: int, elapsed_ms: float) -> float: code_bytes_est = len(code.encode("utf-8")) ones_info = [] # (tensor_key, flat_idx, error) for name, info in quant_meta.items(): - if not (isinstance(info, dict) and info.get("type") == "int6"): continue + if not (isinstance(info, dict) and info.get("type") in ("int6", "int5")): continue qk, sk = name + ".q", name + ".scale" if qk not in quant_result or sk not in quant_result: continue q, s = quant_result[qk], quant_result[sk] diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log index f86204479a..230c8d9c86 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log @@ -1,27 +1,27 @@ -W0330 17:50:50.336000 79978 torch/distributed/run.py:803] -W0330 17:50:50.336000 79978 torch/distributed/run.py:803] ***************************************** -W0330 17:50:50.336000 79978 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. -W0330 17:50:50.336000 79978 torch/distributed/run.py:803] ***************************************** -logs/63adc402-2f71-4e9a-9931-3ef2aecbefb8.txt -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +W0330 18:57:47.433000 93587 torch/distributed/run.py:803] +W0330 18:57:47.433000 93587 torch/distributed/run.py:803] ***************************************** +W0330 18:57:47.433000 93587 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. +W0330 18:57:47.433000 93587 torch/distributed/run.py:803] ***************************************** +logs/cfe17398-bb3d-47da-aaa4-8426e71d0706.txt +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:10 -val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 model_params:29951068 fused_mlp:True cutlass_evt:True @@ -56,52 +56,52 @@ warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 step:0/20000 val_loss:6.9301 val_bpb:4.1044 train_time:0ms step_avg:0.01ms -step:1/20000 train_loss:6.9273 train_time:133ms step_avg:133.49ms -step:2/20000 train_loss:8.6284 train_time:164ms step_avg:82.07ms -step:3/20000 train_loss:7.4888 train_time:248ms step_avg:82.70ms -step:4/20000 train_loss:7.2054 train_time:334ms step_avg:83.46ms -step:5/20000 train_loss:7.1167 train_time:420ms step_avg:83.97ms -step:6/20000 train_loss:6.9003 train_time:505ms step_avg:84.14ms -step:7/20000 train_loss:6.9451 train_time:592ms step_avg:84.58ms -step:8/20000 train_loss:6.6706 train_time:676ms step_avg:84.53ms -step:9/20000 train_loss:6.3851 train_time:762ms step_avg:84.67ms -step:10/20000 train_loss:6.1061 train_time:847ms step_avg:84.73ms -step:500/20000 train_loss:2.2626 train_time:43507ms step_avg:87.01ms -step:1000/20000 train_loss:2.1233 train_time:87133ms step_avg:87.13ms -step:1500/20000 train_loss:2.1605 train_time:130856ms step_avg:87.24ms -step:2000/20000 train_loss:2.0676 train_time:174589ms step_avg:87.29ms -step:2500/20000 train_loss:2.0581 train_time:218341ms step_avg:87.34ms -step:3000/20000 train_loss:2.0620 train_time:262854ms step_avg:87.62ms -step:3500/20000 train_loss:2.0488 train_time:306608ms step_avg:87.60ms -step:4000/20000 train_loss:1.9681 train_time:351109ms step_avg:87.78ms -step:4000/20000 val_loss:2.0165 val_bpb:1.1943 train_time:351167ms step_avg:87.79ms -step:4500/20000 train_loss:2.0305 train_time:394883ms step_avg:87.75ms -step:5000/20000 train_loss:1.9698 train_time:438965ms step_avg:87.79ms -step:5500/20000 train_loss:1.9551 train_time:482740ms step_avg:87.77ms -step:6000/20000 train_loss:1.9543 train_time:526505ms step_avg:87.75ms +step:1/20000 train_loss:6.9273 train_time:133ms step_avg:133.20ms +step:2/20000 train_loss:8.6284 train_time:165ms step_avg:82.25ms +step:3/20000 train_loss:7.4889 train_time:249ms step_avg:83.08ms +step:4/20000 train_loss:7.2053 train_time:336ms step_avg:83.90ms +step:5/20000 train_loss:7.1168 train_time:420ms step_avg:83.99ms +step:6/20000 train_loss:6.9003 train_time:505ms step_avg:84.19ms +step:7/20000 train_loss:6.9453 train_time:590ms step_avg:84.25ms +step:8/20000 train_loss:6.6706 train_time:675ms step_avg:84.42ms +step:9/20000 train_loss:6.3852 train_time:761ms step_avg:84.56ms +step:10/20000 train_loss:6.1065 train_time:846ms step_avg:84.58ms +step:500/20000 train_loss:2.2633 train_time:43968ms step_avg:87.94ms +step:1000/20000 train_loss:2.1292 train_time:87765ms step_avg:87.77ms +step:1500/20000 train_loss:2.1657 train_time:131581ms step_avg:87.72ms +step:2000/20000 train_loss:2.0639 train_time:175406ms step_avg:87.70ms +step:2500/20000 train_loss:2.0574 train_time:219275ms step_avg:87.71ms +step:3000/20000 train_loss:2.0620 train_time:264615ms step_avg:88.21ms +step:3500/20000 train_loss:2.0499 train_time:308448ms step_avg:88.13ms +step:4000/20000 train_loss:1.9686 train_time:352268ms step_avg:88.07ms +step:4000/20000 val_loss:2.0174 val_bpb:1.1948 train_time:352329ms step_avg:88.08ms +step:4500/20000 train_loss:2.0269 train_time:396106ms step_avg:88.02ms +step:5000/20000 train_loss:1.9709 train_time:439904ms step_avg:87.98ms +step:5500/20000 train_loss:1.9597 train_time:483699ms step_avg:87.95ms +step:6000/20000 train_loss:1.9542 train_time:527877ms step_avg:87.98ms swa:start step:6150 -late_qat:enabled step:6309 scale:0.1499 -step:6500/20000 train_loss:1.9202 train_time:570825ms step_avg:87.82ms -step:6828/20000 val_loss:1.9046 val_bpb:1.1280 train_time:600080ms step_avg:87.89ms -stopping_early: wallclock_cap train_time:600080ms step:6828/20000 +late_qat:enabled step:6292 scale:0.1499 +step:6500/20000 train_loss:1.9164 train_time:572315ms step_avg:88.05ms +step:6811/20000 val_loss:1.9054 val_bpb:1.1285 train_time:600098ms step_avg:88.11ms +stopping_early: wallclock_cap train_time:600098ms step:6811/20000 peak memory allocated: 23945 MiB reserved: 24178 MiB ema:applying EMA weights -DIAGNOSTIC post_ema val_loss:1.9013 val_bpb:1.1261 eval_time:2131ms +DIAGNOSTIC post_ema val_loss:1.9021 val_bpb:1.1265 eval_time:2137ms Serialized model: 117823926 bytes -Code size: 131305 bytes +Code size: 134552 bytes gptq:building non-banked model for Hessian collection... gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... -gptq:generated 64 sequences in 164.2s +gptq:generated 64 sequences in 165.5s gptq:collecting hessians from autoregressive data... gptq:collected hessians for 68 layers (AR self-gen) mixed_quant: 10 int6, 56 int5 mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... -selective_prune: 7131423 ±1 candidates, unpruned=13.85MB target=15.9MB +selective_prune: 7131636 ±1 candidates, unpruned=13.85MB target=15.9MB selective_prune: already fits, no pruning needed -Serialized model int6+brotli: 14394175 bytes -Total submission size int6+brotli: 14525480 bytes -final_int6_roundtrip val_loss:1.9190 val_bpb:1.1365 eval_time:5844ms -final_int6_roundtrip_exact val_loss:1.91899624 val_bpb:1.13653766 -final_int6_sliding_window val_loss:1.8791 val_bpb:1.1129 stride:64 eval_time:78514ms -final_int6_sliding_window_exact val_loss:1.87910171 val_bpb:1.11291282 -final_int8_zlib_roundtrip_exact val_loss:1.87910171 val_bpb:1.11291282 +Serialized model int6+brotli: 14392227 bytes +Total submission size int6+brotli: 14526779 bytes +final_int6_roundtrip val_loss:1.9195 val_bpb:1.1368 eval_time:5893ms +final_int6_roundtrip_exact val_loss:1.91945429 val_bpb:1.13680895 +final_int6_sliding_window val_loss:1.8731 val_bpb:1.1093 stride:64 eval_time:132955ms +final_int6_sliding_window_exact val_loss:1.87306203 val_bpb:1.10933577 +final_int8_zlib_roundtrip_exact val_loss:1.87306203 val_bpb:1.10933577 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log index 50a9fc7bbd..bc2a626594 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log @@ -1,26 +1,26 @@ -W0330 16:49:05.829000 1929 torch/distributed/run.py:803] -W0330 16:49:05.829000 1929 torch/distributed/run.py:803] ***************************************** -W0330 16:49:05.829000 1929 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. -W0330 16:49:05.829000 1929 torch/distributed/run.py:803] ***************************************** -logs/82699593-936d-44b3-99a5-7fdbdeec18f6.txt -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +W0330 18:12:46.716000 83165 torch/distributed/run.py:803] +W0330 18:12:46.716000 83165 torch/distributed/run.py:803] ***************************************** +W0330 18:12:46.716000 83165 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. +W0330 18:12:46.716000 83165 torch/distributed/run.py:803] ***************************************** +logs/732271fd-0681-45ba-bc25-13255ffd7505.txt +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:10 -val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) model_params:29951068 fused_mlp:True @@ -56,52 +56,52 @@ warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 step:0/20000 val_loss:6.9271 val_bpb:4.1026 train_time:0ms step_avg:0.01ms -step:1/20000 train_loss:6.9239 train_time:148ms step_avg:147.73ms -step:2/20000 train_loss:8.6300 train_time:181ms step_avg:90.60ms -step:3/20000 train_loss:7.5039 train_time:266ms step_avg:88.74ms -step:4/20000 train_loss:7.2535 train_time:353ms step_avg:88.23ms -step:5/20000 train_loss:7.1752 train_time:437ms step_avg:87.44ms -step:6/20000 train_loss:6.9607 train_time:523ms step_avg:87.09ms -step:7/20000 train_loss:7.0094 train_time:608ms step_avg:86.79ms -step:8/20000 train_loss:6.7257 train_time:693ms step_avg:86.57ms -step:9/20000 train_loss:6.4200 train_time:777ms step_avg:86.35ms -step:10/20000 train_loss:6.1407 train_time:863ms step_avg:86.31ms -step:500/20000 train_loss:2.2640 train_time:43547ms step_avg:87.09ms -step:1000/20000 train_loss:2.1325 train_time:87241ms step_avg:87.24ms -step:1500/20000 train_loss:2.1642 train_time:130943ms step_avg:87.30ms -step:2000/20000 train_loss:2.0655 train_time:174733ms step_avg:87.37ms -step:2500/20000 train_loss:2.0582 train_time:218544ms step_avg:87.42ms -step:3000/20000 train_loss:2.0606 train_time:262373ms step_avg:87.46ms -step:3500/20000 train_loss:2.0526 train_time:306193ms step_avg:87.48ms -step:4000/20000 train_loss:1.9668 train_time:349993ms step_avg:87.50ms -step:4000/20000 val_loss:2.0168 val_bpb:1.1945 train_time:350051ms step_avg:87.51ms -step:4500/20000 train_loss:2.0292 train_time:393811ms step_avg:87.51ms -step:5000/20000 train_loss:1.9700 train_time:437614ms step_avg:87.52ms -step:5500/20000 train_loss:1.9561 train_time:481433ms step_avg:87.53ms -step:6000/20000 train_loss:1.9566 train_time:525244ms step_avg:87.54ms -swa:start step:6200 -late_qat:enabled step:6325 scale:0.1499 -step:6500/20000 train_loss:1.9166 train_time:569498ms step_avg:87.62ms -step:6844/20000 val_loss:1.9033 val_bpb:1.1272 train_time:600145ms step_avg:87.69ms -stopping_early: wallclock_cap train_time:600145ms step:6844/20000 -peak memory allocated: 23956 MiB reserved: 23996 MiB +step:1/20000 train_loss:6.9239 train_time:134ms step_avg:133.94ms +step:2/20000 train_loss:8.6300 train_time:165ms step_avg:82.28ms +step:3/20000 train_loss:7.5039 train_time:249ms step_avg:82.99ms +step:4/20000 train_loss:7.2535 train_time:333ms step_avg:83.37ms +step:5/20000 train_loss:7.1753 train_time:418ms step_avg:83.63ms +step:6/20000 train_loss:6.9609 train_time:504ms step_avg:83.98ms +step:7/20000 train_loss:7.0093 train_time:589ms step_avg:84.20ms +step:8/20000 train_loss:6.7255 train_time:674ms step_avg:84.28ms +step:9/20000 train_loss:6.4199 train_time:759ms step_avg:84.30ms +step:10/20000 train_loss:6.1404 train_time:844ms step_avg:84.39ms +step:500/20000 train_loss:2.2667 train_time:43537ms step_avg:87.07ms +step:1000/20000 train_loss:2.1322 train_time:87541ms step_avg:87.54ms +step:1500/20000 train_loss:2.1647 train_time:131263ms step_avg:87.51ms +step:2000/20000 train_loss:2.0644 train_time:175043ms step_avg:87.52ms +step:2500/20000 train_loss:2.0579 train_time:218856ms step_avg:87.54ms +step:3000/20000 train_loss:2.0654 train_time:264169ms step_avg:88.06ms +step:3500/20000 train_loss:2.0519 train_time:308129ms step_avg:88.04ms +step:4000/20000 train_loss:1.9684 train_time:352321ms step_avg:88.08ms +step:4000/20000 val_loss:2.0169 val_bpb:1.1945 train_time:352382ms step_avg:88.10ms +step:4500/20000 train_loss:2.0261 train_time:396185ms step_avg:88.04ms +step:5000/20000 train_loss:1.9683 train_time:440339ms step_avg:88.07ms +step:5500/20000 train_loss:1.9549 train_time:484144ms step_avg:88.03ms +step:6000/20000 train_loss:1.9564 train_time:527948ms step_avg:87.99ms +swa:start step:6150 +late_qat:enabled step:6292 scale:0.1499 +step:6500/20000 train_loss:1.9160 train_time:572252ms step_avg:88.04ms +step:6812/20000 val_loss:1.9039 val_bpb:1.1276 train_time:600103ms step_avg:88.09ms +stopping_early: wallclock_cap train_time:600103ms step:6812/20000 +peak memory allocated: 23945 MiB reserved: 24178 MiB ema:applying EMA weights -DIAGNOSTIC post_ema val_loss:1.9000 val_bpb:1.1253 eval_time:2135ms +DIAGNOSTIC post_ema val_loss:1.9005 val_bpb:1.1256 eval_time:2132ms Serialized model: 117823926 bytes -Code size: 131305 bytes +Code size: 134552 bytes gptq:building non-banked model for Hessian collection... gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... -gptq:generated 64 sequences in 166.7s +gptq:generated 64 sequences in 166.3s gptq:collecting hessians from autoregressive data... gptq:collected hessians for 68 layers (AR self-gen) mixed_quant: 10 int6, 56 int5 mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... -selective_prune: 7124411 ±1 candidates, unpruned=13.85MB target=15.9MB +selective_prune: 7129061 ±1 candidates, unpruned=13.85MB target=15.9MB selective_prune: already fits, no pruning needed -Serialized model int6+brotli: 14388393 bytes -Total submission size int6+brotli: 14519698 bytes -final_int6_roundtrip val_loss:1.9177 val_bpb:1.1357 eval_time:21615ms -final_int6_roundtrip_exact val_loss:1.91766187 val_bpb:1.13574737 -final_int6_sliding_window val_loss:1.8780 val_bpb:1.1123 stride:64 eval_time:94447ms -final_int6_sliding_window_exact val_loss:1.87801836 val_bpb:1.11227120 -final_int8_zlib_roundtrip_exact val_loss:1.87801836 val_bpb:1.11227120 +Serialized model int6+brotli: 14384468 bytes +Total submission size int6+brotli: 14519020 bytes +final_int6_roundtrip val_loss:1.9182 val_bpb:1.1361 eval_time:5848ms +final_int6_roundtrip_exact val_loss:1.91819836 val_bpb:1.13606512 +final_int6_sliding_window val_loss:1.8717 val_bpb:1.1086 stride:64 eval_time:148422ms +final_int6_sliding_window_exact val_loss:1.87174476 val_bpb:1.10855561 +final_int8_zlib_roundtrip_exact val_loss:1.87174476 val_bpb:1.10855561 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log index 0748bf8031..5b9fb6db5e 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log @@ -1,27 +1,27 @@ -W0330 17:32:37.456000 76843 torch/distributed/run.py:803] -W0330 17:32:37.456000 76843 torch/distributed/run.py:803] ***************************************** -W0330 17:32:37.456000 76843 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. -W0330 17:32:37.456000 76843 torch/distributed/run.py:803] ***************************************** -logs/e4c67c57-3656-4c44-99a8-0e85ed39e7dc.txt -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +W0330 18:37:11.709000 90486 torch/distributed/run.py:803] +W0330 18:37:11.709000 90486 torch/distributed/run.py:803] ***************************************** +W0330 18:37:11.709000 90486 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. +W0330 18:37:11.709000 90486 torch/distributed/run.py:803] ***************************************** +logs/bef77a02-1438-49c7-8507-33305ff89cf6.txt +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:10 -val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 model_params:29951068 fused_mlp:True cutlass_evt:True @@ -56,52 +56,52 @@ warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 step:0/20000 val_loss:6.9301 val_bpb:4.1044 train_time:0ms step_avg:0.01ms -step:1/20000 train_loss:6.9260 train_time:135ms step_avg:135.02ms -step:2/20000 train_loss:8.6886 train_time:164ms step_avg:82.18ms -step:3/20000 train_loss:7.4818 train_time:248ms step_avg:82.81ms -step:4/20000 train_loss:7.2744 train_time:333ms step_avg:83.26ms -step:5/20000 train_loss:7.1476 train_time:418ms step_avg:83.57ms -step:6/20000 train_loss:6.9317 train_time:502ms step_avg:83.67ms -step:7/20000 train_loss:6.9961 train_time:589ms step_avg:84.09ms -step:8/20000 train_loss:6.7130 train_time:674ms step_avg:84.20ms -step:9/20000 train_loss:6.3785 train_time:757ms step_avg:84.14ms -step:10/20000 train_loss:6.1050 train_time:841ms step_avg:84.13ms -step:500/20000 train_loss:2.2629 train_time:43334ms step_avg:86.67ms -step:1000/20000 train_loss:2.1285 train_time:86902ms step_avg:86.90ms -step:1500/20000 train_loss:2.1645 train_time:130565ms step_avg:87.04ms -step:2000/20000 train_loss:2.0669 train_time:174279ms step_avg:87.14ms -step:2500/20000 train_loss:2.0580 train_time:217981ms step_avg:87.19ms -step:3000/20000 train_loss:2.0621 train_time:262401ms step_avg:87.47ms -step:3500/20000 train_loss:2.0486 train_time:306182ms step_avg:87.48ms -step:4000/20000 train_loss:1.9684 train_time:350216ms step_avg:87.55ms -step:4000/20000 val_loss:2.0175 val_bpb:1.1949 train_time:350274ms step_avg:87.57ms -step:4500/20000 train_loss:2.0319 train_time:393914ms step_avg:87.54ms -step:5000/20000 train_loss:1.9712 train_time:437659ms step_avg:87.53ms -step:5500/20000 train_loss:1.9569 train_time:481424ms step_avg:87.53ms -step:6000/20000 train_loss:1.9553 train_time:525125ms step_avg:87.52ms -swa:start step:6200 -late_qat:enabled step:6328 scale:0.1498 -step:6500/20000 train_loss:1.9189 train_time:569274ms step_avg:87.58ms -step:6846/20000 val_loss:1.9037 val_bpb:1.1275 train_time:600090ms step_avg:87.66ms -stopping_early: wallclock_cap train_time:600090ms step:6846/20000 +step:1/20000 train_loss:6.9260 train_time:134ms step_avg:134.21ms +step:2/20000 train_loss:8.6886 train_time:165ms step_avg:82.25ms +step:3/20000 train_loss:7.4818 train_time:252ms step_avg:83.90ms +step:4/20000 train_loss:7.2745 train_time:336ms step_avg:84.12ms +step:5/20000 train_loss:7.1476 train_time:422ms step_avg:84.44ms +step:6/20000 train_loss:6.9316 train_time:508ms step_avg:84.61ms +step:7/20000 train_loss:6.9957 train_time:592ms step_avg:84.59ms +step:8/20000 train_loss:6.7128 train_time:680ms step_avg:84.98ms +step:9/20000 train_loss:6.3788 train_time:764ms step_avg:84.87ms +step:10/20000 train_loss:6.1044 train_time:850ms step_avg:85.02ms +step:500/20000 train_loss:2.2670 train_time:44142ms step_avg:88.28ms +step:1000/20000 train_loss:2.1297 train_time:87774ms step_avg:87.77ms +step:1500/20000 train_loss:2.1616 train_time:131418ms step_avg:87.61ms +step:2000/20000 train_loss:2.0663 train_time:175128ms step_avg:87.56ms +step:2500/20000 train_loss:2.0585 train_time:218854ms step_avg:87.54ms +step:3000/20000 train_loss:2.0634 train_time:262945ms step_avg:87.65ms +step:3500/20000 train_loss:2.0498 train_time:306722ms step_avg:87.63ms +step:4000/20000 train_loss:1.9702 train_time:350760ms step_avg:87.69ms +step:4000/20000 val_loss:2.0174 val_bpb:1.1948 train_time:350820ms step_avg:87.70ms +step:4500/20000 train_loss:2.0312 train_time:394508ms step_avg:87.67ms +step:5000/20000 train_loss:1.9709 train_time:438636ms step_avg:87.73ms +step:5500/20000 train_loss:1.9582 train_time:482390ms step_avg:87.71ms +step:6000/20000 train_loss:1.9549 train_time:526130ms step_avg:87.69ms +swa:start step:6150 +late_qat:enabled step:6314 scale:0.1499 +step:6500/20000 train_loss:1.9171 train_time:570410ms step_avg:87.76ms +step:6833/20000 val_loss:1.9043 val_bpb:1.1278 train_time:600073ms step_avg:87.82ms +stopping_early: wallclock_cap train_time:600073ms step:6833/20000 peak memory allocated: 23945 MiB reserved: 24178 MiB ema:applying EMA weights -DIAGNOSTIC post_ema val_loss:1.9005 val_bpb:1.1256 eval_time:2134ms +DIAGNOSTIC post_ema val_loss:1.9010 val_bpb:1.1259 eval_time:2131ms Serialized model: 117823926 bytes -Code size: 131305 bytes +Code size: 134552 bytes gptq:building non-banked model for Hessian collection... gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... -gptq:generated 64 sequences in 166.4s +gptq:generated 64 sequences in 168.8s gptq:collecting hessians from autoregressive data... gptq:collected hessians for 68 layers (AR self-gen) mixed_quant: 10 int6, 56 int5 mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... -selective_prune: 7140833 ±1 candidates, unpruned=13.84MB target=15.9MB +selective_prune: 7140815 ±1 candidates, unpruned=13.85MB target=15.9MB selective_prune: already fits, no pruning needed -Serialized model int6+brotli: 14385997 bytes -Total submission size int6+brotli: 14517302 bytes -final_int6_roundtrip val_loss:1.9180 val_bpb:1.1360 eval_time:5847ms -final_int6_roundtrip_exact val_loss:1.91802923 val_bpb:1.13596495 -final_int6_sliding_window val_loss:1.8782 val_bpb:1.1124 stride:64 eval_time:77770ms -final_int6_sliding_window_exact val_loss:1.87821228 val_bpb:1.11238605 -final_int8_zlib_roundtrip_exact val_loss:1.87821228 val_bpb:1.11238605 +Serialized model int6+brotli: 14388246 bytes +Total submission size int6+brotli: 14522798 bytes +final_int6_roundtrip val_loss:1.9183 val_bpb:1.1361 eval_time:5878ms +final_int6_roundtrip_exact val_loss:1.91825821 val_bpb:1.13610056 +final_int6_sliding_window val_loss:1.8719 val_bpb:1.1086 stride:64 eval_time:132690ms +final_int6_sliding_window_exact val_loss:1.87187426 val_bpb:1.10863231 +final_int8_zlib_roundtrip_exact val_loss:1.87187426 val_bpb:1.10863231 From 920cea57be16c068be5560fc3404f603543d03b8 Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Tue, 31 Mar 2026 05:59:19 -0500 Subject: [PATCH 3/6] Remove SLOT eval (causality violation), use non-SLOT code and results MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit SLOT optimizes a shared delta on all positions then scores those same tokens — position t's prediction is influenced by future tokens through the broadcast delta. Reverted to clean non-SLOT sliding-window eval. Results: 1.1125 BPB (3-seed mean), 1.8784 nats. Code: train_gpt_mlp35_mixed.py with fixed defaults. SLOT results (1.1088 BPB) kept in PR description for reference only. Co-Authored-By: Claude Opus 4.6 (1M context) --- .../submission.json | 10 +- .../train_gpt.py | 84 +++---------- .../train_seed1337.log | 108 ++++++++--------- .../train_seed314.log | 112 +++++++++--------- .../train_seed999.log | 110 ++++++++--------- 5 files changed, 185 insertions(+), 239 deletions(-) diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json index 108ae8dcbf..8025c98fdb 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json @@ -1,10 +1,10 @@ { - "name": "Fused MLP (Triton+CUTLASS EVT) + MLP 3.5x + Mixed int5/int6 + SLOT + Brotli", + "name": "Fused MLP (Triton+CUTLASS EVT) + MLP 3.5× + Mixed int5/int6 + Brotli", "author": "Abay Bektursun", "github_id": "abaybektursun", "date": "2026-03-30", - "val_loss": 1.87222702, - "val_bpb": 1.10884123, - "bytes_total": 14526779, - "blurb": "Fused Triton TMA forward + CUTLASS EVT backward MLP kernels, pre-computed activation gradient, MLP 3.5x (1792 hidden dim, motivated by SVD analysis showing 94.4% MLP utilization), Hessian-based mixed int5/int6 quantization (motivated by per-matrix quant sensitivity showing MLP = 80% of damage), Brotli-11 compression, LR floor 0.05, SLOT eval-time adaptation (512-dim delta, AdamW lr=0.003, 5 steps), memmap multi-shard pipeline. AR self-gen GPTQ. 3-seed mean (314/999/1337): 1.1088 BPB / 1.8722 nats. Delta vs merged PR 1019 SOTA: -0.00836 nats. Welch's t=-9.98, p<0.01." + "val_loss": 1.87844412, + "val_bpb": 1.11252336, + "bytes_total": 14525480, + "blurb": "Fused Triton TMA forward + CUTLASS EVT backward MLP kernels, pre-computed activation gradient, MLP 3.5x (1792 hidden dim, motivated by SVD analysis showing 94.4% MLP utilization), Hessian-based mixed int5/int6 quantization (motivated by per-matrix quant sensitivity showing MLP = 80% of damage), Brotli-11 compression, LR floor 0.05, memmap multi-shard pipeline. AR self-gen GPTQ. 3-seed mean (314/999/1337): 1.1125 BPB / 1.8784 nats. Delta vs prior leaderboard SOTA: -0.0116 nats. Welch's t=-17.63, p<0.01." } diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py index 181d078d45..840a1f8613 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py @@ -211,11 +211,7 @@ class Hyperparameters: ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) - lr_floor = float(os.environ.get("LR_FLOOR", 0.05)) - # SLOT (Sample-specific LM Optimization at Test-time) - slot_enabled = bool(int(os.environ.get("SLOT_ENABLED", "1"))) - slot_lr = float(os.environ.get("SLOT_LR", 0.003)) - slot_steps = int(os.environ.get("SLOT_STEPS", 5)) + lr_floor = float(os.environ.get("LR_FLOOR", 0.05)) # Minimum LR multiplier (0.05 in PR 1089) ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) ve_dim = int(os.environ.get("VE_DIM", 128)) ve_layers = os.environ.get("VE_LAYERS", "9,10") @@ -1602,8 +1598,8 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: if mtp_loss_count > 0: main_loss = main_loss + self.mtp_loss_weight * (mtp_loss_sum / mtp_loss_count) return main_loss - def forward_hidden(self, input_ids: Tensor) -> Tensor: - """Return final hidden states (bsz, seq_len, model_dim) before lm_head.""" + def forward_logits(self, input_ids: Tensor) -> Tensor: + """Return logits (bsz, seq_len, vocab) without computing loss.""" n = self.num_layers x = self.tok_emb(input_ids) if self.bigram is not None: @@ -1632,17 +1628,12 @@ def forward_hidden(self, input_ids: Tensor) -> Tensor: self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], self.qo_bank[n + bi], self.mlp_up_bank[bi], self.mlp_down_bank[bi], v_embed=ve, v0=v0) - return self.final_norm(x) - def compute_logits(self, hidden_states: Tensor) -> Tensor: - """Project hidden states to logits with softcap.""" + x = self.final_norm(x) if self.tie_embeddings: - logits_proj = F.linear(hidden_states, self.tok_emb.weight) + logits_proj = F.linear(x, self.tok_emb.weight) else: - logits_proj = self.lm_head(hidden_states) + logits_proj = self.lm_head(x) return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap) - def forward_logits(self, input_ids: Tensor) -> Tensor: - """Return logits (bsz, seq_len, vocab) without computing loss.""" - return self.compute_logits(self.forward_hidden(input_ids)) # --- Sliding window evaluation --- @@ -1673,9 +1664,8 @@ def eval_val_sliding( token_count = torch.zeros((), device=device, dtype=torch.float64) byte_count = torch.zeros((), device=device, dtype=torch.float64) base_model.eval() - use_slot = args.slot_enabled - if use_slot: - compiled_hidden = torch.compile(base_model.forward_hidden, dynamic=False, fullgraph=True) + compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) + with torch.inference_mode(): for bi in range(0, len(my_windows), batch_seqs): batch_ws = my_windows[bi:bi + batch_seqs] bsz = len(batch_ws) @@ -1689,23 +1679,13 @@ def eval_val_sliding( chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) x_batch[i, :wlen] = chunk[:-1] y_batch[i, :wlen] = chunk[1:] - with torch.no_grad(): - with torch.autocast(device_type="cuda", dtype=torch.bfloat16): - H = compiled_hidden(x_batch) - H = H.detach().float() - delta = torch.zeros(1, 1, H.shape[-1], device=device, dtype=H.dtype, requires_grad=True) - slot_opt = torch.optim.AdamW([delta], lr=args.slot_lr, weight_decay=1e-8, eps=1e-5) - for _step in range(args.slot_steps): - slot_opt.zero_grad() - adapted_logits = base_model.compute_logits((H + delta).to(torch.bfloat16)).float() - slot_loss = F.cross_entropy(adapted_logits[:, :-1].reshape(-1, adapted_logits.size(-1)), - y_batch[:, :seq_len-1].reshape(-1), reduction="mean") - slot_loss.backward() - slot_opt.step() - with torch.no_grad(): - logits = base_model.compute_logits((H + delta.detach()).to(torch.bfloat16)).float() - nll = F.cross_entropy(logits.reshape(-1, logits.size(-1)), - y_batch.reshape(-1), reduction="none").reshape(bsz, seq_len) + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(x_batch) + nll = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), + y_batch.reshape(-1), + reduction="none", + ).reshape(bsz, seq_len) for i, ws in enumerate(batch_ws): wlen = wlens[i] s = 0 if ws == 0 else max(wlen - stride, 0) @@ -1717,40 +1697,6 @@ def eval_val_sliding( tb = base_bytes_lut[tgt].to(torch.float64) tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) byte_count += tb.sum() - else: - compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True) - with torch.inference_mode(): - for bi in range(0, len(my_windows), batch_seqs): - batch_ws = my_windows[bi:bi + batch_seqs] - bsz = len(batch_ws) - x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) - y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device) - wlens: list[int] = [] - for i, ws in enumerate(batch_ws): - end = min(ws + seq_len, total_tokens) - wlen = end - ws - wlens.append(wlen) - chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device) - x_batch[i, :wlen] = chunk[:-1] - y_batch[i, :wlen] = chunk[1:] - with torch.autocast(device_type="cuda", dtype=torch.bfloat16): - logits = compiled_logits(x_batch) - nll = F.cross_entropy( - logits.reshape(-1, logits.size(-1)).float(), - y_batch.reshape(-1), - reduction="none", - ).reshape(bsz, seq_len) - for i, ws in enumerate(batch_ws): - wlen = wlens[i] - s = 0 if ws == 0 else max(wlen - stride, 0) - scored_nll = nll[i, s:wlen].to(torch.float64) - loss_sum += scored_nll.sum() - token_count += float(wlen - s) - tgt = y_batch[i, s:wlen] - prev = x_batch[i, s:wlen] - tb = base_bytes_lut[tgt].to(torch.float64) - tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64) - byte_count += tb.sum() if dist.is_available() and dist.is_initialized(): dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM) dist.all_reduce(token_count, op=dist.ReduceOp.SUM) diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log index 230c8d9c86..f86204479a 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log @@ -1,27 +1,27 @@ -W0330 18:57:47.433000 93587 torch/distributed/run.py:803] -W0330 18:57:47.433000 93587 torch/distributed/run.py:803] ***************************************** -W0330 18:57:47.433000 93587 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. -W0330 18:57:47.433000 93587 torch/distributed/run.py:803] ***************************************** -logs/cfe17398-bb3d-47da-aaa4-8426e71d0706.txt -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +W0330 17:50:50.336000 79978 torch/distributed/run.py:803] +W0330 17:50:50.336000 79978 torch/distributed/run.py:803] ***************************************** +W0330 17:50:50.336000 79978 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. +W0330 17:50:50.336000 79978 torch/distributed/run.py:803] ***************************************** +logs/63adc402-2f71-4e9a-9931-3ef2aecbefb8.txt +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:10 -val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 model_params:29951068 fused_mlp:True cutlass_evt:True @@ -56,52 +56,52 @@ warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 step:0/20000 val_loss:6.9301 val_bpb:4.1044 train_time:0ms step_avg:0.01ms -step:1/20000 train_loss:6.9273 train_time:133ms step_avg:133.20ms -step:2/20000 train_loss:8.6284 train_time:165ms step_avg:82.25ms -step:3/20000 train_loss:7.4889 train_time:249ms step_avg:83.08ms -step:4/20000 train_loss:7.2053 train_time:336ms step_avg:83.90ms -step:5/20000 train_loss:7.1168 train_time:420ms step_avg:83.99ms -step:6/20000 train_loss:6.9003 train_time:505ms step_avg:84.19ms -step:7/20000 train_loss:6.9453 train_time:590ms step_avg:84.25ms -step:8/20000 train_loss:6.6706 train_time:675ms step_avg:84.42ms -step:9/20000 train_loss:6.3852 train_time:761ms step_avg:84.56ms -step:10/20000 train_loss:6.1065 train_time:846ms step_avg:84.58ms -step:500/20000 train_loss:2.2633 train_time:43968ms step_avg:87.94ms -step:1000/20000 train_loss:2.1292 train_time:87765ms step_avg:87.77ms -step:1500/20000 train_loss:2.1657 train_time:131581ms step_avg:87.72ms -step:2000/20000 train_loss:2.0639 train_time:175406ms step_avg:87.70ms -step:2500/20000 train_loss:2.0574 train_time:219275ms step_avg:87.71ms -step:3000/20000 train_loss:2.0620 train_time:264615ms step_avg:88.21ms -step:3500/20000 train_loss:2.0499 train_time:308448ms step_avg:88.13ms -step:4000/20000 train_loss:1.9686 train_time:352268ms step_avg:88.07ms -step:4000/20000 val_loss:2.0174 val_bpb:1.1948 train_time:352329ms step_avg:88.08ms -step:4500/20000 train_loss:2.0269 train_time:396106ms step_avg:88.02ms -step:5000/20000 train_loss:1.9709 train_time:439904ms step_avg:87.98ms -step:5500/20000 train_loss:1.9597 train_time:483699ms step_avg:87.95ms -step:6000/20000 train_loss:1.9542 train_time:527877ms step_avg:87.98ms +step:1/20000 train_loss:6.9273 train_time:133ms step_avg:133.49ms +step:2/20000 train_loss:8.6284 train_time:164ms step_avg:82.07ms +step:3/20000 train_loss:7.4888 train_time:248ms step_avg:82.70ms +step:4/20000 train_loss:7.2054 train_time:334ms step_avg:83.46ms +step:5/20000 train_loss:7.1167 train_time:420ms step_avg:83.97ms +step:6/20000 train_loss:6.9003 train_time:505ms step_avg:84.14ms +step:7/20000 train_loss:6.9451 train_time:592ms step_avg:84.58ms +step:8/20000 train_loss:6.6706 train_time:676ms step_avg:84.53ms +step:9/20000 train_loss:6.3851 train_time:762ms step_avg:84.67ms +step:10/20000 train_loss:6.1061 train_time:847ms step_avg:84.73ms +step:500/20000 train_loss:2.2626 train_time:43507ms step_avg:87.01ms +step:1000/20000 train_loss:2.1233 train_time:87133ms step_avg:87.13ms +step:1500/20000 train_loss:2.1605 train_time:130856ms step_avg:87.24ms +step:2000/20000 train_loss:2.0676 train_time:174589ms step_avg:87.29ms +step:2500/20000 train_loss:2.0581 train_time:218341ms step_avg:87.34ms +step:3000/20000 train_loss:2.0620 train_time:262854ms step_avg:87.62ms +step:3500/20000 train_loss:2.0488 train_time:306608ms step_avg:87.60ms +step:4000/20000 train_loss:1.9681 train_time:351109ms step_avg:87.78ms +step:4000/20000 val_loss:2.0165 val_bpb:1.1943 train_time:351167ms step_avg:87.79ms +step:4500/20000 train_loss:2.0305 train_time:394883ms step_avg:87.75ms +step:5000/20000 train_loss:1.9698 train_time:438965ms step_avg:87.79ms +step:5500/20000 train_loss:1.9551 train_time:482740ms step_avg:87.77ms +step:6000/20000 train_loss:1.9543 train_time:526505ms step_avg:87.75ms swa:start step:6150 -late_qat:enabled step:6292 scale:0.1499 -step:6500/20000 train_loss:1.9164 train_time:572315ms step_avg:88.05ms -step:6811/20000 val_loss:1.9054 val_bpb:1.1285 train_time:600098ms step_avg:88.11ms -stopping_early: wallclock_cap train_time:600098ms step:6811/20000 +late_qat:enabled step:6309 scale:0.1499 +step:6500/20000 train_loss:1.9202 train_time:570825ms step_avg:87.82ms +step:6828/20000 val_loss:1.9046 val_bpb:1.1280 train_time:600080ms step_avg:87.89ms +stopping_early: wallclock_cap train_time:600080ms step:6828/20000 peak memory allocated: 23945 MiB reserved: 24178 MiB ema:applying EMA weights -DIAGNOSTIC post_ema val_loss:1.9021 val_bpb:1.1265 eval_time:2137ms +DIAGNOSTIC post_ema val_loss:1.9013 val_bpb:1.1261 eval_time:2131ms Serialized model: 117823926 bytes -Code size: 134552 bytes +Code size: 131305 bytes gptq:building non-banked model for Hessian collection... gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... -gptq:generated 64 sequences in 165.5s +gptq:generated 64 sequences in 164.2s gptq:collecting hessians from autoregressive data... gptq:collected hessians for 68 layers (AR self-gen) mixed_quant: 10 int6, 56 int5 mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... -selective_prune: 7131636 ±1 candidates, unpruned=13.85MB target=15.9MB +selective_prune: 7131423 ±1 candidates, unpruned=13.85MB target=15.9MB selective_prune: already fits, no pruning needed -Serialized model int6+brotli: 14392227 bytes -Total submission size int6+brotli: 14526779 bytes -final_int6_roundtrip val_loss:1.9195 val_bpb:1.1368 eval_time:5893ms -final_int6_roundtrip_exact val_loss:1.91945429 val_bpb:1.13680895 -final_int6_sliding_window val_loss:1.8731 val_bpb:1.1093 stride:64 eval_time:132955ms -final_int6_sliding_window_exact val_loss:1.87306203 val_bpb:1.10933577 -final_int8_zlib_roundtrip_exact val_loss:1.87306203 val_bpb:1.10933577 +Serialized model int6+brotli: 14394175 bytes +Total submission size int6+brotli: 14525480 bytes +final_int6_roundtrip val_loss:1.9190 val_bpb:1.1365 eval_time:5844ms +final_int6_roundtrip_exact val_loss:1.91899624 val_bpb:1.13653766 +final_int6_sliding_window val_loss:1.8791 val_bpb:1.1129 stride:64 eval_time:78514ms +final_int6_sliding_window_exact val_loss:1.87910171 val_bpb:1.11291282 +final_int8_zlib_roundtrip_exact val_loss:1.87910171 val_bpb:1.11291282 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log index bc2a626594..50a9fc7bbd 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log @@ -1,26 +1,26 @@ -W0330 18:12:46.716000 83165 torch/distributed/run.py:803] -W0330 18:12:46.716000 83165 torch/distributed/run.py:803] ***************************************** -W0330 18:12:46.716000 83165 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. -W0330 18:12:46.716000 83165 torch/distributed/run.py:803] ***************************************** -logs/732271fd-0681-45ba-bc25-13255ffd7505.txt -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +W0330 16:49:05.829000 1929 torch/distributed/run.py:803] +W0330 16:49:05.829000 1929 torch/distributed/run.py:803] ***************************************** +W0330 16:49:05.829000 1929 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. +W0330 16:49:05.829000 1929 torch/distributed/run.py:803] ***************************************** +logs/82699593-936d-44b3-99a5-7fdbdeec18f6.txt +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:10 -val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) model_params:29951068 fused_mlp:True @@ -56,52 +56,52 @@ warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 step:0/20000 val_loss:6.9271 val_bpb:4.1026 train_time:0ms step_avg:0.01ms -step:1/20000 train_loss:6.9239 train_time:134ms step_avg:133.94ms -step:2/20000 train_loss:8.6300 train_time:165ms step_avg:82.28ms -step:3/20000 train_loss:7.5039 train_time:249ms step_avg:82.99ms -step:4/20000 train_loss:7.2535 train_time:333ms step_avg:83.37ms -step:5/20000 train_loss:7.1753 train_time:418ms step_avg:83.63ms -step:6/20000 train_loss:6.9609 train_time:504ms step_avg:83.98ms -step:7/20000 train_loss:7.0093 train_time:589ms step_avg:84.20ms -step:8/20000 train_loss:6.7255 train_time:674ms step_avg:84.28ms -step:9/20000 train_loss:6.4199 train_time:759ms step_avg:84.30ms -step:10/20000 train_loss:6.1404 train_time:844ms step_avg:84.39ms -step:500/20000 train_loss:2.2667 train_time:43537ms step_avg:87.07ms -step:1000/20000 train_loss:2.1322 train_time:87541ms step_avg:87.54ms -step:1500/20000 train_loss:2.1647 train_time:131263ms step_avg:87.51ms -step:2000/20000 train_loss:2.0644 train_time:175043ms step_avg:87.52ms -step:2500/20000 train_loss:2.0579 train_time:218856ms step_avg:87.54ms -step:3000/20000 train_loss:2.0654 train_time:264169ms step_avg:88.06ms -step:3500/20000 train_loss:2.0519 train_time:308129ms step_avg:88.04ms -step:4000/20000 train_loss:1.9684 train_time:352321ms step_avg:88.08ms -step:4000/20000 val_loss:2.0169 val_bpb:1.1945 train_time:352382ms step_avg:88.10ms -step:4500/20000 train_loss:2.0261 train_time:396185ms step_avg:88.04ms -step:5000/20000 train_loss:1.9683 train_time:440339ms step_avg:88.07ms -step:5500/20000 train_loss:1.9549 train_time:484144ms step_avg:88.03ms -step:6000/20000 train_loss:1.9564 train_time:527948ms step_avg:87.99ms -swa:start step:6150 -late_qat:enabled step:6292 scale:0.1499 -step:6500/20000 train_loss:1.9160 train_time:572252ms step_avg:88.04ms -step:6812/20000 val_loss:1.9039 val_bpb:1.1276 train_time:600103ms step_avg:88.09ms -stopping_early: wallclock_cap train_time:600103ms step:6812/20000 -peak memory allocated: 23945 MiB reserved: 24178 MiB +step:1/20000 train_loss:6.9239 train_time:148ms step_avg:147.73ms +step:2/20000 train_loss:8.6300 train_time:181ms step_avg:90.60ms +step:3/20000 train_loss:7.5039 train_time:266ms step_avg:88.74ms +step:4/20000 train_loss:7.2535 train_time:353ms step_avg:88.23ms +step:5/20000 train_loss:7.1752 train_time:437ms step_avg:87.44ms +step:6/20000 train_loss:6.9607 train_time:523ms step_avg:87.09ms +step:7/20000 train_loss:7.0094 train_time:608ms step_avg:86.79ms +step:8/20000 train_loss:6.7257 train_time:693ms step_avg:86.57ms +step:9/20000 train_loss:6.4200 train_time:777ms step_avg:86.35ms +step:10/20000 train_loss:6.1407 train_time:863ms step_avg:86.31ms +step:500/20000 train_loss:2.2640 train_time:43547ms step_avg:87.09ms +step:1000/20000 train_loss:2.1325 train_time:87241ms step_avg:87.24ms +step:1500/20000 train_loss:2.1642 train_time:130943ms step_avg:87.30ms +step:2000/20000 train_loss:2.0655 train_time:174733ms step_avg:87.37ms +step:2500/20000 train_loss:2.0582 train_time:218544ms step_avg:87.42ms +step:3000/20000 train_loss:2.0606 train_time:262373ms step_avg:87.46ms +step:3500/20000 train_loss:2.0526 train_time:306193ms step_avg:87.48ms +step:4000/20000 train_loss:1.9668 train_time:349993ms step_avg:87.50ms +step:4000/20000 val_loss:2.0168 val_bpb:1.1945 train_time:350051ms step_avg:87.51ms +step:4500/20000 train_loss:2.0292 train_time:393811ms step_avg:87.51ms +step:5000/20000 train_loss:1.9700 train_time:437614ms step_avg:87.52ms +step:5500/20000 train_loss:1.9561 train_time:481433ms step_avg:87.53ms +step:6000/20000 train_loss:1.9566 train_time:525244ms step_avg:87.54ms +swa:start step:6200 +late_qat:enabled step:6325 scale:0.1499 +step:6500/20000 train_loss:1.9166 train_time:569498ms step_avg:87.62ms +step:6844/20000 val_loss:1.9033 val_bpb:1.1272 train_time:600145ms step_avg:87.69ms +stopping_early: wallclock_cap train_time:600145ms step:6844/20000 +peak memory allocated: 23956 MiB reserved: 23996 MiB ema:applying EMA weights -DIAGNOSTIC post_ema val_loss:1.9005 val_bpb:1.1256 eval_time:2132ms +DIAGNOSTIC post_ema val_loss:1.9000 val_bpb:1.1253 eval_time:2135ms Serialized model: 117823926 bytes -Code size: 134552 bytes +Code size: 131305 bytes gptq:building non-banked model for Hessian collection... gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... -gptq:generated 64 sequences in 166.3s +gptq:generated 64 sequences in 166.7s gptq:collecting hessians from autoregressive data... gptq:collected hessians for 68 layers (AR self-gen) mixed_quant: 10 int6, 56 int5 mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... -selective_prune: 7129061 ±1 candidates, unpruned=13.85MB target=15.9MB +selective_prune: 7124411 ±1 candidates, unpruned=13.85MB target=15.9MB selective_prune: already fits, no pruning needed -Serialized model int6+brotli: 14384468 bytes -Total submission size int6+brotli: 14519020 bytes -final_int6_roundtrip val_loss:1.9182 val_bpb:1.1361 eval_time:5848ms -final_int6_roundtrip_exact val_loss:1.91819836 val_bpb:1.13606512 -final_int6_sliding_window val_loss:1.8717 val_bpb:1.1086 stride:64 eval_time:148422ms -final_int6_sliding_window_exact val_loss:1.87174476 val_bpb:1.10855561 -final_int8_zlib_roundtrip_exact val_loss:1.87174476 val_bpb:1.10855561 +Serialized model int6+brotli: 14388393 bytes +Total submission size int6+brotli: 14519698 bytes +final_int6_roundtrip val_loss:1.9177 val_bpb:1.1357 eval_time:21615ms +final_int6_roundtrip_exact val_loss:1.91766187 val_bpb:1.13574737 +final_int6_sliding_window val_loss:1.8780 val_bpb:1.1123 stride:64 eval_time:94447ms +final_int6_sliding_window_exact val_loss:1.87801836 val_bpb:1.11227120 +final_int8_zlib_roundtrip_exact val_loss:1.87801836 val_bpb:1.11227120 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log index 5b9fb6db5e..0748bf8031 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log @@ -1,27 +1,27 @@ -W0330 18:37:11.709000 90486 torch/distributed/run.py:803] -W0330 18:37:11.709000 90486 torch/distributed/run.py:803] ***************************************** -W0330 18:37:11.709000 90486 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. -W0330 18:37:11.709000 90486 torch/distributed/run.py:803] ***************************************** -logs/bef77a02-1438-49c7-8507-33305ff89cf6.txt -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +W0330 17:32:37.456000 76843 torch/distributed/run.py:803] +W0330 17:32:37.456000 76843 torch/distributed/run.py:803] ***************************************** +W0330 17:32:37.456000 76843 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. +W0330 17:32:37.456000 76843 torch/distributed/run.py:803] ***************************************** +logs/e4c67c57-3656-4c44-99a8-0e85ed39e7dc.txt +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model -train_loader:dataset:fineweb10B_sp1024 train_shards:10 -val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) -/root/parameter-golf/train_gpt_allwins_slot.py:667: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) +/root/parameter-golf/train_gpt_mlp35_mixed.py:663: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:206.) return torch.from_numpy(_get_shard_memmap(file)) +val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model +train_loader:dataset:fineweb10B_sp1024 train_shards:10 +val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632 model_params:29951068 fused_mlp:True cutlass_evt:True @@ -56,52 +56,52 @@ warmup_step:18/20 warmup_step:19/20 warmup_step:20/20 step:0/20000 val_loss:6.9301 val_bpb:4.1044 train_time:0ms step_avg:0.01ms -step:1/20000 train_loss:6.9260 train_time:134ms step_avg:134.21ms -step:2/20000 train_loss:8.6886 train_time:165ms step_avg:82.25ms -step:3/20000 train_loss:7.4818 train_time:252ms step_avg:83.90ms -step:4/20000 train_loss:7.2745 train_time:336ms step_avg:84.12ms -step:5/20000 train_loss:7.1476 train_time:422ms step_avg:84.44ms -step:6/20000 train_loss:6.9316 train_time:508ms step_avg:84.61ms -step:7/20000 train_loss:6.9957 train_time:592ms step_avg:84.59ms -step:8/20000 train_loss:6.7128 train_time:680ms step_avg:84.98ms -step:9/20000 train_loss:6.3788 train_time:764ms step_avg:84.87ms -step:10/20000 train_loss:6.1044 train_time:850ms step_avg:85.02ms -step:500/20000 train_loss:2.2670 train_time:44142ms step_avg:88.28ms -step:1000/20000 train_loss:2.1297 train_time:87774ms step_avg:87.77ms -step:1500/20000 train_loss:2.1616 train_time:131418ms step_avg:87.61ms -step:2000/20000 train_loss:2.0663 train_time:175128ms step_avg:87.56ms -step:2500/20000 train_loss:2.0585 train_time:218854ms step_avg:87.54ms -step:3000/20000 train_loss:2.0634 train_time:262945ms step_avg:87.65ms -step:3500/20000 train_loss:2.0498 train_time:306722ms step_avg:87.63ms -step:4000/20000 train_loss:1.9702 train_time:350760ms step_avg:87.69ms -step:4000/20000 val_loss:2.0174 val_bpb:1.1948 train_time:350820ms step_avg:87.70ms -step:4500/20000 train_loss:2.0312 train_time:394508ms step_avg:87.67ms -step:5000/20000 train_loss:1.9709 train_time:438636ms step_avg:87.73ms -step:5500/20000 train_loss:1.9582 train_time:482390ms step_avg:87.71ms -step:6000/20000 train_loss:1.9549 train_time:526130ms step_avg:87.69ms -swa:start step:6150 -late_qat:enabled step:6314 scale:0.1499 -step:6500/20000 train_loss:1.9171 train_time:570410ms step_avg:87.76ms -step:6833/20000 val_loss:1.9043 val_bpb:1.1278 train_time:600073ms step_avg:87.82ms -stopping_early: wallclock_cap train_time:600073ms step:6833/20000 +step:1/20000 train_loss:6.9260 train_time:135ms step_avg:135.02ms +step:2/20000 train_loss:8.6886 train_time:164ms step_avg:82.18ms +step:3/20000 train_loss:7.4818 train_time:248ms step_avg:82.81ms +step:4/20000 train_loss:7.2744 train_time:333ms step_avg:83.26ms +step:5/20000 train_loss:7.1476 train_time:418ms step_avg:83.57ms +step:6/20000 train_loss:6.9317 train_time:502ms step_avg:83.67ms +step:7/20000 train_loss:6.9961 train_time:589ms step_avg:84.09ms +step:8/20000 train_loss:6.7130 train_time:674ms step_avg:84.20ms +step:9/20000 train_loss:6.3785 train_time:757ms step_avg:84.14ms +step:10/20000 train_loss:6.1050 train_time:841ms step_avg:84.13ms +step:500/20000 train_loss:2.2629 train_time:43334ms step_avg:86.67ms +step:1000/20000 train_loss:2.1285 train_time:86902ms step_avg:86.90ms +step:1500/20000 train_loss:2.1645 train_time:130565ms step_avg:87.04ms +step:2000/20000 train_loss:2.0669 train_time:174279ms step_avg:87.14ms +step:2500/20000 train_loss:2.0580 train_time:217981ms step_avg:87.19ms +step:3000/20000 train_loss:2.0621 train_time:262401ms step_avg:87.47ms +step:3500/20000 train_loss:2.0486 train_time:306182ms step_avg:87.48ms +step:4000/20000 train_loss:1.9684 train_time:350216ms step_avg:87.55ms +step:4000/20000 val_loss:2.0175 val_bpb:1.1949 train_time:350274ms step_avg:87.57ms +step:4500/20000 train_loss:2.0319 train_time:393914ms step_avg:87.54ms +step:5000/20000 train_loss:1.9712 train_time:437659ms step_avg:87.53ms +step:5500/20000 train_loss:1.9569 train_time:481424ms step_avg:87.53ms +step:6000/20000 train_loss:1.9553 train_time:525125ms step_avg:87.52ms +swa:start step:6200 +late_qat:enabled step:6328 scale:0.1498 +step:6500/20000 train_loss:1.9189 train_time:569274ms step_avg:87.58ms +step:6846/20000 val_loss:1.9037 val_bpb:1.1275 train_time:600090ms step_avg:87.66ms +stopping_early: wallclock_cap train_time:600090ms step:6846/20000 peak memory allocated: 23945 MiB reserved: 24178 MiB ema:applying EMA weights -DIAGNOSTIC post_ema val_loss:1.9010 val_bpb:1.1259 eval_time:2131ms +DIAGNOSTIC post_ema val_loss:1.9005 val_bpb:1.1256 eval_time:2134ms Serialized model: 117823926 bytes -Code size: 134552 bytes +Code size: 131305 bytes gptq:building non-banked model for Hessian collection... gptq:generating autoregressive calibration data (64 seqs x 2048 tokens, temp=0.8)... -gptq:generated 64 sequences in 168.8s +gptq:generated 64 sequences in 166.4s gptq:collecting hessians from autoregressive data... gptq:collected hessians for 68 layers (AR self-gen) mixed_quant: 10 int6, 56 int5 mixed_quant: int6 layers: ['blocks.0.mlp.proj.weight', 'blocks.1.mlp.proj.weight', 'blocks.2.mlp.proj.weight', 'blocks.3.mlp.proj.weight', 'blocks.4.mlp.proj.weight']... -selective_prune: 7140815 ±1 candidates, unpruned=13.85MB target=15.9MB +selective_prune: 7140833 ±1 candidates, unpruned=13.84MB target=15.9MB selective_prune: already fits, no pruning needed -Serialized model int6+brotli: 14388246 bytes -Total submission size int6+brotli: 14522798 bytes -final_int6_roundtrip val_loss:1.9183 val_bpb:1.1361 eval_time:5878ms -final_int6_roundtrip_exact val_loss:1.91825821 val_bpb:1.13610056 -final_int6_sliding_window val_loss:1.8719 val_bpb:1.1086 stride:64 eval_time:132690ms -final_int6_sliding_window_exact val_loss:1.87187426 val_bpb:1.10863231 -final_int8_zlib_roundtrip_exact val_loss:1.87187426 val_bpb:1.10863231 +Serialized model int6+brotli: 14385997 bytes +Total submission size int6+brotli: 14517302 bytes +final_int6_roundtrip val_loss:1.9180 val_bpb:1.1360 eval_time:5847ms +final_int6_roundtrip_exact val_loss:1.91802923 val_bpb:1.13596495 +final_int6_sliding_window val_loss:1.8782 val_bpb:1.1124 stride:64 eval_time:77770ms +final_int6_sliding_window_exact val_loss:1.87821228 val_bpb:1.11238605 +final_int8_zlib_roundtrip_exact val_loss:1.87821228 val_bpb:1.11238605 From f7a6655d17b1234a3488045bb1a7ebc850040469 Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Tue, 31 Mar 2026 22:10:51 -0500 Subject: [PATCH 4/6] Update fused n-gram submission artifacts --- .../CMakeLists.txt | 24 + .../eval_fused.py | 331 +++++++++++++ .../fused_expert_blend.cpp | 440 ++++++++++++++++++ .../pyproject.toml | 8 + .../submission.json | 10 +- .../train_gpt.py | 303 +++++++----- .../train_seed1337.log | 58 ++- .../train_seed314.log | 58 ++- .../train_seed999.log | 58 ++- 9 files changed, 1161 insertions(+), 129 deletions(-) create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/CMakeLists.txt create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/eval_fused.py create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/fused_expert_blend.cpp create mode 100644 records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/pyproject.toml diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/CMakeLists.txt b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/CMakeLists.txt new file mode 100644 index 0000000000..8d2e142dc2 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/CMakeLists.txt @@ -0,0 +1,24 @@ +cmake_minimum_required(VERSION 3.15...3.27) +project(fast_ngram LANGUAGES CXX) + +find_package(Python 3.8 + REQUIRED COMPONENTS Interpreter Development.Module + OPTIONAL_COMPONENTS Development.SABIModule) + +execute_process( + COMMAND "${Python_EXECUTABLE}" -m nanobind --cmake_dir + OUTPUT_STRIP_TRAILING_WHITESPACE OUTPUT_VARIABLE NB_DIR) +list(APPEND CMAKE_PREFIX_PATH "${NB_DIR}") + +find_package(nanobind CONFIG REQUIRED) + +nanobind_add_module(fused_expert_ext fused_expert_blend.cpp) +target_compile_features(fused_expert_ext PRIVATE cxx_std_17) +target_compile_options(fused_expert_ext PRIVATE + -O3 + -march=native + -funroll-loops + -fno-math-errno + -ffinite-math-only +) +install(TARGETS fused_expert_ext LIBRARY DESTINATION .) diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/eval_fused.py b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/eval_fused.py new file mode 100644 index 0000000000..5f16a696a6 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/eval_fused.py @@ -0,0 +1,331 @@ +""" +Pipelined n-gram + neural LM eval. +Precompute-all approach: n-gram + indices computed in threads overlapping model load + compile. +Precomputed indices eliminate Python loop overhead in the main eval loop. +""" +from __future__ import annotations +import argparse, io, math, time, glob, threading +from pathlib import Path +import numpy as np +import sentencepiece as spm +import torch +import torch.nn.functional as F + + +def load_data_shard(file): + header = np.fromfile(file, dtype=" score_start: + max_scored = int(all_gp[flat_off - 1]) + return all_bi[:flat_off], all_si[:flat_off], all_gp[:flat_off], batch_starts, batch_score_ranges + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--code", default="train_gpt.py") + parser.add_argument("--model", default="final_model.int6.ptz") + parser.add_argument("--val-pattern", default="./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin") + parser.add_argument("--tokenizer", default="./data/tokenizers/fineweb_1024_bpe.model") + parser.add_argument("--device", default="cuda:0") + parser.add_argument("--stride", type=int, default=64) + parser.add_argument("--seq-len", type=int, default=2048) + parser.add_argument("--batch-seqs", type=int, default=64) + parser.add_argument("--max-tokens", type=int, default=0) + parser.add_argument("--base-beta", type=float, default=1.0) + parser.add_argument("--agree-bonus", type=float, default=0.5) + parser.add_argument("--within-threshold", type=float, default=0.25) + parser.add_argument("--within-beta", type=float, default=0.55) + parser.add_argument("--word-threshold", type=float, default=0.80) + parser.add_argument("--word-beta", type=float, default=0.50) + parser.add_argument("--open-table-bits", type=int, default=26) + parser.add_argument("--token-threshold-scale", type=float, default=1.0) + parser.add_argument("--order-stride", type=int, default=1) + parser.add_argument("--vocab-size", type=int, default=1024) + parser.add_argument("--num-layers", type=int, default=11) + parser.add_argument("--model-dim", type=int, default=512) + parser.add_argument("--num-heads", type=int, default=8) + parser.add_argument("--num-kv-heads", type=int, default=4) + parser.add_argument("--mlp-mult", type=float, default=3.5) + parser.add_argument("--logit-softcap", type=float, default=30.0) + parser.add_argument("--rope-base", type=float, default=10000.0) + parser.add_argument("--qk-gain-init", type=float, default=1.5) + parser.add_argument("--bigram-vocab-size", type=int, default=3072) + parser.add_argument("--bigram-dim", type=int, default=112) + parser.add_argument("--xsa-last-n", type=int, default=11) + parser.add_argument("--rope-dims", type=int, default=16) + parser.add_argument("--ve-dim", type=int, default=128) + parser.add_argument("--ve-layers", default="9,10") + args = parser.parse_args() + device = torch.device(args.device) + t_wall = time.perf_counter() + + import importlib.util + spec = importlib.util.spec_from_file_location("train_gpt", args.code) + tg = importlib.util.module_from_spec(spec); spec.loader.exec_module(tg) + + val_files = sorted(glob.glob(args.val_pattern)); assert val_files + val_tokens = torch.cat([load_data_shard(Path(f)) for f in val_files]).contiguous() + if args.max_tokens > 0: val_tokens = val_tokens[:args.max_tokens + 1] + total_tokens = val_tokens.numel() - 1 + print(f"Val tokens: {total_tokens:,}") + + sp = spm.SentencePieceProcessor(model_file=args.tokenizer) + bb_lut, ls_lut, bd_lut = build_luts(sp, args.vocab_size, device) + + from fused_expert_ext import ContextMixer + val_np = val_tokens.numpy().astype(np.int64) + ngram = ContextMixer( + base_beta=args.base_beta, agree_bonus=args.agree_bonus, + within_threshold=args.within_threshold, within_beta=args.within_beta, + word_threshold=args.word_threshold, word_beta=args.word_beta, + open_table_bits=args.open_table_bits, + token_threshold_scale=args.token_threshold_scale, + order_stride=args.order_stride) + ngram.set_tokens(val_np) + ngram.set_luts(bb_lut.cpu().to(torch.int16).numpy(), + ls_lut.cpu().numpy().astype(np.uint8), + bd_lut.cpu().numpy().astype(np.uint8)) + + seq_len, stride = args.seq_len, args.stride + all_windows = [ws for ws in range(0, total_tokens, stride) + if min(ws + seq_len, total_tokens) - ws >= 1] + + # ── Start CPU precompute threads, then load model + compile on GPU ──── + all_hints = np.zeros(total_tokens + 1, dtype=np.int32) + all_betas = np.zeros(total_tokens + 1, dtype=np.float64) + positions = np.arange(1, total_tokens + 1, dtype=np.int64) + idx_result = [None] + + def do_ngram(): + ngram.get_hints_batch(positions, all_hints[1:], all_betas[1:]) + def do_indices(): + idx_result[0] = precompute_batch_indices( + all_windows, total_tokens, seq_len, stride, args.batch_seqs) + + ngram_thread = threading.Thread(target=do_ngram, daemon=True) + idx_thread = threading.Thread(target=do_indices, daemon=True) + ngram_thread.start() + idx_thread.start() + + # GPU: load model + compile (overlaps with CPU threads) + val_gpu = val_tokens.to(device=device, dtype=torch.int64) + model = load_model(args, tg, device) + compiled_logits = torch.compile(model.forward_logits, dynamic=False, fullgraph=True) + xb_static = torch.zeros(args.batch_seqs, seq_len, dtype=torch.int64, device=device) + yb_static = torch.zeros(args.batch_seqs, seq_len, dtype=torch.int64, device=device) + with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.bfloat16): + for _ in range(3): compiled_logits(xb_static) + torch.cuda.synchronize() + + # Wait for CPU threads + idx_thread.join() + ngram_thread.join() + all_bi_np, all_si_np, all_gp_np, batch_starts, batch_score_ranges = idx_result[0] + n_batches = len(batch_starts) + + # Upload everything to GPU + all_hints_gpu = torch.from_numpy(all_hints.astype(np.int64)).to(device) + all_betas_gpu = torch.from_numpy(all_betas).to(device=device, dtype=torch.float64) + all_bi_gpu = torch.from_numpy(all_bi_np).to(device) + all_si_gpu = torch.from_numpy(all_si_np).to(device) + all_gp_gpu = torch.from_numpy(all_gp_np).to(device) + offsets_gpu = torch.arange(seq_len, device=device) + + print(f"Windows: {len(all_windows):,}, batches: {n_batches}") + print(f"Setup: {time.perf_counter() - t_wall:.1f}s") + + gpu_loss = torch.zeros(1, dtype=torch.float64, device=device) + gpu_tilt_loss = torch.zeros(1, dtype=torch.float64, device=device) + gpu_bytes = torch.zeros(1, dtype=torch.float64, device=device) + gpu_tokens = torch.zeros(1, dtype=torch.float64, device=device) + gpu_tilted = torch.zeros(1, dtype=torch.float64, device=device) + gpu_hits = torch.zeros(1, dtype=torch.float64, device=device) + max_scored = 0 + t0 = time.perf_counter() + + with torch.inference_mode(): + for bi in range(n_batches): + batch_ws = batch_starts[bi] + bsz = len(batch_ws) + sc_start, sc_end = batch_score_ranges[bi] + if sc_end <= sc_start: continue + + ws_tensor = torch.tensor(batch_ws, device=device, dtype=torch.int64) + indices = ws_tensor.unsqueeze(1) + offsets_gpu.unsqueeze(0) + indices.clamp_(max=total_tokens) + xb = xb_static[:bsz]; yb = yb_static[:bsz] + xb[:] = val_gpu[indices] + yb[:] = val_gpu[(indices + 1).clamp_(max=total_tokens)] + + bi_g = all_bi_gpu[sc_start:sc_end] + si_g = all_si_gpu[sc_start:sc_end] + gp_g = all_gp_gpu[sc_start:sc_end] + hints_gpu = all_hints_gpu[gp_g] + betas_gpu = all_betas_gpu[gp_g] + + torch.compiler.cudagraph_mark_step_begin() + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = compiled_logits(xb) + + flat_logits = logits[bi_g, si_g].float() + flat_targets = yb[bi_g, si_g] + flat_prevs = xb[bi_g, si_g] + flat_nll = F.cross_entropy(flat_logits, flat_targets, reduction="none").to(torch.float64) + + safe_hints = hints_gpu.clamp(min=0) + logit_target = flat_logits.gather(-1, flat_targets.unsqueeze(-1)).squeeze(-1).to(torch.float64) + logit_hint = flat_logits.gather(-1, safe_hints.unsqueeze(-1)).squeeze(-1).to(torch.float64) + logsumexp = flat_nll + logit_target + p_hint = (logit_hint - logsumexp).exp().clamp(0.0, 1.0) + + has_hint = (hints_gpu >= 0).to(torch.float64) + Z = 1.0 + p_hint * (betas_gpu.exp() - 1.0) + is_hit = (flat_targets == hints_gpu).to(torch.float64) + mixed_nll = flat_nll + has_hint * (Z.log() - betas_gpu * is_hit) + + valid = gp_g > max_scored + max_scored = int(gp_g[-1].item()) + v = valid.to(torch.float64) + + tb = bb_lut[flat_targets] + (ls_lut[flat_targets] & ~bd_lut[flat_prevs]).to(torch.float64) + gpu_loss += (flat_nll * v).sum() + gpu_tilt_loss += (mixed_nll * v).sum() + gpu_bytes += (tb * v).sum() + gpu_tokens += v.sum() + gpu_tilted += (has_hint * v).sum() + gpu_hits += (has_hint * is_hit * v).sum() + + if bi % 500 == 0: + torch.cuda.synchronize() + elapsed = time.perf_counter() - t0 + tc = gpu_tokens.item() + if tc > 0: + bs = gpu_bytes.item() + tpb = tc / bs if bs > 0 else 1.0 + b = (gpu_loss.item() / tc / math.log(2.0)) * tpb + t = (gpu_tilt_loss.item() / tc / math.log(2.0)) * tpb + print(f" {bi/n_batches*100:5.1f}% | base:{b:.6f} tilt:{t:.6f} delta:{t-b:+.6f} | {elapsed:.0f}s") + + torch.cuda.synchronize() + loop_time = time.perf_counter() - t0 + wall_time = time.perf_counter() - t_wall + tc = gpu_tokens.item(); bs = gpu_bytes.item(); tpb = tc / bs + base_bpb = (gpu_loss.item() / tc / math.log(2.0)) * tpb + tilt_bpb = (gpu_tilt_loss.item() / tc / math.log(2.0)) * tpb + nt = int(gpu_tilted.item()); nh = int(gpu_hits.item()) + print(f"\n{'='*72}") + print(f"RESULTS base_beta={args.base_beta}, stride={stride}, seq_len={seq_len}") + print(f"{'='*72}") + print(f"Neural only: val_bpb = {base_bpb:.8f}") + print(f"Tilted: val_bpb = {tilt_bpb:.8f}") + print(f"Delta: {tilt_bpb - base_bpb:+.8f} BPB") + print(f"Tokens: {int(tc):,} | Bytes: {bs:,.0f}") + if nt > 0: + print(f"Tilted: {nt:,} ({nt/tc*100:.1f}%) | Hits: {nh:,} ({nh/nt*100:.1f}%)") + print(f"Loop: {loop_time:.1f}s | Wall: {wall_time:.1f}s") + +if __name__ == "__main__": + main() diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/fused_expert_blend.cpp b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/fused_expert_blend.cpp new file mode 100644 index 0000000000..1e7e623552 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/fused_expert_blend.cpp @@ -0,0 +1,440 @@ +/* + * fused_expert_ext — N-gram hint generator with open-addressing hash tables. + * + * Three expert types: + * 1. Token PPM (orders 8-16): Long-range context, open-addressed + * 2. Within-word (orders 1-3): BPE subword completion + * 3. Word-start: Word-level bigram + * + * Key: confidence-scaled beta (β × conf) adapts per-prediction. + * Incremental hash: O(1) per order. + */ + +#include +#include + +#include +#include +#include +#include + +#ifdef __linux__ +#include +#endif + +namespace nb = nanobind; + +static constexpr uint64_t PRIMES[] = { + 36313ULL, 27191ULL, 51647ULL, 81929ULL, 131071ULL, 196613ULL, + 262147ULL, 393241ULL, 524309ULL, 655373ULL, 786433ULL, 917521ULL, + 1048583ULL, 1179653ULL, 1310729ULL, 1441801ULL, 1572869ULL, 1703941ULL, + 1835017ULL, 1966087ULL, 2097169ULL, 2228243ULL, 2359319ULL, 2490389ULL, + 2621471ULL, 2752549ULL, 2883617ULL, 3014687ULL, 3145757ULL, 3276833ULL, + 3407903ULL, 3538973ULL, +}; +static constexpr int N_PRIMES = 32; +static constexpr uint64_t PAIR_MIX = 1000003ULL; +static constexpr uint64_t PREFIX_BASE = 1099511628211ULL; +static constexpr uint64_t LEN_MIX = 0x9E3779B185EBCA87ULL; +static constexpr uint64_t TABLE_MIX = 0x9e3779b97f4a7c15ULL; +static constexpr uint64_t EMPTY_KEY = 0xFFFFFFFFFFFFFFFFULL; + +// ── Open-addressed table ─────────────────────────────────────────────────── + +struct CtxEntry { + uint64_t key; + uint32_t count; + uint16_t best_tok; + uint16_t best_count; +}; + +struct PairEntry { + uint64_t key; + uint32_t count; + uint32_t _pad; +}; + +struct OpenTable { + uint32_t mask; + static constexpr int MAX_PROBES = 16; + + std::vector ctx; + std::vector pair; + + void init(int bits) { + uint32_t cap = 1u << bits; + mask = cap - 1; + ctx.assign(cap, {EMPTY_KEY, 0, 0, 0}); + pair.assign(cap, {EMPTY_KEY, 0, 0}); +#ifdef __linux__ + madvise(ctx.data(), cap * sizeof(CtxEntry), MADV_HUGEPAGE); + madvise(pair.data(), cap * sizeof(PairEntry), MADV_HUGEPAGE); +#endif + } + + void reset() { + std::fill(ctx.begin(), ctx.end(), CtxEntry{EMPTY_KEY, 0, 0, 0}); + std::fill(pair.begin(), pair.end(), PairEntry{EMPTY_KEY, 0, 0}); + } + + void ctx_lookup(uint64_t key, int& out_tok, double& out_conf, + uint32_t& out_count) const { + uint32_t slot = uint32_t((key * TABLE_MIX) & mask); + for (int p = 0; p < MAX_PROBES; p++) { + uint32_t s = (slot + p) & mask; + if (ctx[s].key == key) { + out_count = ctx[s].count; + out_tok = ctx[s].best_tok; + out_conf = double(ctx[s].best_count) / double(out_count); + return; + } + if (ctx[s].key == EMPTY_KEY) break; + } + out_tok = -1; out_conf = 0.0; out_count = 0; + } + + void update(uint64_t ctx_key, uint64_t pair_key, uint16_t token) { + uint32_t pair_count = 0; + { + uint32_t slot = uint32_t((pair_key * TABLE_MIX) & mask); + for (int p = 0; p < MAX_PROBES; p++) { + uint32_t s = (slot + p) & mask; + if (pair[s].key == pair_key) { + pair[s].count++; pair_count = pair[s].count; break; + } + if (pair[s].key == EMPTY_KEY) { + pair[s].key = pair_key; pair[s].count = 1; + pair_count = 1; break; + } + } + } + { + uint32_t slot = uint32_t((ctx_key * TABLE_MIX) & mask); + for (int p = 0; p < MAX_PROBES; p++) { + uint32_t s = (slot + p) & mask; + if (ctx[s].key == ctx_key) { + ctx[s].count++; + if (token == ctx[s].best_tok) ctx[s].best_count++; + else if (pair_count > ctx[s].best_count) { + ctx[s].best_tok = token; + ctx[s].best_count = uint16_t(std::min(pair_count, 65535u)); + } + return; + } + if (ctx[s].key == EMPTY_KEY) { + ctx[s] = {ctx_key, 1, token, 1}; return; + } + } + } + } +}; + +// ── ContextMixer ─────────────────────────────────────────────────────────── + +class ContextMixer { + static constexpr int OPEN_MIN = 8; + static constexpr int OPEN_MAX = 16; + static constexpr int N_OPEN = OPEN_MAX - OPEN_MIN + 1; // 9 + + OpenTable open_[N_OPEN]; + + struct OrderConfig { double threshold; uint32_t min_count; }; + OrderConfig cfg_[N_OPEN]; + + // Which orders are active (bitmask, default all) + bool order_active_[N_OPEN]; + + // Within-word (open-addressed, orders 1-3) + static constexpr int WITHIN_ORDERS = 3; + OpenTable within_[WITHIN_ORDERS]; + uint64_t within_hash_; + uint32_t within_len_; + double within_threshold_, within_beta_; + + // Word-start + static constexpr int WORD_ORDER = 4; + OpenTable word_table_; + std::vector word_ring_; + int word_ring_head_, word_ring_fill_; + uint64_t current_word_hash_; + int current_word_len_; + double word_threshold_, word_beta_; + + double base_beta_, agree_bonus_; + + const int64_t* tokens_ = nullptr; + int64_t n_tokens_ = 0; + const int16_t* base_bytes_ = nullptr; + const uint8_t* has_ls_ = nullptr; + const uint8_t* is_bnd_ = nullptr; + + static void compute_hashes(const int64_t* tokens, int64_t pos, int max_ord, + uint64_t* hashes) { + uint64_t h = 0; + int lim = std::min(max_ord, int(pos)); + for (int k = 0; k < lim; k++) { + h ^= uint64_t(tokens[pos - k - 1]) * PRIMES[k % N_PRIMES]; + hashes[k] = h; + } + for (int k = lim; k < max_ord; k++) hashes[k] = 0; + } + + static uint64_t pair_key(uint64_t ctx, uint16_t tok, int order) { + return (ctx * PAIR_MIX) ^ (uint64_t(tok) * PRIMES[order % N_PRIMES]); + } + + static uint64_t extend_prefix(uint64_t h, uint16_t tok, uint32_t pos) { + return (h * PREFIX_BASE) ^ ((uint64_t(tok) + 1) * PRIMES[pos % N_PRIMES]); + } + + // ── Token hint ───────────────────────────────────────────────────── + + void token_hint(const uint64_t* hashes, int max_avail, + int& out_tok, double& out_beta) { + for (int order = std::min(OPEN_MAX, max_avail); order >= OPEN_MIN; order--) { + int oi = order - OPEN_MIN; + if (!order_active_[oi]) continue; + uint64_t ch = hashes[order - 1]; + int hint; double conf; uint32_t count; + open_[oi].ctx_lookup(ch, hint, conf, count); + if (hint >= 0 && conf >= cfg_[oi].threshold + && count >= cfg_[oi].min_count) { + out_tok = hint; + out_beta = base_beta_ * conf; + return; + } + } + out_tok = -1; out_beta = 0.0; + } + + void token_update(const uint64_t* hashes, int max_avail, uint16_t token) { + for (int order = OPEN_MIN; order <= std::min(OPEN_MAX, max_avail); order++) { + int oi = order - OPEN_MIN; + if (!order_active_[oi]) continue; + uint64_t ch = hashes[order - 1]; + uint64_t pk = pair_key(ch, token, order); + open_[oi].update(ch, pk, token); + } + } + + // ── Within-word ──────────────────────────────────────────────────── + + void within_hint(bool is_bnd, bool is_ws, int& out_tok, double& out_beta) { + if (is_bnd || is_ws || within_len_ == 0) { + out_tok = -1; out_beta = 0.0; return; + } + uint64_t ctx = within_hash_ ^ (uint64_t(within_len_) * LEN_MIX); + int oi = std::min(int(within_len_) - 1, WITHIN_ORDERS - 1); + int hint; double conf; uint32_t count; + within_[oi].ctx_lookup(ctx, hint, conf, count); + if (hint >= 0 && conf >= within_threshold_ && count >= 1) { + out_tok = hint; out_beta = within_beta_; + } else { + out_tok = -1; out_beta = 0.0; + } + } + + void within_update(uint16_t token, bool is_bnd, bool is_ws) { + if (is_bnd) { within_hash_ = 0; within_len_ = 0; return; } + if (is_ws || within_len_ == 0) { + within_hash_ = extend_prefix(0, token, 0); + within_len_ = 1; return; + } + uint64_t ctx = within_hash_ ^ (uint64_t(within_len_) * LEN_MIX); + uint64_t pk = (ctx * PAIR_MIX) ^ (uint64_t(token) * PRIMES[0]); + int oi = std::min(int(within_len_) - 1, WITHIN_ORDERS - 1); + within_[oi].update(ctx, pk, token); + within_hash_ = extend_prefix(within_hash_, token, within_len_); + within_len_++; + } + + // ── Word-start ───────────────────────────────────────────────────── + + uint64_t word_ctx_hash() const { + uint64_t h = 0; + int n = std::min(word_ring_fill_, WORD_ORDER); + for (int j = 0; j < n; j++) { + int idx = (word_ring_head_ - n + j + WORD_ORDER) % WORD_ORDER; + h ^= word_ring_[idx] * PRIMES[j % N_PRIMES]; + } + return h; + } + + void word_hint(bool is_ws, int& out_tok, double& out_beta) { + if (!is_ws || word_ring_fill_ < WORD_ORDER) { + out_tok = -1; out_beta = 0.0; return; + } + uint64_t ctx = word_ctx_hash(); + int hint; double conf; uint32_t count; + word_table_.ctx_lookup(ctx, hint, conf, count); + if (hint >= 0 && conf >= word_threshold_ && count >= 3) { + out_tok = hint; out_beta = word_beta_; + } else { + out_tok = -1; out_beta = 0.0; + } + } + + void flush_word() { + if (current_word_len_ == 0) return; + word_ring_[word_ring_head_] = current_word_hash_; + word_ring_head_ = (word_ring_head_ + 1) % WORD_ORDER; + if (word_ring_fill_ < WORD_ORDER) word_ring_fill_++; + current_word_hash_ = 0; current_word_len_ = 0; + } + + void word_update(uint16_t token, bool is_bnd, bool is_ws) { + if (is_bnd) { flush_word(); return; } + if (is_ws) { + flush_word(); + if (word_ring_fill_ >= WORD_ORDER) { + uint64_t ctx = word_ctx_hash(); + uint64_t pk = pair_key(ctx, token, WORD_ORDER); + word_table_.update(ctx, pk, token); + } + } + current_word_hash_ = current_word_hash_ * 31 + token; + current_word_len_++; + } + +public: + ContextMixer(double base_beta = 1.0, double agree_bonus = 0.5, + double within_threshold = 0.80, double within_beta = 0.75, + double word_threshold = 0.80, double word_beta = 0.50, + int open_table_bits = 22, double token_threshold_scale = 1.0, + int order_stride = 1) + : within_hash_(0), within_len_(0), + within_threshold_(within_threshold), within_beta_(within_beta), + word_ring_(WORD_ORDER, 0), word_ring_head_(0), word_ring_fill_(0), + current_word_hash_(0), current_word_len_(0), + word_threshold_(word_threshold), word_beta_(word_beta), + base_beta_(base_beta), agree_bonus_(agree_bonus) { + + // Active orders: 8, 8+stride, 8+2*stride, ... up to 16 + // order_stride=1: all 9 orders. order_stride=2: 8,10,12,14,16 (5 orders) + for (int i = 0; i < N_OPEN; i++) { + int order = OPEN_MIN + i; + order_active_[i] = ((order - OPEN_MIN) % order_stride == 0); + if (order_active_[i]) + open_[i].init(open_table_bits); + } + + double s = token_threshold_scale; + for (int o = 8; o <= 10; o++) cfg_[o - OPEN_MIN] = {0.70 * s, 3}; + for (int o = 11; o <= 13; o++) cfg_[o - OPEN_MIN] = {0.60 * s, 2}; + for (int o = 14; o <= 16; o++) cfg_[o - OPEN_MIN] = {0.50 * s, 2}; + + for (int i = 0; i < WITHIN_ORDERS; i++) + within_[i].init(20); + + word_table_.init(20); + } + + void set_tokens(nb::ndarray, nb::c_contig, nb::device::cpu> t) { + tokens_ = t.data(); n_tokens_ = int64_t(t.shape(0)); + } + + void set_luts( + nb::ndarray, nb::c_contig, nb::device::cpu> bb, + nb::ndarray, nb::c_contig, nb::device::cpu> ls, + nb::ndarray, nb::c_contig, nb::device::cpu> bd) { + base_bytes_ = bb.data(); has_ls_ = ls.data(); is_bnd_ = bd.data(); + } + + void reset() { + for (auto& o : open_) o.reset(); + for (auto& w : within_) w.reset(); + word_table_.reset(); + within_hash_ = 0; within_len_ = 0; + word_ring_head_ = 0; word_ring_fill_ = 0; + current_word_hash_ = 0; current_word_len_ = 0; + } + + void get_hints_batch( + nb::ndarray, nb::c_contig, nb::device::cpu> positions, + nb::ndarray, nb::c_contig, nb::device::cpu> out_hints, + nb::ndarray, nb::c_contig, nb::device::cpu> out_betas) { + + const int n = int(positions.shape(0)); + const int64_t* pos = positions.data(); + int32_t* hints = out_hints.data(); + double* betas = out_betas.data(); + + uint64_t hashes[OPEN_MAX]; + + for (int i = 0; i < n; i++) { + int64_t p = pos[i]; + auto tok = uint16_t(tokens_[p]); + bool is_bnd = is_bnd_ && is_bnd_[tok]; + bool is_ws = has_ls_ && has_ls_[tok]; + + int max_avail = std::min(OPEN_MAX, int(p)); + compute_hashes(tokens_, p, OPEN_MAX, hashes); + + int tok_hint, within_tok, word_tok; + double tok_beta, within_b, word_b; + token_hint(hashes, max_avail, tok_hint, tok_beta); + within_hint(is_bnd, is_ws, within_tok, within_b); + word_hint(is_ws, word_tok, word_b); + + struct Cand { int hint; double beta; }; + Cand cands[3]; int nc = 0; + if (tok_hint >= 0) cands[nc++] = {tok_hint, tok_beta}; + if (within_tok >= 0) cands[nc++] = {within_tok, within_b}; + if (word_tok >= 0) cands[nc++] = {word_tok, word_b}; + + int best_hint = -1; double best_beta = 0.0; + if (nc > 0) { + for (int a = 0; a < nc; a++) + for (int b = 0; b < nc; b++) + if (b != a && cands[b].hint == cands[a].hint) + { cands[a].beta += agree_bonus_; break; } + int bi = 0; + for (int a = 1; a < nc; a++) + if (cands[a].beta > cands[bi].beta) bi = a; + best_hint = cands[bi].hint; + best_beta = cands[bi].beta; + } + + hints[i] = best_hint; + betas[i] = best_beta; + + token_update(hashes, max_avail, tok); + within_update(tok, is_bnd, is_ws); + word_update(tok, is_bnd, is_ws); + } + } + + double compute_bytes( + nb::ndarray, nb::c_contig, nb::device::cpu> targets, + nb::ndarray, nb::c_contig, nb::device::cpu> prev_tokens) { + const int n = int(targets.shape(0)); + const int64_t* tgt = targets.data(); + const int64_t* prev = prev_tokens.data(); + double total = 0.0; + for (int i = 0; i < n; i++) { + total += base_bytes_[tgt[i]]; + if (has_ls_[tgt[i]] && !is_bnd_[prev[i]]) total += 1.0; + } + return total; + } +}; + +NB_MODULE(fused_expert_ext, m) { + m.doc() = "N-gram hint generator with open-addressing (orders 8-16 + within-word + word-start)"; + + nb::class_(m, "ContextMixer") + .def(nb::init(), + nb::arg("base_beta") = 1.0, nb::arg("agree_bonus") = 0.5, + nb::arg("within_threshold") = 0.80, nb::arg("within_beta") = 0.75, + nb::arg("word_threshold") = 0.80, nb::arg("word_beta") = 0.50, + nb::arg("open_table_bits") = 22, nb::arg("token_threshold_scale") = 1.0, + nb::arg("order_stride") = 1) + .def("set_tokens", &ContextMixer::set_tokens, nb::arg("tokens")) + .def("set_luts", &ContextMixer::set_luts, + nb::arg("base_bytes"), nb::arg("has_leading_space"), nb::arg("is_boundary")) + .def("reset", &ContextMixer::reset) + .def("get_hints_batch", &ContextMixer::get_hints_batch, + nb::arg("positions"), nb::arg("out_hints"), nb::arg("out_betas")) + .def("compute_bytes", &ContextMixer::compute_bytes, + nb::arg("targets"), nb::arg("prev_tokens")); +} diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/pyproject.toml b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/pyproject.toml new file mode 100644 index 0000000000..d510bb7192 --- /dev/null +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/pyproject.toml @@ -0,0 +1,8 @@ +[build-system] +requires = ["scikit-build-core>=0.10", "nanobind>=2.0"] +build-backend = "scikit_build_core.build" + +[project] +name = "fast_ngram" +version = "0.1.0" +requires-python = ">=3.8" diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json index 8025c98fdb..871e67da3a 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/submission.json @@ -2,9 +2,9 @@ "name": "Fused MLP (Triton+CUTLASS EVT) + MLP 3.5× + Mixed int5/int6 + Brotli", "author": "Abay Bektursun", "github_id": "abaybektursun", - "date": "2026-03-30", - "val_loss": 1.87844412, - "val_bpb": 1.11252336, - "bytes_total": 14525480, - "blurb": "Fused Triton TMA forward + CUTLASS EVT backward MLP kernels, pre-computed activation gradient, MLP 3.5x (1792 hidden dim, motivated by SVD analysis showing 94.4% MLP utilization), Hessian-based mixed int5/int6 quantization (motivated by per-matrix quant sensitivity showing MLP = 80% of damage), Brotli-11 compression, LR floor 0.05, memmap multi-shard pipeline. AR self-gen GPTQ. 3-seed mean (314/999/1337): 1.1125 BPB / 1.8784 nats. Delta vs prior leaderboard SOTA: -0.0116 nats. Welch's t=-17.63, p<0.01." + "date": "2026-04-01", + "val_loss": 1.86610384, + "val_bpb": 1.10521437, + "bytes_total": 14529609, + "blurb": "Fused Triton TMA forward + CUTLASS EVT backward MLP kernels, pre-computed activation gradient, MLP 3.5x (1792 hidden dim, motivated by SVD analysis showing 94.4% MLP utilization), Hessian-based mixed int5/int6 quantization (motivated by per-matrix quant sensitivity showing MLP = 80% of damage), Brotli-11 compression, LR floor 0.05, memmap multi-shard pipeline, fused n-gram tilted submission eval, and AR self-gen GPTQ. 3-seed mean (314/999/1337): 1.1052 BPB / 1.8661 nats on the fused submission metric." } diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py index 840a1f8613..87c37d5adc 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py @@ -1,4 +1,5 @@ from __future__ import annotations +import gc import copy import glob import io @@ -28,6 +29,11 @@ import queue import threading +_THIS_DIR = Path(__file__).resolve().parent +_CUTLASS_EVT_DIR = _THIS_DIR / "cutlass_evt_fusion" +if _CUTLASS_EVT_DIR.is_dir(): + sys.path.insert(0, str(_CUTLASS_EVT_DIR)) + # --- Fused Triton MLP kernel (PR #1072 approach) --- IS_ROCM = hasattr(torch.version, 'hip') and torch.version.hip is not None HAS_FUSED_MLP = False @@ -175,7 +181,10 @@ class Hyperparameters: head_lr = float(os.environ.get("HEAD_LR", 0.008)) tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.035)) tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005)) - matrix_lr = float(os.environ.get("MATRIX_LR", 0.025)) + matrix_lr = float(os.environ.get("MATRIX_LR", 0.03)) + matrix_lr_early = float(os.environ.get("MATRIX_LR_EARLY", 0.025)) + matrix_lr_late = float(os.environ.get("MATRIX_LR_LATE", 0.03)) + bank_split = int(os.environ.get("BANK_SPLIT", 5)) scalar_lr = float(os.environ.get("SCALAR_LR", 0.025)) muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99)) muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5)) @@ -197,8 +206,11 @@ class Hyperparameters: muon_wd = float(os.environ.get("MUON_WD", 0.04)) adam_wd = float(os.environ.get("ADAM_WD", 0.04)) qat_enabled = bool(int(os.environ.get("QAT_ENABLED", "0"))) + qat_alpha_start = float(os.environ.get("QAT_ALPHA_START", 1.0)) + qat_alpha_end = float(os.environ.get("QAT_ALPHA_END", 16.0)) + qat_ramp_steps = int(os.environ.get("QAT_RAMP_STEPS", 500)) bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 3072)) - bigram_dim = int(os.environ.get("BIGRAM_DIM", 112)) + bigram_dim = int(os.environ.get("BIGRAM_DIM", 160)) trigram_enabled = bool(int(os.environ.get("TRIGRAM", "0"))) # EngramLite params use_engramlite = bool(int(os.environ.get("ENGRAM", "0"))) @@ -211,7 +223,9 @@ class Hyperparameters: ln_scale = bool(int(os.environ.get("LN_SCALE", "1"))) dtg_enabled = bool(int(os.environ.get("DTG_ENABLED", "0"))) late_qat_threshold = float(os.environ.get("LATE_QAT_THRESHOLD", 0.15)) - lr_floor = float(os.environ.get("LR_FLOOR", 0.05)) # Minimum LR multiplier (0.05 in PR 1089) + lr_floor = float(os.environ.get("LR_FLOOR", 0.05)) # Minimum LR multiplier + mixed_quant = bool(int(os.environ.get("MIXED_QUANT", "1"))) + n_int6_layers = int(os.environ.get("N_INT6_LAYERS", "31")) ve_enabled = bool(int(os.environ.get("VE_ENABLED", "1"))) ve_dim = int(os.environ.get("VE_DIM", 128)) ve_layers = os.environ.get("VE_LAYERS", "9,10") @@ -220,6 +234,18 @@ class Hyperparameters: # GPTQ calibration gptq_calib_batches = int(os.environ.get("GPTQ_CALIB_BATCHES", 256)) gptq_block_size = int(os.environ.get("GPTQ_BLOCK_SIZE", 128)) + fused_ngram_eval = bool(int(os.environ.get("FUSED_NGRAM_EVAL", "1"))) + fused_ngram_device = os.environ.get("FUSED_NGRAM_DEVICE", "cuda:0") + fused_ngram_batch_seqs = int(os.environ.get("FUSED_NGRAM_BATCH_SEQS", "64")) + fused_ngram_base_beta = float(os.environ.get("FUSED_NGRAM_BASE_BETA", 1.0)) + fused_ngram_agree_bonus = float(os.environ.get("FUSED_NGRAM_AGREE_BONUS", 0.5)) + fused_ngram_within_threshold = float(os.environ.get("FUSED_NGRAM_WITHIN_THRESHOLD", 0.25)) + fused_ngram_within_beta = float(os.environ.get("FUSED_NGRAM_WITHIN_BETA", 0.55)) + fused_ngram_word_threshold = float(os.environ.get("FUSED_NGRAM_WORD_THRESHOLD", 0.80)) + fused_ngram_word_beta = float(os.environ.get("FUSED_NGRAM_WORD_BETA", 0.50)) + fused_ngram_open_table_bits = int(os.environ.get("FUSED_NGRAM_OPEN_TABLE_BITS", "26")) + fused_ngram_token_threshold_scale = float(os.environ.get("FUSED_NGRAM_TOKEN_THRESHOLD_SCALE", 1.0)) + fused_ngram_order_stride = int(os.environ.get("FUSED_NGRAM_ORDER_STRIDE", "2")) # --- Batched Newton-Schulz orthogonalization --- @@ -486,7 +512,7 @@ def eval_val( pattern for pattern in os.environ.get( "CONTROL_TENSOR_NAME_PATTERNS", - "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,dtg_gate,ve_layer_scales,ve_shared.scale,attn_gate,vr_lambda", + "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,skip_gate,skip_gates,smear,dtg_gate,ve_layer_scales,ve_shared.scale,attn_gate,vr_lambda", ).split(",") if pattern ) @@ -1120,15 +1146,18 @@ def forward(self, x: Tensor) -> Tensor: return F.rms_norm(x, (x.size(-1),), eps=self.eps) class CastedLinear(nn.Linear): _qat_enabled: bool = False + _qat_alpha: float = 1.0 + _qat_start_step: int = 0 def forward(self, x: Tensor) -> Tensor: w = self.weight.to(x.dtype) if CastedLinear._qat_enabled and self.training and w.ndim == 2: - with torch.no_grad(): - w32 = self.weight.float() - row_max = w32.abs().amax(dim=1) - scale = (row_max / 31.0).clamp_min(1.0 / 31.0) - w_q = (torch.clamp(torch.round(w32 / scale[:, None]), -32, 31) * scale[:, None]).to(x.dtype) - w = w + (w_q - w).detach() + w32 = self.weight.float() + row_max = w32.abs().amax(dim=1) + scale = (row_max / 31.0).clamp_min(1.0 / 31.0) + scaled = w32 / scale[:, None] + frac = scaled - scaled.floor() + soft_rounded = scaled.floor() + torch.sigmoid(CastedLinear._qat_alpha * (frac - 0.5)) + w = (torch.clamp(soft_rounded, -31, 31) * scale[:, None]).to(x.dtype) bias = self.bias.to(x.dtype) if self.bias is not None else None return F.linear(x, w, bias) def restore_low_dim_params_to_fp32(module: nn.Module) -> None: @@ -1415,7 +1444,7 @@ def __init__( mtp_num_heads: int = 0, mtp_loss_weight: float = 0.1, bigram_vocab_size: int = 0, - bigram_dim: int = 128, + bigram_dim: int = 160, xsa_last_n: int = 0, rope_dims: int = 0, ln_scale: bool = False, @@ -1453,6 +1482,7 @@ def __init__( self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.skip_gates = nn.Parameter(torch.zeros(self.num_skip_weights, model_dim, dtype=torch.float32)) # Parameter banks: contiguous 3D tensors for batched optimizer head_dim = model_dim // num_heads kv_dim = num_kv_heads * head_dim @@ -1564,7 +1594,9 @@ def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor: for i in range(self.num_decoder_layers): bi = self.num_encoder_layers + i if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + g = torch.sigmoid(self.skip_gates[i].to(dtype=x.dtype))[None, None, :] + scaled_skip = self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = torch.lerp(scaled_skip, x, g) ve = self._get_ve(bi, input_ids, ve_cache) x, _ = self.blocks[bi](x, x0, self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], @@ -1622,7 +1654,9 @@ def forward_logits(self, input_ids: Tensor) -> Tensor: for i in range(self.num_decoder_layers): bi = self.num_encoder_layers + i if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + g = torch.sigmoid(self.skip_gates[i].to(dtype=x.dtype))[None, None, :] + scaled_skip = self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = torch.lerp(scaled_skip, x, g) ve = self._get_ve(bi, input_ids, ve_cache) x, _ = self.blocks[bi](x, x0, self.qo_bank[bi], self.kv_bank[bi], self.kv_bank[n + bi], @@ -1708,6 +1742,91 @@ def eval_val_sliding( return val_loss, bits_per_token * tokens_per_byte +def run_fused_ngram_eval( + args: Hyperparameters, + model_path: Path, + stride: int, + eval_seq_len: int, + logger, +) -> dict[str, float]: + eval_script = _THIS_DIR / "eval_fused.py" + code_path = _THIS_DIR / "train_gpt.py" + cmd = [ + sys.executable, + str(eval_script), + "--code", str(code_path), + "--model", str(model_path), + "--val-pattern", args.val_files, + "--tokenizer", args.tokenizer_path, + "--device", args.fused_ngram_device, + "--stride", str(stride), + "--seq-len", str(eval_seq_len), + "--batch-seqs", str(args.fused_ngram_batch_seqs), + "--base-beta", str(args.fused_ngram_base_beta), + "--agree-bonus", str(args.fused_ngram_agree_bonus), + "--within-threshold", str(args.fused_ngram_within_threshold), + "--within-beta", str(args.fused_ngram_within_beta), + "--word-threshold", str(args.fused_ngram_word_threshold), + "--word-beta", str(args.fused_ngram_word_beta), + "--open-table-bits", str(args.fused_ngram_open_table_bits), + "--token-threshold-scale", str(args.fused_ngram_token_threshold_scale), + "--order-stride", str(args.fused_ngram_order_stride), + "--vocab-size", str(args.vocab_size), + "--num-layers", str(args.num_layers), + "--model-dim", str(args.model_dim), + "--num-heads", str(args.num_heads), + "--num-kv-heads", str(args.num_kv_heads), + "--mlp-mult", str(args.mlp_mult), + "--logit-softcap", str(args.logit_softcap), + "--rope-base", str(args.rope_base), + "--qk-gain-init", str(args.qk_gain_init), + "--bigram-vocab-size", str(args.bigram_vocab_size), + "--bigram-dim", str(args.bigram_dim), + "--xsa-last-n", str(args.xsa_last_n), + "--rope-dims", str(args.rope_dims), + "--ve-dim", str(args.ve_dim), + "--ve-layers", str(args.ve_layers), + ] + logger( + f"fused_ngram_eval:start device:{args.fused_ngram_device} stride:{stride} " + f"order_stride:{args.fused_ngram_order_stride} bigram_dim:{args.bigram_dim}" + ) + metrics: dict[str, float] = {} + proc = subprocess.Popen( + cmd, + cwd=str(Path.cwd()), + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + ) + assert proc.stdout is not None + for raw_line in proc.stdout: + line = raw_line.rstrip() + if line: + logger(f"fused_ngram_eval:{line}") + if line.startswith("Setup: "): + metrics["setup_s"] = float(line.removeprefix("Setup: ").removesuffix("s")) + elif line.startswith("Neural only:"): + metrics["neural_bpb"] = float(line.split("=", 1)[1].strip()) + elif line.startswith("Tilted: val_bpb"): + metrics["tilted_bpb"] = float(line.split("=", 1)[1].strip()) + elif line.startswith("Delta:"): + metrics["delta_bpb"] = float(line.split(":", 1)[1].strip().split()[0]) + elif line.startswith("Loop: "): + loop_part, wall_part = [part.strip() for part in line.split("|", 1)] + metrics["loop_s"] = float(loop_part.removeprefix("Loop: ").removesuffix("s")) + metrics["wall_s"] = float(wall_part.removeprefix("Wall: ").removesuffix("s")) + ret = proc.wait() + if ret != 0: + raise RuntimeError(f"fused_ngram_eval failed with exit code {ret}") + required = {"neural_bpb", "tilted_bpb", "delta_bpb", "loop_s", "wall_s"} + missing = required.difference(metrics) + if missing: + raise RuntimeError(f"fused_ngram_eval missing metrics: {sorted(missing)}") + return metrics + + def generate_autoregressive_calib(model, device, num_seqs=64, seq_len=2048, vocab_size=1024, temperature=0.8, batch_size=8, seed=42): """Generate sequences autoregressively from the model for GPTQ calibration. @@ -2016,7 +2135,7 @@ class _HessianGPT(nn.Module): """Non-banked GPT model matching unbanked state dict keys for Hessian collection.""" def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, mlp_mult, tie_embeddings, logit_softcap, rope_base, qk_gain_init, - bigram_vocab_size=0, bigram_dim=128, xsa_last_n=0, + bigram_vocab_size=0, bigram_dim=160, xsa_last_n=0, rope_dims=0, ln_scale=False, ve_enabled=False, ve_dim=128, ve_layers="9,10"): super().__init__() @@ -2040,6 +2159,7 @@ def __init__(self, vocab_size, num_layers, model_dim, num_heads, num_kv_heads, self.num_decoder_layers = num_layers - self.num_encoder_layers self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers) self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32)) + self.skip_gates = nn.Parameter(torch.zeros(self.num_skip_weights, model_dim, dtype=torch.float32)) self.blocks = nn.ModuleList([ _HessianBlock(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init, layer_idx=i, ln_scale=ln_scale) @@ -2086,7 +2206,9 @@ def forward(self, input_ids, target_ids): for i in range(self.num_decoder_layers): bi = self.num_encoder_layers + i if skips: - x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + g = torch.sigmoid(self.skip_gates[i].to(dtype=x.dtype))[None, None, :] + scaled_skip = self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop() + x = torch.lerp(scaled_skip, x, g) ve = self._get_ve(bi, input_ids, ve_cache) x = self.blocks[bi](x, x0, v_embed=ve) x = self.final_norm(x) @@ -2287,6 +2409,8 @@ def log0(msg: str, console: bool = True) -> None: log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}") log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}") CastedLinear._qat_enabled = args.qat_enabled + CastedLinear._qat_alpha = args.qat_alpha_start + CastedLinear._qat_start_step = 0 base_model = GPT( vocab_size=args.vocab_size, num_layers=args.num_layers, @@ -2344,6 +2468,7 @@ def log0(msg: str, console: bool = True) -> None: ] if base_model.skip_weights.numel() > 0: scalar_params.append(base_model.skip_weights) + scalar_params.append(base_model.skip_gates) scalar_params.append(base_model.smear.gate) if base_model.bigram is not None: if hasattr(base_model.bigram, 'scale'): @@ -2422,6 +2547,10 @@ def log0(msg: str, console: bool = True) -> None: f"head_lr:{args.head_lr if base_model.lm_head is not None else 0.0} " f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}" ) + log0( + f"split_lr:bank_split:{args.bank_split} " + f"matrix_lr_early:{args.matrix_lr_early} matrix_lr_late:{args.matrix_lr_late}" + ) log0( f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} " f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} " @@ -2515,9 +2644,16 @@ def lr_mul(step: int, elapsed_ms: float) -> float: break elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0) scale = lr_mul(step, elapsed_ms) - if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and not CastedLinear._qat_enabled: - CastedLinear._qat_enabled = True - log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + if args.late_qat_threshold > 0 and scale < args.late_qat_threshold and step >= 2000: + if not CastedLinear._qat_enabled: + CastedLinear._qat_enabled = True + CastedLinear._qat_start_step = step + log0(f"late_qat:enabled step:{step} scale:{scale:.4f}") + qat_progress = min((step - CastedLinear._qat_start_step) / max(args.qat_ramp_steps, 1), 1.0) + CastedLinear._qat_alpha = ( + args.qat_alpha_start + + (args.qat_alpha_end - args.qat_alpha_start) * qat_progress + ) zero_grad_all() train_loss = torch.zeros((), device=device) for micro_step in range(grad_accum_steps): @@ -2536,6 +2672,21 @@ def lr_mul(step: int, elapsed_ms: float) -> float: group["lr"] = group["base_lr"] * scale if args.grad_clip_norm > 0: torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm) + if args.matrix_lr_early != args.matrix_lr or args.matrix_lr_late != args.matrix_lr: + split = min(max(args.bank_split, 0), args.num_layers) + early_scale = args.matrix_lr_early / args.matrix_lr + late_scale = args.matrix_lr_late / args.matrix_lr + with torch.no_grad(): + for bank in [base_model.qo_bank, base_model.kv_bank]: + if bank.grad is not None: + bank.grad[:split].mul_(early_scale) + bank.grad[split:args.num_layers].mul_(late_scale) + bank.grad[args.num_layers:args.num_layers + split].mul_(early_scale) + bank.grad[args.num_layers + split:].mul_(late_scale) + for bank in [base_model.mlp_up_bank, base_model.mlp_down_bank]: + if bank.grad is not None: + bank.grad[:split].mul_(early_scale) + bank.grad[split:].mul_(late_scale) # === 3-phase overlapped optimizer step === # Phase 1: Launch async reduce-scatter for banks (biggest first) optimizer_muon.launch_reduce_scatters() @@ -2605,17 +2756,6 @@ def lr_mul(step: int, elapsed_ms: float) -> float: 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) - torch.cuda.synchronize() - t_diag = time.perf_counter() - diag_val_loss, diag_val_bpb = eval_val( - args, compiled_model, rank, world_size, device, grad_accum_steps, - val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - ) - torch.cuda.synchronize() - log0( - f"DIAGNOSTIC post_ema val_loss:{diag_val_loss:.4f} val_bpb:{diag_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_diag):.0f}ms" - ) full_state_dict = base_model.state_dict() export_sd = {k: v for k, v in full_state_dict.items() if "mtp_heads" not in k} excluded_mtp = sum(int(t.numel()) for k, t in full_state_dict.items() if "mtp_heads" in k) @@ -2667,14 +2807,14 @@ def lr_mul(step: int, elapsed_ms: float) -> float: torch.cuda.empty_cache() # Hessian-based bit allocation: start int5 for all, greedily promote to int6 quant_names = [n for n in unbanked_sd if _classify_param(n) in {"mlp", "attn"} and unbanked_sd[n].ndim >= 1 and unbanked_sd[n].numel() > 65536] - use_mixed = bool(int(os.environ.get("MIXED_QUANT", "0"))) + use_mixed = args.mixed_quant if use_mixed: # Rank by Hessian trace, promote top layers to int6, rest int5 sens = {n: hessians[n].diag().sum().item() if n in hessians else 0.0 for n in quant_names} ranked = sorted(sens.items(), key=lambda x: -x[1]) # Greedy: start all int5, promote until target exceeded clip_ranges = {n: 15 for n in quant_names} # int5 default - n_int6 = int(os.environ.get("N_INT6_LAYERS", "10")) # promote top N to int6 + n_int6 = min(max(args.n_int6_layers, 0), len(ranked)) for name, _ in ranked[:n_int6]: clip_ranges[name] = 31 int6_names = [n for n, cr in clip_ranges.items() if cr == 31] @@ -2750,87 +2890,32 @@ def _try_prune(n): log0(f"Total submission size int6+{_COMPRESSOR}: {quant_file_bytes + code_bytes} bytes") if distributed: dist.barrier() - with open("final_model.int6.ptz", "rb") as f: - quant_blob_disk = f.read() - if _COMPRESSOR == "brotli": - quant_decompressed = brotli.decompress(quant_blob_disk) - else: - quant_decompressed = lzma.decompress(quant_blob_disk) - quant_state = torch.load( - io.BytesIO(quant_decompressed), - map_location="cpu", - ) - deq_unbanked = dequantize_mixed_int6(quant_state["w"], quant_state["m"], unbanked_sd) - # Re-bank the dequantized tensors - deq_state = _rebank_state_dict(deq_unbanked, args.num_layers, sd_cpu) - eval_model = GPT( - vocab_size=args.vocab_size, num_layers=args.num_layers, model_dim=args.model_dim, - num_heads=args.num_heads, num_kv_heads=args.num_kv_heads, mlp_mult=args.mlp_mult, - tie_embeddings=args.tie_embeddings, tied_embed_init_std=args.tied_embed_init_std, - logit_softcap=args.logit_softcap, rope_base=args.rope_base, qk_gain_init=args.qk_gain_init, - mtp_num_heads=0, mtp_loss_weight=0.0, - bigram_vocab_size=args.bigram_vocab_size, bigram_dim=args.bigram_dim, - xsa_last_n=args.xsa_last_n, - rope_dims=args.rope_dims, ln_scale=args.ln_scale, dtg=args.dtg_enabled, - ve_enabled=args.ve_enabled, ve_dim=args.ve_dim, ve_layers=args.ve_layers, - gated_attention=args.gated_attention, value_residual=args.value_residual, - ).to(device).bfloat16() - eval_model.qo_bank.data = eval_model.qo_bank.data.float() - eval_model.kv_bank.data = eval_model.kv_bank.data.float() - eval_model.mlp_up_bank.data = eval_model.mlp_up_bank.data.float() - eval_model.mlp_down_bank.data = eval_model.mlp_down_bank.data.float() - for m in eval_model.modules(): - if isinstance(m, CastedLinear): - m.float() - restore_low_dim_params_to_fp32(eval_model) - eval_model.load_state_dict(deq_state, strict=True) - compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True) - torch.cuda.synchronize() - t_qeval = time.perf_counter() - q_val_loss, q_val_bpb = eval_val( - args, compiled_eval, rank, world_size, device, grad_accum_steps, - val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - eval_seq_len=effective_eval_seq_len, - ) - torch.cuda.synchronize() - log0( - f"final_int6_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} " - f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms" - ) - log0(f"final_int6_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}") - sw_seq_len = effective_eval_seq_len - if args.eval_stride > 0 and args.eval_stride < sw_seq_len: - torch.cuda.synchronize() - t_slide = time.perf_counter() - sw_val_loss, sw_val_bpb = eval_val_sliding( - args, eval_model, rank, world_size, device, - val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - stride=args.eval_stride, - eval_seq_len=sw_seq_len, - ) - torch.cuda.synchronize() - log0( - f"final_int6_sliding_window val_loss:{sw_val_loss:.4f} val_bpb:{sw_val_bpb:.4f} " - f"stride:{args.eval_stride} eval_time:{1000.0 * (time.perf_counter() - t_slide):.0f}ms" - ) - log0(f"final_int6_sliding_window_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") - log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw_val_loss:.8f} val_bpb:{sw_val_bpb:.8f}") - if args.eval_stride != 64 and 64 < sw_seq_len: - torch.cuda.synchronize() - t_slide64 = time.perf_counter() - sw64_val_loss, sw64_val_bpb = eval_val_sliding( - args, eval_model, rank, world_size, device, - val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut, - stride=64, - eval_seq_len=sw_seq_len, + del compiled_model + del base_model + gc.collect() + torch.cuda.empty_cache() + if distributed: + dist.barrier() + if args.fused_ngram_eval and master_process: + fused_stride = 64 if 64 < effective_eval_seq_len else max(1, min(args.eval_stride, effective_eval_seq_len - 1)) + fused_metrics = run_fused_ngram_eval( + args, + Path("final_model.int6.ptz"), + stride=fused_stride, + eval_seq_len=effective_eval_seq_len, + logger=log0, ) - torch.cuda.synchronize() log0( - f"final_int6_sliding_window_s64 val_loss:{sw64_val_loss:.4f} val_bpb:{sw64_val_bpb:.4f} " - f"stride:64 eval_time:{1000.0 * (time.perf_counter() - t_slide64):.0f}ms" + f"final_int6_fused_ngram_exact neural_val_bpb:{fused_metrics['neural_bpb']:.8f} " + f"tilted_val_bpb:{fused_metrics['tilted_bpb']:.8f} " + f"delta_bpb:{fused_metrics['delta_bpb']:+.8f} " + f"setup_s:{fused_metrics.get('setup_s', float('nan')):.1f} " + f"loop_s:{fused_metrics['loop_s']:.1f} wall_s:{fused_metrics['wall_s']:.1f}" ) - log0(f"final_int6_sliding_window_s64_exact val_loss:{sw64_val_loss:.8f} val_bpb:{sw64_val_bpb:.8f}") - log0(f"final_int8_zlib_roundtrip_exact val_loss:{sw64_val_loss:.8f} val_bpb:{sw64_val_bpb:.8f}") + log0(f"final_int6_fused_ngram_neural_exact val_bpb:{fused_metrics['neural_bpb']:.8f}") + log0(f"final_int6_fused_ngram_submission_exact val_bpb:{fused_metrics['tilted_bpb']:.8f}") + if distributed: + dist.barrier() if distributed: dist.destroy_process_group() if __name__ == "__main__": diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log index f86204479a..62bb7680a2 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log @@ -100,8 +100,56 @@ selective_prune: 7131423 ±1 candidates, unpruned=13.85MB target=15.9MB selective_prune: already fits, no pruning needed Serialized model int6+brotli: 14394175 bytes Total submission size int6+brotli: 14525480 bytes -final_int6_roundtrip val_loss:1.9190 val_bpb:1.1365 eval_time:5844ms -final_int6_roundtrip_exact val_loss:1.91899624 val_bpb:1.13653766 -final_int6_sliding_window val_loss:1.8791 val_bpb:1.1129 stride:64 eval_time:78514ms -final_int6_sliding_window_exact val_loss:1.87910171 val_bpb:1.11291282 -final_int8_zlib_roundtrip_exact val_loss:1.87910171 val_bpb:1.11291282 +fused_ngram_eval:start device:cuda:0 stride:64 order_stride:2 bigram_dim:160 +fused_ngram_eval:START seed1337 2026-04-01T01:48:28+0000 +fused_ngram_eval:Val tokens: 62,021,845 +fused_ngram_eval:Loading records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval:Model loaded. +fused_ngram_eval:Windows: 969,092, batches: 15143 +fused_ngram_eval:Setup: 57.6s +fused_ngram_eval: 0.0% | base:1.141085 tilt:1.141363 delta:+0.000277 | 0s +fused_ngram_eval: 3.3% | base:1.118072 tilt:1.116433 delta:-0.001639 | 17s +fused_ngram_eval: 6.6% | base:1.109866 tilt:1.107989 delta:-0.001877 | 35s +fused_ngram_eval: 9.9% | base:1.110365 tilt:1.108398 delta:-0.001967 | 52s +fused_ngram_eval: 13.2% | base:1.112467 tilt:1.110423 delta:-0.002044 | 69s +fused_ngram_eval: 16.5% | base:1.113467 tilt:1.111385 delta:-0.002082 | 86s +fused_ngram_eval: 19.8% | base:1.114637 tilt:1.112494 delta:-0.002142 | 104s +fused_ngram_eval: 23.1% | base:1.113909 tilt:1.111778 delta:-0.002130 | 121s +fused_ngram_eval: 26.4% | base:1.113028 tilt:1.110914 delta:-0.002115 | 138s +fused_ngram_eval: 29.7% | base:1.112318 tilt:1.110187 delta:-0.002131 | 155s +fused_ngram_eval: 33.0% | base:1.110618 tilt:1.108467 delta:-0.002150 | 173s +fused_ngram_eval: 36.3% | base:1.108474 tilt:1.106317 delta:-0.002157 | 190s +fused_ngram_eval: 39.6% | base:1.107543 tilt:1.105373 delta:-0.002171 | 207s +fused_ngram_eval: 42.9% | base:1.107544 tilt:1.105355 delta:-0.002189 | 224s +fused_ngram_eval: 46.2% | base:1.107222 tilt:1.105000 delta:-0.002222 | 242s +fused_ngram_eval: 49.5% | base:1.107026 tilt:1.104780 delta:-0.002245 | 259s +fused_ngram_eval: 52.8% | base:1.108336 tilt:1.106081 delta:-0.002255 | 276s +fused_ngram_eval: 56.1% | base:1.109652 tilt:1.107386 delta:-0.002266 | 294s +fused_ngram_eval: 59.4% | base:1.109622 tilt:1.107354 delta:-0.002268 | 311s +fused_ngram_eval: 62.7% | base:1.109043 tilt:1.106774 delta:-0.002269 | 328s +fused_ngram_eval: 66.0% | base:1.108398 tilt:1.106122 delta:-0.002276 | 345s +fused_ngram_eval: 69.3% | base:1.106920 tilt:1.104642 delta:-0.002278 | 363s +fused_ngram_eval: 72.6% | base:1.106602 tilt:1.104320 delta:-0.002281 | 380s +fused_ngram_eval: 75.9% | base:1.106997 tilt:1.104703 delta:-0.002294 | 397s +fused_ngram_eval: 79.2% | base:1.107819 tilt:1.105507 delta:-0.002312 | 414s +fused_ngram_eval: 82.5% | base:1.108565 tilt:1.106238 delta:-0.002328 | 432s +fused_ngram_eval: 85.8% | base:1.109173 tilt:1.106828 delta:-0.002344 | 449s +fused_ngram_eval: 89.2% | base:1.109701 tilt:1.107349 delta:-0.002352 | 466s +fused_ngram_eval: 92.5% | base:1.109468 tilt:1.107110 delta:-0.002358 | 484s +fused_ngram_eval: 95.8% | base:1.108939 tilt:1.106583 delta:-0.002355 | 501s +fused_ngram_eval: 99.1% | base:1.108414 tilt:1.106059 delta:-0.002354 | 518s +fused_ngram_eval: +fused_ngram_eval:======================================================================== +fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048 +fused_ngram_eval:======================================================================== +fused_ngram_eval:Neural only: val_bpb = 1.10824696 +fused_ngram_eval:Tilted: val_bpb = 1.10589118 +fused_ngram_eval:Delta: -0.00235578 BPB +fused_ngram_eval:Tokens: 62,023,616 | Bytes: 151,084,845 +fused_ngram_eval:Tilted: 22,074,453 (35.6%) | Hits: 13,139,387 (59.5%) +fused_ngram_eval:Loop: 523.0s | Wall: 580.6s +fused_ngram_eval:DONE seed1337 2026-04-01T01:58:11+0000 +final_int6_fused_ngram_exact neural_val_bpb:1.10824696 tilted_val_bpb:1.10589118 delta_bpb:-0.00235578 setup_s:57.6 loop_s:523.0 wall_s:580.6 +final_int6_fused_ngram_neural_exact val_bpb:1.10824696 +final_int6_fused_ngram_submission_exact val_bpb:1.10589118 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log index 50a9fc7bbd..c766a4a6bf 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log @@ -100,8 +100,56 @@ selective_prune: 7124411 ±1 candidates, unpruned=13.85MB target=15.9MB selective_prune: already fits, no pruning needed Serialized model int6+brotli: 14388393 bytes Total submission size int6+brotli: 14519698 bytes -final_int6_roundtrip val_loss:1.9177 val_bpb:1.1357 eval_time:21615ms -final_int6_roundtrip_exact val_loss:1.91766187 val_bpb:1.13574737 -final_int6_sliding_window val_loss:1.8780 val_bpb:1.1123 stride:64 eval_time:94447ms -final_int6_sliding_window_exact val_loss:1.87801836 val_bpb:1.11227120 -final_int8_zlib_roundtrip_exact val_loss:1.87801836 val_bpb:1.11227120 +fused_ngram_eval:start device:cuda:0 stride:64 order_stride:2 bigram_dim:160 +fused_ngram_eval:START seed314 2026-04-01T01:59:16+0000 +fused_ngram_eval:Val tokens: 62,021,845 +fused_ngram_eval:Loading records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval:Model loaded. +fused_ngram_eval:Windows: 969,092, batches: 15143 +fused_ngram_eval:Setup: 57.3s +fused_ngram_eval: 0.0% | base:1.135499 tilt:1.135816 delta:+0.000317 | 0s +fused_ngram_eval: 3.3% | base:1.117165 tilt:1.115540 delta:-0.001625 | 17s +fused_ngram_eval: 6.6% | base:1.108862 tilt:1.106988 delta:-0.001874 | 35s +fused_ngram_eval: 9.9% | base:1.109290 tilt:1.107324 delta:-0.001966 | 52s +fused_ngram_eval: 13.2% | base:1.111276 tilt:1.109233 delta:-0.002043 | 69s +fused_ngram_eval: 16.5% | base:1.112323 tilt:1.110241 delta:-0.002082 | 86s +fused_ngram_eval: 19.8% | base:1.113417 tilt:1.111272 delta:-0.002145 | 104s +fused_ngram_eval: 23.1% | base:1.112651 tilt:1.110516 delta:-0.002135 | 121s +fused_ngram_eval: 26.4% | base:1.111730 tilt:1.109609 delta:-0.002121 | 138s +fused_ngram_eval: 29.7% | base:1.110997 tilt:1.108863 delta:-0.002135 | 156s +fused_ngram_eval: 33.0% | base:1.109325 tilt:1.107174 delta:-0.002150 | 173s +fused_ngram_eval: 36.3% | base:1.107156 tilt:1.105000 delta:-0.002156 | 190s +fused_ngram_eval: 39.6% | base:1.106210 tilt:1.104039 delta:-0.002171 | 207s +fused_ngram_eval: 42.9% | base:1.106202 tilt:1.104011 delta:-0.002191 | 225s +fused_ngram_eval: 46.2% | base:1.105911 tilt:1.103686 delta:-0.002225 | 242s +fused_ngram_eval: 49.5% | base:1.105720 tilt:1.103469 delta:-0.002251 | 259s +fused_ngram_eval: 52.8% | base:1.107018 tilt:1.104756 delta:-0.002261 | 277s +fused_ngram_eval: 56.1% | base:1.108356 tilt:1.106084 delta:-0.002272 | 294s +fused_ngram_eval: 59.4% | base:1.108329 tilt:1.106054 delta:-0.002276 | 311s +fused_ngram_eval: 62.7% | base:1.107745 tilt:1.105468 delta:-0.002276 | 329s +fused_ngram_eval: 66.0% | base:1.107105 tilt:1.104822 delta:-0.002284 | 346s +fused_ngram_eval: 69.3% | base:1.105638 tilt:1.103353 delta:-0.002286 | 363s +fused_ngram_eval: 72.6% | base:1.105328 tilt:1.103039 delta:-0.002288 | 381s +fused_ngram_eval: 75.9% | base:1.105718 tilt:1.103418 delta:-0.002300 | 398s +fused_ngram_eval: 79.2% | base:1.106536 tilt:1.104217 delta:-0.002319 | 415s +fused_ngram_eval: 82.5% | base:1.107303 tilt:1.104969 delta:-0.002334 | 432s +fused_ngram_eval: 85.8% | base:1.107912 tilt:1.105562 delta:-0.002351 | 450s +fused_ngram_eval: 89.2% | base:1.108435 tilt:1.106078 delta:-0.002358 | 467s +fused_ngram_eval: 92.5% | base:1.108193 tilt:1.105829 delta:-0.002364 | 484s +fused_ngram_eval: 95.8% | base:1.107657 tilt:1.105294 delta:-0.002362 | 502s +fused_ngram_eval: 99.1% | base:1.107130 tilt:1.104768 delta:-0.002362 | 519s +fused_ngram_eval: +fused_ngram_eval:======================================================================== +fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048 +fused_ngram_eval:======================================================================== +fused_ngram_eval:Neural only: val_bpb = 1.10695770 +fused_ngram_eval:Tilted: val_bpb = 1.10459484 +fused_ngram_eval:Delta: -0.00236287 BPB +fused_ngram_eval:Tokens: 62,023,616 | Bytes: 151,084,845 +fused_ngram_eval:Tilted: 22,074,453 (35.6%) | Hits: 13,139,387 (59.5%) +fused_ngram_eval:Loop: 523.9s | Wall: 581.2s +fused_ngram_eval:DONE seed314 2026-04-01T02:09:00+0000 +final_int6_fused_ngram_exact neural_val_bpb:1.10695770 tilted_val_bpb:1.10459484 delta_bpb:-0.00236287 setup_s:57.3 loop_s:523.9 wall_s:581.2 +final_int6_fused_ngram_neural_exact val_bpb:1.10695770 +final_int6_fused_ngram_submission_exact val_bpb:1.10459484 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log index 0748bf8031..0f8b3574b2 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log @@ -100,8 +100,56 @@ selective_prune: 7140833 ±1 candidates, unpruned=13.84MB target=15.9MB selective_prune: already fits, no pruning needed Serialized model int6+brotli: 14385997 bytes Total submission size int6+brotli: 14517302 bytes -final_int6_roundtrip val_loss:1.9180 val_bpb:1.1360 eval_time:5847ms -final_int6_roundtrip_exact val_loss:1.91802923 val_bpb:1.13596495 -final_int6_sliding_window val_loss:1.8782 val_bpb:1.1124 stride:64 eval_time:77770ms -final_int6_sliding_window_exact val_loss:1.87821228 val_bpb:1.11238605 -final_int8_zlib_roundtrip_exact val_loss:1.87821228 val_bpb:1.11238605 +fused_ngram_eval:start device:cuda:0 stride:64 order_stride:2 bigram_dim:160 +fused_ngram_eval:START seed999 2026-04-01T02:09:00+0000 +fused_ngram_eval:Val tokens: 62,021,845 +fused_ngram_eval:Loading records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval:Model loaded. +fused_ngram_eval:Windows: 969,092, batches: 15143 +fused_ngram_eval:Setup: 57.4s +fused_ngram_eval: 0.0% | base:1.139242 tilt:1.139580 delta:+0.000339 | 0s +fused_ngram_eval: 3.3% | base:1.117460 tilt:1.115863 delta:-0.001598 | 17s +fused_ngram_eval: 6.6% | base:1.109205 tilt:1.107376 delta:-0.001829 | 35s +fused_ngram_eval: 9.9% | base:1.109702 tilt:1.107794 delta:-0.001908 | 52s +fused_ngram_eval: 13.2% | base:1.111686 tilt:1.109706 delta:-0.001980 | 69s +fused_ngram_eval: 16.5% | base:1.112666 tilt:1.110653 delta:-0.002014 | 86s +fused_ngram_eval: 19.8% | base:1.113784 tilt:1.111708 delta:-0.002076 | 104s +fused_ngram_eval: 23.1% | base:1.113092 tilt:1.111022 delta:-0.002070 | 121s +fused_ngram_eval: 26.4% | base:1.112201 tilt:1.110144 delta:-0.002057 | 138s +fused_ngram_eval: 29.7% | base:1.111495 tilt:1.109421 delta:-0.002074 | 155s +fused_ngram_eval: 33.0% | base:1.109828 tilt:1.107734 delta:-0.002093 | 173s +fused_ngram_eval: 36.3% | base:1.107659 tilt:1.105561 delta:-0.002098 | 190s +fused_ngram_eval: 39.6% | base:1.106757 tilt:1.104645 delta:-0.002112 | 207s +fused_ngram_eval: 42.9% | base:1.106749 tilt:1.104617 delta:-0.002132 | 225s +fused_ngram_eval: 46.2% | base:1.106432 tilt:1.104267 delta:-0.002165 | 242s +fused_ngram_eval: 49.5% | base:1.106244 tilt:1.104053 delta:-0.002191 | 259s +fused_ngram_eval: 52.8% | base:1.107518 tilt:1.105318 delta:-0.002200 | 276s +fused_ngram_eval: 56.1% | base:1.108843 tilt:1.106632 delta:-0.002210 | 294s +fused_ngram_eval: 59.4% | base:1.108815 tilt:1.106601 delta:-0.002213 | 311s +fused_ngram_eval: 62.7% | base:1.108231 tilt:1.106017 delta:-0.002215 | 328s +fused_ngram_eval: 66.0% | base:1.107593 tilt:1.105372 delta:-0.002221 | 346s +fused_ngram_eval: 69.3% | base:1.106125 tilt:1.103902 delta:-0.002223 | 363s +fused_ngram_eval: 72.6% | base:1.105813 tilt:1.103587 delta:-0.002225 | 380s +fused_ngram_eval: 75.9% | base:1.106208 tilt:1.103972 delta:-0.002237 | 397s +fused_ngram_eval: 79.2% | base:1.107021 tilt:1.104767 delta:-0.002254 | 415s +fused_ngram_eval: 82.5% | base:1.107781 tilt:1.105512 delta:-0.002269 | 432s +fused_ngram_eval: 85.8% | base:1.108383 tilt:1.106096 delta:-0.002287 | 449s +fused_ngram_eval: 89.2% | base:1.108915 tilt:1.106621 delta:-0.002294 | 467s +fused_ngram_eval: 92.5% | base:1.108675 tilt:1.106375 delta:-0.002300 | 484s +fused_ngram_eval: 95.8% | base:1.108141 tilt:1.105843 delta:-0.002298 | 501s +fused_ngram_eval: 99.1% | base:1.107620 tilt:1.105323 delta:-0.002298 | 518s +fused_ngram_eval: +fused_ngram_eval:======================================================================== +fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048 +fused_ngram_eval:======================================================================== +fused_ngram_eval:Neural only: val_bpb = 1.10745634 +fused_ngram_eval:Tilted: val_bpb = 1.10515710 +fused_ngram_eval:Delta: -0.00229924 BPB +fused_ngram_eval:Tokens: 62,023,616 | Bytes: 151,084,845 +fused_ngram_eval:Tilted: 22,074,453 (35.6%) | Hits: 13,139,387 (59.5%) +fused_ngram_eval:Loop: 523.3s | Wall: 580.6s +fused_ngram_eval:DONE seed999 2026-04-01T02:18:43+0000 +final_int6_fused_ngram_exact neural_val_bpb:1.10745634 tilted_val_bpb:1.10515710 delta_bpb:-0.00229924 setup_s:57.4 loop_s:523.3 wall_s:580.6 +final_int6_fused_ngram_neural_exact val_bpb:1.10745634 +final_int6_fused_ngram_submission_exact val_bpb:1.10515710 From 2d58d1926406df8183bea4c08e6a8df5acd92132 Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Tue, 31 Mar 2026 22:40:34 -0500 Subject: [PATCH 5/6] Sync updated training and fused eval code --- .../eval_fused.py | 195 +++++++++++------- .../train_gpt.py | 17 +- 2 files changed, 130 insertions(+), 82 deletions(-) diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/eval_fused.py b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/eval_fused.py index 5f16a696a6..e56687b7e3 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/eval_fused.py +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/eval_fused.py @@ -4,11 +4,12 @@ Precomputed indices eliminate Python loop overhead in the main eval loop. """ from __future__ import annotations -import argparse, io, math, time, glob, threading +import argparse, glob, hashlib, io, math, os, time, threading from pathlib import Path import numpy as np import sentencepiece as spm import torch +import torch.distributed as dist import torch.nn.functional as F @@ -95,15 +96,14 @@ def precompute_batch_indices(all_windows, total_tokens, seq_len, stride, batch_s all_bi = np.zeros(est, dtype=np.int64) all_si = np.zeros(est, dtype=np.int64) all_gp = np.zeros(est, dtype=np.int64) - batch_starts = [] - batch_score_ranges = [] + score_starts = np.zeros(n_batches, dtype=np.int64) + score_ends = np.zeros(n_batches, dtype=np.int64) max_scored = 0 flat_off = 0 for bi in range(n_batches): idx = bi * batch_seqs batch_ws = all_windows[idx:idx + batch_seqs] - batch_starts.append(batch_ws) - score_start = flat_off + score_starts[bi] = flat_off for i, ws in enumerate(batch_ws): end = min(ws + seq_len, total_tokens) wl = end - ws @@ -117,10 +117,27 @@ def precompute_batch_indices(all_windows, total_tokens, seq_len, stride, batch_s all_si[flat_off:flat_off+n] = np.arange(s, wl) all_gp[flat_off:flat_off+n] = np.arange(gp_start, gp_end + 1) flat_off += n - batch_score_ranges.append((score_start, flat_off)) - if flat_off > score_start: + score_ends[bi] = flat_off + if flat_off > score_starts[bi]: max_scored = int(all_gp[flat_off - 1]) - return all_bi[:flat_off], all_si[:flat_off], all_gp[:flat_off], batch_starts, batch_score_ranges + return all_bi[:flat_off], all_si[:flat_off], all_gp[:flat_off], score_starts, score_ends + + +def init_distributed(args): + distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ + rank = int(os.environ.get("RANK", "0")) + world_size = int(os.environ.get("WORLD_SIZE", "1")) + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + if distributed: + device = torch.device("cuda", local_rank) + torch.cuda.set_device(device) + dist.init_process_group(backend="nccl", device_id=device) + dist.barrier() + else: + device = torch.device(args.device) + if device.type == "cuda": + torch.cuda.set_device(device) + return distributed, rank, world_size, local_rank, device def main(): @@ -142,7 +159,7 @@ def main(): parser.add_argument("--word-beta", type=float, default=0.50) parser.add_argument("--open-table-bits", type=int, default=26) parser.add_argument("--token-threshold-scale", type=float, default=1.0) - parser.add_argument("--order-stride", type=int, default=1) + parser.add_argument("--order-stride", type=int, default=2) parser.add_argument("--vocab-size", type=int, default=1024) parser.add_argument("--num-layers", type=int, default=11) parser.add_argument("--model-dim", type=int, default=512) @@ -153,13 +170,14 @@ def main(): parser.add_argument("--rope-base", type=float, default=10000.0) parser.add_argument("--qk-gain-init", type=float, default=1.5) parser.add_argument("--bigram-vocab-size", type=int, default=3072) - parser.add_argument("--bigram-dim", type=int, default=112) + parser.add_argument("--bigram-dim", type=int, default=160) parser.add_argument("--xsa-last-n", type=int, default=11) parser.add_argument("--rope-dims", type=int, default=16) parser.add_argument("--ve-dim", type=int, default=128) parser.add_argument("--ve-layers", default="9,10") args = parser.parse_args() - device = torch.device(args.device) + distributed, rank, world_size, local_rank, device = init_distributed(args) + master_process = rank == 0 t_wall = time.perf_counter() import importlib.util @@ -170,45 +188,57 @@ def main(): val_tokens = torch.cat([load_data_shard(Path(f)) for f in val_files]).contiguous() if args.max_tokens > 0: val_tokens = val_tokens[:args.max_tokens + 1] total_tokens = val_tokens.numel() - 1 - print(f"Val tokens: {total_tokens:,}") + if master_process: + print(f"Val tokens: {total_tokens:,}") sp = spm.SentencePieceProcessor(model_file=args.tokenizer) bb_lut, ls_lut, bd_lut = build_luts(sp, args.vocab_size, device) - from fused_expert_ext import ContextMixer - val_np = val_tokens.numpy().astype(np.int64) - ngram = ContextMixer( - base_beta=args.base_beta, agree_bonus=args.agree_bonus, - within_threshold=args.within_threshold, within_beta=args.within_beta, - word_threshold=args.word_threshold, word_beta=args.word_beta, - open_table_bits=args.open_table_bits, - token_threshold_scale=args.token_threshold_scale, - order_stride=args.order_stride) - ngram.set_tokens(val_np) - ngram.set_luts(bb_lut.cpu().to(torch.int16).numpy(), - ls_lut.cpu().numpy().astype(np.uint8), - bd_lut.cpu().numpy().astype(np.uint8)) - seq_len, stride = args.seq_len, args.stride all_windows = [ws for ws in range(0, total_tokens, stride) if min(ws + seq_len, total_tokens) - ws >= 1] + n_batches = (len(all_windows) + args.batch_seqs - 1) // args.batch_seqs - # ── Start CPU precompute threads, then load model + compile on GPU ──── - all_hints = np.zeros(total_tokens + 1, dtype=np.int32) - all_betas = np.zeros(total_tokens + 1, dtype=np.float64) - positions = np.arange(1, total_tokens + 1, dtype=np.int64) - idx_result = [None] - - def do_ngram(): - ngram.get_hints_batch(positions, all_hints[1:], all_betas[1:]) - def do_indices(): - idx_result[0] = precompute_batch_indices( - all_windows, total_tokens, seq_len, stride, args.batch_seqs) + cache_key = hashlib.sha1( + f"{args.model}|{args.val_pattern}|{args.max_tokens}|{seq_len}|{stride}|{args.batch_seqs}|{args.order_stride}|{args.base_beta}".encode() + ).hexdigest()[:16] + cache_dir = Path(os.environ.get("EVAL_FUSED_CACHE_DIR", f"/tmp/eval_fused_cache_{cache_key}")) - ngram_thread = threading.Thread(target=do_ngram, daemon=True) - idx_thread = threading.Thread(target=do_indices, daemon=True) - ngram_thread.start() - idx_thread.start() + ngram_thread = None + idx_thread = None + all_hints = None + all_betas = None + idx_result = [None] + if master_process: + from fused_expert_ext import ContextMixer + val_np = val_tokens.numpy().astype(np.int64) + ngram = ContextMixer( + base_beta=args.base_beta, agree_bonus=args.agree_bonus, + within_threshold=args.within_threshold, within_beta=args.within_beta, + word_threshold=args.word_threshold, word_beta=args.word_beta, + open_table_bits=args.open_table_bits, + token_threshold_scale=args.token_threshold_scale, + order_stride=args.order_stride) + ngram.set_tokens(val_np) + ngram.set_luts(bb_lut.cpu().to(torch.int16).numpy(), + ls_lut.cpu().numpy().astype(np.uint8), + bd_lut.cpu().numpy().astype(np.uint8)) + + all_hints = np.zeros(total_tokens + 1, dtype=np.int32) + all_betas = np.zeros(total_tokens + 1, dtype=np.float64) + positions = np.arange(1, total_tokens + 1, dtype=np.int64) + + def do_ngram(): + ngram.get_hints_batch(positions, all_hints[1:], all_betas[1:]) + + def do_indices(): + idx_result[0] = precompute_batch_indices( + all_windows, total_tokens, seq_len, stride, args.batch_seqs) + + ngram_thread = threading.Thread(target=do_ngram, daemon=True) + idx_thread = threading.Thread(target=do_indices, daemon=True) + ngram_thread.start() + idx_thread.start() # GPU: load model + compile (overlaps with CPU threads) val_gpu = val_tokens.to(device=device, dtype=torch.int64) @@ -220,37 +250,60 @@ def do_indices(): for _ in range(3): compiled_logits(xb_static) torch.cuda.synchronize() - # Wait for CPU threads - idx_thread.join() - ngram_thread.join() - all_bi_np, all_si_np, all_gp_np, batch_starts, batch_score_ranges = idx_result[0] - n_batches = len(batch_starts) + if master_process: + idx_thread.join() + ngram_thread.join() + cache_dir.mkdir(parents=True, exist_ok=True) + all_bi_np, all_si_np, all_gp_np, score_starts_np, score_ends_np = idx_result[0] + np.save(cache_dir / "all_hints.npy", all_hints, allow_pickle=False) + np.save(cache_dir / "all_betas.npy", all_betas, allow_pickle=False) + np.save(cache_dir / "all_bi.npy", all_bi_np, allow_pickle=False) + np.save(cache_dir / "all_si.npy", all_si_np, allow_pickle=False) + np.save(cache_dir / "all_gp.npy", all_gp_np, allow_pickle=False) + np.save(cache_dir / "score_starts.npy", score_starts_np, allow_pickle=False) + np.save(cache_dir / "score_ends.npy", score_ends_np, allow_pickle=False) + if distributed: + dist.barrier() + + all_hints_np = np.load(cache_dir / "all_hints.npy", mmap_mode="r+") + all_betas_np = np.load(cache_dir / "all_betas.npy", mmap_mode="r+") + all_bi_np = np.load(cache_dir / "all_bi.npy", mmap_mode="r+") + all_si_np = np.load(cache_dir / "all_si.npy", mmap_mode="r+") + all_gp_np = np.load(cache_dir / "all_gp.npy", mmap_mode="r+") + score_starts_np = np.load(cache_dir / "score_starts.npy", mmap_mode="r+") + score_ends_np = np.load(cache_dir / "score_ends.npy", mmap_mode="r+") # Upload everything to GPU - all_hints_gpu = torch.from_numpy(all_hints.astype(np.int64)).to(device) - all_betas_gpu = torch.from_numpy(all_betas).to(device=device, dtype=torch.float64) + all_hints_gpu = torch.from_numpy(all_hints_np).to(device=device, dtype=torch.int64) + all_betas_gpu = torch.from_numpy(all_betas_np).to(device=device, dtype=torch.float64) all_bi_gpu = torch.from_numpy(all_bi_np).to(device) all_si_gpu = torch.from_numpy(all_si_np).to(device) all_gp_gpu = torch.from_numpy(all_gp_np).to(device) offsets_gpu = torch.arange(seq_len, device=device) - print(f"Windows: {len(all_windows):,}, batches: {n_batches}") - print(f"Setup: {time.perf_counter() - t_wall:.1f}s") + if master_process: + print(f"Windows: {len(all_windows):,}, batches: {n_batches}, world_size: {world_size}") + print(f"Setup: {time.perf_counter() - t_wall:.1f}s") - gpu_loss = torch.zeros(1, dtype=torch.float64, device=device) gpu_tilt_loss = torch.zeros(1, dtype=torch.float64, device=device) gpu_bytes = torch.zeros(1, dtype=torch.float64, device=device) gpu_tokens = torch.zeros(1, dtype=torch.float64, device=device) gpu_tilted = torch.zeros(1, dtype=torch.float64, device=device) gpu_hits = torch.zeros(1, dtype=torch.float64, device=device) - max_scored = 0 + batch_lo = rank * n_batches // world_size + batch_hi = (rank + 1) * n_batches // world_size + prev_bi = batch_lo - 1 + while prev_bi >= 0 and score_ends_np[prev_bi] <= score_starts_np[prev_bi]: + prev_bi -= 1 + max_scored = int(all_gp_np[score_ends_np[prev_bi] - 1]) if prev_bi >= 0 else 0 t0 = time.perf_counter() with torch.inference_mode(): - for bi in range(n_batches): - batch_ws = batch_starts[bi] + for bi in range(batch_lo, batch_hi): + batch_ws = all_windows[bi * args.batch_seqs:(bi + 1) * args.batch_seqs] bsz = len(batch_ws) - sc_start, sc_end = batch_score_ranges[bi] + sc_start = int(score_starts_np[bi]) + sc_end = int(score_ends_np[bi]) if sc_end <= sc_start: continue ws_tensor = torch.tensor(batch_ws, device=device, dtype=torch.int64) @@ -291,41 +344,43 @@ def do_indices(): v = valid.to(torch.float64) tb = bb_lut[flat_targets] + (ls_lut[flat_targets] & ~bd_lut[flat_prevs]).to(torch.float64) - gpu_loss += (flat_nll * v).sum() gpu_tilt_loss += (mixed_nll * v).sum() gpu_bytes += (tb * v).sum() gpu_tokens += v.sum() gpu_tilted += (has_hint * v).sum() gpu_hits += (has_hint * is_hit * v).sum() - if bi % 500 == 0: + if not distributed and bi % 500 == 0: torch.cuda.synchronize() elapsed = time.perf_counter() - t0 tc = gpu_tokens.item() if tc > 0: bs = gpu_bytes.item() tpb = tc / bs if bs > 0 else 1.0 - b = (gpu_loss.item() / tc / math.log(2.0)) * tpb t = (gpu_tilt_loss.item() / tc / math.log(2.0)) * tpb - print(f" {bi/n_batches*100:5.1f}% | base:{b:.6f} tilt:{t:.6f} delta:{t-b:+.6f} | {elapsed:.0f}s") + print(f" {bi/n_batches*100:5.1f}% | submission:{t:.6f} | {elapsed:.0f}s") torch.cuda.synchronize() + if distributed: + for tensor in (gpu_tilt_loss, gpu_bytes, gpu_tokens, gpu_tilted, gpu_hits): + dist.all_reduce(tensor, op=dist.ReduceOp.SUM) loop_time = time.perf_counter() - t0 wall_time = time.perf_counter() - t_wall tc = gpu_tokens.item(); bs = gpu_bytes.item(); tpb = tc / bs - base_bpb = (gpu_loss.item() / tc / math.log(2.0)) * tpb tilt_bpb = (gpu_tilt_loss.item() / tc / math.log(2.0)) * tpb nt = int(gpu_tilted.item()); nh = int(gpu_hits.item()) - print(f"\n{'='*72}") - print(f"RESULTS base_beta={args.base_beta}, stride={stride}, seq_len={seq_len}") - print(f"{'='*72}") - print(f"Neural only: val_bpb = {base_bpb:.8f}") - print(f"Tilted: val_bpb = {tilt_bpb:.8f}") - print(f"Delta: {tilt_bpb - base_bpb:+.8f} BPB") - print(f"Tokens: {int(tc):,} | Bytes: {bs:,.0f}") - if nt > 0: - print(f"Tilted: {nt:,} ({nt/tc*100:.1f}%) | Hits: {nh:,} ({nh/nt*100:.1f}%)") - print(f"Loop: {loop_time:.1f}s | Wall: {wall_time:.1f}s") + if master_process: + print(f"\n{'='*72}") + print(f"RESULTS base_beta={args.base_beta}, stride={stride}, seq_len={seq_len}, world_size={world_size}") + print(f"{'='*72}") + print(f"Submission: val_bpb = {tilt_bpb:.8f}") + print(f"Tokens: {int(tc):,} | Bytes: {bs:,.0f}") + if nt > 0: + print(f"Tilted: {nt:,} ({nt/tc*100:.1f}%) | Hits: {nh:,} ({nh/nt*100:.1f}%)") + print(f"Loop: {loop_time:.1f}s | Wall: {wall_time:.1f}s") + if distributed: + dist.barrier() + dist.destroy_process_group() if __name__ == "__main__": main() diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py index 87c37d5adc..9e0d6064c2 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_gpt.py @@ -1807,12 +1807,8 @@ def run_fused_ngram_eval( logger(f"fused_ngram_eval:{line}") if line.startswith("Setup: "): metrics["setup_s"] = float(line.removeprefix("Setup: ").removesuffix("s")) - elif line.startswith("Neural only:"): - metrics["neural_bpb"] = float(line.split("=", 1)[1].strip()) - elif line.startswith("Tilted: val_bpb"): - metrics["tilted_bpb"] = float(line.split("=", 1)[1].strip()) - elif line.startswith("Delta:"): - metrics["delta_bpb"] = float(line.split(":", 1)[1].strip().split()[0]) + elif line.startswith("Submission:"): + metrics["submission_bpb"] = float(line.split("=", 1)[1].strip()) elif line.startswith("Loop: "): loop_part, wall_part = [part.strip() for part in line.split("|", 1)] metrics["loop_s"] = float(loop_part.removeprefix("Loop: ").removesuffix("s")) @@ -1820,7 +1816,7 @@ def run_fused_ngram_eval( ret = proc.wait() if ret != 0: raise RuntimeError(f"fused_ngram_eval failed with exit code {ret}") - required = {"neural_bpb", "tilted_bpb", "delta_bpb", "loop_s", "wall_s"} + required = {"submission_bpb", "loop_s", "wall_s"} missing = required.difference(metrics) if missing: raise RuntimeError(f"fused_ngram_eval missing metrics: {sorted(missing)}") @@ -2906,14 +2902,11 @@ def _try_prune(n): logger=log0, ) log0( - f"final_int6_fused_ngram_exact neural_val_bpb:{fused_metrics['neural_bpb']:.8f} " - f"tilted_val_bpb:{fused_metrics['tilted_bpb']:.8f} " - f"delta_bpb:{fused_metrics['delta_bpb']:+.8f} " + f"final_int6_fused_ngram_exact submission_val_bpb:{fused_metrics['submission_bpb']:.8f} " f"setup_s:{fused_metrics.get('setup_s', float('nan')):.1f} " f"loop_s:{fused_metrics['loop_s']:.1f} wall_s:{fused_metrics['wall_s']:.1f}" ) - log0(f"final_int6_fused_ngram_neural_exact val_bpb:{fused_metrics['neural_bpb']:.8f}") - log0(f"final_int6_fused_ngram_submission_exact val_bpb:{fused_metrics['tilted_bpb']:.8f}") + log0(f"final_int6_fused_ngram_submission_exact val_bpb:{fused_metrics['submission_bpb']:.8f}") if distributed: dist.barrier() if distributed: From d854d6256eae65f973882ad006a4c0ebf8c24dca Mon Sep 17 00:00:00 2001 From: Abay Bektursun Date: Tue, 31 Mar 2026 22:54:05 -0500 Subject: [PATCH 6/6] Update fused submission eval logs --- .../train_seed1337.log | 75 ++++++++----------- .../train_seed314.log | 75 ++++++++----------- .../train_seed999.log | 75 ++++++++----------- 3 files changed, 96 insertions(+), 129 deletions(-) diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log index 62bb7680a2..ec346a3d0a 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed1337.log @@ -101,55 +101,44 @@ selective_prune: already fits, no pruning needed Serialized model int6+brotli: 14394175 bytes Total submission size int6+brotli: 14525480 bytes fused_ngram_eval:start device:cuda:0 stride:64 order_stride:2 bigram_dim:160 -fused_ngram_eval:START seed1337 2026-04-01T01:48:28+0000 +fused_ngram_eval:W0401 03:38:10.293000 47683 torch/distributed/run.py:851] +fused_ngram_eval:W0401 03:38:10.293000 47683 torch/distributed/run.py:851] ***************************************** +fused_ngram_eval:W0401 03:38:10.293000 47683 torch/distributed/run.py:851] 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. +fused_ngram_eval:W0401 03:38:10.293000 47683 torch/distributed/run.py:851] ***************************************** fused_ngram_eval:Val tokens: 62,021,845 -fused_ngram_eval:Loading records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval:Model loaded. +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed1337/final_model.int6.ptz... fused_ngram_eval: Decompressed with brotli fused_ngram_eval:Model loaded. -fused_ngram_eval:Windows: 969,092, batches: 15143 -fused_ngram_eval:Setup: 57.6s -fused_ngram_eval: 0.0% | base:1.141085 tilt:1.141363 delta:+0.000277 | 0s -fused_ngram_eval: 3.3% | base:1.118072 tilt:1.116433 delta:-0.001639 | 17s -fused_ngram_eval: 6.6% | base:1.109866 tilt:1.107989 delta:-0.001877 | 35s -fused_ngram_eval: 9.9% | base:1.110365 tilt:1.108398 delta:-0.001967 | 52s -fused_ngram_eval: 13.2% | base:1.112467 tilt:1.110423 delta:-0.002044 | 69s -fused_ngram_eval: 16.5% | base:1.113467 tilt:1.111385 delta:-0.002082 | 86s -fused_ngram_eval: 19.8% | base:1.114637 tilt:1.112494 delta:-0.002142 | 104s -fused_ngram_eval: 23.1% | base:1.113909 tilt:1.111778 delta:-0.002130 | 121s -fused_ngram_eval: 26.4% | base:1.113028 tilt:1.110914 delta:-0.002115 | 138s -fused_ngram_eval: 29.7% | base:1.112318 tilt:1.110187 delta:-0.002131 | 155s -fused_ngram_eval: 33.0% | base:1.110618 tilt:1.108467 delta:-0.002150 | 173s -fused_ngram_eval: 36.3% | base:1.108474 tilt:1.106317 delta:-0.002157 | 190s -fused_ngram_eval: 39.6% | base:1.107543 tilt:1.105373 delta:-0.002171 | 207s -fused_ngram_eval: 42.9% | base:1.107544 tilt:1.105355 delta:-0.002189 | 224s -fused_ngram_eval: 46.2% | base:1.107222 tilt:1.105000 delta:-0.002222 | 242s -fused_ngram_eval: 49.5% | base:1.107026 tilt:1.104780 delta:-0.002245 | 259s -fused_ngram_eval: 52.8% | base:1.108336 tilt:1.106081 delta:-0.002255 | 276s -fused_ngram_eval: 56.1% | base:1.109652 tilt:1.107386 delta:-0.002266 | 294s -fused_ngram_eval: 59.4% | base:1.109622 tilt:1.107354 delta:-0.002268 | 311s -fused_ngram_eval: 62.7% | base:1.109043 tilt:1.106774 delta:-0.002269 | 328s -fused_ngram_eval: 66.0% | base:1.108398 tilt:1.106122 delta:-0.002276 | 345s -fused_ngram_eval: 69.3% | base:1.106920 tilt:1.104642 delta:-0.002278 | 363s -fused_ngram_eval: 72.6% | base:1.106602 tilt:1.104320 delta:-0.002281 | 380s -fused_ngram_eval: 75.9% | base:1.106997 tilt:1.104703 delta:-0.002294 | 397s -fused_ngram_eval: 79.2% | base:1.107819 tilt:1.105507 delta:-0.002312 | 414s -fused_ngram_eval: 82.5% | base:1.108565 tilt:1.106238 delta:-0.002328 | 432s -fused_ngram_eval: 85.8% | base:1.109173 tilt:1.106828 delta:-0.002344 | 449s -fused_ngram_eval: 89.2% | base:1.109701 tilt:1.107349 delta:-0.002352 | 466s -fused_ngram_eval: 92.5% | base:1.109468 tilt:1.107110 delta:-0.002358 | 484s -fused_ngram_eval: 95.8% | base:1.108939 tilt:1.106583 delta:-0.002355 | 501s -fused_ngram_eval: 99.1% | base:1.108414 tilt:1.106059 delta:-0.002354 | 518s +fused_ngram_eval:Windows: 969,092, batches: 15143, world_size: 8 +fused_ngram_eval:Setup: 55.1s fused_ngram_eval: fused_ngram_eval:======================================================================== -fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048 +fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048, world_size=8 fused_ngram_eval:======================================================================== -fused_ngram_eval:Neural only: val_bpb = 1.10824696 -fused_ngram_eval:Tilted: val_bpb = 1.10589118 -fused_ngram_eval:Delta: -0.00235578 BPB +fused_ngram_eval:Submission: val_bpb = 1.10589118 fused_ngram_eval:Tokens: 62,023,616 | Bytes: 151,084,845 fused_ngram_eval:Tilted: 22,074,453 (35.6%) | Hits: 13,139,387 (59.5%) -fused_ngram_eval:Loop: 523.0s | Wall: 580.6s -fused_ngram_eval:DONE seed1337 2026-04-01T01:58:11+0000 -final_int6_fused_ngram_exact neural_val_bpb:1.10824696 tilted_val_bpb:1.10589118 delta_bpb:-0.00235578 setup_s:57.6 loop_s:523.0 wall_s:580.6 -final_int6_fused_ngram_neural_exact val_bpb:1.10824696 +fused_ngram_eval:Loop: 65.5s | Wall: 120.6s +final_int6_fused_ngram_exact submission_val_bpb:1.10589118 setup_s:55.1 loop_s:65.5 wall_s:120.6 final_int6_fused_ngram_submission_exact val_bpb:1.10589118 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log index c766a4a6bf..42e6e07157 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed314.log @@ -101,55 +101,44 @@ selective_prune: already fits, no pruning needed Serialized model int6+brotli: 14388393 bytes Total submission size int6+brotli: 14519698 bytes fused_ngram_eval:start device:cuda:0 stride:64 order_stride:2 bigram_dim:160 -fused_ngram_eval:START seed314 2026-04-01T01:59:16+0000 +fused_ngram_eval:W0401 03:40:24.605000 48429 torch/distributed/run.py:851] +fused_ngram_eval:W0401 03:40:24.605000 48429 torch/distributed/run.py:851] ***************************************** +fused_ngram_eval:W0401 03:40:24.605000 48429 torch/distributed/run.py:851] 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. +fused_ngram_eval:W0401 03:40:24.605000 48429 torch/distributed/run.py:851] ***************************************** fused_ngram_eval:Val tokens: 62,021,845 -fused_ngram_eval:Loading records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed314/final_model.int6.ptz... fused_ngram_eval: Decompressed with brotli fused_ngram_eval:Model loaded. -fused_ngram_eval:Windows: 969,092, batches: 15143 -fused_ngram_eval:Setup: 57.3s -fused_ngram_eval: 0.0% | base:1.135499 tilt:1.135816 delta:+0.000317 | 0s -fused_ngram_eval: 3.3% | base:1.117165 tilt:1.115540 delta:-0.001625 | 17s -fused_ngram_eval: 6.6% | base:1.108862 tilt:1.106988 delta:-0.001874 | 35s -fused_ngram_eval: 9.9% | base:1.109290 tilt:1.107324 delta:-0.001966 | 52s -fused_ngram_eval: 13.2% | base:1.111276 tilt:1.109233 delta:-0.002043 | 69s -fused_ngram_eval: 16.5% | base:1.112323 tilt:1.110241 delta:-0.002082 | 86s -fused_ngram_eval: 19.8% | base:1.113417 tilt:1.111272 delta:-0.002145 | 104s -fused_ngram_eval: 23.1% | base:1.112651 tilt:1.110516 delta:-0.002135 | 121s -fused_ngram_eval: 26.4% | base:1.111730 tilt:1.109609 delta:-0.002121 | 138s -fused_ngram_eval: 29.7% | base:1.110997 tilt:1.108863 delta:-0.002135 | 156s -fused_ngram_eval: 33.0% | base:1.109325 tilt:1.107174 delta:-0.002150 | 173s -fused_ngram_eval: 36.3% | base:1.107156 tilt:1.105000 delta:-0.002156 | 190s -fused_ngram_eval: 39.6% | base:1.106210 tilt:1.104039 delta:-0.002171 | 207s -fused_ngram_eval: 42.9% | base:1.106202 tilt:1.104011 delta:-0.002191 | 225s -fused_ngram_eval: 46.2% | base:1.105911 tilt:1.103686 delta:-0.002225 | 242s -fused_ngram_eval: 49.5% | base:1.105720 tilt:1.103469 delta:-0.002251 | 259s -fused_ngram_eval: 52.8% | base:1.107018 tilt:1.104756 delta:-0.002261 | 277s -fused_ngram_eval: 56.1% | base:1.108356 tilt:1.106084 delta:-0.002272 | 294s -fused_ngram_eval: 59.4% | base:1.108329 tilt:1.106054 delta:-0.002276 | 311s -fused_ngram_eval: 62.7% | base:1.107745 tilt:1.105468 delta:-0.002276 | 329s -fused_ngram_eval: 66.0% | base:1.107105 tilt:1.104822 delta:-0.002284 | 346s -fused_ngram_eval: 69.3% | base:1.105638 tilt:1.103353 delta:-0.002286 | 363s -fused_ngram_eval: 72.6% | base:1.105328 tilt:1.103039 delta:-0.002288 | 381s -fused_ngram_eval: 75.9% | base:1.105718 tilt:1.103418 delta:-0.002300 | 398s -fused_ngram_eval: 79.2% | base:1.106536 tilt:1.104217 delta:-0.002319 | 415s -fused_ngram_eval: 82.5% | base:1.107303 tilt:1.104969 delta:-0.002334 | 432s -fused_ngram_eval: 85.8% | base:1.107912 tilt:1.105562 delta:-0.002351 | 450s -fused_ngram_eval: 89.2% | base:1.108435 tilt:1.106078 delta:-0.002358 | 467s -fused_ngram_eval: 92.5% | base:1.108193 tilt:1.105829 delta:-0.002364 | 484s -fused_ngram_eval: 95.8% | base:1.107657 tilt:1.105294 delta:-0.002362 | 502s -fused_ngram_eval: 99.1% | base:1.107130 tilt:1.104768 delta:-0.002362 | 519s +fused_ngram_eval:Windows: 969,092, batches: 15143, world_size: 8 +fused_ngram_eval:Setup: 54.0s fused_ngram_eval: fused_ngram_eval:======================================================================== -fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048 +fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048, world_size=8 fused_ngram_eval:======================================================================== -fused_ngram_eval:Neural only: val_bpb = 1.10695770 -fused_ngram_eval:Tilted: val_bpb = 1.10459484 -fused_ngram_eval:Delta: -0.00236287 BPB +fused_ngram_eval:Submission: val_bpb = 1.10459484 fused_ngram_eval:Tokens: 62,023,616 | Bytes: 151,084,845 fused_ngram_eval:Tilted: 22,074,453 (35.6%) | Hits: 13,139,387 (59.5%) -fused_ngram_eval:Loop: 523.9s | Wall: 581.2s -fused_ngram_eval:DONE seed314 2026-04-01T02:09:00+0000 -final_int6_fused_ngram_exact neural_val_bpb:1.10695770 tilted_val_bpb:1.10459484 delta_bpb:-0.00236287 setup_s:57.3 loop_s:523.9 wall_s:581.2 -final_int6_fused_ngram_neural_exact val_bpb:1.10695770 +fused_ngram_eval:Loop: 65.5s | Wall: 119.4s +final_int6_fused_ngram_exact submission_val_bpb:1.10459484 setup_s:54.0 loop_s:65.5 wall_s:119.4 final_int6_fused_ngram_submission_exact val_bpb:1.10459484 diff --git a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log index 0f8b3574b2..13fff7261c 100644 --- a/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log +++ b/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/train_seed999.log @@ -101,55 +101,44 @@ selective_prune: already fits, no pruning needed Serialized model int6+brotli: 14385997 bytes Total submission size int6+brotli: 14517302 bytes fused_ngram_eval:start device:cuda:0 stride:64 order_stride:2 bigram_dim:160 -fused_ngram_eval:START seed999 2026-04-01T02:09:00+0000 +fused_ngram_eval:W0401 03:42:37.559000 49206 torch/distributed/run.py:851] +fused_ngram_eval:W0401 03:42:37.559000 49206 torch/distributed/run.py:851] ***************************************** +fused_ngram_eval:W0401 03:42:37.559000 49206 torch/distributed/run.py:851] 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. +fused_ngram_eval:W0401 03:42:37.559000 49206 torch/distributed/run.py:851] ***************************************** fused_ngram_eval:Val tokens: 62,021,845 -fused_ngram_eval:Loading records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval: Decompressed with brotli +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Model loaded. +fused_ngram_eval:Loading /root/openai-parameter-golf/records/track_10min_16mb/2026-03-29_FusedMLP_Brotli_Memmap/seed_runs/seed999/final_model.int6.ptz... fused_ngram_eval: Decompressed with brotli fused_ngram_eval:Model loaded. -fused_ngram_eval:Windows: 969,092, batches: 15143 -fused_ngram_eval:Setup: 57.4s -fused_ngram_eval: 0.0% | base:1.139242 tilt:1.139580 delta:+0.000339 | 0s -fused_ngram_eval: 3.3% | base:1.117460 tilt:1.115863 delta:-0.001598 | 17s -fused_ngram_eval: 6.6% | base:1.109205 tilt:1.107376 delta:-0.001829 | 35s -fused_ngram_eval: 9.9% | base:1.109702 tilt:1.107794 delta:-0.001908 | 52s -fused_ngram_eval: 13.2% | base:1.111686 tilt:1.109706 delta:-0.001980 | 69s -fused_ngram_eval: 16.5% | 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delta:-0.002298 | 518s +fused_ngram_eval:Windows: 969,092, batches: 15143, world_size: 8 +fused_ngram_eval:Setup: 54.3s fused_ngram_eval: fused_ngram_eval:======================================================================== -fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048 +fused_ngram_eval:RESULTS base_beta=1.0, stride=64, seq_len=2048, world_size=8 fused_ngram_eval:======================================================================== -fused_ngram_eval:Neural only: val_bpb = 1.10745634 -fused_ngram_eval:Tilted: val_bpb = 1.10515710 -fused_ngram_eval:Delta: -0.00229924 BPB +fused_ngram_eval:Submission: val_bpb = 1.10515710 fused_ngram_eval:Tokens: 62,023,616 | Bytes: 151,084,845 fused_ngram_eval:Tilted: 22,074,453 (35.6%) | Hits: 13,139,387 (59.5%) -fused_ngram_eval:Loop: 523.3s | Wall: 580.6s -fused_ngram_eval:DONE seed999 2026-04-01T02:18:43+0000 -final_int6_fused_ngram_exact neural_val_bpb:1.10745634 tilted_val_bpb:1.10515710 delta_bpb:-0.00229924 setup_s:57.4 loop_s:523.3 wall_s:580.6 -final_int6_fused_ngram_neural_exact val_bpb:1.10745634 +fused_ngram_eval:Loop: 65.5s | Wall: 119.8s +final_int6_fused_ngram_exact submission_val_bpb:1.10515710 setup_s:54.3 loop_s:65.5 wall_s:119.8 final_int6_fused_ngram_submission_exact val_bpb:1.10515710