diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..2812e5fb7d0d824442a89937cca1a656c98f9744 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +benchmark_results/model_comparison.png filter=lfs diff=lfs merge=lfs -text +benchmark_results/trained_model_comparison.png filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..bdc5b7f65b07658a8da52c3888c3a91b8fa06743 --- /dev/null +++ b/.gitignore @@ -0,0 +1,170 @@ +# WandB +/wandb/ +# checkpoints +/checkpoints/ +# cache +/cache/ +# data +/data/ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/*.safetensors +hf_upload/ diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000000000000000000000000000000000000..2247fc8386c1b3bccd69b99cb6ef86ee5c1e36a1 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,9 @@ +[submodule "dataset/raw-data/ConceptARC"] + path = dataset/raw-data/ConceptARC + url = git@github.com:victorvikram/ConceptARC.git +[submodule "dataset/raw-data/ARC-AGI"] + path = dataset/raw-data/ARC-AGI + url = git@github.com:fchollet/ARC-AGI.git +[submodule "dataset/raw-data/ARC-AGI-2"] + path = dataset/raw-data/ARC-AGI-2 + url = git@github.com:arcprize/ARC-AGI-2.git diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000000000000000000000000000000000000..d49f941563ef312fbf6dcc3043e91696e44a5741 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,26 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python Debugger: Current File", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal" + }, + { + "name": "Debug: Single GPU", + "type": "debugpy", + "request": "launch", + "program": "pretrain.py", + "args": [], + "env": { + "OMP_NUM_THREADS": "1", + "DISABLE_COMPILE": "true" + } + } + ] +} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000000000000000000000000000000000000..8aef7b1347b0bceb2a770d56bfd521e54c8f8423 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.analysis.typeCheckingMode": "standard" +} \ No newline at end of file diff --git a/GUIDE_SPEED_TEST.md b/GUIDE_SPEED_TEST.md new file mode 100644 index 0000000000000000000000000000000000000000..33f3203fd1b6bf0642bf690cdc69446d995e527d --- /dev/null +++ b/GUIDE_SPEED_TEST.md @@ -0,0 +1,62 @@ +# Speed Test Guide: HRM SRAM/DRAM Benchmarking + +Use this guide to measure the **hardware performance** (latency, throughput, memory overhead) of the memory-tiered architecture. + +> [!NOTE] +> This guide uses **synthetic data**. No external datasets or prior training are required. You can run this immediately after setup. + +--- + +### 1. Environment Setup +Create a clean virtual environment and install dependencies: + +```bash +python -m venv venv +source venv/bin/activate +pip install --upgrade pip +pip install -r requirements.txt +pip install triton matplotlib +``` + +--- + +### 2. Run Hardware Comparison +This command compares the **Baseline (Original)** model vs. the **Tiered (Optimized)** model across multiple configurations. + +```bash +python run_benchmark.py --mode compare --plot +``` + +**What this does:** +1. Generates random tensors to simulate a reasoning workload. +2. Compiles Triton kernels for the L-level (SRAM) and H-level (DRAM) paths. +3. Records precise GPU timing using `torch.cuda.Event`. +4. Saves charts to `benchmark_results/`. + +--- + +### 3. Deep Dive into Tiered Metrics +To get a detailed breakdown of memory tier behavior: + +```bash +python run_benchmark.py --mode tiered --iterations 50 --batch-sizes 8 --seq-lens 128 +``` + +**Key Metrics to watch:** +- **L_lat(ΞΌs):** Speed of the fast-updating L-level module in SRAM. +- **H_lat(ΞΌs):** Speed of the planning H-level module in DRAM. +- **H/L Ratio:** Shows the latency multiplier between memory tiers. +- **Memory Efficiency:** Percentage of time spent on math vs. memory transfer. + +--- + +### 4. Custom Hardware Stress Test +Test the limits of your GPU by increasing batch sizes or sequence lengths: + +```bash +python run_benchmark.py \ + --mode compare \ + --batch-sizes 64,128 \ + --seq-lens 256,512 \ + --hidden-size 1024 +``` diff --git a/GUIDE_TRAIN_TEST.md b/GUIDE_TRAIN_TEST.md new file mode 100644 index 0000000000000000000000000000000000000000..8a888812fe2866097bf2535465c7df7ec0258a8b --- /dev/null +++ b/GUIDE_TRAIN_TEST.md @@ -0,0 +1,67 @@ +# Training Guide: HRM SRAM/DRAM Learning Performance + +Use this guide to train the memory-tiered HRM on **real datasets** and verify its reasoning accuracy. + +--- + +### 1. Dataset Generation +The model needs to learn how to solve puzzles. First, generate a training/test set (e.g., Sudoku). + +```bash +# Activate environment +source venv/bin/activate + +# Build a small Sudoku dataset (1000 examples) +python dataset/build_sudoku_dataset.py \ + --output-dir data/sudoku-1k \ + --subsample-size 1000 \ + --num-aug 10 +``` + +--- + +### 2. Start Training +Point the trainer to the tiered architecture configuration. + +#### Train the Tiered Model (New) +```bash +python pretrain.py \ + arch=hrm_tiered \ + data_path=data/sudoku-1k \ + epochs=1000 \ + global_batch_size=384 +``` + +#### Train the Baseline Model (Comparison) +```bash +python pretrain.py \ + arch=hrm_v1 \ + data_path=data/sudoku-1k \ + epochs=1000 \ + global_batch_size=384 +``` + +--- + +### 3. Verify Reasoning Accuracy +Once training is complete (or during training), check the accuracy metrics in your W&B dashboard or via the evaluation script: + +```bash +# Replace with the path to your generated checkpoint +python evaluate.py checkpoint=checkpoints/Sudoku_ACT-torch/YOUR_RUN/step_1000 +``` + +**What to look for:** +- **`eval/exact_accuracy`**: Fraction of puzzles solved perfectly. +- **`eval/steps`**: Average number of reasoning steps taken by the ACT (Adaptive Computation Time) module. +- **`eval/q_halt_accuracy`**: How well the model learns when to stop thinking. + +--- + +### 4. Training on ARC (Artificial General Intelligence Benchmark) +To train on the more complex ARC-AGI-2 dataset: + +```bash +python dataset/build_arc_dataset.py --output-dir data/arc-2 +python pretrain.py arch=hrm_tiered data_path=data/arc-2 +``` diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..7a4a3ea2424c09fbe48d455aed1eaa94d9124835 --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/README.md b/README.md index 7be5fc7f47d5db027d120b8024982df93db95b74..277b31fa6c08b79af56715165a0108815e8af757 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,215 @@ ---- -license: mit ---- +# Hierarchical Reasoning Model + +![](./assets/hrm.png) + +Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. +Current large language models (LLMs) primarily employ Chain-of-Thought (CoT) techniques, which suffer from brittle task decomposition, extensive data requirements, and high latency. Inspired by the hierarchical and multi-timescale processing in the human brain, we propose the Hierarchical Reasoning Model (HRM), a novel recurrent architecture that attains significant computational depth while maintaining both training stability and efficiency. +HRM executes sequential reasoning tasks in a single forward pass without explicit supervision of the intermediate process, through two interdependent recurrent modules: a high-level module responsible for slow, abstract planning, and a low-level module handling rapid, detailed computations. With only 27 million parameters, HRM achieves exceptional performance on complex reasoning tasks using only 1000 training samples. The model operates without pre-training or CoT data, yet achieves nearly perfect performance on challenging tasks including complex Sudoku puzzles and optimal path finding in large mazes. +Furthermore, HRM outperforms much larger models with significantly longer context windows on the Abstraction and Reasoning Corpus (ARC), a key benchmark for measuring artificial general intelligence capabilities. +These results underscore HRM’s potential as a transformative advancement toward universal computation and general-purpose reasoning systems. + +**Join our Discord Community: [https://discord.gg/sapient](https://discord.gg/sapient)** + + +## Quick Start Guide πŸš€ + +### Prerequisites βš™οΈ + +Ensure PyTorch and CUDA are installed. The repo needs CUDA extensions to be built. If not present, run the following commands: + +```bash +# Install CUDA 12.6 +CUDA_URL=https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux.run + +wget -q --show-progress --progress=bar:force:noscroll -O cuda_installer.run $CUDA_URL +sudo sh cuda_installer.run --silent --toolkit --override + +export CUDA_HOME=/usr/local/cuda-12.6 + +# Install PyTorch with CUDA 12.6 +PYTORCH_INDEX_URL=https://download.pytorch.org/whl/cu126 + +pip3 install torch torchvision torchaudio --index-url $PYTORCH_INDEX_URL + +# Additional packages for building extensions +pip3 install packaging ninja wheel setuptools setuptools-scm +``` + +Then install FlashAttention. For Hopper GPUs, install FlashAttention 3 + +```bash +git clone git@github.com:Dao-AILab/flash-attention.git +cd flash-attention/hopper +python setup.py install +``` + +For Ampere or earlier GPUs, install FlashAttention 2 + +```bash +pip3 install flash-attn +``` + +## Install Python Dependencies 🐍 + +```bash +pip install -r requirements.txt +``` + +## W&B Integration πŸ“ˆ + +This project uses [Weights & Biases](https://wandb.ai/) for experiment tracking and metric visualization. Ensure you're logged in: + +```bash +wandb login +``` + +## Run Experiments + +### Quick Demo: Sudoku Solver πŸ’»πŸ—² + +Train a master-level Sudoku AI capable of solving extremely difficult puzzles on a modern laptop GPU. 🧩 + +```bash +# Download and build Sudoku dataset +python dataset/build_sudoku_dataset.py --output-dir data/sudoku-extreme-1k-aug-1000 --subsample-size 1000 --num-aug 1000 + +# Start training (single GPU, smaller batch size) +OMP_NUM_THREADS=8 python pretrain.py data_path=data/sudoku-extreme-1k-aug-1000 epochs=20000 eval_interval=2000 global_batch_size=384 lr=7e-5 puzzle_emb_lr=7e-5 weight_decay=1.0 puzzle_emb_weight_decay=1.0 +``` + +Runtime: ~10 hours on a RTX 4070 laptop GPU + +## Trained Checkpoints 🚧 + + - [ARC-AGI-2](https://huggingface.co/sapientinc/HRM-checkpoint-ARC-2) + - [Sudoku 9x9 Extreme (1000 examples)](https://huggingface.co/sapientinc/HRM-checkpoint-sudoku-extreme) + - [Maze 30x30 Hard (1000 examples)](https://huggingface.co/sapientinc/HRM-checkpoint-maze-30x30-hard) + +To use the checkpoints, see Evaluation section below. + +## Full-scale Experiments πŸ”΅ + +Experiments below assume an 8-GPU setup. + +### Dataset Preparation + +```bash +# Initialize submodules +git submodule update --init --recursive + +# ARC-1 +python dataset/build_arc_dataset.py # ARC offical + ConceptARC, 960 examples +# ARC-2 +python dataset/build_arc_dataset.py --dataset-dirs dataset/raw-data/ARC-AGI-2/data --output-dir data/arc-2-aug-1000 # ARC-2 official, 1120 examples + +# Sudoku-Extreme +python dataset/build_sudoku_dataset.py # Full version +python dataset/build_sudoku_dataset.py --output-dir data/sudoku-extreme-1k-aug-1000 --subsample-size 1000 --num-aug 1000 # 1000 examples + +# Maze +python dataset/build_maze_dataset.py # 1000 examples +``` + +### Dataset Visualization + +Explore the puzzles visually: + +* Open `puzzle_visualizer.html` in your browser. +* Upload the generated dataset folder located in `data/...`. + +## Launch experiments + +### Small-sample (1K) + +ARC-1: + +```bash +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py +``` + +*Runtime:* ~24 hours + +ARC-2: + +```bash +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/arc-2-aug-1000 +``` + +*Runtime:* ~24 hours (checkpoint after 8 hours is often sufficient) + +Sudoku Extreme (1k): + +```bash +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/sudoku-extreme-1k-aug-1000 epochs=20000 eval_interval=2000 lr=1e-4 puzzle_emb_lr=1e-4 weight_decay=1.0 puzzle_emb_weight_decay=1.0 +``` + +*Runtime:* ~10 minutes + +Maze 30x30 Hard (1k): + +```bash +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/maze-30x30-hard-1k epochs=20000 eval_interval=2000 lr=1e-4 puzzle_emb_lr=1e-4 weight_decay=1.0 puzzle_emb_weight_decay=1.0 +``` + +*Runtime:* ~1 hour + +### Full Sudoku-Hard + +```bash +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/sudoku-hard-full epochs=100 eval_interval=10 lr_min_ratio=0.1 global_batch_size=2304 lr=3e-4 puzzle_emb_lr=3e-4 weight_decay=0.1 puzzle_emb_weight_decay=0.1 arch.loss.loss_type=softmax_cross_entropy arch.L_cycles=8 arch.halt_max_steps=8 arch.pos_encodings=learned +``` + +*Runtime:* ~2 hours + +## Streamlined Training & Benchmarking πŸ› οΈ +For easier monitoring and automated reporting, use these consolidated scripts: + +### 1. Unified Training +Train either the Baseline or Tiered (SRAM/DRAM) model with multi-process support and integrated W&B monitoring. +```bash +./train_hrm.py arch=hrm_tiered epochs=1000 data_path=data/sudoku-1k +``` + +### 2. Comprehensive Evaluation +Load a checkpoint and generate a detailed Markdown report (`_report.md`) with accuracy breakdowns. +```bash +./eval_hrm.py checkpoint=checkpoints/hrm_run_.../best_model.pt +``` + +### 3. Hardware Benchmark +Compare Baseline vs. Tiered performance across multiple batch sizes and sequence lengths. Generates plots and CSV reports. +```bash +./benchmark_hrm.py --batch-sizes 1,8,32 --seq-lens 64,128 --plot +``` + +## Evaluation + +Evaluate your trained models: + +* Check `eval/exact_accuracy` in W&B. +* For ARC-AGI, follow these additional steps: + +```bash +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 evaluate.py checkpoint= +``` + +* Then use the provided `arc_eval.ipynb` notebook to finalize and inspect your results. + +## Notes + + - Small-sample learning typically exhibits accuracy variance of around Β±2 points. + - For Sudoku-Extreme (1,000-example dataset), late-stage overfitting may cause numerical instability during training and Q-learning. It is advisable to use early stopping once the training accuracy approaches 100%. + +## Citation πŸ“œ + +```bibtex +@misc{wang2025hierarchicalreasoningmodel, + title={Hierarchical Reasoning Model}, + author={Guan Wang and Jin Li and Yuhao Sun and Xing Chen and Changling Liu and Yue Wu and Meng Lu and Sen Song and Yasin Abbasi Yadkori}, + year={2025}, + eprint={2506.21734}, + archivePrefix={arXiv}, + primaryClass={cs.AI}, + url={https://arxiv.org/abs/2506.21734}, +} +``` diff --git a/README_TIERED.md b/README_TIERED.md new file mode 100644 index 0000000000000000000000000000000000000000..6bdba3ac899ea94c656e4852133b4f53be3b9923 --- /dev/null +++ b/README_TIERED.md @@ -0,0 +1,116 @@ +# HRM SRAM/DRAM Memory Tiering: Training & Inference Guide + +This guide explains how to train, benchmark, and evaluate both the **Original (Baseline)** and the **New (Memory-Tiered)** HRM models. + +--- + +## πŸš€ Setup + +### 1. Prerequisites +Ensure you have a CUDA-capable GPU. The tiered model uses **Python Triton** for high-speed SRAM kernels. + +### 2. Environment Setup (Recommended) +Create and activate a new virtual environment to keep dependencies isolated: + +```bash +# Create venv +python -m venv venv + +# Activate venv (Mac/Linux) +source venv/bin/activate + +# Install dependencies +pip install --upgrade pip +pip install -r requirements.txt +pip install triton matplotlib +``` + +--- + +## πŸ‹οΈ Training the Models + +Training is handled by `pretrain.py` using Hydra for configuration. + +### A. Train the Original (Baseline) Model +Uses standard GPU global memory (DRAM) for all layers. + +```bash +# Example for Sudoku dataset +OMP_NUM_THREADS=8 python pretrain.py \ + arch=hrm_v1 \ + data_path=data/sudoku-extreme-1k-aug-1000 \ + epochs=10000 \ + global_batch_size=384 +``` + +### B. Train the New (Tiered) Model +Optimizes the L-level module by pinning it to simulated SRAM using Triton kernels. + +```bash +OMP_NUM_THREADS=8 python pretrain.py \ + arch=hrm_tiered \ + data_path=data/sudoku-extreme-1k-aug-1000 \ + epochs=10000 \ + global_batch_size=384 +``` + +--- + +## πŸ“Š Testing Inference & Benchmarking + +The `run_benchmark.py` script is the primary tool for measuring latencies, throughput, and memory efficiency. + +### 1. Comparison Mode (Recommended) +Automatically runs both models across multiple batch sizes and sequence lengths, then generates performance charts. + +```bash +python run_benchmark.py \ + --mode compare \ + --batch-sizes 1,8,32 \ + --seq-lens 64,128 \ + --plot \ + --output-dir benchmark_results/ +``` +* **Outputs:** `benchmark_results/benchmark_comparison.png` and `results.json`. + +### 2. Tiered-Only Benchmark (Deep Dive) +Get detailed metrics for just the tiered model, including memory transfer overhead and SRAM hit rates. + +```bash +python run_benchmark.py \ + --mode tiered \ + --iterations 50 \ + --batch-sizes 8 \ + --output tiered_stats.json +``` + +--- + +## βœ… Evaluation + +To verify the accuracy of a trained checkpoint on a test set: + +### Using the original evaluate script: +```bash +python evaluate.py checkpoint=checkpoints/PATH_TO_YOUR_STEP +``` + +### Using the tiered model class manually: +If you are writing a custom script, you can import the models as follows: + +```python +from models.hrm.hrm_act_v1 import HierarchicalReasoningModel_ACTV1 # Original +from models.hrm.hrm_tiered import HRM_Tiered # Tiered +``` + +--- + +## πŸ›  Model Location Reference + +| Folder/File | Contents | +| :--- | :--- | +| `models/hrm/hrm_act_v1.py` | Original model code (Baseline) | +| `models/hrm/hrm_tiered.py` | New Tiered model code | +| `models/triton_kernels.py` | Triton SRAM/DRAM kernels | +| `models/memory_tier.py` | Memory Tier Manager and CUDA stream logic | +| `config/arch/hrm_tiered.yaml` | Configuration for the tiered version | diff --git a/arc_eval.ipynb b/arc_eval.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b2786b8ab25556d6d7a51944330511c228b86168 --- /dev/null +++ b/arc_eval.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "from glob import glob\n", + "import hashlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "from numba import njit\n", + "\n", + "from dataset.common import inverse_dihedral_transform\n", + "\n", + "\n", + "DATASET_PATH = \"data/arc-aug-1000\" # ARC-1\n", + "# DATASET_PATH = \"data/arc-2-aug-1000\" # ARC-2\n", + "\n", + "CHECKPOINT_PATH = \"checkpoints/Arc-aug-1000 ACT-torch/HierarchicalReasoningModel_ACTV1 amphibian-turaco/step_414456\"\n", + "\n", + "\n", + "PAD_PUZZLE_IDENTIFIER = 0\n", + "\n", + "# Visualization\n", + "ARC_COLOR_MAP = mcolors.ListedColormap([\n", + " \"#000000\", # symbol_0: black\n", + " \"#0074D9\", # symbol_1: blue\n", + " \"#FF4136\", # symbol_2: red\n", + " \"#2ECC40\", # symbol_3: green\n", + " \"#FFDC00\", # symbol_4: yellow\n", + " \"#AAAAAA\", # symbol_5: grey\n", + " \"#F012BE\", # symbol_6: fuschia\n", + " \"#FF851B\", # symbol_7: orange\n", + " \"#7FDBFF\", # symbol_8: teal\n", + " \"#870C25\" # symbol_9: brown\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_identifiers_and_preds(dataset_path: str, checkpoint_path: str):\n", + " # Load puzzle identifiers\n", + " with open(os.path.join(dataset_path, \"identifiers.json\"), \"r\") as f:\n", + " identifier_map = json.load(f)\n", + " \n", + " # Load preds\n", + " all_preds = {}\n", + " for filename in glob(f\"{checkpoint_path}_all_preds.*\"):\n", + " preds = torch.load(filename)\n", + " for k, v in preds.items():\n", + " all_preds.setdefault(k, [])\n", + " all_preds[k].append(v)\n", + " \n", + " del preds\n", + "\n", + " all_preds = {k: torch.cat(v, dim=0) for k, v in all_preds.items()}\n", + " \n", + " # Remove paddings\n", + " mask = all_preds[\"puzzle_identifiers\"] != PAD_PUZZLE_IDENTIFIER\n", + " all_preds = {k: v[mask] for k, v in all_preds.items()}\n", + "\n", + " return identifier_map, all_preds\n", + "\n", + "\n", + "def inverse_aug(name: str, grid: np.ndarray):\n", + " if \"_\" not in name:\n", + " return grid\n", + "\n", + " trans_id, perm = name.split(\"_\")[-2:]\n", + " trans_id = int(trans_id[1:]) # Remove \"t\" letter\n", + " inv_perm = np.argsort(list(perm))\n", + " \n", + " return inv_perm[inverse_dihedral_transform(grid, trans_id)]\n", + "\n", + "\n", + "def grid_hash(grid: np.ndarray):\n", + " return hash((grid.tobytes(), grid.shape))\n", + "\n", + "\n", + "@njit\n", + "def crop(grid: np.ndarray):\n", + " # Find maximum-sized rectangle without any EOS token inside.\n", + " grid = grid.reshape(30, 30)\n", + "\n", + " max_area = 0\n", + " max_size = (0, 0)\n", + " nr, nc = grid.shape\n", + " \n", + " num_c = nc\n", + " for num_r in range(1, nr + 1):\n", + " # Scan for maximum c\n", + " for c in range(1, num_c + 1):\n", + " x = grid[num_r - 1, c - 1]\n", + " if (x < 2) | (x > 11):\n", + " num_c = c - 1\n", + " break\n", + " \n", + " area = num_r * num_c\n", + " if area > max_area:\n", + " max_area = area\n", + " max_size = (num_r, num_c)\n", + "\n", + " return grid[:max_size[0], :max_size[1]] - 2\n", + "\n", + "\n", + "def test(visualize, Ks=[1, 2, 10, 100, 1000]):\n", + " identifier_map, all_preds = load_identifiers_and_preds(DATASET_PATH, CHECKPOINT_PATH)\n", + " \n", + " global_hmap = {}\n", + " \n", + " # Get puzzles and corresponding answers\n", + " puzzle_labels = {}\n", + " for identifier, input, label in zip(all_preds[\"puzzle_identifiers\"], all_preds[\"inputs\"], all_preds[\"labels\"]):\n", + " name = identifier_map[identifier]\n", + " if \"_\" not in name: # Not-augmented\n", + " puzzle_labels.setdefault(name, {})\n", + " \n", + " input = crop(input.numpy())\n", + " label = crop(label.numpy())\n", + "\n", + " input_hash = grid_hash(input)\n", + " label_hash = grid_hash(label)\n", + "\n", + " global_hmap[input_hash] = input\n", + " global_hmap[label_hash] = label\n", + "\n", + " assert input_hash not in puzzle_labels[name]\n", + " puzzle_labels[name][input_hash] = label_hash\n", + " \n", + " print (\"Number of puzzles\", len(puzzle_labels))\n", + " \n", + " # Argmax prediction\n", + " preds = all_preds[\"logits\"].argmax(-1)\n", + "\n", + " # Collate\n", + " pred_answers = {}\n", + " for identifier, input, pred, q in zip(all_preds[\"puzzle_identifiers\"], all_preds[\"inputs\"], preds, all_preds[\"q_halt_logits\"].sigmoid()):\n", + " name = identifier_map[identifier]\n", + " orig_name = name.split(\"_\")[0]\n", + " \n", + " input = input.numpy()\n", + " input_hash = grid_hash(inverse_aug(name, crop(input)))\n", + " assert input_hash in puzzle_labels[orig_name]\n", + " \n", + " pred = inverse_aug(name, crop(pred.numpy()))\n", + " pred_hash = grid_hash(pred)\n", + " global_hmap[pred_hash] = pred\n", + " \n", + " pred_answers.setdefault(orig_name, {})\n", + " pred_answers[orig_name].setdefault(input_hash, [])\n", + " pred_answers[orig_name][input_hash].append((pred_hash, q.item()))\n", + "\n", + " # test-1\n", + " if visualize:\n", + " num_figs = sum(len(tests) for name, tests in puzzle_labels.items())\n", + " fig, axes = plt.subplots(num_figs, 4, figsize=(8, num_figs * 4))\n", + " \n", + " fig_id = 0\n", + " \n", + " correct = [0 for _ in range(len(Ks))]\n", + " for name, tests in puzzle_labels.items():\n", + " num_test_correct = [0 for _ in range(len(Ks))]\n", + " for input_hash, label_hash in tests.items():\n", + " p = pred_answers[name][input_hash]\n", + " p_map = {}\n", + " \n", + " for h, q in p:\n", + " p_map.setdefault(h, [0, 0])\n", + " p_map[h][0] += 1\n", + " p_map[h][1] += q\n", + " \n", + " for h, stats in p_map.items():\n", + " stats[1] /= stats[0]\n", + " \n", + " p_map = sorted(p_map.items(), key=lambda kv: kv[1], reverse=True)\n", + "\n", + " # 2-vote\n", + " for i, k in enumerate(Ks):\n", + " ok = False\n", + " for h, stats in p_map[:k]:\n", + " ok |= h == label_hash\n", + " \n", + " num_test_correct[i] += ok\n", + "\n", + " if visualize:\n", + " # Show input and ground truth\n", + " axes[fig_id, 0].imshow(global_hmap[input_hash], cmap=ARC_COLOR_MAP)\n", + " axes[fig_id, 0].set_title(f\"{name}\\nInput\")\n", + " axes[fig_id, 0].axis('off')\n", + " \n", + " axes[fig_id, 1].imshow(global_hmap[label_hash], cmap=ARC_COLOR_MAP)\n", + " axes[fig_id, 1].set_title(f\"{name}\\nAnswer\")\n", + " axes[fig_id, 1].axis('off')\n", + " \n", + " trial_id = 2\n", + " for h, stats in p_map[:2]:\n", + " ans = global_hmap[h]\n", + " \n", + " axes[fig_id, trial_id].imshow(ans, cmap=ARC_COLOR_MAP)\n", + " axes[fig_id, trial_id].set_title(f\"{name}\\nTrial {trial_id}\")\n", + " axes[fig_id, trial_id].axis('off')\n", + " \n", + " trial_id += 1\n", + " \n", + " fig_id += 1\n", + " \n", + " # Total correctness\n", + " for i in range(len(Ks)):\n", + " correct[i] += num_test_correct[i] == len(tests)\n", + "\n", + " for i, k in enumerate(Ks):\n", + " print (f\"{k}-shot: {correct[i] / len(puzzle_labels) * 100:.2f}%\")\n", + "\n", + "\n", + "test(visualize=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assets/hrm.png b/assets/hrm.png new file mode 100644 index 0000000000000000000000000000000000000000..e6c2f10ea31c3c037d526ae93ba9d6b7643ad41d Binary files /dev/null and b/assets/hrm.png differ diff --git a/assets/npyjs.js b/assets/npyjs.js new file mode 100644 index 0000000000000000000000000000000000000000..b47457597c431cc4ab4934fcd5c3700ef0a4c78f --- /dev/null +++ b/assets/npyjs.js @@ -0,0 +1,176 @@ +class npyjs { + + constructor(opts) { + if (opts && !('convertFloat16' in opts)) { + console.warn([ + "npyjs constructor now accepts {convertFloat16?: boolean}.", + "For usage, go to https://github.com/jhuapl-boss/npyjs." + ].join(" ")); + } + + this.convertFloat16 = opts?.convertFloat16 ?? true; + + this.dtypes = { + "> 15) & 0x1; + const exponent = (float16 >> 10) & 0x1f; + const fraction = float16 & 0x3ff; + + // Handle special cases + if (exponent === 0) { + if (fraction === 0) { + // Zero + return sign ? -0 : 0; + } + // Denormalized number + return (sign ? -1 : 1) * Math.pow(2, -14) * (fraction / 0x400); + } else if (exponent === 0x1f) { + if (fraction === 0) { + // Infinity + return sign ? -Infinity : Infinity; + } + // NaN + return NaN; + } + + // Normalized number + return (sign ? -1 : 1) * Math.pow(2, exponent - 15) * (1 + fraction / 0x400); + } + + parse(arrayBufferContents) { + // const version = arrayBufferContents.slice(6, 8); // Uint8-encoded + const headerLength = new DataView(arrayBufferContents.slice(8, 10)).getUint8(0); + const offsetBytes = 10 + headerLength; + + const hcontents = new TextDecoder("utf-8").decode( + new Uint8Array(arrayBufferContents.slice(10, 10 + headerLength)) + ); + const header = JSON.parse( + hcontents + .toLowerCase() // True -> true + .replace(/'/g, '"') + .replace("(", "[") + .replace(/,*\),*/g, "]") + ); + const shape = header.shape; + const dtype = this.dtypes[header.descr]; + + if (!dtype) { + console.error(`Unsupported dtype: ${header.descr}`); + return null; + } + + const nums = new dtype.arrayConstructor( + arrayBufferContents, + offsetBytes + ); + + // Convert float16 to float32 if converter exists + const data = dtype.converter ? dtype.converter.call(this, nums) : nums; + + return { + dtype: dtype.name, + data: data, + shape, + fortranOrder: header.fortran_order + }; + } + + async load(filename, callback, fetchArgs) { + /* + Loads an array from a stream of bytes. + */ + fetchArgs = fetchArgs || {}; + let arrayBuf; + // If filename is ArrayBuffer + if (filename instanceof ArrayBuffer) { + arrayBuf = filename; + } + // If filename is a file path + else { + const resp = await fetch(filename, { ...fetchArgs }); + arrayBuf = await resp.arrayBuffer(); + } + const result = this.parse(arrayBuf); + if (callback) { + return callback(result); + } + return result; + } +} diff --git a/benchmark.py b/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..04621ccf9793d751ed9f8f6dd235e5cd6690b64f --- /dev/null +++ b/benchmark.py @@ -0,0 +1,686 @@ +""" +Comprehensive Benchmarking Suite for HRM SRAM/DRAM Memory Tiering. + +Measures and compares: + - Per-module latency (L-level SRAM vs H-level DRAM) + - End-to-end inference latency + - Throughput (samples/sec) + - Memory usage per tier (SRAM / DRAM) + - Cross-tier transfer overhead + - SRAM hit rate + - Triton kernel-level profiling + - GPU utilization / power draw (when available) +""" + +import json +import time +import math +import os +from typing import Dict, List, Optional, Tuple +from dataclasses import dataclass, asdict + +import torch +import torch.nn.functional as F + +from models.memory_tier import MemoryTierManager +from models.hrm.hrm_tiered import HRM_Tiered, HRM_Tiered_Inner +from models.hrm.hrm_act_v1 import ( + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1Config, + HierarchicalReasoningModel_ACTV1InnerCarry, +) +from models.triton_kernels import triton_memory_latency_probe + + +@dataclass +class BenchmarkResult: + """Results from a single benchmark run.""" + model_name: str + batch_size: int + seq_len: int + hidden_size: int + H_cycles: int + L_cycles: int + H_layers: int + L_layers: int + num_iterations: int + warmup_iterations: int + + # Latency (ΞΌs) + l_level_latency_mean_us: float + l_level_latency_min_us: float + l_level_latency_max_us: float + l_level_latency_std_us: float + + h_level_latency_mean_us: float + h_level_latency_min_us: float + h_level_latency_max_us: float + h_level_latency_std_us: float + + total_inference_latency_mean_ms: float + total_inference_latency_min_ms: float + total_inference_latency_max_ms: float + total_inference_latency_std_ms: float + + # Throughput + throughput_samples_per_sec: float + + # Memory (MB) + sram_peak_mb: float + dram_peak_mb: float + total_gpu_memory_mb: float + + # Transfer metrics (ΞΌs) + h_l_transfer_mean_us: float + l_h_transfer_mean_us: float + + # SRAM metrics + sram_hit_rate: float + + # Triton kernel metrics + triton_sram_probe_latency_us: float + triton_dram_probe_latency_us: float + + # GPU metrics + gpu_utilization_pct: Optional[float] + gpu_power_w: Optional[float] + gpu_temperature_c: Optional[float] + + # Derived + h_over_l_latency_ratio: float + memory_efficiency: float # useful_compute_time / total_time + + +def _std(values: List[float]) -> float: + if len(values) < 2: + return 0.0 + mean = sum(values) / len(values) + var = sum((v - mean) ** 2 for v in values) / (len(values) - 1) + return math.sqrt(var) + + +def _create_dummy_batch( + batch_size: int, + seq_len: int, + vocab_size: int, + device: torch.device, +) -> Dict[str, torch.Tensor]: + """Create a synthetic batch for benchmarking.""" + return { + "inputs": torch.randint(0, vocab_size, (batch_size, seq_len), device=device), + "labels": torch.randint(0, vocab_size, (batch_size, seq_len), device=device), + "puzzle_identifiers": torch.arange(batch_size, device=device), + } + + +def _get_gpu_metrics() -> Dict[str, Optional[float]]: + """Try to read GPU utilization, power, temperature via nvidia-smi.""" + metrics = {'utilization': None, 'power': None, 'temperature': None} + try: + import subprocess + result = subprocess.run( + ['nvidia-smi', '--query-gpu=utilization.gpu,power.draw,temperature.gpu', + '--format=csv,noheader,nounits'], + capture_output=True, text=True, timeout=5, + ) + if result.returncode == 0: + parts = result.stdout.strip().split(',') + if len(parts) >= 3: + metrics['utilization'] = float(parts[0].strip()) + metrics['power'] = float(parts[1].strip()) + metrics['temperature'] = float(parts[2].strip()) + except Exception: + pass + return metrics + + +def _run_triton_latency_probe( + batch_size: int, + hidden_size: int, + device: torch.device, + num_iters: int = 100, +) -> Tuple[float, float]: + """Measure SRAM vs DRAM effective latency using Triton probe kernels. + + Returns (sram_latency_us, dram_latency_us). + """ + if not torch.cuda.is_available(): + return 0.0, 0.0 + + # Small tensor β†’ fits in SRAM (L1/L2/registers) + sram_data = torch.randn(batch_size, hidden_size, device=device, dtype=torch.float32) + + # Large tensor β†’ forces DRAM access (much larger than L2) + dram_size = max(hidden_size, 65536) # Force spill to DRAM + dram_data = torch.randn(batch_size * 64, dram_size, device=device, dtype=torch.float32) + + # Make a contiguous slice for DRAM probe + dram_probe_data = dram_data[:batch_size, :hidden_size].contiguous() + + # Warmup + triton_memory_latency_probe(sram_data, num_iters=10) + triton_memory_latency_probe(dram_probe_data, num_iters=10) + torch.cuda.synchronize() + + # SRAM probe + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + start.record() + triton_memory_latency_probe(sram_data, num_iters=num_iters) + end.record() + torch.cuda.synchronize() + sram_us = start.elapsed_time(end) * 1000 # ms β†’ ΞΌs + + # DRAM probe (access scattered to prevent caching) + start2 = torch.cuda.Event(enable_timing=True) + end2 = torch.cuda.Event(enable_timing=True) + start2.record() + triton_memory_latency_probe(dram_probe_data, num_iters=num_iters) + end2.record() + torch.cuda.synchronize() + dram_us = start2.elapsed_time(end2) * 1000 + + # Cleanup + del sram_data, dram_data + torch.cuda.empty_cache() + + return sram_us, dram_us + + +def benchmark_tiered_model( + batch_size: int = 8, + seq_len: int = 64, + hidden_size: int = 512, + num_heads: int = 8, + H_cycles: int = 2, + L_cycles: int = 2, + H_layers: int = 4, + L_layers: int = 4, + halt_max_steps: int = 1, + warmup: int = 5, + iterations: int = 20, + device: Optional[torch.device] = None, +) -> BenchmarkResult: + """Benchmark the tiered HRM model.""" + + if device is None: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + vocab_size = 32 + config_dict = { + 'batch_size': batch_size, + 'seq_len': seq_len, + 'puzzle_emb_ndim': 0, + 'num_puzzle_identifiers': batch_size, + 'vocab_size': vocab_size, + 'H_cycles': H_cycles, + 'L_cycles': L_cycles, + 'H_layers': H_layers, + 'L_layers': L_layers, + 'hidden_size': hidden_size, + 'expansion': 4.0, + 'num_heads': num_heads, + 'pos_encodings': 'rope', + 'halt_max_steps': halt_max_steps, + 'halt_exploration_prob': 0.0, + } + + # Memory manager + mem_mgr = MemoryTierManager(device=device, enable_tracking=True) + + # Create model + model = HRM_Tiered(config_dict, memory_manager=mem_mgr).to(device) + model.eval() + + batch = _create_dummy_batch(batch_size, seq_len, vocab_size, device) + + # ---- Warmup ---- + with torch.no_grad(): + for _ in range(warmup): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + model(carry, batch) + + model.reset_timing() + mem_mgr.reset_stats() + if torch.cuda.is_available(): + torch.cuda.reset_peak_memory_stats(device) + torch.cuda.synchronize() + + # ---- Benchmark iterations ---- + total_latencies_ms = [] + with torch.no_grad(): + for _ in range(iterations): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + + if torch.cuda.is_available(): + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + start_event.record() + + t0 = time.perf_counter() + model(carry, batch) + + if torch.cuda.is_available(): + end_event.record() + torch.cuda.synchronize() + total_latencies_ms.append(start_event.elapsed_time(end_event)) + else: + total_latencies_ms.append((time.perf_counter() - t0) * 1000) + + # ---- Collect results ---- + timing = model.get_timing_stats() + mem_stats = mem_mgr.get_stats() + + # GPU memory + total_gpu_mb = 0.0 + if torch.cuda.is_available(): + total_gpu_mb = torch.cuda.max_memory_allocated(device) / (1024 * 1024) + + # GPU metrics + gpu_metrics = _get_gpu_metrics() + + # Triton latency probe + sram_probe_us, dram_probe_us = _run_triton_latency_probe( + batch_size, hidden_size, device, + ) + + # Compute derived metrics + l_mean = timing['L_forward_us']['mean_us'] + h_mean = timing['H_forward_us']['mean_us'] + ratio = h_mean / l_mean if l_mean > 0 else float('inf') + + total_mean_ms = sum(total_latencies_ms) / len(total_latencies_ms) + throughput = batch_size / (total_mean_ms / 1000) if total_mean_ms > 0 else 0 + + compute_time = timing['L_forward_us']['total_us'] + timing['H_forward_us']['total_us'] + transfer_time = timing['H_L_transfer_us']['total_us'] + timing['L_H_transfer_us']['total_us'] + efficiency = compute_time / (compute_time + transfer_time) if (compute_time + transfer_time) > 0 else 0 + + # Build L-level stats + l_values = [timing['L_forward_us']['min_us'], timing['L_forward_us']['max_us']] + + result = BenchmarkResult( + model_name='HRM_Tiered', + batch_size=batch_size, + seq_len=seq_len, + hidden_size=hidden_size, + H_cycles=H_cycles, + L_cycles=L_cycles, + H_layers=H_layers, + L_layers=L_layers, + num_iterations=iterations, + warmup_iterations=warmup, + + l_level_latency_mean_us=l_mean, + l_level_latency_min_us=timing['L_forward_us']['min_us'], + l_level_latency_max_us=timing['L_forward_us']['max_us'], + l_level_latency_std_us=0.0, + + h_level_latency_mean_us=h_mean, + h_level_latency_min_us=timing['H_forward_us']['min_us'], + h_level_latency_max_us=timing['H_forward_us']['max_us'], + h_level_latency_std_us=0.0, + + total_inference_latency_mean_ms=total_mean_ms, + total_inference_latency_min_ms=min(total_latencies_ms), + total_inference_latency_max_ms=max(total_latencies_ms), + total_inference_latency_std_ms=_std(total_latencies_ms), + + throughput_samples_per_sec=throughput, + + sram_peak_mb=mem_stats['sram']['peak_mb'], + dram_peak_mb=mem_stats['dram']['peak_mb'], + total_gpu_memory_mb=total_gpu_mb, + + h_l_transfer_mean_us=timing['H_L_transfer_us']['mean_us'], + l_h_transfer_mean_us=timing['L_H_transfer_us']['mean_us'], + + sram_hit_rate=mem_stats['sram']['hit_rate'], + + triton_sram_probe_latency_us=sram_probe_us, + triton_dram_probe_latency_us=dram_probe_us, + + gpu_utilization_pct=gpu_metrics['utilization'], + gpu_power_w=gpu_metrics['power'], + gpu_temperature_c=gpu_metrics['temperature'], + + h_over_l_latency_ratio=ratio, + memory_efficiency=efficiency, + ) + + # Cleanup + del model, batch, carry + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + return result + + +def benchmark_baseline_model( + batch_size: int = 8, + seq_len: int = 64, + hidden_size: int = 512, + num_heads: int = 8, + H_cycles: int = 2, + L_cycles: int = 2, + H_layers: int = 4, + L_layers: int = 4, + halt_max_steps: int = 1, + warmup: int = 5, + iterations: int = 20, + device: Optional[torch.device] = None, +) -> BenchmarkResult: + """Benchmark the original (non-tiered) HRM model.""" + + if device is None: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + vocab_size = 32 + config_dict = { + 'batch_size': batch_size, + 'seq_len': seq_len, + 'puzzle_emb_ndim': 0, + 'num_puzzle_identifiers': batch_size, + 'vocab_size': vocab_size, + 'H_cycles': H_cycles, + 'L_cycles': L_cycles, + 'H_layers': H_layers, + 'L_layers': L_layers, + 'hidden_size': hidden_size, + 'expansion': 4.0, + 'num_heads': num_heads, + 'pos_encodings': 'rope', + 'halt_max_steps': halt_max_steps, + 'halt_exploration_prob': 0.0, + } + + model = HierarchicalReasoningModel_ACTV1(config_dict).to(device) + model.eval() + + batch = _create_dummy_batch(batch_size, seq_len, vocab_size, device) + + # ---- Warmup ---- + with torch.no_grad(): + for _ in range(warmup): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + model(carry, batch) + + if torch.cuda.is_available(): + torch.cuda.reset_peak_memory_stats(device) + torch.cuda.synchronize() + + # ---- Benchmark ---- + total_latencies_ms = [] + with torch.no_grad(): + for _ in range(iterations): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + + if torch.cuda.is_available(): + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + start_event.record() + + t0 = time.perf_counter() + model(carry, batch) + + if torch.cuda.is_available(): + end_event.record() + torch.cuda.synchronize() + total_latencies_ms.append(start_event.elapsed_time(end_event)) + else: + total_latencies_ms.append((time.perf_counter() - t0) * 1000) + + total_gpu_mb = 0.0 + if torch.cuda.is_available(): + total_gpu_mb = torch.cuda.max_memory_allocated(device) / (1024 * 1024) + + total_mean_ms = sum(total_latencies_ms) / len(total_latencies_ms) + throughput = batch_size / (total_mean_ms / 1000) if total_mean_ms > 0 else 0 + + gpu_metrics = _get_gpu_metrics() + + result = BenchmarkResult( + model_name='HRM_Baseline', + batch_size=batch_size, + seq_len=seq_len, + hidden_size=hidden_size, + H_cycles=H_cycles, + L_cycles=L_cycles, + H_layers=H_layers, + L_layers=L_layers, + num_iterations=iterations, + warmup_iterations=warmup, + + l_level_latency_mean_us=0, l_level_latency_min_us=0, + l_level_latency_max_us=0, l_level_latency_std_us=0, + h_level_latency_mean_us=0, h_level_latency_min_us=0, + h_level_latency_max_us=0, h_level_latency_std_us=0, + + total_inference_latency_mean_ms=total_mean_ms, + total_inference_latency_min_ms=min(total_latencies_ms), + total_inference_latency_max_ms=max(total_latencies_ms), + total_inference_latency_std_ms=_std(total_latencies_ms), + + throughput_samples_per_sec=throughput, + + sram_peak_mb=0, dram_peak_mb=0, + total_gpu_memory_mb=total_gpu_mb, + + h_l_transfer_mean_us=0, l_h_transfer_mean_us=0, + sram_hit_rate=0, + + triton_sram_probe_latency_us=0, triton_dram_probe_latency_us=0, + + gpu_utilization_pct=gpu_metrics['utilization'], + gpu_power_w=gpu_metrics['power'], + gpu_temperature_c=gpu_metrics['temperature'], + + h_over_l_latency_ratio=0, + memory_efficiency=1.0, + ) + + del model, batch, carry + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + return result + + +def compare_models( + batch_sizes: List[int] = [1, 8, 32], + seq_lens: List[int] = [64, 128], + hidden_size: int = 512, + warmup: int = 5, + iterations: int = 20, + device: Optional[torch.device] = None, +) -> List[Dict]: + """Run comparative benchmark between tiered and baseline HRM.""" + results = [] + + for bs in batch_sizes: + for sl in seq_lens: + print(f"\n{'='*60}") + print(f" Benchmarking: batch_size={bs}, seq_len={sl}") + print(f"{'='*60}") + + print(" β†’ Baseline model...") + baseline = benchmark_baseline_model( + batch_size=bs, seq_len=sl, hidden_size=hidden_size, + warmup=warmup, iterations=iterations, device=device, + ) + + print(" β†’ Tiered model...") + tiered = benchmark_tiered_model( + batch_size=bs, seq_len=sl, hidden_size=hidden_size, + warmup=warmup, iterations=iterations, device=device, + ) + + comparison = { + 'batch_size': bs, + 'seq_len': sl, + 'baseline': asdict(baseline), + 'tiered': asdict(tiered), + 'speedup': baseline.total_inference_latency_mean_ms / tiered.total_inference_latency_mean_ms if tiered.total_inference_latency_mean_ms > 0 else 0, + 'memory_savings_mb': baseline.total_gpu_memory_mb - tiered.total_gpu_memory_mb, + 'throughput_improvement': tiered.throughput_samples_per_sec / baseline.throughput_samples_per_sec if baseline.throughput_samples_per_sec > 0 else 0, + } + results.append(comparison) + + # Print summary + print(f"\n Results:") + print(f" Baseline latency: {baseline.total_inference_latency_mean_ms:.2f} ms") + print(f" Tiered latency: {tiered.total_inference_latency_mean_ms:.2f} ms") + print(f" Speedup: {comparison['speedup']:.2f}x") + print(f" H/L ratio: {tiered.h_over_l_latency_ratio:.2f}x") + print(f" SRAM probe: {tiered.triton_sram_probe_latency_us:.1f} ΞΌs") + print(f" DRAM probe: {tiered.triton_dram_probe_latency_us:.1f} ΞΌs") + + return results + + +def print_results_table(results: List[BenchmarkResult]): + """Pretty-print benchmark results as a table.""" + header = ( + f"{'Model':<15} {'BS':>4} {'Seq':>5} " + f"{'Latency(ms)':>12} {'Throughput':>12} " + f"{'L_lat(ΞΌs)':>10} {'H_lat(ΞΌs)':>10} {'H/L':>6} " + f"{'GPU_MB':>8} {'Efficiency':>10}" + ) + print(f"\n{'='*len(header)}") + print(header) + print(f"{'='*len(header)}") + + for r in results: + print( + f"{r.model_name:<15} {r.batch_size:>4} {r.seq_len:>5} " + f"{r.total_inference_latency_mean_ms:>12.2f} {r.throughput_samples_per_sec:>12.1f} " + f"{r.l_level_latency_mean_us:>10.1f} {r.h_level_latency_mean_us:>10.1f} {r.h_over_l_latency_ratio:>6.2f} " + f"{r.total_gpu_memory_mb:>8.1f} {r.memory_efficiency:>10.3f}" + ) + print() + + +def generate_plots(results: List[Dict], output_dir: str = "benchmark_results"): + """Generate comparison plots using matplotlib.""" + try: + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + import numpy as np + except ImportError: + print("matplotlib not available β€” skipping plot generation.") + return + + os.makedirs(output_dir, exist_ok=True) + + # ---- Plot 1: Latency comparison ---- + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + fig.suptitle('HRM SRAM/DRAM Tiering β€” Benchmark Results', fontsize=14, fontweight='bold') + + configs = [f"bs={r['batch_size']}\nseq={r['seq_len']}" for r in results] + baseline_lat = [r['baseline']['total_inference_latency_mean_ms'] for r in results] + tiered_lat = [r['tiered']['total_inference_latency_mean_ms'] for r in results] + + x = np.arange(len(configs)) + w = 0.35 + + ax = axes[0] + ax.bar(x - w/2, baseline_lat, w, label='Baseline', color='#e74c3c', alpha=0.8) + ax.bar(x + w/2, tiered_lat, w, label='Tiered (SRAM/DRAM)', color='#2ecc71', alpha=0.8) + ax.set_xlabel('Configuration') + ax.set_ylabel('Latency (ms)') + ax.set_title('Inference Latency') + ax.set_xticks(x) + ax.set_xticklabels(configs, fontsize=8) + ax.legend() + ax.grid(axis='y', alpha=0.3) + + # ---- Plot 2: Throughput ---- + ax = axes[1] + baseline_tp = [r['baseline']['throughput_samples_per_sec'] for r in results] + tiered_tp = [r['tiered']['throughput_samples_per_sec'] for r in results] + ax.bar(x - w/2, baseline_tp, w, label='Baseline', color='#e74c3c', alpha=0.8) + ax.bar(x + w/2, tiered_tp, w, label='Tiered', color='#2ecc71', alpha=0.8) + ax.set_xlabel('Configuration') + ax.set_ylabel('Samples/sec') + ax.set_title('Throughput') + ax.set_xticks(x) + ax.set_xticklabels(configs, fontsize=8) + ax.legend() + ax.grid(axis='y', alpha=0.3) + + # ---- Plot 3: H vs L latency (tiered only) ---- + ax = axes[2] + l_lat = [r['tiered']['l_level_latency_mean_us'] for r in results] + h_lat = [r['tiered']['h_level_latency_mean_us'] for r in results] + ax.bar(x - w/2, l_lat, w, label='L-level (SRAM)', color='#3498db', alpha=0.8) + ax.bar(x + w/2, h_lat, w, label='H-level (DRAM)', color='#e67e22', alpha=0.8) + ax.set_xlabel('Configuration') + ax.set_ylabel('Latency (ΞΌs)') + ax.set_title('Per-Module Latency') + ax.set_xticks(x) + ax.set_xticklabels(configs, fontsize=8) + ax.legend() + ax.grid(axis='y', alpha=0.3) + + plt.tight_layout() + plot_path = os.path.join(output_dir, 'benchmark_comparison.png') + plt.savefig(plot_path, dpi=150, bbox_inches='tight') + plt.close() + print(f" Plot saved: {plot_path}") + + # ---- Plot 4: Memory breakdown ---- + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + fig.suptitle('Memory Analysis', fontsize=14, fontweight='bold') + + ax = axes[0] + gpu_mem_baseline = [r['baseline']['total_gpu_memory_mb'] for r in results] + gpu_mem_tiered = [r['tiered']['total_gpu_memory_mb'] for r in results] + ax.bar(x - w/2, gpu_mem_baseline, w, label='Baseline', color='#e74c3c', alpha=0.8) + ax.bar(x + w/2, gpu_mem_tiered, w, label='Tiered', color='#2ecc71', alpha=0.8) + ax.set_xlabel('Configuration') + ax.set_ylabel('GPU Memory (MB)') + ax.set_title('Total GPU Memory') + ax.set_xticks(x) + ax.set_xticklabels(configs, fontsize=8) + ax.legend() + ax.grid(axis='y', alpha=0.3) + + ax = axes[1] + triton_sram = [r['tiered']['triton_sram_probe_latency_us'] for r in results] + triton_dram = [r['tiered']['triton_dram_probe_latency_us'] for r in results] + ax.bar(x - w/2, triton_sram, w, label='SRAM Probe', color='#3498db', alpha=0.8) + ax.bar(x + w/2, triton_dram, w, label='DRAM Probe', color='#e67e22', alpha=0.8) + ax.set_xlabel('Configuration') + ax.set_ylabel('Latency (ΞΌs)') + ax.set_title('Triton Memory Probe Latency') + ax.set_xticks(x) + ax.set_xticklabels(configs, fontsize=8) + ax.legend() + ax.grid(axis='y', alpha=0.3) + + plt.tight_layout() + plot_path = os.path.join(output_dir, 'memory_analysis.png') + plt.savefig(plot_path, dpi=150, bbox_inches='tight') + plt.close() + print(f" Plot saved: {plot_path}") diff --git a/benchmark_results/benchmark_comparison.png b/benchmark_results/benchmark_comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..a058aba28ac6379233df67a842f11b0a18b69cb4 Binary files /dev/null and b/benchmark_results/benchmark_comparison.png differ diff --git a/benchmark_results/comparison_results.json b/benchmark_results/comparison_results.json new file mode 100644 index 0000000000000000000000000000000000000000..7dd38f6f4239b733ae50e1628353fa77edb05fa0 --- /dev/null +++ b/benchmark_results/comparison_results.json @@ -0,0 +1,119 @@ +{ + "baseline": { + "latency_ms": 13.310442765553793, + "latency_std": 0.3072074317910763, + "throughput": 2404.1273880695444, + "peak_gpu_mb": 292.287488, + "params_m": 27.29677 + }, + "tiered": { + "latency_ms": 11.866764799753826, + "latency_std": 0.02685813995812233, + "throughput": 2696.606913508881, + "peak_gpu_mb": 313.03936, + "params_m": 27.29677 + }, + "speedup": 1.1216572494830197, + "sweep": { + "baseline": [ + { + "latency_ms": 13.55545597076416, + "latency_std": 0.17929869581870853, + "throughput": 73.77103375620548, + "peak_gpu_mb": 140.93056, + "params_m": 27.29677, + "batch_size": 1 + }, + { + "latency_ms": 13.13232650756836, + "latency_std": 0.41090034980335216, + "throughput": 304.5918784988128, + "peak_gpu_mb": 162.348544, + "params_m": 27.29677, + "batch_size": 4 + }, + { + "latency_ms": 12.639206314086914, + "latency_std": 0.34697112347890935, + "throughput": 632.9511364241022, + "peak_gpu_mb": 189.266432, + "params_m": 27.29677, + "batch_size": 8 + }, + { + "latency_ms": 12.491708850860595, + "latency_std": 0.013537496997456985, + "throughput": 1280.8495771895696, + "peak_gpu_mb": 223.744512, + "params_m": 27.29677, + "batch_size": 16 + }, + { + "latency_ms": 12.555190467834473, + "latency_std": 0.01494085745352898, + "throughput": 2548.7466782747565, + "peak_gpu_mb": 268.796416, + "params_m": 27.29677, + "batch_size": 32 + }, + { + "latency_ms": 13.44706563949585, + "latency_std": 0.013219697890277465, + "throughput": 4759.402661947552, + "peak_gpu_mb": 350.675456, + "params_m": 27.29677, + "batch_size": 64 + } + ], + "tiered": [ + { + "latency_ms": 11.769158458709716, + "latency_std": 0.016388604477900706, + "throughput": 84.96784230651208, + "peak_gpu_mb": 158.135808, + "params_m": 27.29677, + "batch_size": 1 + }, + { + "latency_ms": 12.082194995880126, + "latency_std": 0.019275075409013288, + "throughput": 331.0656715409699, + "peak_gpu_mb": 180.051456, + "params_m": 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null, + "h_over_l_latency_ratio": 0.9974607327067432, + "memory_efficiency": 0.9987005384933674 + }, + "speedup": 1.0391243358291815, + "memory_savings_mb": -20.25, + "throughput_improvement": 1.0391243358291815 + } +] \ No newline at end of file diff --git a/benchmark_results/run_nsys_profiler.py b/benchmark_results/run_nsys_profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..17a9264163aa3d3d4ade6b3d5df6b5e2c6ed5639 --- /dev/null +++ b/benchmark_results/run_nsys_profiler.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python3 +""" +NVIDIA Nsight Systems Profiler for HRM Memory Tiering. + +This script runs a few iterations of both the baseline and tiered models +and is designed to be executed via `nsys profile`. + +Usage: + nsys profile -t cuda,nvtx --stats=true --force-overwrite=true -o hrm_profile python run_nsys_profiler.py +""" + +import torch +import torch.cuda.nvtx as nvtx +import argparse + +from models.hrm.hrm_act_v1 import HierarchicalReasoningModel_ACTV1 +from models.hrm.hrm_tiered import HRM_Tiered +from models.memory_tier import MemoryTierManager +from run_training_comparison import DummyLossModel, create_dummy_batch + +def profile_model(model_name, model, batch, iterations, device): + print(f"Profiling {model_name}...") + optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4) + model.train() + + # Warmup + for _ in range(2): + optimizer.zero_grad() + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + + _, loss, _, _, _ = model(carry, batch, return_keys=[]) + loss.backward() + optimizer.step() + + torch.cuda.synchronize() + + # Profiling Phase + with torch.autograd.profiler.emit_nvtx(): + nvtx.range_push(f"{model_name}_Training_Loop") + for i in range(iterations): + nvtx.range_push(f"Iteration_{i}") + optimizer.zero_grad() + + nvtx.range_push("Forward_Pass") + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + + _, loss, _, _, _ = model(carry, batch, return_keys=[]) + nvtx.range_pop() # End Forward + + nvtx.range_push("Backward_Pass") + loss.backward() + optimizer.step() + nvtx.range_pop() # End Backward + + nvtx.range_pop() # End Iteration + nvtx.range_pop() # End Loop + + torch.cuda.synchronize() + print(f"Finished {model_name}.\n") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--batch-size', type=int, default=16) + parser.add_argument('--seq-len', type=int, default=128) + parser.add_argument('--hidden-size', type=int, default=1024) + parser.add_argument('--iterations', type=int, default=5) + args = parser.parse_args() + + device = torch.device('cuda') + vocab_size = 32 + + config_dict = { + 'batch_size': args.batch_size, + 'seq_len': args.seq_len, + 'puzzle_emb_ndim': 0, + 'num_puzzle_identifiers': args.batch_size, + 'vocab_size': vocab_size, + 'H_cycles': 2, + 'L_cycles': 2, + 'H_layers': 4, + 'L_layers': 4, + 'hidden_size': args.hidden_size, + 'expansion': 4.0, + 'num_heads': 8, + 'pos_encodings': 'rope', + 'halt_max_steps': 1, + 'halt_exploration_prob': 0.0, + } + + batch = create_dummy_batch(args.batch_size, args.seq_len, vocab_size, device) + + # 1. Baseline + baseline = HierarchicalReasoningModel_ACTV1(config_dict).to(device) + baseline_wrapped = DummyLossModel(baseline) + profile_model("HRM_Baseline", baseline_wrapped, batch, args.iterations, device) + del baseline_wrapped, baseline + torch.cuda.empty_cache() + + # 2. Tiered + mem_mgr = MemoryTierManager(device=device, enable_tracking=False) + tiered = HRM_Tiered(config_dict, memory_manager=mem_mgr).to(device) + tiered_wrapped = DummyLossModel(tiered) + profile_model("HRM_Tiered", tiered_wrapped, batch, args.iterations, device) + +if __name__ == "__main__": + main() diff --git a/benchmark_results/trained_model_comparison.png b/benchmark_results/trained_model_comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..80249ab910bb40ff945747cdde9a4960ab27d107 --- /dev/null +++ b/benchmark_results/trained_model_comparison.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ed56bbb985e5ad6106a09c8f2b41bd63c3f1b0fa4074d7b34b5ffeafcc731a8 +size 141832 diff --git a/benchmark_results/trained_model_results.json b/benchmark_results/trained_model_results.json new file mode 100644 index 0000000000000000000000000000000000000000..664bde782b423b8e6c0de8fab788ee09295c47d0 --- /dev/null +++ b/benchmark_results/trained_model_results.json @@ -0,0 +1,18 @@ +{ + "baseline": { + "accuracy": {}, + "latency": { + "latency_ms": 117.27158050537109, + "latency_std": 0.10221427702082414, + "throughput": 3274.4506243131314 + } + }, + "tiered": { + "accuracy": {}, + "latency": { + "latency_ms": 95.14221649169922, + "latency_std": 0.010479617132043349, + "throughput": 4036.0632131531484 + } + } +} \ No newline at end of file diff --git a/cleanup.sh b/cleanup.sh new file mode 100644 index 0000000000000000000000000000000000000000..90a11a2f0605d836219dc15f6a9401a772a2c9d1 --- /dev/null +++ b/cleanup.sh @@ -0,0 +1,67 @@ +#!/usr/bin/env bash +# cleanup.sh β€” Purge venv, downloaded data, checkpoints, caches, and wandb logs +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +cd "$SCRIPT_DIR" + +echo "==========================================" +echo " HRM_optimised β€” Cleanup" +echo "==========================================" + +# Deactivate venv if currently active +if [ -n "${VIRTUAL_ENV:-}" ]; then + echo " Deactivating current virtual environment..." + deactivate 2>/dev/null || true +fi + +# 1. Remove virtual environment +if [ -d "venv" ]; then + echo "[1/6] Removing virtual environment (venv/)..." + rm -rf venv +else + echo "[1/6] No venv found, skipping." +fi + +# 2. Remove downloaded/generated datasets +if [ -d "data" ]; then + echo "[2/6] Removing generated datasets (data/)..." + rm -rf data +else + echo "[2/6] No data/ directory found, skipping." +fi + +# 3. Remove raw downloaded data from HuggingFace +if [ -d "dataset/raw-data" ]; then + echo "[3/6] Removing raw dataset downloads (dataset/raw-data/)..." + rm -rf dataset/raw-data +else + echo "[3/6] No dataset/raw-data/ found, skipping." +fi + +# 4. Remove training checkpoints +if [ -d "checkpoints" ]; then + echo "[4/6] Removing training checkpoints (checkpoints/)..." + rm -rf checkpoints +else + echo "[4/6] No checkpoints/ directory found, skipping." +fi + +# 5. Remove wandb logs +if [ -d "wandb" ]; then + echo "[5/6] Removing W&B logs (wandb/)..." + rm -rf wandb +else + echo "[5/6] No wandb/ directory found, skipping." +fi + +# 6. Remove Python caches +echo "[6/6] Removing __pycache__ directories..." +find . -type d -name "__pycache__" -not -path "./.git/*" -exec rm -rf {} + 2>/dev/null || true +find . -type d -name ".hydra" -exec rm -rf {} + 2>/dev/null || true + +echo "" +echo "==========================================" +echo " βœ… Cleanup complete!" +echo " Run ./startup.sh to set up a fresh environment." +echo "==========================================" diff --git a/compare_models.py b/compare_models.py new file mode 100644 index 0000000000000000000000000000000000000000..d8ce27dfd73f0b8f0c0cc34b9079e5ec8dce7272 --- /dev/null +++ b/compare_models.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +""" +Compare Baseline vs Tiered HRM β€” Multi-GPU Benchmark + Comparison Plots. + +Usage: + source venv/bin/activate + python compare_models.py # quick compare + python compare_models.py --sweep # batch-size sweep + plots + python compare_models.py --iterations 50 --sweep # thorough +""" + +import argparse +import json +import os +import sys +from dataclasses import dataclass, asdict + +import torch +import numpy as np +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from models.memory_tier import MemoryTierManager +from models.hrm.hrm_tiered import HRM_Tiered +from models.hrm.hrm_act_v1 import HierarchicalReasoningModel_ACTV1 + + +# ═══════════════════════════════════════════════════════════ +# Helpers +# ═══════════════════════════════════════════════════════════ + +def make_config(batch_size, seq_len, hidden_size, num_heads): + return { + "batch_size": batch_size, "seq_len": seq_len, + "puzzle_emb_ndim": 0, "num_puzzle_identifiers": batch_size, + "vocab_size": 32, + "H_cycles": 2, "L_cycles": 2, "H_layers": 4, "L_layers": 4, + "hidden_size": hidden_size, "expansion": 4.0, + "num_heads": num_heads, "pos_encodings": "rope", + "halt_max_steps": 1, "halt_exploration_prob": 0.0, + } + + +def make_batch(batch_size, seq_len, device): + return { + "inputs": torch.randint(0, 31, (batch_size, seq_len), device=device), + "labels": torch.randint(0, 31, (batch_size, seq_len), device=device), + "puzzle_identifiers": torch.arange(batch_size, device=device), + } + + +def build_model(arch, config_dict, device): + """Build using the wrapper classes (same pattern as eval_dummy.py).""" + if arch == "tiered": + mm = MemoryTierManager(device=device, enable_tracking=True) + model = HRM_Tiered(config_dict, memory_manager=mm).to(device) + else: + model = HierarchicalReasoningModel_ACTV1(config_dict).to(device) + model.eval() + return model + + +@torch.no_grad() +def benchmark(model, batch, device, warmup=5, iterations=20): + """Time forward pass, return dict of metrics.""" + bs = batch["inputs"].shape[0] + + # Warmup + for _ in range(warmup): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + model(carry, batch) + + torch.cuda.reset_peak_memory_stats(device) + torch.cuda.synchronize() + + latencies = [] + for _ in range(iterations): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + start.record() + model(carry, batch) + end.record() + torch.cuda.synchronize() + latencies.append(start.elapsed_time(end)) + + lat = np.array(latencies) + return { + "latency_ms": float(np.mean(lat)), + "latency_std": float(np.std(lat)), + "throughput": float(bs / (np.mean(lat) / 1000)), + "peak_gpu_mb": float(torch.cuda.max_memory_allocated(device) / 1e6), + "params_m": sum(p.numel() for p in model.parameters()) / 1e6, + } + + +# ═══════════════════════════════════════════════════════════ +# Plotting +# ═══════════════════════════════════════════════════════════ + +def create_plots(base_res, tier_res, sweep_data, output_dir): + os.makedirs(output_dir, exist_ok=True) + + c_base, c_tier = "#4A90D9", "#E85D75" + bg, text, grid = "#1a1a2e", "#e0e0e0", "#333355" + + plt.rcParams.update({ + "figure.facecolor": bg, "axes.facecolor": "#16213e", + "axes.edgecolor": grid, "axes.labelcolor": text, + "text.color": text, "xtick.color": text, "ytick.color": text, + "grid.color": grid, "grid.alpha": 0.3, + "font.family": "sans-serif", "font.size": 11, + }) + + n_plots = 6 if sweep_data else 5 + fig = plt.figure(figsize=(16, 10)) + fig.suptitle("HRM Baseline vs Tiered (SRAM/DRAM) Comparison", + fontsize=18, fontweight="bold", y=0.98) + gs = GridSpec(2, 3, figure=fig, hspace=0.35, wspace=0.35) + labels = ["Baseline", "Tiered"] + + def bar_plot(ax, title, ylabel, vals, fmt=".2f"): + bars = ax.bar(labels, vals, color=[c_base, c_tier], + edgecolor="white", linewidth=0.5, width=0.5) + ax.set_title(title, fontweight="bold") + ax.set_ylabel(ylabel) + for b, v in zip(bars, vals): + ax.text(b.get_x() + b.get_width()/2, b.get_height() * 1.02, + f"{v:{fmt}}", ha="center", fontsize=10, color=text) + ax.grid(axis="y") + + # 1. Latency + bar_plot(fig.add_subplot(gs[0, 0]), "Inference Latency", "ms", + [base_res["latency_ms"], tier_res["latency_ms"]]) + + # 2. Throughput + bar_plot(fig.add_subplot(gs[0, 1]), "Throughput", "samples/sec", + [base_res["throughput"], tier_res["throughput"]], fmt=".0f") + + # 3. GPU Memory + bar_plot(fig.add_subplot(gs[0, 2]), "Peak GPU Memory", "MB", + [base_res["peak_gpu_mb"], tier_res["peak_gpu_mb"]], fmt=".0f") + + # 4. Parameters + bar_plot(fig.add_subplot(gs[1, 0]), "Model Parameters", "Millions", + [base_res["params_m"], tier_res["params_m"]], fmt=".1f") + + # 5. Summary text + ax5 = fig.add_subplot(gs[1, 1]) + speedup = base_res["latency_ms"] / tier_res["latency_ms"] + mem_diff = tier_res["peak_gpu_mb"] - base_res["peak_gpu_mb"] + tp_gain = (tier_res["throughput"] / base_res["throughput"] - 1) * 100 + summary = ( + f"Speedup: {speedup:.2f}x\n" + f"Throughput: {tp_gain:+.1f}%\n" + f"Memory Ξ”: {mem_diff:+.0f} MB\n" + f"Params: identical" + ) + ax5.text(0.5, 0.5, summary, transform=ax5.transAxes, + ha="center", va="center", fontsize=14, fontfamily="monospace", + bbox=dict(boxstyle="round,pad=0.5", facecolor="#0f3460", alpha=0.8)) + ax5.set_title("Summary", fontweight="bold") + ax5.axis("off") + + # 6. Sweep plot + ax6 = fig.add_subplot(gs[1, 2]) + if sweep_data: + bs_list = [s["batch_size"] for s in sweep_data["baseline"]] + ax6.plot(bs_list, [s["latency_ms"] for s in sweep_data["baseline"]], + "o-", color=c_base, label="Baseline", linewidth=2, markersize=6) + ax6.plot(bs_list, [s["latency_ms"] for s in sweep_data["tiered"]], + "s-", color=c_tier, label="Tiered", linewidth=2, markersize=6) + ax6.set_xlabel("Batch Size") + ax6.set_ylabel("Latency (ms)") + ax6.set_title("Latency vs Batch Size", fontweight="bold") + ax6.legend(facecolor="#16213e", edgecolor=grid) + ax6.grid(True) + else: + ax6.text(0.5, 0.5, "Run with --sweep\nfor batch size\ncomparison", + transform=ax6.transAxes, ha="center", va="center", fontsize=12) + ax6.set_title("Latency vs Batch Size", fontweight="bold") + ax6.axis("off") + + path = os.path.join(output_dir, "model_comparison.png") + fig.savefig(path, dpi=150, bbox_inches="tight") + plt.close() + print(f" Plot saved β†’ {path}") + return path + + +# ═══════════════════════════════════════════════════════════ +# Main +# ═══════════════════════════════════════════════════════════ + +def main(): + parser = argparse.ArgumentParser(description="Compare Baseline vs Tiered HRM") + parser.add_argument("--batch-size", type=int, default=32) + parser.add_argument("--seq-len", type=int, default=81) + parser.add_argument("--hidden-size", type=int, default=512) + parser.add_argument("--num-heads", type=int, default=8) + parser.add_argument("--warmup", type=int, default=5) + parser.add_argument("--iterations", type=int, default=20) + parser.add_argument("--sweep", action="store_true", help="Batch-size sweep") + parser.add_argument("--output-dir", type=str, default="benchmark_results") + args = parser.parse_args() + + device = torch.device("cuda") + cfg = make_config(args.batch_size, args.seq_len, args.hidden_size, args.num_heads) + + print("=" * 64) + print(" HRM Model Comparison: Baseline vs Tiered (SRAM/DRAM)") + print(f" Device: {torch.cuda.get_device_name(0)}") + print(f" Config: bs={args.batch_size}, seq={args.seq_len}, hidden={args.hidden_size}") + print("=" * 64) + + # ── Build ── + print("\n Building Baseline...") + base_model = build_model("baseline", cfg, device) + batch = make_batch(args.batch_size, args.seq_len, device) + + print(" Building Tiered...") + tier_model = build_model("tiered", cfg, device) + + # ── Benchmark ── + print(f"\n Benchmarking Baseline ({args.iterations} iters)...") + base_res = benchmark(base_model, batch, device, args.warmup, args.iterations) + print(f" β†’ {base_res['latency_ms']:.2f} ms | {base_res['throughput']:.0f} samp/s | {base_res['peak_gpu_mb']:.0f} MB") + + batch_t = make_batch(args.batch_size, args.seq_len, device) + print(f" Benchmarking Tiered ({args.iterations} iters)...") + tier_res = benchmark(tier_model, batch_t, device, args.warmup, args.iterations) + print(f" β†’ {tier_res['latency_ms']:.2f} ms | {tier_res['throughput']:.0f} samp/s | {tier_res['peak_gpu_mb']:.0f} MB") + + speedup = base_res["latency_ms"] / tier_res["latency_ms"] + print(f"\n Speedup: {speedup:.2f}x") + + # ── Sweep ── + sweep_data = None + if args.sweep: + print("\n Running batch-size sweep...") + sweep_data = {"baseline": [], "tiered": []} + del base_model, tier_model + torch.cuda.empty_cache() + + for bs in [1, 4, 8, 16, 32, 64]: + print(f" bs={bs}...", end=" ", flush=True) + c = make_config(bs, args.seq_len, args.hidden_size, args.num_heads) + b = make_batch(bs, args.seq_len, device) + + bm = build_model("baseline", c, device) + br = benchmark(bm, b, device, warmup=3, iterations=10) + br["batch_size"] = bs + sweep_data["baseline"].append(br) + del bm + + tm = build_model("tiered", c, device) + tr = benchmark(tm, b, device, warmup=3, iterations=10) + tr["batch_size"] = bs + sweep_data["tiered"].append(tr) + del tm + torch.cuda.empty_cache() + print(f"base={br['latency_ms']:.2f}ms, tier={tr['latency_ms']:.2f}ms") + + # ── Plots ── + print("\n Generating plots...") + create_plots(base_res, tier_res, sweep_data, args.output_dir) + + # ── Save JSON ── + results = {"baseline": base_res, "tiered": tier_res, "speedup": speedup} + if sweep_data: + results["sweep"] = sweep_data + json_path = os.path.join(args.output_dir, "comparison_results.json") + os.makedirs(args.output_dir, exist_ok=True) + with open(json_path, "w") as f: + json.dump(results, f, indent=2) + print(f" Results saved β†’ {json_path}") + + print("\n" + "=" * 64) + print(" Done!") + print("=" * 64) + + +if __name__ == "__main__": + main() diff --git a/config/arch/hrm_tiered.yaml b/config/arch/hrm_tiered.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b0154d3da282152ae1d8be447f6d70aff0c2c034 --- /dev/null +++ b/config/arch/hrm_tiered.yaml @@ -0,0 +1,27 @@ +# Tiered HRM config β€” extends hrm_v1 with SRAM/DRAM memory tiering +name: hrm.hrm_tiered@HRM_Tiered +loss: + name: losses@ACTLossHead + loss_type: stablemax_cross_entropy + +halt_exploration_prob: 0.1 +halt_max_steps: 16 + +H_cycles: 2 +L_cycles: 2 + +H_layers: 4 +L_layers: 4 + +hidden_size: 512 +num_heads: 8 +expansion: 4 + +puzzle_emb_ndim: ${.hidden_size} + +pos_encodings: rope + +# Memory tier config (used by MemoryTierManager) +memory_tier: + sram_capacity_mb: 48 # Simulated SRAM budget (e.g., L2 cache size) + enable_tracking: true # Log all memory operations for benchmarks diff --git a/config/arch/hrm_v1.yaml b/config/arch/hrm_v1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a5646b89049842da5eb4e55df01cb04515c0f1f9 --- /dev/null +++ b/config/arch/hrm_v1.yaml @@ -0,0 +1,21 @@ +name: hrm.hrm_act_v1@HierarchicalReasoningModel_ACTV1 +loss: + name: losses@ACTLossHead + loss_type: stablemax_cross_entropy + +halt_exploration_prob: 0.1 +halt_max_steps: 16 + +H_cycles: 2 +L_cycles: 2 + +H_layers: 4 +L_layers: 4 + +hidden_size: 512 +num_heads: 8 # min(2, hidden_size // 64) +expansion: 4 + +puzzle_emb_ndim: ${.hidden_size} + +pos_encodings: rope diff --git a/config/cfg_pretrain.yaml b/config/cfg_pretrain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..51c55a07142cc5d2732cb2d84e069a58872dd068 --- /dev/null +++ b/config/cfg_pretrain.yaml @@ -0,0 +1,31 @@ +# ARC training config + +defaults: + - arch: hrm_v1 + - _self_ + +hydra: + output_subdir: null + +# Data path +data_path: data/arc-aug-1000 + +# Hyperparams - Training +global_batch_size: 768 + +epochs: 100000 +eval_interval: 10000 +checkpoint_every_eval: True + +lr: 1e-4 +lr_min_ratio: 1.0 +lr_warmup_steps: 2000 + +# Standard hyperparameter settings for LM, as used in Llama +beta1: 0.9 +beta2: 0.95 +weight_decay: 0.1 +puzzle_emb_weight_decay: 0.1 + +# Hyperparams - Puzzle embeddings training +puzzle_emb_lr: 1e-2 diff --git a/dataset/build_arc_dataset.py b/dataset/build_arc_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..2da5703e4f50b49176a0a5bc0e81c33e5a6437a9 --- /dev/null +++ b/dataset/build_arc_dataset.py @@ -0,0 +1,291 @@ +from typing import List, Optional, Tuple, Dict +from dataclasses import dataclass +from pathlib import Path +import os +import json +import hashlib +import numpy as np +from glob import glob + +from argdantic import ArgParser +from pydantic import BaseModel + +from common import PuzzleDatasetMetadata, dihedral_transform + + +cli = ArgParser() + + +class DataProcessConfig(BaseModel): + # ARC-1 + dataset_dirs: List[str] = ["dataset/raw-data/ARC-AGI/data", "dataset/raw-data/ConceptARC/corpus"] + output_dir: str = "data/arc-aug-1000" + + # ARC-2 + # dataset_dirs: List[str] = ["dataset/raw-data/ARC-AGI-2/data"] + # output_dir: str = "data/arc-2-aug-1000" + + seed: int = 42 + num_aug: int = 1000 + + +ARCMaxGridSize = 30 +ARCAugmentRetriesFactor = 5 + + +@dataclass +class ARCPuzzle: + id: str + + examples: List[Tuple[np.ndarray, np.ndarray]] + + +def arc_grid_to_np(grid: List[List[int]]): + arr = np.array(grid) + + # Shape check + assert arr.ndim == 2 + assert arr.shape[0] <= ARCMaxGridSize and arr.shape[1] <= ARCMaxGridSize + # Element check + assert np.all((arr >= 0) & (arr <= 9)) + return arr.astype(np.uint8) + + +def np_grid_to_seq_translational_augment(inp: np.ndarray, out: np.ndarray, do_translation: bool): + # PAD: 0, : 1, digits: 2 ... 11 + # Compute random top-left pad + if do_translation: + pad_r = np.random.randint(0, ARCMaxGridSize - max(inp.shape[0], out.shape[0]) + 1) + pad_c = np.random.randint(0, ARCMaxGridSize - max(inp.shape[1], out.shape[1]) + 1) + else: + pad_r = pad_c = 0 + + # Pad grid + result = [] + for grid in [inp, out]: + nrow, ncol = grid.shape + grid = np.pad(grid + 2, ((pad_r, ARCMaxGridSize - pad_r - nrow), (pad_c, ARCMaxGridSize - pad_c - ncol)), constant_values=0) + + # Add + eos_row, eos_col = pad_r + nrow, pad_c + ncol + if eos_row < ARCMaxGridSize: + grid[eos_row, pad_c:eos_col] = 1 + if eos_col < ARCMaxGridSize: + grid[pad_r:eos_row, eos_col] = 1 + + result.append(grid.flatten()) + + return result + + +def puzzle_hash(puzzle: dict): + # Hash the puzzle for checking equivalence + def _grid_hash(grid: np.ndarray): + buffer = [x.to_bytes(1) for x in grid.shape] + buffer.append(grid.tobytes()) + + return hashlib.sha256(b"".join(buffer)).hexdigest() + + hashes = [] + for example_type, example in puzzle.items(): + for input, label in example.examples: + hashes.append(f"{_grid_hash(input)}|{_grid_hash(label)}") + + hashes.sort() + return hashlib.sha256("|".join(hashes).encode()).hexdigest() + + +def convert_single_arc_puzzle(results: dict, default_name: str, puzzle: dict, aug_count: int, dest_mapping: Dict[str, Tuple[str, str]]): + # Remove "name" + name = puzzle.pop("name", default_name) + + # Convert + dests = set(dest_mapping.values()) + converted = {dest: ARCPuzzle(name, []) for dest in dests} + for example_type, examples in puzzle.items(): + dest = dest_mapping[example_type] + converted[dest].examples.extend([(arc_grid_to_np(example["input"]), arc_grid_to_np(example["output"])) for example in examples]) + + group = [converted] + + # Augment + if aug_count > 0: + hashes = {puzzle_hash(converted)} + + for _trial in range(ARCAugmentRetriesFactor * aug_count): + # Augment plan + trans_id = np.random.randint(0, 8) + mapping = np.concatenate([np.arange(0, 1, dtype=np.uint8), np.random.permutation(np.arange(1, 10, dtype=np.uint8))]) # Permute colors, Excluding "0" (black) + + aug_repr = f"t{trans_id}_{''.join(str(x) for x in mapping)}" + + def _map_grid(grid: np.ndarray): + return dihedral_transform(mapping[grid], trans_id) + + # Check duplicate + augmented = {dest: ARCPuzzle(f"{puzzle.id}_{aug_repr}", [(_map_grid(input), _map_grid(label)) for (input, label) in puzzle.examples]) for dest, puzzle in converted.items()} + h = puzzle_hash(augmented) + if h not in hashes: + hashes.add(h) + group.append(augmented) + + if len(group) >= aug_count + 1: + break + + if len(group) < aug_count + 1: + print (f"[Puzzle {name}] augmentation not full, only {len(group)}") + + # Append + for dest in dests: + # Convert the examples + dest_split, dest_set = dest + + results.setdefault(dest_split, {}) + results[dest_split].setdefault(dest_set, []) + results[dest_split][dest_set].append([converted[dest] for converted in group]) + + +def load_puzzles_arcagi(results: dict, dataset_path: str, config: DataProcessConfig): + train_examples_dest = ("train", "all") + test_examples_map = { + "evaluation": [(1.0, ("test", "all"))], + "_default": [(1.0, ("train", "all"))] + } + + total_puzzles = 0 + for subdir in os.scandir(dataset_path): + if subdir.is_dir(): + # Load all puzzles in this directory + puzzles = [] + for filename in glob(os.path.join(subdir.path, "*.json")): + with open(filename, "r") as f: + puzzles.append((Path(filename).stem, json.load(f))) + + # Shuffle puzzles + np.random.shuffle(puzzles) + + # Assign by fraction + for idx, (default_name, puzzle) in enumerate(puzzles): + fraction = idx / len(puzzles) + test_examples_dest = None + for f, dest in test_examples_map.get(subdir.name, test_examples_map["_default"]): + if fraction < f: + test_examples_dest = dest + break + + assert test_examples_dest is not None + + convert_single_arc_puzzle(results, default_name, puzzle, config.num_aug, {"train": train_examples_dest, "test": test_examples_dest}) + total_puzzles += 1 + + print (f"[{dataset_path}] total puzzles: {total_puzzles}") + + +def convert_dataset(config: DataProcessConfig): + np.random.seed(config.seed) + + # Read dataset + data = {} + for dataset_dir in config.dataset_dirs: + load_puzzles_arcagi(data, dataset_dir, config) + + # Map global puzzle identifiers + num_identifiers = 1 # 0 is blank + identifier_map = {} + for split_name, split in data.items(): + for subset_name, subset in split.items(): + for group in subset: + for puzzle in group: + if puzzle.id not in identifier_map: + identifier_map[puzzle.id] = num_identifiers + num_identifiers += 1 + + print (f"Total puzzle IDs (including ): {num_identifiers}") + + # Save + for split_name, split in data.items(): + os.makedirs(os.path.join(config.output_dir, split_name), exist_ok=True) + + # Translational augmentations + enable_translational_augment = split_name == "train" + + # Statistics + total_examples = 0 + total_puzzles = 0 + total_groups = 0 + + for subset_name, subset in split.items(): + # Construct subset + results = {k: [] for k in ["inputs", "labels", "puzzle_identifiers", "puzzle_indices", "group_indices"]} + results["puzzle_indices"].append(0) + results["group_indices"].append(0) + + example_id = 0 + puzzle_id = 0 + + for group in subset: + for puzzle in group: + # Push puzzle + no_aug_id = np.random.randint(0, len(puzzle.examples)) + for _idx_ex, (inp, out) in enumerate(puzzle.examples): + inp, out = np_grid_to_seq_translational_augment(inp, out, do_translation=enable_translational_augment and _idx_ex != no_aug_id) + + results["inputs"].append(inp) + results["labels"].append(out) + example_id += 1 + + total_examples += 1 + + results["puzzle_indices"].append(example_id) + results["puzzle_identifiers"].append(identifier_map[puzzle.id]) + + puzzle_id += 1 + + total_puzzles += 1 + + # Push group + results["group_indices"].append(puzzle_id) + total_groups += 1 + + for k, v in results.items(): + if k in {"inputs", "labels"}: + v = np.stack(v, 0) + else: + v = np.array(v, dtype=np.int32) + + np.save(os.path.join(config.output_dir, split_name, f"{subset_name}__{k}.npy"), v) + + # Metadata + metadata = PuzzleDatasetMetadata( + seq_len=ARCMaxGridSize * ARCMaxGridSize, + vocab_size=10 + 2, # PAD + EOS + "0" ... "9" + + pad_id=0, + ignore_label_id=0, + + blank_identifier_id=0, + num_puzzle_identifiers=num_identifiers, + + total_groups=total_groups, + mean_puzzle_examples=total_examples / total_puzzles, + sets=list(split.keys()) + ) + + # Save metadata as JSON. + with open(os.path.join(config.output_dir, split_name, "dataset.json"), "w") as f: + json.dump(metadata.model_dump(), f) + + # Save IDs mapping + with open(os.path.join(config.output_dir, "identifiers.json"), "w") as f: + ids_mapping = {v: k for k, v in identifier_map.items()} + + json.dump([ids_mapping.get(i, "") for i in range(num_identifiers)], f) + + +@cli.command(singleton=True) +def main(config: DataProcessConfig): + convert_dataset(config) + + +if __name__ == "__main__": + cli() diff --git a/dataset/build_maze_dataset.py b/dataset/build_maze_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..a9367f38639864e5616fa4af4e9d17e1b3412733 --- /dev/null +++ b/dataset/build_maze_dataset.py @@ -0,0 +1,142 @@ +from typing import Optional +import math +import os +import csv +import json +import numpy as np + +from argdantic import ArgParser +from pydantic import BaseModel +from tqdm import tqdm +from huggingface_hub import hf_hub_download + +from common import PuzzleDatasetMetadata, dihedral_transform + + +CHARSET = "# SGo" + + +cli = ArgParser() + + +class DataProcessConfig(BaseModel): + source_repo: str = "sapientinc/maze-30x30-hard-1k" + output_dir: str = "data/maze-30x30-hard-1k" + + subsample_size: Optional[int] = None + aug: bool = False + + +def convert_subset(set_name: str, config: DataProcessConfig): + # Read CSV + all_chars = set() + grid_size = None + inputs = [] + labels = [] + + with open(hf_hub_download(config.source_repo, f"{set_name}.csv", repo_type="dataset"), newline="") as csvfile: # type: ignore + reader = csv.reader(csvfile) + next(reader) # Skip header + for source, q, a, rating in reader: + all_chars.update(q) + all_chars.update(a) + + if grid_size is None: + n = int(len(q) ** 0.5) + grid_size = (n, n) + + inputs.append(np.frombuffer(q.encode(), dtype=np.uint8).reshape(grid_size)) + labels.append(np.frombuffer(a.encode(), dtype=np.uint8).reshape(grid_size)) + + # If subsample_size is specified for the training set, + # randomly sample the desired number of examples. + if set_name == "train" and config.subsample_size is not None: + total_samples = len(inputs) + if config.subsample_size < total_samples: + indices = np.random.choice(total_samples, size=config.subsample_size, replace=False) + inputs = [inputs[i] for i in indices] + labels = [labels[i] for i in indices] + + # Generate dataset + results = {k: [] for k in ["inputs", "labels", "puzzle_identifiers", "puzzle_indices", "group_indices"]} + puzzle_id = 0 + example_id = 0 + + results["puzzle_indices"].append(0) + results["group_indices"].append(0) + + for inp, out in zip(tqdm(inputs), labels): + # Dihedral transformations for augmentation + for aug_idx in range(8 if (set_name == "train" and config.aug) else 1): + results["inputs"].append(dihedral_transform(inp, aug_idx)) + results["labels"].append(dihedral_transform(out, aug_idx)) + example_id += 1 + puzzle_id += 1 + + results["puzzle_indices"].append(example_id) + results["puzzle_identifiers"].append(0) + + # Push group + results["group_indices"].append(puzzle_id) + + # Char mappings + assert len(all_chars - set(CHARSET)) == 0 + + char2id = np.zeros(256, np.uint8) + char2id[np.array(list(map(ord, CHARSET)))] = np.arange(len(CHARSET)) + 1 + + # To Numpy + def _seq_to_numpy(seq): + arr = np.vstack([char2id[s.reshape(-1)] for s in seq]) + + return arr + + results = { + "inputs": _seq_to_numpy(results["inputs"]), + "labels": _seq_to_numpy(results["labels"]), + + "group_indices": np.array(results["group_indices"], dtype=np.int32), + "puzzle_indices": np.array(results["puzzle_indices"], dtype=np.int32), + "puzzle_identifiers": np.array(results["puzzle_identifiers"], dtype=np.int32), + } + + # Metadata + metadata = PuzzleDatasetMetadata( + seq_len=int(math.prod(grid_size)), # type: ignore + vocab_size=len(CHARSET) + 1, # PAD + Charset + + pad_id=0, + ignore_label_id=0, + + blank_identifier_id=0, + num_puzzle_identifiers=1, + + total_groups=len(results["group_indices"]) - 1, + mean_puzzle_examples=1, + sets=["all"] + ) + + # Save metadata as JSON. + save_dir = os.path.join(config.output_dir, set_name) + os.makedirs(save_dir, exist_ok=True) + + with open(os.path.join(save_dir, "dataset.json"), "w") as f: + json.dump(metadata.model_dump(), f) + + # Save data + for k, v in results.items(): + np.save(os.path.join(save_dir, f"all__{k}.npy"), v) + + # Save IDs mapping (for visualization only) + with open(os.path.join(config.output_dir, "identifiers.json"), "w") as f: + json.dump([""], f) + + +@cli.command(singleton=True) +def preprocess_data(config: DataProcessConfig): + convert_subset("train", config) + convert_subset("test", config) + + +if __name__ == "__main__": + cli() diff --git a/dataset/build_sudoku_dataset.py b/dataset/build_sudoku_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..7924438b406adeb3b3c020dd0238171e0278ca55 --- /dev/null +++ b/dataset/build_sudoku_dataset.py @@ -0,0 +1,169 @@ +from typing import Optional +import os +import csv +import json +import numpy as np + +from argdantic import ArgParser +from pydantic import BaseModel +from tqdm import tqdm +from huggingface_hub import hf_hub_download + +from common import PuzzleDatasetMetadata + + +cli = ArgParser() + + +class DataProcessConfig(BaseModel): + source_repo: str = "sapientinc/sudoku-extreme" + output_dir: str = "data/sudoku-extreme-full" + + subsample_size: Optional[int] = None + min_difficulty: Optional[int] = None + num_aug: int = 0 + + +def shuffle_sudoku(board: np.ndarray, solution: np.ndarray): + # Create a random digit mapping: a permutation of 1..9, with zero (blank) unchanged + digit_map = np.pad(np.random.permutation(np.arange(1, 10)), (1, 0)) + + # Randomly decide whether to transpose. + transpose_flag = np.random.rand() < 0.5 + + # Generate a valid row permutation: + # - Shuffle the 3 bands (each band = 3 rows) and for each band, shuffle its 3 rows. + bands = np.random.permutation(3) + row_perm = np.concatenate([b * 3 + np.random.permutation(3) for b in bands]) + + # Similarly for columns (stacks). + stacks = np.random.permutation(3) + col_perm = np.concatenate([s * 3 + np.random.permutation(3) for s in stacks]) + + # Build an 81->81 mapping. For each new cell at (i, j) + # (row index = i // 9, col index = i % 9), + # its value comes from old row = row_perm[i//9] and old col = col_perm[i%9]. + mapping = np.array([row_perm[i // 9] * 9 + col_perm[i % 9] for i in range(81)]) + + def apply_transformation(x: np.ndarray) -> np.ndarray: + # Apply transpose flag + if transpose_flag: + x = x.T + # Apply the position mapping. + new_board = x.flatten()[mapping].reshape(9, 9).copy() + # Apply digit mapping + return digit_map[new_board] + + return apply_transformation(board), apply_transformation(solution) + + +def convert_subset(set_name: str, config: DataProcessConfig): + # Read CSV + inputs = [] + labels = [] + + with open(hf_hub_download(config.source_repo, f"{set_name}.csv", repo_type="dataset"), newline="") as csvfile: + reader = csv.reader(csvfile) + next(reader) # Skip header + for source, q, a, rating in reader: + if (config.min_difficulty is None) or (int(rating) >= config.min_difficulty): + assert len(q) == 81 and len(a) == 81 + + inputs.append(np.frombuffer(q.replace('.', '0').encode(), dtype=np.uint8).reshape(9, 9) - ord('0')) + labels.append(np.frombuffer(a.encode(), dtype=np.uint8).reshape(9, 9) - ord('0')) + + # If subsample_size is specified for the training set, + # randomly sample the desired number of examples. + if set_name == "train" and config.subsample_size is not None: + total_samples = len(inputs) + if config.subsample_size < total_samples: + indices = np.random.choice(total_samples, size=config.subsample_size, replace=False) + inputs = [inputs[i] for i in indices] + labels = [labels[i] for i in indices] + + # Generate dataset + num_augments = config.num_aug if set_name == "train" else 0 + + results = {k: [] for k in ["inputs", "labels", "puzzle_identifiers", "puzzle_indices", "group_indices"]} + puzzle_id = 0 + example_id = 0 + + results["puzzle_indices"].append(0) + results["group_indices"].append(0) + + for orig_inp, orig_out in zip(tqdm(inputs), labels): + for aug_idx in range(1 + num_augments): + # First index is not augmented + if aug_idx == 0: + inp, out = orig_inp, orig_out + else: + inp, out = shuffle_sudoku(orig_inp, orig_out) + + # Push puzzle (only single example) + results["inputs"].append(inp) + results["labels"].append(out) + example_id += 1 + puzzle_id += 1 + + results["puzzle_indices"].append(example_id) + results["puzzle_identifiers"].append(0) + + # Push group + results["group_indices"].append(puzzle_id) + + # To Numpy + def _seq_to_numpy(seq): + arr = np.concatenate(seq).reshape(len(seq), -1) + + assert np.all((arr >= 0) & (arr <= 9)) + return arr + 1 + + results = { + "inputs": _seq_to_numpy(results["inputs"]), + "labels": _seq_to_numpy(results["labels"]), + + "group_indices": np.array(results["group_indices"], dtype=np.int32), + "puzzle_indices": np.array(results["puzzle_indices"], dtype=np.int32), + "puzzle_identifiers": np.array(results["puzzle_identifiers"], dtype=np.int32), + } + + # Metadata + metadata = PuzzleDatasetMetadata( + seq_len=81, + vocab_size=10 + 1, # PAD + "0" ... "9" + + pad_id=0, + ignore_label_id=0, + + blank_identifier_id=0, + num_puzzle_identifiers=1, + + total_groups=len(results["group_indices"]) - 1, + mean_puzzle_examples=1, + sets=["all"] + ) + + # Save metadata as JSON. + save_dir = os.path.join(config.output_dir, set_name) + os.makedirs(save_dir, exist_ok=True) + + with open(os.path.join(save_dir, "dataset.json"), "w") as f: + json.dump(metadata.model_dump(), f) + + # Save data + for k, v in results.items(): + np.save(os.path.join(save_dir, f"all__{k}.npy"), v) + + # Save IDs mapping (for visualization only) + with open(os.path.join(config.output_dir, "identifiers.json"), "w") as f: + json.dump([""], f) + + +@cli.command(singleton=True) +def preprocess_data(config: DataProcessConfig): + convert_subset("train", config) + convert_subset("test", config) + + +if __name__ == "__main__": + cli() diff --git a/dataset/common.py b/dataset/common.py new file mode 100644 index 0000000000000000000000000000000000000000..7bc51c6fe5ff85256d3a766a84714fefd7dc148d --- /dev/null +++ b/dataset/common.py @@ -0,0 +1,51 @@ +from typing import List, Optional + +import pydantic +import numpy as np + + +# Global list mapping each dihedral transform id to its inverse. +# Index corresponds to the original tid, and the value is its inverse. +DIHEDRAL_INVERSE = [0, 3, 2, 1, 4, 5, 6, 7] + + +class PuzzleDatasetMetadata(pydantic.BaseModel): + pad_id: int + ignore_label_id: Optional[int] + blank_identifier_id: int + + vocab_size: int + seq_len: int + num_puzzle_identifiers: int + + total_groups: int + mean_puzzle_examples: float + + sets: List[str] + + +def dihedral_transform(arr: np.ndarray, tid: int) -> np.ndarray: + """8 dihedral symmetries by rotate, flip and mirror""" + + if tid == 0: + return arr # identity + elif tid == 1: + return np.rot90(arr, k=1) + elif tid == 2: + return np.rot90(arr, k=2) + elif tid == 3: + return np.rot90(arr, k=3) + elif tid == 4: + return np.fliplr(arr) # horizontal flip + elif tid == 5: + return np.flipud(arr) # vertical flip + elif tid == 6: + return arr.T # transpose (reflection along main diagonal) + elif tid == 7: + return np.fliplr(np.rot90(arr, k=1)) # anti-diagonal reflection + else: + return arr + + +def inverse_dihedral_transform(arr: np.ndarray, tid: int) -> np.ndarray: + return dihedral_transform(arr, DIHEDRAL_INVERSE[tid]) diff --git a/docs/END_TO_END_EXPLANATION.md b/docs/END_TO_END_EXPLANATION.md new file mode 100644 index 0000000000000000000000000000000000000000..9d499f7d037b08394635e946c77c9313240e5936 --- /dev/null +++ b/docs/END_TO_END_EXPLANATION.md @@ -0,0 +1,1193 @@ +# End-to-End Explanation: PyTorch & Triton Implementation +## For someone with basic Python knowledge + +--- + +## PART 1: What Are the Available Benchmarks? + +Before diving into code, let's understand what benchmarks are available to measure performance. + +### Overview of Benchmarking Suite + +The benchmarking system measures how fast the model runs and how efficiently it uses memory. It compares two setups: +- **Tiered Model**: Uses the SRAM/DRAM memory hierarchy (what we want to study) +- **Baseline Model**: Standard model without memory tiering + +### Files Involved: `benchmark.py` and `run_benchmark.py` + +#### A. `run_benchmark.py` - The Command-Line Interface + +This file lets you **run benchmarks from the terminal**. Think of it as a control panel. + +**Key command-line options:** + +```bash +# See available benchmarks and options +python run_benchmark.py --help + +# Compare tiered vs baseline models +python run_benchmark.py --mode compare --batch-sizes 1,8,32 --seq-lens 64,128 + +# Benchmark only the tiered (memory-aware) model +python run_benchmark.py --mode tiered --warmup 5 --iterations 50 --output results.json + +# Quick test (for learning) +python run_benchmark.py --mode tiered --warmup 1 --iterations 3 --batch-sizes 2 --seq-lens 16 +``` + +**What each option means:** + +| Option | Meaning | +|--------|---------| +| `--mode` | What to benchmark: `tiered` (new model), `baseline` (standard), or `compare` (both) | +| `--batch-sizes` | How many samples to process at once (comma-separated: `1,8,32` means test with 1, 8, and 32 samples) | +| `--seq-lens` | Length of input sequences: `64,128` means test with sequences of 64 and 128 tokens | +| `--hidden-size` | Internal dimension of the model (default: 512) | +| `--H-cycles` | Number of times H-level (slow) processes data (default: 2) | +| `--L-cycles` | Number of times L-level (fast) processes data (default: 2) | +| `--H-layers` | Number of H-level transformer layers (default: 4) | +| `--L-layers` | Number of L-level transformer layers (default: 4) | +| `--warmup` | How many runs to discard before measuring (to let GPU settle) | +| `--iterations` | How many actual measurements to take | +| `--output` | Save results to JSON file | + +--- + +### B. What Does `benchmark.py` Actually Measure? + +Inside `benchmark.py`, the `BenchmarkResult` class records these metrics: + +#### **Latency Metrics** (How fast things run, in microseconds `ΞΌs`) + +- **L-level latency**: Time for the "fast" tier to process data +- **H-level latency**: Time for the "slow" tier to process data +- **Total inference latency**: End-to-end prediction time (in milliseconds `ms`) +- **h_over_l_latency_ratio**: How many times slower H-level is than L-level + +**Why this matters:** If H-level takes 10Γ— longer than L-level, we know the memory hierarchy is working β€” slow operations vs. fast ones. + +#### **Memory Metrics** (How much GPU memory used) + +- **sram_peak_mb**: Peak memory in the "fast" tier +- **dram_peak_mb**: Peak memory in the "slow" tier +- **total_gpu_memory_mb**: Total GPU memory used + +#### **Transfer Metrics** (Cost of moving data between tiers, in microseconds) + +- **h_l_transfer_mean_us**: Average time to copy data from Hβ†’L +- **l_h_transfer_mean_us**: Average time to copy data from Lβ†’H + +**Why this matters:** If transfers are expensive, the tiering strategy might not be worth it. + +#### **Hit Rate & Efficiency** + +- **sram_hit_rate**: How often data we tried to put in "fast" memory actually fit (0.0 = never, 1.0 = always) +- **memory_efficiency**: Useful compute time / total time (higher is better) + +**Why this matters:** If hit_rate is low, data keeps spilling to slow memory and we're not getting the optimization benefit. + +#### **Triton Kernel Probes** (Direct measurement of memory latency) + +- **triton_sram_probe_latency_us**: Measured latency of "fast" memory via Triton kernels +- **triton_dram_probe_latency_us**: Measured latency of "slow" memory via Triton kernels + +**Why this matters:** These are the "ground truth" measurements showing the actual speed difference between SRAM and DRAM. + +--- + +### C. How Benchmarks Are Run + +**Flow of a benchmark:** + +1. **Create dummy data** (synthetic input tensors) +2. **Warmup phase** (run N times to let GPU caches warm up) + - These runs are NOT counted in final results +3. **Timing phase** (run N times with GPU timers active) + - Start GPU timer + - Run model inference + - Stop GPU timer + - Record elapsed time +4. **Collect statistics**: min, max, mean, std dev from all the timed runs +5. **Measure memory** (peak usage during runs) +6. **Calculate derived metrics** (ratios, efficiency scores) +7. **Output results** (to console and/or JSON file) + +--- + +## PART 2: PyTorch Implementation β€” Line-by-Line + +### File 1: `memory_tier.py` β€” The Memory Manager + +This is the **PyTorch foundation** for the two-tier memory system. It mimics how GPU memory is organized (SRAM = fast cache vs. DRAM = main memory). + +#### **Header & Imports (Lines 1-22)** + +```python +""" +Memory Tier Manager for HRM SRAM/DRAM implementation. + +Manages the placement of H-level and L-level hidden states across +GPU memory tiers and tracks all memory operations for benchmarking. + +- SRAM tier: Uses CUDA pinned memory + explicit prefetching. + L-level states are kept GPU-resident with minimal transfers. +- DRAM tier: Standard GPU global memory with transfer tracking. + H-level states go through normal allocation paths. +""" + +import time +from typing import Dict, List, Optional, Tuple +from dataclasses import dataclass, field +from contextlib import contextmanager + +import torch +``` + +**Explanation:** +- The docstring explains the purpose: manage two memory tiers +- `dataclass` and `field` are used to create structured data containers +- `contextmanager` creates a context (like `with` statements in Python) +- `torch` is imported to use PyTorch tensor operations + +#### **Section 1: MemoryEvent β€” Tracking Individual Operations (Lines 26-31)** + +```python +@dataclass +class MemoryEvent: + """A single tracked memory operation.""" + tier: str # 'sram' or 'dram' + operation: str # 'alloc', 'load', 'store', 'transfer' + bytes: int + duration_us: float # microseconds + timestamp: float +``` + +**Explanation:** +- A `@dataclass` is like a **lightweight container** for data +- Each `MemoryEvent` records ONE memory operation (e.g., "allocated 1024 bytes in SRAM in 50 microseconds") +- Fields: + - `tier`: Which tier (fast or slow)? + - `operation`: What happened? (allocate new memory, load, store, transfer between tiers) + - `bytes`: How much data? + - `duration_us`: How long did it take? (in microseconds, where 1000 ΞΌs = 1 ms) + - `timestamp`: When did it happen? + +**Real example:** +```python +event = MemoryEvent( + tier='sram', + operation='alloc', + bytes=65536, + duration_us=123.45, + timestamp=1704067200.123 +) +# Created a record: "SRAM allocation of 65536 bytes took 123.45 microseconds" +``` + +#### **Section 2: TierStats β€” Accumulated Statistics (Lines 35-56)** + +```python +@dataclass +class TierStats: + """Accumulated statistics for one memory tier.""" + total_alloc_bytes: int = 0 + peak_alloc_bytes: int = 0 + current_alloc_bytes: int = 0 + num_loads: int = 0 + num_stores: int = 0 + num_transfers: int = 0 + total_load_us: float = 0.0 + total_store_us: float = 0.0 + total_transfer_us: float = 0.0 + hit_count: int = 0 + miss_count: int = 0 + + @property + def hit_rate(self) -> float: + total = self.hit_count + self.miss_count + return self.hit_count / total if total > 0 else 0.0 + + @property + def avg_load_us(self) -> float: + return self.total_load_us / self.num_loads if self.num_loads > 0 else 0.0 + + @property + def avg_store_us(self) -> float: + return self.total_store_us / self.num_stores if self.num_stores > 0 else 0.0 +``` + +**Explanation:** +- This accumulates **total statistics** for a tier (all operations combined) +- `total_alloc_bytes`: Total memory ever allocated +- `peak_alloc_bytes`: Maximum memory used at any point +- `current_alloc_bytes`: Memory currently in use +- `num_loads`, `num_stores`: Counters for how many times data was read/written +- `hit_count` / `miss_count`: Success/failure for fitting data in SRAM +- **Properties** (`@property`): Computed on-the-fly from raw counts + - `hit_rate = hit_count / (hit_count + miss_count)` β€” percentage of successful SRAM fits + - `avg_load_us = total_load_us / num_loads` β€” average speed per load operation + - `avg_store_us = total_store_us / num_stores` β€” average speed per store operation + +**Why properties are useful:** +Instead of storing `hit_rate` separately and having to update it constantly, we compute it whenever asked: `stats.hit_rate` automatically calculates the current rate. + +--- + +#### **Section 3: MemoryTierManager β€” The Main Manager Class (Lines 60-90)** + +```python +class MemoryTierManager: + """Coordinates SRAM/DRAM memory placement and tracking for HRM. + + In the Triton context: + - SRAM tier: Tensors allocated with `pin_memory` and kept on the + same CUDA stream as L-level computation. Triton kernels keep + these values in registers/shared memory via data reuse. + - DRAM tier: Standard `torch.cuda` tensors. Triton kernels load + these from global memory each time. + """ + + def __init__( + self, + device: torch.device, + enable_tracking: bool = True, + sram_capacity_mb: float = 48.0, # Typical L2 cache size + ): + self.device = device + self.enable_tracking = enable_tracking + self.sram_capacity_bytes = int(sram_capacity_mb * 1024 * 1024) + + # State registries + self._sram_tensors: Dict[str, torch.Tensor] = {} + self._dram_tensors: Dict[str, torch.Tensor] = {} + + # Event log + self._events: List[MemoryEvent] = [] + self._sram_stats = TierStats() + self._dram_stats = TierStats() + + # CUDA events for GPU timing + self._use_cuda = device.type == 'cuda' + if self._use_cuda: + self._sram_stream = torch.cuda.Stream(device=device) + self._dram_stream = torch.cuda.Stream(device=device) + else: + self._sram_stream = None + self._dram_stream = None +``` + +**Explanation:** + +- **`__init__` method**: Initializes the manager when created + - `device`: Where to allocate (CPU or GPU?) + - `enable_tracking`: Should we record all operations? + - `sram_capacity_mb`: How much "fast" memory is available? (Typical GPU L2 cache = 48 MB) + +- **State registries:** + - `_sram_tensors`: Dictionary storing all tensors in SRAM (key = name, value = tensor) + - `_dram_tensors`: Dictionary storing all tensors in DRAM + - Example: `_sram_tensors['layer1_hidden'] = torch.tensor(...)` + +- **Event log:** + - `_events`: A list of `MemoryEvent` objects (every operation is recorded) + - `_sram_stats`, `_dram_stats`: Running statistics for each tier + +- **CUDA streams:** + - A stream is like a **"lane"** for GPU operations (operations in same lane execute sequentially, different lanes can run in parallel) + - Two separate streams (`_sram_stream`, `_dram_stream`) let us potentially run fast and slow operations concurrently + +--- + +#### **Section 4A: SRAM Allocation (Lines 95-123)** + +```python +def alloc_sram(self, name: str, shape: Tuple, dtype: torch.dtype) -> torch.Tensor: + """Allocate a tensor in the SRAM tier (GPU-resident, pinned).""" + # Calculate size in bytes + nbytes = torch.tensor([], dtype=dtype).element_size() + for s in shape: + nbytes *= s + + # Check capacity: does it fit? + if self._sram_stats.current_alloc_bytes + nbytes > self.sram_capacity_bytes: + # Doesn't fit β†’ spill to DRAM and record a "miss" + self._sram_stats.miss_count += 1 + return self.alloc_dram(name, shape, dtype) + + # It fits! Record a "hit" + self._sram_stats.hit_count += 1 + + # Time the allocation + t0 = self._timer_start() + tensor = torch.zeros(shape, dtype=dtype, device=self.device) + + # Hint: keep GPU-resident (don't swap to CPU) + if self._use_cuda: + with torch.cuda.stream(self._sram_stream): + tensor = tensor.contiguous() + + self._sram_tensors[name] = tensor + dur = self._timer_end(t0) + + # Update statistics + self._sram_stats.total_alloc_bytes += nbytes + self._sram_stats.current_alloc_bytes += nbytes + self._sram_stats.peak_alloc_bytes = max( + self._sram_stats.peak_alloc_bytes, + self._sram_stats.current_alloc_bytes, + ) + + # Record the operation + self._record_event('sram', 'alloc', nbytes, dur) + return tensor +``` + +**Explanation (line by line):** + +1. **Calculate size:** Convert shape into bytes + - Example: shape `(128, 512)` with `float32` (4 bytes) = 128 Γ— 512 Γ— 4 = 262,144 bytes + +2. **Capacity check:** Does this allocation fit in SRAM? + - If `current_alloc_bytes + nbytes > sram_capacity_bytes`, it doesn't fit + - **Fall back to DRAM** and **count as a miss** (failed to use SRAM) + - Otherwise, **count as a hit** (successfully allocated in SRAM) + +3. **Timing:** Record when allocation starts (`t0`) + +4. **Create tensor:** `torch.zeros(shape, ...)` creates a tensor of zeros + - `shape`: dimensions + - `dtype`: data type (e.g., float32) + - `device`: 'cuda' or 'cpu' + +5. **GPU optimization:** `tensor.contiguous()` makes the data **contiguous in memory** (important for GPU performance) + - Done on the SRAM stream to suggest GPU placement + +6. **Store in registry:** Save for later: `_sram_tensors[name] = tensor` + +7. **Update stats:** + - Add `nbytes` to total and current + - Update peak if we're now using more than before + +8. **Record event:** Add this operation to the event log + +--- + +#### **Section 4B: DRAM Allocation (Lines 126-142)** + +```python +def alloc_dram(self, name: str, shape: Tuple, dtype: torch.dtype) -> torch.Tensor: + """Allocate a tensor in the DRAM tier (standard GPU memory).""" + nbytes = torch.tensor([], dtype=dtype).element_size() + for s in shape: + nbytes *= s + + t0 = self._timer_start() + tensor = torch.zeros(shape, dtype=dtype, device=self.device) + self._dram_tensors[name] = tensor + dur = self._timer_end(t0) + + self._dram_stats.total_alloc_bytes += nbytes + self._dram_stats.current_alloc_bytes += nbytes + self._dram_stats.peak_alloc_bytes = max( + self._dram_stats.peak_alloc_bytes, + self._dram_stats.current_alloc_bytes, + ) + + self._record_event('dram', 'alloc', nbytes, dur) + return tensor +``` + +**Explanation:** +- **Almost identical to `alloc_sram`**, but: + - **No capacity check** (unrestricted DRAM) + - **No stream optimization** (standard allocation) + - **Stores in `_dram_tensors`** instead of `_sram_tensors` + +--- + +#### **Section 5: Cross-Tier Transfers (Lines 147-177)** + +```python +def transfer_sram_to_dram(self, name: str) -> torch.Tensor: + """Copy a tensor from SRAM tier to DRAM tier.""" + src = self._sram_tensors[name] + t0 = self._timer_start() + dst = src.clone() + if self._use_cuda: + torch.cuda.synchronize(self.device) + dur = self._timer_end(t0) + + self._dram_tensors[name + '_from_sram'] = dst + self._sram_stats.num_transfers += 1 + self._sram_stats.total_transfer_us += dur + self._record_event('sram', 'transfer', src.nelement() * src.element_size(), dur) + return dst + +def transfer_dram_to_sram(self, name: str) -> torch.Tensor: + """Copy a tensor from DRAM tier to SRAM tier.""" + src = self._dram_tensors[name] + t0 = self._timer_start() + dst = src.clone() + if self._use_cuda: + torch.cuda.synchronize(self.device) + dur = self._timer_end(t0) + + self._sram_tensors[name + '_from_dram'] = dst + self._dram_stats.num_transfers += 1 + self._dram_stats.total_transfer_us += dur + self._record_event('dram', 'transfer', src.nelement() * src.element_size(), dur) + return dst +``` + +**Explanation:** + +- **Hβ†’L transfer (SRAM to DRAM):** + 1. Get source tensor from SRAM registry + 2. `clone()` = make a complete copy + 3. `torch.cuda.synchronize()` = wait for GPU to finish (so we time actual operation) + 4. Calculate duration + 5. Store in DRAM registry with new name + 6. Update transfer counter and total transfer time + 7. Record the event + +- **Lβ†’H transfer (DRAM to SRAM):** Same logic in reverse + +**Why clone?** We don't want to move the original; we want a copy at the destination. + +**Why synchronize?** GPU operations often run asynchronously (GPU schedules them but CPU moves on). Synchronize makes CPU wait, so we measure actual GPU time. + +--- + +#### **Section 6: Context Managers for Streams (Lines 182-198)** + +```python +@contextmanager +def sram_context(self): + """Context manager that runs operations on the SRAM stream.""" + if self._use_cuda and self._sram_stream is not None: + with torch.cuda.stream(self._sram_stream): + yield self._sram_stream + else: + yield None + +@contextmanager +def dram_context(self): + """Context manager that runs operations on the DRAM stream.""" + if self._use_cuda and self._dram_stream is not None: + with torch.cuda.stream(self._dram_stream): + yield self._dram_stream + else: + yield None +``` + +**Explanation:** + +A `@contextmanager` lets you use `with` statements: + +```python +# Usage: +with memory_manager.sram_context() as stream: + # Operations here run on the SRAM stream + x = torch.matmul(a, b) # GPU runs this on _sram_stream + y = x + 1 + +# When we exit the block, stream is restored +``` + +**Why useful?** Different operations can run on different streams in parallel, potentially overlapping computation and memory transfer. + +--- + +#### **Section 7: Statistics & Reporting (Lines 203-243)** + +```python +def get_stats(self) -> Dict: + """Return all memory tier statistics.""" + return { + 'sram': { + 'peak_mb': self._sram_stats.peak_alloc_bytes / (1024 * 1024), + 'current_mb': self._sram_stats.current_alloc_bytes / (1024 * 1024), + 'hit_rate': self._sram_stats.hit_rate, + 'num_loads': self._sram_stats.num_loads, + 'num_stores': self._sram_stats.num_stores, + 'num_transfers': self._sram_stats.num_transfers, + 'avg_load_us': self._sram_stats.avg_load_us, + 'avg_store_us': self._sram_stats.avg_store_us, + 'total_transfer_us': self._sram_stats.total_transfer_us, + }, + 'dram': { + # Similar for DRAM... + }, + 'num_events': len(self._events), + } + +def get_events(self) -> List[MemoryEvent]: + """Return raw event log.""" + return list(self._events) + +def reset_stats(self): + """Clear all statistics and event log.""" + self._events.clear() + self._sram_stats = TierStats() + self._dram_stats = TierStats() + +def free_all(self): + """Release all managed tensors.""" + self._sram_tensors.clear() + self._dram_tensors.clear() + self._sram_stats.current_alloc_bytes = 0 + self._dram_stats.current_alloc_bytes = 0 +``` + +**Explanation:** + +- **`get_stats()`**: Returns a dictionary with all collected metrics (ready to print or save) + - Converts bytes to MB (divide by 1024Β²) + - Includes hit rate, counts, timing + +- **`get_events()`**: Returns the raw event log (for detailed analysis) + +- **`reset_stats()`**: Clear counters before a new benchmark run (don't contaminate results with previous runs) + +- **`free_all()`**: Release memory and reset current allocations (cleanup) + +--- + +#### **Section 8: Internal Timing Helpers (Lines 248-257)** + +```python +def _timer_start(self) -> float: + if self._use_cuda: + torch.cuda.synchronize(self.device) + return time.perf_counter() + +def _timer_end(self, t0: float) -> float: + if self._use_cuda: + torch.cuda.synchronize(self.device) + return (time.perf_counter() - t0) * 1e6 # β†’ microseconds + +def _record_event(self, tier: str, op: str, nbytes: int, dur_us: float): + if self.enable_tracking: + self._events.append(MemoryEvent( + tier=tier, operation=op, bytes=nbytes, + duration_us=dur_us, timestamp=time.time(), + )) +``` + +**Explanation:** + +- **`_timer_start()`:** + - Synchronize GPU first (wait for all pending operations) + - Record CPU time + - Returns the start time + +- **`_timer_end(t0)`:** + - Synchronize GPU (wait for all operations since start) + - Calculate elapsed time + - Convert to microseconds (Γ— 1,000,000) + +- **`_record_event()`:** + - If tracking is enabled, create a `MemoryEvent` and add to log + - Otherwise, skip (for performance when we don't need detailed logs) + +--- + +### File 2: `softmax.py` β€” Simple PyTorch Reference + +```python +import torch +import torch.nn.functional as F +sample = torch.tensor([[1,2,3,4,5], [5,4,3,2,1]], dtype=torch.float32, device='cuda') +ref = F.softmax(sample, dim=1) +print(f"Softmax result: {ref=}") +``` + +**Explanation:** + +This is a **minimal reference implementation** showing: +1. Create a sample tensor: 2 rows Γ— 5 columns of numbers +2. Apply softmax along dimension 1 (across columns) +3. Print the result + +**What softmax does:** Converts scores to probabilities (sum to 1). Example: +- Input `[1, 2, 3, 4, 5]` +- Output ~`[0.67, 0.18, 0.05, 0.01, 0.006]` (largest inputs become largest probabilities) + +This is a building block used in attention mechanisms in transformers. + +--- + +## PART 3: Triton Implementation β€” Line-by-Line + +### File: `triton_kernels.py` β€” GPU Kernels for Memory-Aware Compute + +Triton is a language for writing GPU kernels (low-level GPU programs). Unlike PyTorch which relies on pre-built library functions, Triton lets us control **exactly** how data flows through GPU memory. + +#### **Header & Imports (Lines 1-23)** + +```python +""" +Triton kernels for HRM SRAM/DRAM memory-tiered operations. + +Key idea: In Triton, SRAM = registers + shared memory (managed by compiler +within a block). DRAM = global memory (GPU HBM). By structuring kernels to keep +L-level state tile-resident (loaded once, reused many times within a block), +we ensure L-level stays in SRAM. H-level state is loaded from global memory +(DRAM) each cycle, paying the full memory bandwidth cost. + +This gives us real, measurable latency differences that map to the HRM's +hierarchical update frequencies. +""" + +import torch +import triton +import triton.language as tl +import math +``` + +**Explanation:** + +- Key insight: **Data locality matters enormously** on GPUs + - SRAM (L1/L2 cache, registers, shared memory): ~1-4 cycles latency, 100s of GB/s bandwidth + - DRAM (global memory, HBM): ~200-400 cycles latency, 100s of GB/s like SRAM but way higher latency! + +**The trick:** Keep "important" data (L-level) in SRAM by reusing it within a kernel block. Load "less critical" data (H-level) fresh each time from DRAM. + +--- + +#### **Kernel 1: SRAM-Resident RMS-Norm + Residual (Lines 28-64)** + +```python +@triton.jit +def _rms_norm_residual_fused_kernel( + X_ptr, # Input tensor (residual branch) + Residual_ptr, # Residual connection input + Out_ptr, # Output tensor + N: tl.constexpr, # Hidden dimension (constexpr β†’ compiler tiles in SRAM) + eps: tl.constexpr, + BLOCK_N: tl.constexpr, +): + """Fused RMS-norm + residual add. + + By making N and BLOCK_N constexpr, the compiler keeps the entire hidden + vector in registers/shared-memory (SRAM) across the norm computation. + This is the kernel used for L-level (fast path). + """ + row = tl.program_id(0) + cols = tl.arange(0, BLOCK_N) + mask = cols < N + + # ---- Load both inputs into SRAM (registers) in one shot ---- + x = tl.load(X_ptr + row * N + cols, mask=mask, other=0.0).to(tl.float32) + r = tl.load(Residual_ptr + row * N + cols, mask=mask, other=0.0).to(tl.float32) + + # Residual add β€” stays in registers + h = x + r + + # RMS norm β€” entirely in registers, no global memory round-trip + variance = tl.sum(h * h, axis=0) / N + h_norm = h * tl.math.rsqrt(variance + eps) + + # ---- Store back to global memory ---- + tl.store(Out_ptr + row * N + cols, h_norm.to(tl.bfloat16), mask=mask) +``` + +**Explanation (line by line):** + +1. **Function signature:** + - `@triton.jit`: This is GPU code (JIT-compiled, not Python) + - `X_ptr`, `Residual_ptr`, `Out_ptr`: **Pointers** to tensors in GPU memory + - `N: tl.constexpr`: "constexpr" = compiler treats as a constant (compile-time value, not runtime) + - `BLOCK_N: tl.constexpr`: Block size (constexpr tells compiler to optimize for fixed size) + +2. **Get thread ID:** + - `row = tl.program_id(0)`: Which "row" is this GPU thread processing? + - On GPU, thousands of threads run in parallel; each processes one row + +3. **Create index array:** + - `cols = tl.arange(0, BLOCK_N)`: Array `[0, 1, 2, ..., BLOCK_N-1]` + - `mask = cols < N`: Boolean mask to handle if N < BLOCK_N (some threads might not have data) + +4. **Load data into registers:** + - `tl.load(X_ptr + row * N + cols, ...)`: Load elements from memory + - `row * N`: Start at row-th row + - `+ cols`: Load all columns for this row + - `mask=mask`: Only load valid columns + - `other=0.0`: If invalid column, use 0.0 + - `.to(tl.float32)`: Convert to 32-bit floats for math + - **KEY**: All data now in registers/shared memory (SRAM)! + +5. **Compute residual:** + - `h = x + r`: Add the two inputs (element-wise, all in registers) + +6. **Compute RMS norm:** + - RMS = sqrt(mean(xΒ²)) + - `variance = tl.sum(h * h, axis=0) / N`: Compute mean of squares + - `h_norm = h * tl.math.rsqrt(variance + eps)`: Normalize by reciprocal square root + - `eps`: Small number to avoid division by zero + - **KEY**: All arithmetic in registers! + +7. **Store result:** + - `tl.store(Out_ptr + ..., h_norm.to(tl.bfloat16), mask=mask)` + - Write normalized result back to global memory + - `.to(tl.bfloat16)`: Convert to lower precision for storage (saves memory bandwidth) + +**Why this is fast:** +- Load data once β†’ keep in SRAM β†’ do many operations β†’ store once +- If we did this in PyTorch with separate ops (add, then norm), we'd load/store twice + +--- + +#### **Kernel 2: DRAM-Sourced RMS-Norm + Residual (Lines 68-95)** + +```python +@triton.jit +def _rms_norm_residual_dram_kernel( + X_ptr, + Residual_ptr, + Out_ptr, + N: tl.constexpr, + eps: tl.constexpr, + BLOCK_N: tl.constexpr, +): + """RMS-norm + residual for H-level. + + Structurally identical but designed to be called with larger strides + and without re-use inside a meta-kernel. Each call does a full + DRAM round-trip, modeling the slower H-level memory access pattern. + """ + row = tl.program_id(0) + cols = tl.arange(0, BLOCK_N) + mask = cols < N + + # Global memory load (DRAM) + x = tl.load(X_ptr + row * N + cols, mask=mask, other=0.0).to(tl.float32) + r = tl.load(Residual_ptr + row * N + cols, mask=mask, other=0.0).to(tl.float32) + + h = x + r + variance = tl.sum(h * h, axis=0) / N + h_norm = h * tl.math.rsqrt(variance + eps) + + tl.store(Out_ptr + row * N + cols, h_norm.to(tl.bfloat16), mask=mask) +``` + +**Explanation:** + +**Identical kernel code**, but: +- **Intent is different**: This is called less frequently (H-level runs slower) +- **Memory behavior differs** via **calling context**: + - SRAM kernel: Data reused many times within tight loops β†’ stays in cache + - DRAM kernel: Data used once then discarded β†’ evicted from cache + +**In code**, they're identical because the difference is **how often and how much** they're called, not the kernel itself. + +--- + +#### **Kernel 3: SwiGLU Activation (Lines 100-126)** + +```python +@triton.jit +def _swiglu_fused_sram_kernel( + GateUp_ptr, # [rows, 2 * inter] β€” gate and up projections concatenated + Out_ptr, # [rows, inter] + inter: tl.constexpr, + BLOCK_INTER: tl.constexpr, +): + """Fused SiLU(gate) * up in a single kernel pass. + + For L-level: The gate and up vectors are loaded once into registers + and the activation is computed without spilling to DRAM. + """ + row = tl.program_id(0) + cols = tl.arange(0, BLOCK_INTER) + mask = cols < inter + + # Load gate and up from contiguous memory β€” both go into SRAM + gate = tl.load(GateUp_ptr + row * 2 * inter + cols, mask=mask, other=0.0).to(tl.float32) + up = tl.load(GateUp_ptr + row * 2 * inter + inter + cols, mask=mask, other=0.0).to(tl.float32) + + # SiLU(gate) * up β€” entirely in registers + silu_gate = gate * tl.sigmoid(gate) + result = silu_gate * up + + tl.store(Out_ptr + row * inter + cols, result.to(tl.bfloat16), mask=mask) +``` + +**Explanation:** + +**SwiGLU** = A gating mechanism in transformers. Formula: `output = sigmoid(gate) * up` + +- **Input layout:** `GateUp_ptr` contains concatenated `[gate | up]` (e.g., first half is gate, second half is up) +- **Load both halves:** + - Gate: `row * 2 * inter + cols` (first half) + - Up: `row * 2 * inter + inter + cols` (second half) +- **Compute SiLU:** + - `tl.sigmoid(gate)`: Apply sigmoid (s-shaped function) to gate + - `silu_gate = gate * sigmoid(gate)`: SiLU = gate * sigmoid(gate) + - `result = silu_gate * up`: Gated output +- **Store:** Result back to global memory + +**Why fused?** If done separately: +1. Load gate +2. Compute sigmoid +3. Write intermediate +4. Load intermediate +5. Compute SiLU +6. Write intermediate +7. Load up +8. Compute product +9. Write result + +Fused does it in one kernel β†’ one load, one store. + +--- + +#### **Kernel 4: State Transfer (Lines 131-148)** + +```python +@triton.jit +def _state_transfer_kernel( + Src_ptr, + Dst_ptr, + numel: tl.constexpr, + BLOCK: tl.constexpr, +): + """Explicit memory copy kernel for cross-tier state transfer. + + Used when H-level needs to read L-level output (or vice versa). + Triton compiles this into optimized async memcpy instructions. + """ + pid = tl.program_id(0) + offsets = pid * BLOCK + tl.arange(0, BLOCK) + mask = offsets < numel + + data = tl.load(Src_ptr + offsets, mask=mask, other=0.0) + tl.store(Dst_ptr + offsets, data, mask=mask) +``` + +**Explanation:** + +Simple memcpy kernel (copy data from source to destination): +- `pid = tl.program_id(0)`: Program/thread block ID +- `offsets = pid * BLOCK + tl.arange(0, BLOCK)`: Calculate which elements this block handles + - If BLOCK=1024, pid=0 handles offsets 0-1023, pid=1 handles 1024-2047, etc. +- `mask = offsets < numel`: Don't copy past the end +- Load and store in parallel across many threads + +**Why separate kernel?** GPU memcopy can be optimized by the compiler (uses memory controllers, not just compute cores). + +--- + +#### **Kernel 5: Memory Latency Probe (Lines 153-176)** + +```python +@triton.jit +def _memory_latency_probe_kernel( + Data_ptr, + Out_ptr, + N: tl.constexpr, + BLOCK_N: tl.constexpr, + NUM_ITERS: tl.constexpr, +): + """Probe kernel to measure effective memory latency. + + Performs NUM_ITERS dependent loads to measure true SRAM vs DRAM latency. + The data dependency chain prevents compiler reordering. + """ + pid = tl.program_id(0) + cols = tl.arange(0, BLOCK_N) + mask = cols < N + + # Initial load from global memory + acc = tl.load(Data_ptr + pid * N + cols, mask=mask, other=0.0) + + # Dependent iteration chain β€” forces sequential memory access + for _ in range(NUM_ITERS): + # This stays in SRAM (registers) because acc is reused + acc = acc * 1.00001 + 0.00001 + + tl.store(Out_ptr + pid * N + cols, acc, mask=mask) +``` + +**Explanation:** + +This kernel **measures latency** by creating a **dependency chain** that can't be optimized away: + +1. Load initial data +2. For N iterations: + - Multiply by 1.00001 + add 0.00001 (cheap operations) + - Result depends on previous iteration (creates dependency) +3. Store result + +**Why not just load/store?** The compiler could optimize away separate load-stores, but a **dependency chain** forces real latency measurement. + +**For SRAM data:** +- Data stays in registers +- All iterations hit registers (super fast) +- Total time β‰ˆ NUM_ITERS Γ— 1 cycle β‰ˆ very fast + +**For DRAM data:** +- Data in global memory +- Each iteration reloads from DRAM +- Total time β‰ˆ NUM_ITERS Γ— 200-400 cycles β‰ˆ slow! + +**Result:** By comparing SRAM vs DRAM probe times, we measure the latency difference. + +--- + +#### **Python Wrappers (Lines 184-265)** + +```python +def _next_power_of_2(n: int) -> int: + return 1 << (n - 1).bit_length() + +def triton_rms_norm_residual_sram( + x: torch.Tensor, + residual: torch.Tensor, + eps: float = 1e-5, +) -> torch.Tensor: + """SRAM-optimized fused RMS-norm + residual for L-level.""" + assert x.shape == residual.shape + assert x.is_contiguous() and residual.is_contiguous() + + rows, N = x.shape[0] * (x.shape[1] if x.ndim == 3 else 1), x.shape[-1] + flat_x = x.reshape(rows, N) + flat_r = residual.reshape(rows, N) + out = torch.empty_like(flat_x) + + BLOCK_N = _next_power_of_2(N) + + _rms_norm_residual_fused_kernel[(rows,)]( + flat_x, flat_r, out, + N=N, eps=eps, BLOCK_N=BLOCK_N, + ) + return out.reshape(x.shape) +``` + +**Explanation:** + +- **`_next_power_of_2(n)`**: Find smallest power of 2 β‰₯ n + - Example: `_next_power_of_2(512)` β†’ 512, `_next_power_of_2(513)` β†’ 1024 + - (Bit manipulation: `(n-1).bit_length()` gives number of bits, `1 << x` is 2^x) + - GPUs work best with powers of 2 (thread block sizes) + +- **`triton_rms_norm_residual_sram(...)`**: Python wrapper to call the Triton kernel + - Checks inputs are same shape and contiguous + - Flattens to 2D (rows Γ— hidden_size) + - Creates output tensor + - Picks block size (nearest power of 2) + - Launches kernel with `[(rows,)]` β€” one thread block per row + - Reshapes output back to original + +**Why a wrapper?** Triton kernels are GPU code; we need Python to: +1. Prepare data (reshape, allocate output) +2. Launch the kernel (call the JIT-compiled function) +3. Return result to CPU + +--- + +## PART 4: How PyTorch & Triton Work Together + +### The Full Pipeline + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Input Data (e.g., transformer input) β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ MemoryTierManager β”‚ + β”‚ allocates L-level ────┐ β”‚ + β”‚ allocates H-level ──┐ β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”˜ β”‚ β”‚ + β”‚ β”‚ β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β” β”‚ β”‚ + β”‚ L-level (Fast Path) β”‚ β”‚ β”‚ + β”‚ Triton kernels: β”‚ β”‚ β”‚ + β”‚ - SRAM RMS+Residual β”‚ β”‚ β”‚ + β”‚ - SRAM SwiGLU β”‚ β”‚ β”‚ + β”‚ (Data in regs/cache) β”‚ β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ + β”‚ β”‚ β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ + β”‚ H-level (Slow Path) β”‚ β”‚ β”‚ + β”‚ Triton kernels: β”‚ β”‚ β”‚ + β”‚ - DRAM RMS+Residual β”‚ β”‚ β”‚ + β”‚ (Data in global memory) β”‚ β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ + β”‚ β”‚ β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”β”‚ β”‚ + β”‚ Triton State Transfer: β”‚β”‚ β”‚ + β”‚ Move Lβ†’H and Hβ†’L states β”€β”€β”€β”€β”€β”€β”€β”Όβ”˜ β”‚ + β”‚ (what MemoryTierManager times) β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ + β”‚ β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ + β”‚ Output β”‚ β”‚ + β”‚ (result tensor) β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ + β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Ό + β”‚ Statistics collected: + β”‚ - L/H latency + β”‚ - Transfer time + β”‚ - Memory usage + β”‚ - Hit rate +``` + +### Real Example: Forward Pass + +```python +# 1. Create memory manager +mem_mgr = MemoryTierManager(device='cuda') + +# 2. Allocate L-level (fast) hidden state +L_hidden = mem_mgr.alloc_sram('L_state', shape=(batch, hidden_dim), dtype=torch.float32) +# β†’ Records allocation in SRAM if it fits, otherwise spills to DRAM + +# 3. Allocate H-level (slow) hidden state +H_hidden = mem_mgr.alloc_dram('H_state', shape=(batch, hidden_dim), dtype=torch.float32) +# β†’ Records allocation in DRAM + +# 4. L-level computes using SRAM kernel (fast) +with mem_mgr.sram_context(): + L_out = triton_rms_norm_residual_sram(L_hidden, residual, eps=1e-5) + L_out = triton_swiglu_sram(gate_up, inter_dim) + # All data in registers/shared memory + # Time recorded: ~10-100 microseconds per operation + +# 5. H-level computes using DRAM kernel (slower) +with mem_mgr.dram_context(): + H_out = triton_rms_norm_residual_dram(H_hidden, residual, eps=1e-5) + # Data loaded from global memory each time + # Time recorded: ~100-1000 microseconds per operation + +# 6. Transfer L-level output to H-level +L_to_H_output = mem_mgr.transfer_sram_to_dram('L_out') +# β†’ Records transfer size and time + +# 7. Collect metrics +stats = mem_mgr.get_stats() +print(f"L/H latency ratio: {stats['H_latency'] / stats['L_latency']}") +# β†’ Usually 10-100x difference +``` + +--- + +## PART 5: Bringing It All Together + +### What Happens When You Run a Benchmark + +``` +python run_benchmark.py --mode tiered --iterations 10 +``` + +1. **Setup:** + - Create MemoryTierManager + - Create model (HRM_Tiered) + - Create dummy batch of data + +2. **Warmup phase (first 5 runs):** + - Forward pass (not timed) + - GPU caches warm up, compilers warm up + - Discarded from results + +3. **Benchmark phase (10 timed runs):** + - For each iteration: + - Start GPU timer + - Forward pass: + - L-level uses SRAM kernels (fast) + - H-level uses DRAM kernels (slow) + - Record latencies in mem_mgr + - Stop GPU timer + - Record elapsed time + +4. **Statistics:** + - Calculate mean, min, max, std dev of times + - Get memory stats (peak, current usage) + - Get transfer stats (H↔L copy times) + - Calculate derived metrics (ratios, efficiency) + - Get Triton probe latencies (direct SRAM vs DRAM measurement) + +5. **Output:** + - Print table of results + - Save to JSON + - Optionally generate plots + +--- + +## Key Insights + +### Why Two Implementations? + +| Aspect | PyTorch | Triton | +|--------|---------|--------| +| **What it expresses** | Algorithmic intent | Micro-architecture intent | +| **Memory control** | Coarse (allocate tensor) | Fine (exact register usage) | +| **Performance** | Relies on library kernels | Direct GPU control | +| **Latency** | Can hide memory issues | Exposes memory latency differences | +| **Usability** | Easy to write | Low-level, harder to write | + +### Why SRAM vs DRAM? + +**The 200Γ— Latency Difference:** +- SRAM (L2 cache): Load time ~4 cycles = ~2 nanoseconds = **very fast** +- DRAM (HBM): Load time ~400 cycles = **200 nanoseconds = 200x slower!** + +By **exploiting this difference**, we can: +- Keep "important" (L-level) data in fast SRAM +- Allow "less important" (H-level) data to use slow DRAM +- Model hierarchical computation: fast frequent + slow infrequent + +### Why Fuse Operations? + +**Without fusion:** +``` +Load β†’ Compute β†’ Store β†’ Load β†’ Compute β†’ Store +``` +Multiple memory round-trips! + +**With fusion:** +``` +Load β†’ Compute β†’ Compute β†’ Compute β†’ Store +``` +One load, many operations, one store. **Massive speedup!** + +--- + +## Testing It Yourself + +### Quick test (2 minutes): +```bash +cd test-env/HRM_optimised +python run_benchmark.py --mode tiered --warmup 1 --iterations 3 --batch-sizes 2 --seq-lens 16 +``` + +### Full benchmark (10 minutes): +```bash +python run_benchmark.py --mode compare --warmup 5 --iterations 20 --batch-sizes 1,8,32 --seq-lens 64,128 +``` + +### Analyze results: +```python +import json +with open('benchmark_results/results.json') as f: + results = json.load(f) +print(f"L/H latency ratio: {results[0]['h_over_l_latency_ratio']}") +print(f"SRAM hit rate: {results[0]['sram_hit_rate']}") +print(f"Memory efficiency: {results[0]['memory_efficiency']}") +``` + +--- + +## Summary + +You now understand: + +βœ… **Benchmarks** β†’ What metrics are collected and why +βœ… **PyTorch layer** β†’ How `MemoryTierManager` tracks two memory tiers +βœ… **Triton layer** β†’ How kernels control GPU memory access patterns +βœ… **Integration** β†’ How they work together to model hierarchical computation +βœ… **Performance** β†’ Why SRAM vs DRAM and data fusion matter for speed + +The key idea: **Different parts of the model operate at different timescales (L-level fast, H-level slow), and we can model this using GPU memory hierarchy (SRAM fast, DRAM slow).** diff --git a/docs/IMPLEMENTATION.md b/docs/IMPLEMENTATION.md new file mode 100644 index 0000000000000000000000000000000000000000..c1c36fd99e5cdee22c404c09ee505aa14c042d33 --- /dev/null +++ b/docs/IMPLEMENTATION.md @@ -0,0 +1,49 @@ +**Overview** +- **Purpose**: Explains how the HRM model implements a two-tier memory hierarchy (L-level β‰ˆ SRAM, H-level β‰ˆ DRAM) in both PyTorch and Triton, and how the two implementations map to each other. + +**Files to reference** +- `PyTorch ref`: [softmax.py](softmax.py) +- `Triton ref`: [softmax_triton.py](softmax_triton.py) +- `Memory tier manager`: [test-env/HRM_optimised/models/memory_tier.py](test-env/HRM_optimised/models/memory_tier.py) +- `Triton kernels`: [test-env/HRM_optimised/models/triton_kernels.py](test-env/HRM_optimised/models/triton_kernels.py) +- `Sparse embedding + optimizer`: [test-env/HRM_optimised/models/sparse_embedding.py](test-env/HRM_optimised/models/sparse_embedding.py) + +**Conceptual summary** +- **Two-tier idea**: L-level (fast, frequently-updated state) is treated as SRAM: kept resident and reused heavily inside a compute tile. H-level (large, infrequently-updated state) is treated as DRAM: loaded from global memory when needed. +- **Why two implementations**: PyTorch expresses algorithmic intent using tensors, streams and explicit copies; Triton encodes the micro-kernel behavior (register/shared-memory reuse, explicit loads/stores) so we can enforce the intended locality and measure latency/bandwidth differences. + +**PyTorch implementation (what to look for)** +- **`MemoryTierManager` (`memory_tier.py`)**: central piece for the PyTorch-side memory-tiering. + - **Allocation**: `alloc_sram` and `alloc_dram` allocate tensors and track sizes. SRAM allocation is guarded by a capacity limit (spill-to-DRAM on overflow). + - **Contexts / Streams**: `sram_context` and `dram_context` provide CUDA stream scopes to suggest where operations should run (helps overlap/ordering and can hint locality). + - **Transfers**: `transfer_sram_to_dram` and `transfer_dram_to_sram` perform explicit `clone()` copies and record timing via CPU timers with `torch.cuda.synchronize()` for correctness. + - **Tracking & metrics**: `MemoryEvent` log and `TierStats` collect hit/miss counts, transfer counts, and timing statistics used for benchmarking. +- **Pure-PyTorch ops**: Higher-level routines (e.g. a softmax demo in `softmax.py`) use standard `torch`/`torch.nn.functional` ops. These rely on the allocator/streams above to approximate tier behavior but cannot force register/shared-memory residency the way Triton kernels do. +- **Sparse embedding** (`sparse_embedding.py`): shows a training path where local slices are copied into a local buffer for computation and a distributed SignSGD step reduces gradients and writes back slices β€” this mirrors a β€œlocal fast working set + global parameter store” design. + +**Triton implementation (what to look for)** +- **Kernels are explicit**: `triton_kernels.py` contains JIT-ed kernels that declare which data is intended to remain in SRAM (registers/shared-memory) vs. be sourced from DRAM (global memory). + - **`constexpr` parameters** (e.g. `N`, `BLOCK_N`) cause the Triton compiler to tile loops and keep whole tiles in registers/shared memory. This is how L-level residency is enforced. + - **SRAM kernels**: `_rms_norm_residual_fused_kernel` and `_swiglu_fused_sram_kernel` load tile data into registers/shared memory, perform fused compute (residual add, RMS-norm, SwiGLU) entirely in-register, then store results β€” minimal global memory traffic. + - **DRAM kernels**: `_rms_norm_residual_dram_kernel` uses the same math but is intended to be invoked without tile reuse; each invocation does full DRAM round-trips. + - **State transfer**: `_state_transfer_kernel` is an explicit copy kernel used to move state between tiers (H↔L). This is the Triton analogue of `transfer_*` in `MemoryTierManager` but compiled down to efficient device copies. + - **Latency probes**: `_memory_latency_probe_kernel` builds dependent load chains to measure effective latency and expose the practical difference between SRAM-like reuse and DRAM round trips. + - **Wrappers** (Python functions at the bottom of `triton_kernels.py`): reshape inputs, pick grid/block sizes, and launch the kernels (e.g. `triton_rms_norm_residual_sram`, `triton_state_transfer`). These are the call sites you'd use in place of the plain PyTorch ops where you need the explicit micro-architecture behavior. + +**How the mapping works: PyTorch ↔ Triton** +- **Operator mapping**: Where PyTorch would call `x + residual` followed by `F.layer_norm` or `F.softmax`, the Triton path provides fused kernels that implement the same math but with different memory patterns (fused to avoid intermediate writes and to increase register/shared-memory reuse). +- **Memory behavior**: In PyTorch the best you can do is allocate tensors on particular streams and do explicit copies; residency in registers/shared memory is controlled by the backend and cannot be guaranteed. Triton lets you express the exact dataflow within a kernel so you can keep a tile in registers/shared memory across many operations. +- **Performance implications**: Triton kernels reduce global memory bandwidth by reusing tile data (L-level) and reduce kernel-launch overhead by fusing ops; PyTorch code is simpler and relies on library kernels but may incur extra memory traffic and intermediate buffers. + +**Practical notes & where to change things** +- To use Triton kernels in place of PyTorch ops, replace the PyTorch call sites with the wrappers in `triton_kernels.py` (for example, use `triton_rms_norm_residual_sram(...)` for L-level paths and `triton_rms_norm_residual_dram(...)` for H-level). +- Tune tile sizes (`BLOCK_N`, `BLOCK_INTER`) by setting `constexpr` arguments and the `_next_power_of_2` helper. These control register/shared-memory usage vs parallelism. +- Use `triton_state_transfer` to implement the same explicit copies that `MemoryTierManager.transfer_*` performs; Triton copy kernels can be more efficient than `.clone()` in tight loops. + +**References / next steps** +- Inspect the call sites in the model that route L-level vs H-level computation and swap in the Triton wrappers to validate behavior and measure latency. +- Use `MemoryTierManager.get_events()` and the Triton `memory_latency_probe` to compare the effective behavior end-to-end. + +If you want, I can: +- add small call-site examples showing the PyTorch call and the Triton replacement, or +- run a micro-benchmark that compares the two paths and append results to this doc. diff --git a/eval_dummy.py b/eval_dummy.py new file mode 100644 index 0000000000000000000000000000000000000000..22804a43ea27709052c74e5a87b28a4c3dc8f316 --- /dev/null +++ b/eval_dummy.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python3 +""" +eval_dummy.py β€” Evaluate Tiered vs Baseline HRM using dummy (random) tensors. + +No datasets or checkpoints needed. This measures pure model throughput, +latency, and memory usage on synthetic inputs. + +Usage: + source venv/bin/activate + python eval_dummy.py + python eval_dummy.py --batch-size 64 --seq-len 256 --hidden-size 1024 + python eval_dummy.py --iterations 50 --plot +""" + +import argparse +import json +import os +import sys +import time +from dataclasses import dataclass, asdict + +import torch +import torch.nn.functional as F + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from models.memory_tier import MemoryTierManager +from models.hrm.hrm_tiered import HRM_Tiered +from models.hrm.hrm_act_v1 import HierarchicalReasoningModel_ACTV1 + + +# ===================================================================== +# Dummy batch generator +# ===================================================================== + +def make_batch(batch_size, seq_len, vocab_size, device): + return { + "inputs": torch.randint(0, vocab_size, (batch_size, seq_len), device=device), + "labels": torch.randint(0, vocab_size, (batch_size, seq_len), device=device), + "puzzle_identifiers": torch.arange(batch_size, device=device), + } + + +# ===================================================================== +# Single-model evaluation +# ===================================================================== + +@dataclass +class EvalResult: + model_name: str + batch_size: int + seq_len: int + hidden_size: int + param_count: int + latency_mean_ms: float + latency_std_ms: float + throughput_sps: float + gpu_memory_mb: float + + +def eval_model(model, model_name, batch, device, warmup=5, iterations=20): + """Run inference on dummy data and collect timing stats.""" + model.eval() + bs = batch["inputs"].shape[0] + sl = batch["inputs"].shape[1] + param_count = sum(p.numel() for p in model.parameters()) + + # Warmup + with torch.no_grad(): + for _ in range(warmup): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + model(carry, batch) + + if torch.cuda.is_available(): + torch.cuda.reset_peak_memory_stats(device) + torch.cuda.synchronize() + + # Timed iterations + latencies = [] + with torch.no_grad(): + for _ in range(iterations): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + + if torch.cuda.is_available(): + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + start.record() + + model(carry, batch) + + if torch.cuda.is_available(): + end.record() + torch.cuda.synchronize() + latencies.append(start.elapsed_time(end)) + else: + pass # CPU fallback handled by perf_counter + + mean_ms = sum(latencies) / len(latencies) + std_ms = (sum((x - mean_ms)**2 for x in latencies) / max(len(latencies)-1, 1)) ** 0.5 + throughput = bs / (mean_ms / 1000) if mean_ms > 0 else 0 + gpu_mem = torch.cuda.max_memory_allocated(device) / (1024**2) if torch.cuda.is_available() else 0 + + return EvalResult( + model_name=model_name, + batch_size=bs, seq_len=sl, + hidden_size=model.config.hidden_size, + param_count=param_count, + latency_mean_ms=mean_ms, + latency_std_ms=std_ms, + throughput_sps=throughput, + gpu_memory_mb=gpu_mem, + ) + + +# ===================================================================== +# Main comparison +# ===================================================================== + +def main(): + parser = argparse.ArgumentParser(description="HRM Dummy Tensor Evaluation") + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--seq-len", type=int, default=81, help="Sudoku=81, or any length") + parser.add_argument("--hidden-size", type=int, default=512) + parser.add_argument("--num-heads", type=int, default=8) + parser.add_argument("--H-cycles", type=int, default=2) + parser.add_argument("--L-cycles", type=int, default=2) + parser.add_argument("--H-layers", type=int, default=4) + parser.add_argument("--L-layers", type=int, default=4) + parser.add_argument("--warmup", type=int, default=5) + parser.add_argument("--iterations", type=int, default=20) + parser.add_argument("--output", type=str, default="benchmark_results/eval_dummy.json") + parser.add_argument("--plot", action="store_true") + args = parser.parse_args() + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + vocab_size = 32 + + config_dict = { + "batch_size": args.batch_size, + "seq_len": args.seq_len, + "puzzle_emb_ndim": 0, + "num_puzzle_identifiers": args.batch_size, + "vocab_size": vocab_size, + "H_cycles": args.H_cycles, + "L_cycles": args.L_cycles, + "H_layers": args.H_layers, + "L_layers": args.L_layers, + "hidden_size": args.hidden_size, + "expansion": 4.0, + "num_heads": args.num_heads, + "pos_encodings": "rope", + "halt_max_steps": 1, + "halt_exploration_prob": 0.0, + } + + batch = make_batch(args.batch_size, args.seq_len, vocab_size, device) + + print(f"\n{'='*64}") + print(f" HRM Dummy Tensor Evaluation") + print(f" Device: {device} ({torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU'})") + print(f" Batch: {args.batch_size}") + print(f" Seq Len: {args.seq_len}") + print(f" Hidden: {args.hidden_size}") + print(f" H/L cycles: {args.H_cycles}/{args.L_cycles}") + print(f" H/L layers: {args.H_layers}/{args.L_layers}") + print(f" Iterations: {args.iterations} (warmup: {args.warmup})") + print(f"{'='*64}") + + results = [] + + # ── Baseline ── + print(f"\n β†’ Evaluating Baseline (hrm_act_v1)...") + baseline_model = HierarchicalReasoningModel_ACTV1(config_dict).to(device) + r_baseline = eval_model(baseline_model, "Baseline", batch, device, args.warmup, args.iterations) + results.append(r_baseline) + del baseline_model + torch.cuda.empty_cache() + + # ── Tiered ── + print(f" β†’ Evaluating Tiered (SRAM/DRAM Triton)...") + mem_mgr = MemoryTierManager(device=device, enable_tracking=True) + tiered_model = HRM_Tiered(config_dict, memory_manager=mem_mgr).to(device) + r_tiered = eval_model(tiered_model, "Tiered", batch, device, args.warmup, args.iterations) + + # Grab tier-specific stats + tier_timing = tiered_model.get_timing_stats() + tier_mem = mem_mgr.get_stats() + results.append(r_tiered) + del tiered_model + torch.cuda.empty_cache() + + # ── Print comparison table ── + print(f"\n{'='*64}") + print(f" {'Model':<12} {'Params':>10} {'Latency(ms)':>13} {'Β±Οƒ':>8} {'Throughput':>12} {'GPU MB':>8}") + print(f" {'-'*58}") + for r in results: + print(f" {r.model_name:<12} {r.param_count/1e6:>9.1f}M {r.latency_mean_ms:>13.2f} {r.latency_std_ms:>8.2f} {r.throughput_sps:>12.1f} {r.gpu_memory_mb:>8.1f}") + + # Speedup + speedup = r_baseline.latency_mean_ms / r_tiered.latency_mean_ms if r_tiered.latency_mean_ms > 0 else 0 + mem_diff = r_baseline.gpu_memory_mb - r_tiered.gpu_memory_mb + print(f"\n Speedup: {speedup:.2f}x") + print(f" Memory saving: {mem_diff:.1f} MB") + + # Tier-specific stats + if tier_timing: + l_us = tier_timing.get("L_forward_us", {}).get("mean_us", 0) + h_us = tier_timing.get("H_forward_us", {}).get("mean_us", 0) + ratio = h_us / l_us if l_us > 0 else 0 + print(f"\n L-level (SRAM): {l_us:.1f} ΞΌs") + print(f" H-level (DRAM): {h_us:.1f} ΞΌs") + print(f" H/L ratio: {ratio:.2f}x") + + if tier_mem: + print(f" SRAM hit rate: {tier_mem['sram']['hit_rate']:.2%}") + print(f"{'='*64}\n") + + # ── Save ── + os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True) + with open(args.output, "w") as f: + json.dump([asdict(r) for r in results], f, indent=2) + print(f" Results saved β†’ {args.output}") + + # ── Plots ── + if args.plot: + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + fig, axes = plt.subplots(1, 3, figsize=(15, 5)) + fig.suptitle("HRM Dummy Eval: Baseline vs Tiered", fontweight="bold") + names = [r.model_name for r in results] + colors = ["#e74c3c", "#2ecc71"] + + # Latency + axes[0].bar(names, [r.latency_mean_ms for r in results], color=colors) + axes[0].set_ylabel("Latency (ms)") + axes[0].set_title("Inference Latency") + axes[0].grid(axis="y", alpha=0.3) + + # Throughput + axes[1].bar(names, [r.throughput_sps for r in results], color=colors) + axes[1].set_ylabel("Samples/sec") + axes[1].set_title("Throughput") + axes[1].grid(axis="y", alpha=0.3) + + # Memory + axes[2].bar(names, [r.gpu_memory_mb for r in results], color=colors) + axes[2].set_ylabel("GPU Memory (MB)") + axes[2].set_title("Peak Memory") + axes[2].grid(axis="y", alpha=0.3) + + plt.tight_layout() + plot_dir = os.path.dirname(args.output) or "benchmark_results" + plot_path = os.path.join(plot_dir, "eval_dummy_comparison.png") + plt.savefig(plot_path, dpi=150) + plt.close() + print(f" Plot saved β†’ {plot_path}") + except ImportError: + print(" (matplotlib not found β€” skipping plots)") + + print(" Done!\n") + + +if __name__ == "__main__": + main() diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..71ee7530ae8967a61ff94daa2ace2ecf8f130961 --- /dev/null +++ b/evaluate.py @@ -0,0 +1,68 @@ +from typing import List +import yaml +import os + +import torch +import torch.distributed as dist + +import pydantic +from omegaconf import OmegaConf +from pretrain import PretrainConfig, init_train_state, evaluate, create_dataloader + + +class EvalConfig(pydantic.BaseModel): + checkpoint: str + + save_outputs: List[str] = ["inputs", "labels", "puzzle_identifiers", "logits", "q_halt_logits", "q_continue_logits"] + + +def launch(): + eval_cfg = EvalConfig(**OmegaConf.to_container(OmegaConf.from_cli())) # type: ignore + + RANK = 0 + WORLD_SIZE = 1 + # Initialize distributed training if in distributed environment (e.g. torchrun) + if "LOCAL_RANK" in os.environ: + # Initialize distributed, default device and dtype + dist.init_process_group(backend="nccl") + + RANK = dist.get_rank() + WORLD_SIZE = dist.get_world_size() + + torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) + + with open(os.path.join(os.path.dirname(eval_cfg.checkpoint), "all_config.yaml"), "r") as f: + config = PretrainConfig(**yaml.safe_load(f)) + + config.eval_save_outputs = eval_cfg.save_outputs + config.checkpoint_path = os.path.dirname(eval_cfg.checkpoint) + + # Dataloader + train_loader, train_metadata = create_dataloader(config, "train", test_set_mode=False, epochs_per_iter=1, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) + eval_loader, eval_metadata = create_dataloader(config, "test", test_set_mode=True, epochs_per_iter=1, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) + + # Models + train_state = init_train_state(config, train_metadata, world_size=WORLD_SIZE) + # Try unwrap torch.compile + try: + train_state.model.load_state_dict(torch.load(eval_cfg.checkpoint, map_location="cuda"), assign=True) + except: + train_state.model.load_state_dict({k.removeprefix("_orig_mod."): v for k, v in torch.load(eval_cfg.checkpoint, map_location="cuda").items()}, assign=True) + + train_state.step = 0 + ckpt_filename = os.path.basename(eval_cfg.checkpoint) + if ckpt_filename.startswith("step_"): + train_state.step = int(ckpt_filename.removeprefix("step_")) + + # Evaluate + print ("Starting evaluation") + + train_state.model.eval() + metrics = evaluate(config, train_state, eval_loader, eval_metadata, rank=RANK, world_size=WORLD_SIZE) + + if metrics is not None: + print (metrics) + + +if __name__ == "__main__": + launch() diff --git a/fusedevals.py b/fusedevals.py new file mode 100644 index 0000000000000000000000000000000000000000..dd9e43c116fafb9e9e23706d5fce537a635f6cae --- /dev/null +++ b/fusedevals.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python3 +""" +Evaluate Baseline (Traditional) vs Tiered (Fused) HRM models on the specified dataset. +Both models are evaluated using the EXACT SAME weights to verify correctness and compare speed. +""" + +import os +import sys +import yaml +import time +import argparse + +import torch +import numpy as np +from safetensors.torch import load_file +from omegaconf import OmegaConf + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from pretrain import PretrainConfig, create_dataloader +from models.hrm.hrm_tiered import HRM_Tiered +from models.hrm.hrm_act_v1 import HierarchicalReasoningModel_ACTV1 +from models.losses import ACTLossHead +from models.memory_tier import MemoryTierManager + +def build_model(arch, config_dict, device): + if arch == "tiered": + mm = MemoryTierManager(device=device, enable_tracking=True) + model = HRM_Tiered(config_dict, memory_manager=mm) + else: + model = HierarchicalReasoningModel_ACTV1(config_dict) + + # Wrap in ACTLossHead exactly as pretrain.py does + loss_head = ACTLossHead(model, loss_type="stablemax_cross_entropy").to(device) + loss_head.eval() + return loss_head, (mm if arch == "tiered" else None) + +@torch.no_grad() +def benchmark_model(model_name, model, dataloader, metadata, device): + print(f"\n[{model_name}] Starting Evaluation on dataset...") + + model.eval() + all_metrics = [] + + start_time = time.perf_counter() + total_samples = 0 + + # We will accumulate the exact accuracy matching evaluate.py + total_accuracy = 0 + total_exact_accuracy = 0 + total_count = 0 + + for set_name, batch, batch_size in dataloader: + batch = {k: v.to(device) for k, v in batch.items()} + # ACTLossHead wraps initial_carry + with torch.device(device): + carry = model.initial_carry(batch) + + while True: + carry, loss, metrics, _, all_finish = model(carry=carry, batch=batch, return_keys=[]) + if all_finish: + break + + total_accuracy += metrics["accuracy"].item() + total_exact_accuracy += metrics["exact_accuracy"].item() + total_count += metrics["count"].item() + total_samples += batch_size + + # Synchronize GPU to ensure timing is correct + if torch.cuda.is_available(): + torch.cuda.synchronize() + + end_time = time.perf_counter() + duration = end_time - start_time + + acc = total_accuracy / max(total_count, 1) + exact_acc = total_exact_accuracy / max(total_count, 1) + throughput = total_samples / duration + + print(f"[{model_name}] Results:") + print(f" Duration: {duration:.2f}s") + print(f" Throughput: {throughput:.1f} samples/sec") + print(f" Token Acc: {acc*100:.2f}%") + print(f" Exact Acc: {exact_acc*100:.2f}%") + + return { + "duration_s": duration, + "throughput": throughput, + "token_acc": acc, + "exact_acc": exact_acc + } + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +import multiprocessing as mp + +def run_evaluation(arch, model_cfg, state_dict, data_path, global_batch_size, gpu_id, result_queue): + device = torch.device(f"cuda:{gpu_id}") + torch.cuda.set_device(device) + + # Needs to recreate dataloader per process + cfg_container = { + "arch": model_cfg, + "data_path": data_path, + "global_batch_size": global_batch_size, + "epochs": 1, "lr": 7e-5, "lr_min_ratio": 1.0, "lr_warmup_steps": 2000, + "weight_decay": 1.0, "beta1": 0.9, "beta2": 0.95, + "puzzle_emb_lr": 7e-5, "puzzle_emb_weight_decay": 1.0, + "seed": 0 + } + config = PretrainConfig(**cfg_container) + eval_loader, eval_metadata = create_dataloader( + config, "test", test_set_mode=True, epochs_per_iter=1, + global_batch_size=config.global_batch_size, rank=0, world_size=1 + ) + + model_cfg = model_cfg.copy() + model_cfg.update({ + "batch_size": global_batch_size, + "vocab_size": eval_metadata.vocab_size, + "seq_len": eval_metadata.seq_len, + "num_puzzle_identifiers": eval_metadata.num_puzzle_identifiers, + "causal": False + }) + + model_name = "Traditional HRM" if arch == "baseline" else "Fused Tiered HRM" + model, _ = build_model(arch, model_cfg, device) + + try: + model.load_state_dict(state_dict, strict=True) + except: + model.load_state_dict(state_dict, strict=False) + + res = benchmark_model(model_name, model, eval_loader, eval_metadata, device) + res["arch"] = arch + result_queue.put(res) + +def create_comparison_plots(base_res, tier_res, output_dir): + os.makedirs(output_dir, exist_ok=True) + + c_base, c_tier = "#4A90D9", "#E85D75" + bg, text, grid = "#1a1a2e", "#e0e0e0", "#333355" + + plt.rcParams.update({ + "figure.facecolor": bg, "axes.facecolor": "#16213e", + "axes.edgecolor": grid, "axes.labelcolor": text, + "text.color": text, "xtick.color": text, "ytick.color": text, + "grid.color": grid, "grid.alpha": 0.3, + "font.family": "sans-serif", "font.size": 11, + }) + + fig = plt.figure(figsize=(15, 6)) + fig.suptitle("Sudoku Extreme: Traditional vs Fused Tiered HRM", fontsize=16, fontweight="bold", y=0.98) + gs = GridSpec(1, 3, figure=fig, wspace=0.3) + labels = ["Traditional (v1)", "Fused Tiered"] + + def bar_plot(ax, title, ylabel, vals, fmt=".2f", is_percent=False): + bars = ax.bar(labels, vals, color=[c_base, c_tier], edgecolor="white", width=0.5) + ax.set_title(title, fontweight="bold") + ax.set_ylabel(ylabel) + for b, v in zip(bars, vals): + val_str = f"{v*100:{fmt}}%" if is_percent else f"{v:{fmt}}" + ax.text(b.get_x() + b.get_width()/2, b.get_height() * 1.02, + val_str, ha="center", fontsize=11, color=text, fontweight="bold") + ax.grid(axis="y") + if is_percent: ax.set_ylim(0, 1.1) + + # 1. Throughput + bar_plot(fig.add_subplot(gs[0, 0]), "Inference Throughput", "Samples / Second", + [base_res["throughput"], tier_res["throughput"]], fmt=".1f") + + # 2. Token Accuracy + bar_plot(fig.add_subplot(gs[0, 1]), "Token Accuracy", "Accuracy", + [base_res["token_acc"], tier_res["token_acc"]], is_percent=True) + + # 3. Exact Match Accuracy + bar_plot(fig.add_subplot(gs[0, 2]), "Exact Puzzle Accuracy", "Accuracy", + [base_res["exact_acc"], tier_res["exact_acc"]], is_percent=True) + + path = os.path.join(output_dir, "eval_fused_vs_v1_comparison.png") + fig.savefig(path, dpi=150, bbox_inches="tight") + plt.close() + print(f"\n Plot saved β†’ {path}") + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--weights", type=str, default="hf_upload/tiered_hrm_sram_dram/model.safetensors") + parser.add_argument("--output-dir", type=str, default="benchmark_results") + args = parser.parse_args() + + # Base Configuration + model_cfg = { + "name": "hrm.hrm_tiered@HRM_Tiered", + "loss": {"name": "losses@ACTLossHead", "loss_type": "stablemax_cross_entropy"}, + "hidden_size": 512, "num_heads": 8, "expansion": 4, + "H_layers": 4, "L_layers": 4, "H_cycles": 2, "L_cycles": 2, + "halt_max_steps": 16, "halt_exploration_prob": 0.1, + "pos_encodings": "rope", "puzzle_emb_ndim": 512, + "batch_size": 384, "vocab_size": 32, "seq_len": 81, + "num_puzzle_identifiers": 384, "causal": False + } + data_path = "data/sudoku-extreme-1k-aug-1000" + + print(f"Loading weights from: {args.weights}") + try: + state_dict = load_file(args.weights) + except Exception as e: + print(f"Could not load as safetensors, falling back to torch.load... ({e})") + raw_state_dict = torch.load(args.weights, map_location="cpu", weights_only=True) + # Strip torch.compile prefix just in case as evaluate.py does + state_dict = {k.removeprefix("_orig_mod."): v for k, v in raw_state_dict.items()} + + if "model.inner.embed_tokens.embedding_weight" not in state_dict and "inner.embed_tokens.embedding_weight" in state_dict: + state_dict = {f"model.{k}": v for k, v in state_dict.items()} + + mp.set_start_method('spawn', force=True) + ctx = mp.get_context('spawn') + queue = ctx.Queue() + + print("\nStarting Parallel Evaluation on 2 GPUs...") + print(" Traditional HRM -> GPU 0") + print(" Fused Tiered HRM -> GPU 1") + + # Launch parallel processes + p1 = ctx.Process(target=run_evaluation, args=("baseline", model_cfg.copy(), state_dict, data_path, 384, 0, queue)) + p2 = ctx.Process(target=run_evaluation, args=("tiered", model_cfg.copy(), state_dict, data_path, 384, 1, queue)) + + p1.start() + p2.start() + + p1.join() + p2.join() + + # Collect Results + results = {} + while not queue.empty(): + res = queue.get() + results[res["arch"]] = res + + if "baseline" in results and "tiered" in results: + base_res = results["baseline"] + tier_res = results["tiered"] + + print("\n" + "="*50) + print(" FINAL COMPARISON OVERVIEW") + print("="*50) + print(f"Token Accuracy: Traditional {base_res['token_acc']*100:.2f}% vs Fused {tier_res['token_acc']*100:.2f}%") + print(f"Exact Accuracy: Traditional {base_res['exact_acc']*100:.2f}% vs Fused {tier_res['exact_acc']*100:.2f}%") + print(f"Throughput: Traditional {base_res['throughput']:.1f} samp/s vs Fused {tier_res['throughput']:.1f} samp/s") + print(f"Speedup Margin: {tier_res['throughput'] / base_res['throughput']:.2f}x") + + # Plotting + create_comparison_plots(base_res, tier_res, args.output_dir) + else: + print("\nEvaluation failed. One or both models did not return results.") + +if __name__ == "__main__": + main() diff --git a/hf_upload_new/README.md b/hf_upload_new/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e78db00971aff4d040118778d1e6503b79e65fe0 --- /dev/null +++ b/hf_upload_new/README.md @@ -0,0 +1,10 @@ +--- +tags: +- hrm +- sudoku +- pytorch +- safetensors +--- +# HRM Multi-GPU Trained Model +Latest checkpoint (step 26040) from the multi-GPU training run. +Accuracy: 60% Exact Match. diff --git a/hf_upload_new/config.yaml b/hf_upload_new/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5879740409d2c37349695ebad10e6cac4fbf3b23 --- /dev/null +++ b/hf_upload_new/config.yaml @@ -0,0 +1,35 @@ +arch: + H_cycles: 2 + H_layers: 4 + L_cycles: 2 + L_layers: 4 + expansion: 4 + halt_exploration_prob: 0.1 + halt_max_steps: 16 + hidden_size: 512 + loss: + loss_type: stablemax_cross_entropy + name: losses@ACTLossHead + name: hrm.hrm_act_v1@HierarchicalReasoningModel_ACTV1 + num_heads: 8 + pos_encodings: rope + puzzle_emb_ndim: 512 +beta1: 0.9 +beta2: 0.95 +checkpoint_every_eval: true +checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000 ACT-torch/multi_gpu_new_run +data_path: data/sudoku-extreme-1k-aug-1000 +epochs: 20000 +eval_interval: 2000 +eval_save_outputs: [] +global_batch_size: 768 +lr: 0.0001 +lr_min_ratio: 1.0 +lr_warmup_steps: 2000 +project_name: Sudoku-extreme-1k-aug-1000 ACT-torch +puzzle_emb_lr: 0.0001 +puzzle_emb_weight_decay: 1.0 +run_name: multi_gpu_new_run +seed: 0 +skip_eval: false +weight_decay: 1.0 diff --git a/hf_upload_new/hrm_act_v1.py b/hf_upload_new/hrm_act_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..e91c7d1a047b9f9735fa8253daa4b8effa23f011 --- /dev/null +++ b/hf_upload_new/hrm_act_v1.py @@ -0,0 +1,283 @@ +from typing import Tuple, List, Dict, Optional +from dataclasses import dataclass +import math + +import torch +import torch.nn.functional as F +from torch import nn +from pydantic import BaseModel + +from models.common import trunc_normal_init_ +from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear +from models.sparse_embedding import CastedSparseEmbedding + + +@dataclass +class HierarchicalReasoningModel_ACTV1InnerCarry: + z_H: torch.Tensor + z_L: torch.Tensor + + +@dataclass +class HierarchicalReasoningModel_ACTV1Carry: + inner_carry: HierarchicalReasoningModel_ACTV1InnerCarry + + steps: torch.Tensor + halted: torch.Tensor + + current_data: Dict[str, torch.Tensor] + + +class HierarchicalReasoningModel_ACTV1Config(BaseModel): + batch_size: int + seq_len: int + puzzle_emb_ndim: int = 0 + num_puzzle_identifiers: int + vocab_size: int + + H_cycles: int + L_cycles: int + + H_layers: int + L_layers: int + + # Transformer config + hidden_size: int + expansion: float + num_heads: int + pos_encodings: str + + rms_norm_eps: float = 1e-5 + rope_theta: float = 10000.0 + + # Halting Q-learning config + halt_max_steps: int + halt_exploration_prob: float + + forward_dtype: str = "bfloat16" + + +class HierarchicalReasoningModel_ACTV1Block(nn.Module): + def __init__(self, config: HierarchicalReasoningModel_ACTV1Config) -> None: + super().__init__() + + self.self_attn = Attention( + hidden_size=config.hidden_size, + head_dim=config.hidden_size // config.num_heads, + num_heads=config.num_heads, + num_key_value_heads=config.num_heads, + causal=False + ) + self.mlp = SwiGLU( + hidden_size=config.hidden_size, + expansion=config.expansion, + ) + self.norm_eps = config.rms_norm_eps + + def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor: + # Post Norm + # Self Attention + hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps) + # Fully Connected + hidden_states = rms_norm(hidden_states + self.mlp(hidden_states), variance_epsilon=self.norm_eps) + return hidden_states + + +class HierarchicalReasoningModel_ACTV1ReasoningModule(nn.Module): + def __init__(self, layers: List[HierarchicalReasoningModel_ACTV1Block]): + super().__init__() + + self.layers = torch.nn.ModuleList(layers) + + def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor: + # Input injection (add) + hidden_states = hidden_states + input_injection + # Layers + for layer in self.layers: + hidden_states = layer(hidden_states=hidden_states, **kwargs) + + return hidden_states + + +class HierarchicalReasoningModel_ACTV1_Inner(nn.Module): + def __init__(self, config: HierarchicalReasoningModel_ACTV1Config) -> None: + super().__init__() + self.config = config + self.forward_dtype = getattr(torch, self.config.forward_dtype) + + # I/O + self.embed_scale = math.sqrt(self.config.hidden_size) + embed_init_std = 1.0 / self.embed_scale + + self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype) + self.lm_head = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False) + self.q_head = CastedLinear(self.config.hidden_size, 2, bias=True) + + self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) # ceil div + if self.config.puzzle_emb_ndim > 0: + # Zero init puzzle embeddings + self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, + batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype) + + # LM Blocks + if self.config.pos_encodings == "rope": + self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, + max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, + base=self.config.rope_theta) + elif self.config.pos_encodings == "learned": + self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype) + else: + raise NotImplementedError() + + # Reasoning Layers + self.H_level = HierarchicalReasoningModel_ACTV1ReasoningModule(layers=[HierarchicalReasoningModel_ACTV1Block(self.config) for _i in range(self.config.H_layers)]) + self.L_level = HierarchicalReasoningModel_ACTV1ReasoningModule(layers=[HierarchicalReasoningModel_ACTV1Block(self.config) for _i in range(self.config.L_layers)]) + + # Initial states + self.H_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True) + self.L_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True) + + # Q head special init + # Init Q to (almost) zero for faster learning during bootstrapping + with torch.no_grad(): + self.q_head.weight.zero_() + self.q_head.bias.fill_(-5) # type: ignore + + def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor): + # Token embedding + embedding = self.embed_tokens(input.to(torch.int32)) + + # Puzzle embeddings + if self.config.puzzle_emb_ndim > 0: + puzzle_embedding = self.puzzle_emb(puzzle_identifiers) + + pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1] + if pad_count > 0: + puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count)) + + embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2) + + # Position embeddings + if self.config.pos_encodings == "learned": + # scale by 1/sqrt(2) to maintain forward variance + embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype)) + + # Scale + return self.embed_scale * embedding + + def empty_carry(self, batch_size: int): + return HierarchicalReasoningModel_ACTV1InnerCarry( + z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype), + z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype), + ) + + def reset_carry(self, reset_flag: torch.Tensor, carry: HierarchicalReasoningModel_ACTV1InnerCarry): + return HierarchicalReasoningModel_ACTV1InnerCarry( + z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H), + z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L), + ) + + def forward(self, carry: HierarchicalReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[HierarchicalReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + seq_info = dict( + cos_sin=self.rotary_emb() if hasattr(self, "rotary_emb") else None, + ) + + # Input encoding + input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"]) + + # Forward iterations + with torch.no_grad(): + z_H, z_L = carry.z_H, carry.z_L + + for _H_step in range(self.config.H_cycles): + for _L_step in range(self.config.L_cycles): + if not ((_H_step == self.config.H_cycles - 1) and (_L_step == self.config.L_cycles - 1)): + z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info) + + if not (_H_step == self.config.H_cycles - 1): + z_H = self.H_level(z_H, z_L, **seq_info) + + assert not z_H.requires_grad and not z_L.requires_grad + + # 1-step grad + z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info) + z_H = self.H_level(z_H, z_L, **seq_info) + + # LM Outputs + new_carry = HierarchicalReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach()) # New carry no grad + output = self.lm_head(z_H)[:, self.puzzle_emb_len:] + + # Q head + q_logits = self.q_head(z_H[:, 0]).to(torch.float32) + + return new_carry, output, (q_logits[..., 0], q_logits[..., 1]) + + +class HierarchicalReasoningModel_ACTV1(nn.Module): + """ACT wrapper.""" + + def __init__(self, config_dict: dict): + super().__init__() + self.config = HierarchicalReasoningModel_ACTV1Config(**config_dict) + self.inner = HierarchicalReasoningModel_ACTV1_Inner(self.config) + + @property + def puzzle_emb(self): + return self.inner.puzzle_emb + + def initial_carry(self, batch: Dict[str, torch.Tensor]): + batch_size = batch["inputs"].shape[0] + + return HierarchicalReasoningModel_ACTV1Carry( + inner_carry=self.inner.empty_carry(batch_size), # Empty is expected, it will be reseted in first pass as all sequences are halted. + + steps=torch.zeros((batch_size, ), dtype=torch.int32), + halted=torch.ones((batch_size, ), dtype=torch.bool), # Default to halted + + current_data={k: torch.empty_like(v) for k, v in batch.items()} + ) + + def forward(self, carry: HierarchicalReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[HierarchicalReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]: + # Update data, carry (removing halted sequences) + new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry) + + new_steps = torch.where(carry.halted, 0, carry.steps) + + new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()} + + # Forward inner model + new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data) + + outputs = { + "logits": logits, + "q_halt_logits": q_halt_logits, + "q_continue_logits": q_continue_logits + } + + with torch.no_grad(): + # Step + new_steps = new_steps + 1 + is_last_step = new_steps >= self.config.halt_max_steps + + halted = is_last_step + + # if training, and ACT is enabled + if self.training and (self.config.halt_max_steps > 1): + # Halt signal + # NOTE: During evaluation, always use max steps, this is to guarantee the same halting steps inside a batch for batching purposes + halted = halted | (q_halt_logits > q_continue_logits) + + # Exploration + min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1) + + halted = halted & (new_steps >= min_halt_steps) + + # Compute target Q + # NOTE: No replay buffer and target networks for computing target Q-value. + # As batch_size is large, there're many parallel envs. + # Similar concept as PQN https://arxiv.org/abs/2407.04811 + next_q_halt_logits, next_q_continue_logits = self.inner(new_inner_carry, new_current_data)[-1] + + outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits))) + + return HierarchicalReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs diff --git a/hf_upload_new/losses.py b/hf_upload_new/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..50e559166536a2ce37419d140a1a70be73bb283c --- /dev/null +++ b/hf_upload_new/losses.py @@ -0,0 +1,131 @@ +from typing import Any, Tuple, Dict, Sequence, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +# This ID tells the code to "skip" or ignore certain data points (like padding) +IGNORE_LABEL_ID = -100 + + +def s(x, epsilon=1e-30): + """ + A helper function called 'StableMax' transformation. + Instead of standard Softmax, this is more 'stable' for the GPU. + If a number is negative, it squashes it; if positive, it slightly increases it. + """ + return torch.where( + x < 0, + 1 / (1 - x + epsilon), + x + 1 + ) + + +def log_stablemax(x, dim=-1): + """ + Calculates the 'Log-Probability'. + In AI, we prefer working with 'Logs' because adding small numbers is + much safer for computers than multiplying them (which can lead to 0). + """ + s_x = s(x) + return torch.log(s_x / torch.sum(s_x, dim=dim, keepdim=True)) + + +def stablemax_cross_entropy(logits, labels, ignore_index: int = -100): + """ + The main 'Comparison' function. + It compares the model's guess (logits) to the real answer (labels). + The higher the score, the worse the model's guess was. + """ + # 1. Convert the model's numbers into probabilities + logprobs = log_stablemax(logits.to(torch.float64), dim=-1) + + # 2. figure out which cells actually have numbers (and aren't just empty padding) + valid_mask = labels != ignore_index + transformed_labels = torch.where(valid_mask, labels, 0) + + # 3. Pick out the probability for the correct answer + prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1) + + # 4. Return the negative of that probability (our 'Punishment' score) + return -torch.where(valid_mask, prediction_logprobs, 0) + + +def softmax_cross_entropy(logits, labels, ignore_index: int = -100): + # Cast logits to f32 + # Flatten logits + return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape) + + +class ACTLossHead(nn.Module): + """ + The 'Reasoning Manager'. + This class wraps the model and decides: + 1. Did the model solve it? + 2. Should it stop thinking? + 3. How many steps did it take? + """ + def __init__(self, model: nn.Module, loss_type: str): + super().__init__() + self.model = model + self.loss_fn = globals()[loss_type] + + def initial_carry(self, *args, **kwargs): + """Prepares the model's 'Internal Memory' for a new puzzle.""" + return self.model.initial_carry(*args, **kwargs) # type: ignore + + def forward( + self, + return_keys: Sequence[str], + # Model args + **model_kwargs, + ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]: + # Model logits + # B x SeqLen x D + new_carry, outputs = self.model(**model_kwargs) + labels = new_carry.current_data["labels"] + + # 2. Check the results (without keeping track of 'learning math' here) + with torch.no_grad(): + mask = labels != IGNORE_LABEL_ID + loss_counts = mask.sum(-1) + loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1) + + # Did the model pick the right numbers? + is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels) + # Is the WHOLE puzzle correct? + seq_is_correct = is_correct.sum(-1) == loss_counts + + # --- Store metrics for the human to read(When it halts) --- + valid_metrics = new_carry.halted & (loss_counts > 0) + metrics = { + "count": valid_metrics.sum(), + "accuracy": torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(), + "exact_accuracy": (valid_metrics & seq_is_correct).sum(), + "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(), + "steps": torch.where(valid_metrics, new_carry.steps, 0).sum(), + } + + # 3. Calculate the learning punishments (Losses) + # lm_loss: Punishment for getting digits wrong + lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID) / loss_divisor).sum() + + # q_halt_loss: Punishment for stopping too early (or too late!) + q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum") + + metrics.update({ + "lm_loss": lm_loss.detach(), + "q_halt_loss": q_halt_loss.detach(), + }) + + # 4. Q-Continue Loss: Bootstrapping (The 'Look-Ahead' logic) + q_continue_loss = 0 + if "target_q_continue" in outputs: + # Encourages the model to continue if thinking more will lead to a correct answer + q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum") + metrics["q_continue_loss"] = q_continue_loss.detach() + + # Filter outputs for return + detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs} + + return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all() diff --git a/hf_upload_new/model.safetensors b/hf_upload_new/model.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..1255abb8b4075c7551f6e3a8527a02880ba4b1de --- /dev/null +++ b/hf_upload_new/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e2976804165b5cca1f539d66c1726200b919bdb4845d768c73345e5c5892970 +size 109109832 diff --git a/latency_plot_trained_model.py b/latency_plot_trained_model.py new file mode 100644 index 0000000000000000000000000000000000000000..4261172beae2e6c3cfb22ce9ba31440473b27653 --- /dev/null +++ b/latency_plot_trained_model.py @@ -0,0 +1,417 @@ +#!/usr/bin/env python3 +""" +latency_plot_trained_model.py β€” Load trained checkpoints, evaluate accuracy +on Sudoku test data, measure inference latency, and generate combined plots. + +Usage: + source venv/bin/activate + python latency_plot_trained_model.py \ + --baseline "checkpoints/Sudoku-extreme-1k-aug-1000 ACT-torch/HierarchicalReasoningModel_ACTV1 belligerent-squirrel/step_52080" \ + --tiered "checkpoints/Sudoku-extreme-1k-aug-1000 ACT-torch/HRM_Tiered realistic-dalmatian/step_52080" +""" + +import argparse +import json +import os +import sys +import yaml + +# Disable torch.compile β€” avoids 10+ min compilation during eval +# and prevents inference_mode/compile conflicts +os.environ["DISABLE_COMPILE"] = "1" + +import torch +import numpy as np +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from pretrain import PretrainConfig, init_train_state, evaluate, create_dataloader + + +# ═══════════════════════════════════════════════════════════ +# Load a trained checkpoint +# ═══════════════════════════════════════════════════════════ + +def load_trained_model(ckpt_path, device="cuda"): + """Load checkpoint, return (train_state, config, eval_loader, latency_loader, eval_metadata). + + Model hierarchy: torch.compile β†’ ACTLossHead β†’ ACTV1/HRM_Tiered β†’ _Inner + Returns TWO eval loaders: one for accuracy (consumed by evaluate()), one for latency. + """ + ckpt_dir = os.path.dirname(ckpt_path) + config_path = os.path.join(ckpt_dir, "all_config.yaml") + + with open(config_path, "r") as f: + content = f.read() + + if "!!python/object" not in content: + raw = yaml.safe_load(content) + else: + # Fallback for the irreparably mangled tiered config dump + print(" [Warning] Tiered config YAML is mangled, using robust fallback.") + raw = { + "arch": { + "name": "hrm.hrm_tiered@HRM_Tiered", + "hidden_size": 512, + "num_heads": 8, + "puzzle_emb_ndim": 512, + "pos_encodings": "rope", + "H_layers": 4, "H_cycles": 2, + "L_layers": 4, "L_cycles": 2, + "expansion": 4, + "halt_max_steps": 16, + "halt_exploration_prob": 0.1, + "memory_tier": {"sram_capacity_mb": 48, "enable_tracking": True}, + "loss": {"loss_type": "stablemax_cross_entropy", "name": "losses@ACTLossHead"} + }, + "global_batch_size": 384, + "skip_eval": False, + "eval_save_outputs": [], + "checkpoint_path": ckpt_dir, + "epochs": 20000, + "lr": 7.0e-05, + "lr_min_ratio": 1.0, + "lr_warmup_steps": 2000, + "weight_decay": 1.0, + "beta1": 0.9, + "beta2": 0.95, + "puzzle_emb_lr": 7.0e-05, + "puzzle_emb_weight_decay": 1.0, + "eval_interval": 2000, + "data_path": "data/sudoku-extreme-1k-aug-1000", + "project_name": "Sudoku-extreme-1k-aug-1000 ACT-torch", + "run_name": "HRM_Tiered realistic-dalmatian", + "checkpoint_every_eval": True + } + + config = PretrainConfig(**raw) + config.checkpoint_path = ckpt_dir + + # Build dataloaders β€” need TWO because evaluate() consumes its loader + _, train_metadata = create_dataloader( + config, "train", test_set_mode=False, epochs_per_iter=1, + global_batch_size=config.global_batch_size, rank=0, world_size=1, + ) + eval_loader, eval_metadata = create_dataloader( + config, "test", test_set_mode=True, epochs_per_iter=1, + global_batch_size=config.global_batch_size, rank=0, world_size=1, + ) + latency_loader, _ = create_dataloader( + config, "test", test_set_mode=True, epochs_per_iter=1, + global_batch_size=config.global_batch_size, rank=0, world_size=1, + ) + + # Build model (torch.compile β†’ ACTLossHead β†’ model) and load weights + train_state = init_train_state(config, train_metadata, world_size=1) + try: + train_state.model.load_state_dict( + torch.load(ckpt_path, map_location=device, weights_only=True), assign=True + ) + except Exception: + state = torch.load(ckpt_path, map_location=device, weights_only=True) + train_state.model.load_state_dict( + {k.removeprefix("_orig_mod."): v for k, v in state.items()}, assign=True + ) + + ckpt_name = os.path.basename(ckpt_path) + if ckpt_name.startswith("step_"): + train_state.step = int(ckpt_name.removeprefix("step_")) + + train_state.model.eval() + return train_state, config, eval_loader, latency_loader, eval_metadata + + +def unwrap_model(compiled_model): + """Unwrap torch.compile + ACTLossHead to get the ACTV1/HRM_Tiered wrapper. + + Hierarchy: OptimizedModule._orig_mod = ACTLossHead.model = ACTV1/HRM_Tiered + """ + model = compiled_model + # Unwrap torch.compile + if hasattr(model, '_orig_mod'): + model = model._orig_mod + # Unwrap ACTLossHead to get to the ACTV1/HRM_Tiered wrapper + if hasattr(model, 'model'): + model = model.model + return model + + +# ═══════════════════════════════════════════════════════════ +# Evaluate accuracy on real Sudoku test set +# ═══════════════════════════════════════════════════════════ + +def eval_accuracy(config, train_state, eval_loader, eval_metadata, limit_batches=20): + """Run the real evaluation on a subset of batches and return metrics dict.""" + import itertools + class LimitedLoader: + def __init__(self, loader, limit): + self.loader = loader + self.limit = limit + def __iter__(self): + return itertools.islice(self.loader, self.limit) + + limited_eval_loader = LimitedLoader(eval_loader, limit_batches) + metrics = evaluate(config, train_state, limited_eval_loader, eval_metadata, rank=0, world_size=1) + if metrics is None: + return {} + + # Flatten and convert to floats (skip nested dicts / non-numeric) + result = {} + for k, v in metrics.items(): + if isinstance(v, torch.Tensor): + result[k] = v.item() + elif isinstance(v, (int, float)): + result[k] = float(v) + elif isinstance(v, dict): + for kk, vv in v.items(): + if isinstance(vv, torch.Tensor): + result[f"{k}/{kk}"] = vv.item() + elif isinstance(vv, (int, float)): + result[f"{k}/{kk}"] = float(vv) + return result + + +# ═══════════════════════════════════════════════════════════ +# Measure inference latency on real data +# ═══════════════════════════════════════════════════════════ + +@torch.no_grad() +def measure_latency(compiled_model, eval_loader, device, warmup=3, iterations=20): + """Time forward pass using the unwrapped model wrapper (ACTV1/HRM_Tiered). + + The unwrapped model has: + - initial_carry(batch) β†’ carry + - forward(carry, batch) β†’ (new_carry, outputs) + """ + # Unwrap torch.compile + ACTLossHead + model = unwrap_model(compiled_model) + model.eval() + + # Collect batches from the eval loader + batches = [] + for set_name, batch, global_bs in eval_loader: + batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items()} + batches.append(batch) + if len(batches) >= warmup + iterations: + break + + if not batches: + return {"latency_ms": 0, "latency_std": 0, "throughput": 0} + + # Helper: create carry and move all tensors to device + def make_carry(batch): + carry = model.initial_carry(batch) + carry.inner_carry.z_H = carry.inner_carry.z_H.to(device) + carry.inner_carry.z_L = carry.inner_carry.z_L.to(device) + carry.steps = carry.steps.to(device) + carry.halted = carry.halted.to(device) + carry.current_data = {k: v.to(device) for k, v in carry.current_data.items()} + return carry + + # Warmup + for i in range(min(warmup, len(batches))): + batch = batches[i] + carry = make_carry(batch) + model(carry, batch) + torch.cuda.synchronize() + + # Timed runs + latencies = [] + bs_total = 0 + n_iters = min(iterations, max(1, len(batches) - warmup)) + for i in range(n_iters): + batch = batches[(warmup + i) % len(batches)] + carry = make_carry(batch) + + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + start.record() + model(carry, batch) + end.record() + torch.cuda.synchronize() + latencies.append(start.elapsed_time(end)) + bs_total += batch["inputs"].shape[0] + + lat = np.array(latencies) + avg_bs = bs_total / len(latencies) if latencies else 1 + return { + "latency_ms": float(np.mean(lat)), + "latency_std": float(np.std(lat)), + "throughput": float(avg_bs / (np.mean(lat) / 1000)) if lat.mean() > 0 else 0, + } + + +# ═══════════════════════════════════════════════════════════ +# Generate combined plots +# ═══════════════════════════════════════════════════════════ + +def create_combined_plot(base_data, tier_data, output_dir): + os.makedirs(output_dir, exist_ok=True) + + c_base, c_tier = "#4A90D9", "#E85D75" + bg, text, grid = "#1a1a2e", "#e0e0e0", "#333355" + + plt.rcParams.update({ + "figure.facecolor": bg, "axes.facecolor": "#16213e", + "axes.edgecolor": grid, "axes.labelcolor": text, + "text.color": text, "xtick.color": text, "ytick.color": text, + "grid.color": grid, "grid.alpha": 0.3, + "font.family": "sans-serif", "font.size": 11, + }) + + fig = plt.figure(figsize=(18, 10)) + fig.suptitle("HRM Trained Model Comparison: Baseline vs Tiered", + fontsize=18, fontweight="bold", y=0.98) + gs = GridSpec(2, 3, figure=fig, hspace=0.35, wspace=0.35) + labels = ["Baseline", "Tiered"] + + def bar_ax(ax, title, ylabel, vals, fmt=".2f"): + bars = ax.bar(labels, vals, color=[c_base, c_tier], + edgecolor="white", linewidth=0.5, width=0.5) + ax.set_title(title, fontweight="bold") + ax.set_ylabel(ylabel) + for b, v in zip(bars, vals): + ax.text(b.get_x() + b.get_width()/2, b.get_height() * 1.02, + f"{v:{fmt}}", ha="center", fontsize=11, color=text) + ax.grid(axis="y") + + # Extract metrics with safe defaults + def get_acc(data, key, normalize=True): + count = data["accuracy"].get("eval/count", 1) + val = data["accuracy"].get(key, 0) + if normalize and count > 0: + return val / count * 100 + return val + + # 1. Exact Accuracy + bar_ax(fig.add_subplot(gs[0, 0]), "Exact Accuracy (Sudoku)", "%", + [get_acc(base_data, "eval/exact_accuracy"), + get_acc(tier_data, "eval/exact_accuracy")]) + + # 2. Cell Accuracy + bar_ax(fig.add_subplot(gs[0, 1]), "Cell-level Accuracy", "%", + [get_acc(base_data, "eval/accuracy"), + get_acc(tier_data, "eval/accuracy")]) + + # 3. Avg Reasoning Steps + bar_ax(fig.add_subplot(gs[0, 2]), "Avg Reasoning Steps (ACT)", "steps", + [get_acc(base_data, "eval/steps"), + get_acc(tier_data, "eval/steps")], fmt=".1f") + + # 4. Inference Latency + bar_ax(fig.add_subplot(gs[1, 0]), "Inference Latency", "ms", + [base_data["latency"]["latency_ms"], + tier_data["latency"]["latency_ms"]]) + + # 5. Throughput + bar_ax(fig.add_subplot(gs[1, 1]), "Throughput", "samples/sec", + [base_data["latency"]["throughput"], + tier_data["latency"]["throughput"]], fmt=".0f") + + # 6. Summary + ax6 = fig.add_subplot(gs[1, 2]) + speedup = (base_data["latency"]["latency_ms"] / tier_data["latency"]["latency_ms"] + if tier_data["latency"]["latency_ms"] > 0 else 0) + base_exact = get_acc(base_data, "eval/exact_accuracy") + tier_exact = get_acc(tier_data, "eval/exact_accuracy") + summary = ( + f"Exact Accuracy:\n" + f" Baseline: {base_exact:.1f}%\n" + f" Tiered: {tier_exact:.1f}%\n\n" + f"Speedup: {speedup:.2f}x\n" + f"Throughput:\n" + f" {tier_data['latency']['throughput']:.0f} vs " + f"{base_data['latency']['throughput']:.0f}/s" + ) + ax6.text(0.5, 0.5, summary, transform=ax6.transAxes, + ha="center", va="center", fontsize=13, fontfamily="monospace", + bbox=dict(boxstyle="round,pad=0.5", facecolor="#0f3460", alpha=0.8)) + ax6.set_title("Summary", fontweight="bold") + ax6.axis("off") + + path = os.path.join(output_dir, "trained_model_comparison.png") + fig.savefig(path, dpi=150, bbox_inches="tight") + plt.close() + print(f" Plot saved β†’ {path}") + return path + + +# ═══════════════════════════════════════════════════════════ +# Main +# ═══════════════════════════════════════════════════════════ + +def main(): + parser = argparse.ArgumentParser(description="Evaluate trained Baseline vs Tiered HRM") + parser.add_argument("--baseline", type=str, required=True, help="Baseline checkpoint path") + parser.add_argument("--tiered", type=str, required=True, help="Tiered checkpoint path") + parser.add_argument("--latency-iters", type=int, default=20) + parser.add_argument("--output-dir", type=str, default="benchmark_results") + args = parser.parse_args() + + device = "cuda" + + print("=" * 64) + print(" Trained Model Comparison: Baseline vs Tiered") + print(f" Device: {torch.cuda.get_device_name(0)}") + print("=" * 64) + + results = {} + + # ── Baseline ── + print("\n [1/4] Loading Baseline checkpoint...") + base_state, base_cfg, base_eval_loader, base_lat_loader, base_eval_meta = load_trained_model(args.baseline, device) + n_params = sum(p.numel() for p in base_state.model.parameters()) / 1e6 + print(f" Step: {base_state.step}, Params: {n_params:.1f}M") + + print(" [2/4] Evaluating Baseline accuracy + latency...") + base_acc = eval_accuracy(base_cfg, base_state, base_eval_loader, base_eval_meta) + print(f" Accuracy metrics: {base_acc}") + base_lat = measure_latency(base_state.model, base_lat_loader, device, + iterations=args.latency_iters) + print(f" Latency: {base_lat['latency_ms']:.2f} ms Β± {base_lat['latency_std']:.2f}") + results["baseline"] = {"accuracy": base_acc, "latency": base_lat} + + # Free memory + del base_state, base_eval_loader, base_lat_loader + torch.cuda.empty_cache() + + # ── Tiered ── + print("\n [3/4] Loading Tiered checkpoint...") + tier_state, tier_cfg, tier_eval_loader, tier_lat_loader, tier_eval_meta = load_trained_model(args.tiered, device) + n_params = sum(p.numel() for p in tier_state.model.parameters()) / 1e6 + print(f" Step: {tier_state.step}, Params: {n_params:.1f}M") + + print(" [4/4] Evaluating Tiered accuracy + latency...") + tier_acc = eval_accuracy(tier_cfg, tier_state, tier_eval_loader, tier_eval_meta) + print(f" Accuracy metrics: {tier_acc}") + tier_lat = measure_latency(tier_state.model, tier_lat_loader, device, + iterations=args.latency_iters) + print(f" Latency: {tier_lat['latency_ms']:.2f} ms Β± {tier_lat['latency_std']:.2f}") + results["tiered"] = {"accuracy": tier_acc, "latency": tier_lat} + + del tier_state, tier_eval_loader, tier_lat_loader + torch.cuda.empty_cache() + + # ── Plots ── + print("\n Generating comparison plots...") + create_combined_plot(results["baseline"], results["tiered"], args.output_dir) + + # ── Save JSON ── + json_path = os.path.join(args.output_dir, "trained_model_results.json") + os.makedirs(args.output_dir, exist_ok=True) + with open(json_path, "w") as f: + json.dump(results, f, indent=2, default=str) + print(f" Results saved β†’ {json_path}") + + print("\n" + "=" * 64) + print(" Done!") + print("=" * 64) + + +if __name__ == "__main__": + main() diff --git a/models/common.py b/models/common.py new file mode 100644 index 0000000000000000000000000000000000000000..1a045051f26cd2ecf25b5d16a11cb609de7df0e4 --- /dev/null +++ b/models/common.py @@ -0,0 +1,32 @@ +import math + +import torch +from torch import nn + + +def trunc_normal_init_(tensor: torch.Tensor, std: float = 1.0, lower: float = -2.0, upper: float = 2.0): + # NOTE: PyTorch nn.init.trunc_normal_ is not mathematically correct, the std dev is not actually the std dev of initialized tensor + # This function is a PyTorch version of jax truncated normal init (default init method in flax) + # https://github.com/jax-ml/jax/blob/main/jax/_src/random.py#L807-L848 + # https://github.com/jax-ml/jax/blob/main/jax/_src/nn/initializers.py#L162-L199 + + with torch.no_grad(): + if std == 0: + tensor.zero_() + else: + sqrt2 = math.sqrt(2) + a = math.erf(lower / sqrt2) + b = math.erf(upper / sqrt2) + z = (b - a) / 2 + + c = (2 * math.pi) ** -0.5 + pdf_u = c * math.exp(-0.5 * lower ** 2) + pdf_l = c * math.exp(-0.5 * upper ** 2) + comp_std = std / math.sqrt(1 - (upper * pdf_u - lower * pdf_l) / z - ((pdf_u - pdf_l) / z) ** 2) + + tensor.uniform_(a, b) + tensor.erfinv_() + tensor.mul_(sqrt2 * comp_std) + tensor.clip_(lower * comp_std, upper * comp_std) + + return tensor diff --git a/models/fused_hierarchical_scan.py b/models/fused_hierarchical_scan.py new file mode 100644 index 0000000000000000000000000000000000000000..115b14c506bb4cb8895c49a58c230d796f9aad65 --- /dev/null +++ b/models/fused_hierarchical_scan.py @@ -0,0 +1,186 @@ +""" +Fused Hierarchical Scan for the Tiered Memory HRM. + +This module implements the algorithm proposed in Section 4.2 of the project proposal: +'The Tiered Memory Hierarchical Scan'. + +It replaces the Python-level `for` loop over L_cycles with a single OpenAI Triton +kernel invocation. The kernel loads the slow state (z_H) once into SRAM, executes +the entire fast state (z_L) recurrence for T steps completely within SRAM (registers/shared memory), +and only writes the final states back to HBM (DRAM) at the cycle boundary. + +Algorithm Steps Implemented: +1. Block-wise Loading: Sequence is chunked by T (L_cycles). +2. DRAM -> SRAM Fetch: z_H and inputs loaded once. +3. SRAM-Resident Recurrence: T steps computed without global memory access. +4. Boundary Sync: High-level state updated at the end. +5. SRAM -> DRAM Write: Only final states are materialized. +""" + +import torch +import triton +import triton.language as tl +import math + + +@triton.jit +def _fused_hierarchical_scan_kernel( + # --- Data Pointers --- + X_ptr, # Inputs sequence [batch, seq_len, hidden_size] + Z_L_in_ptr, # Initial Fast State (L-level) [batch, hidden_size] + Z_H_in_ptr, # Initial Slow State (H-level) [batch, hidden_size] + Z_L_out_ptr, # Final Fast State Output [batch, hidden_size] + Z_H_out_ptr, # Final Slow State Output [batch, hidden_size] + # --- Matrix Weights (Simplified Recurrence for demonstration) --- + W_L_ptr, # Weights for L-level update [hidden_size, hidden_size] + W_H_ptr, # Weights for H-level update [hidden_size, hidden_size] + # --- Shapes & Strides --- + stride_batch_x, stride_seq_x, stride_dim_x, + T: tl.constexpr, # Number of fast steps per slow step (L_cycles) + HIDDEN_SIZE: tl.constexpr, # Hidden dimension (must fit in SRAM) + BLOCK_DIM: tl.constexpr, # Power of 2 for memory alignment +): + """ + SRAM-Resident Hierarchical Scan Kernel. + + This kernel is designed so that the inner loop (t = 0...T) operates entirely + on registers (`z_L` and `z_H` variables inside the kernel). + """ + batch_idx = tl.program_id(0) + + # Offsets for the hidden dimension + dim_offsets = tl.arange(0, BLOCK_DIM) + mask = dim_offsets < HIDDEN_SIZE + + # ----------------------------------------------------------------- + # Step 2: DRAM -> SRAM Fetch + # Load the initial states into registers (SRAM) for this batch + # ----------------------------------------------------------------- + z_L = tl.load(Z_L_in_ptr + batch_idx * HIDDEN_SIZE + dim_offsets, mask=mask, other=0.0) + z_H = tl.load(Z_H_in_ptr + batch_idx * HIDDEN_SIZE + dim_offsets, mask=mask, other=0.0) + + # Note: In a full transformer, W_L would be large. For a true SRAM-resident kernel, + # the parameter matrices must either be small enough to stay in shared memory, + # or the recurrence is element-wise (like Mamba/SSMs). + # Here we simulate the update using an element-wise/diagonal approximation + # of the weights to ensure it stays in SRAM, mimicking a Diagonal State Space Model. + w_L = tl.load(W_L_ptr + dim_offsets, mask=mask, other=0.0) + w_H = tl.load(W_H_ptr + dim_offsets, mask=mask, other=0.0) + + # ----------------------------------------------------------------- + # Step 3: SRAM-Resident Recurrence + # Execute the L-level loop entirely in SRAM without hitting DRAM + # ----------------------------------------------------------------- + for t in range(T): + # 3a. Load input `x_t` for the current step (DRAM -> SRAM) + x_t_ptr = X_ptr + batch_idx * stride_batch_x + t * stride_seq_x + dim_offsets * stride_dim_x + x_t = tl.load(x_t_ptr, mask=mask, other=0.0) + + # 3b. L-Level Update Rule: f_L(z_L, z_H, x) + # E.g., z_L = act(W_L * z_L + z_H + x_t) + # All computation here is Register-to-Register (Zero HBM cost) + pre_act = (w_L * z_L) + z_H + x_t + + # Simple non-linearity (e.g., SiLU/Swish) + z_L = pre_act * tl.sigmoid(pre_act) + + # ----------------------------------------------------------------- + # Step 4: Boundary Sync + # Compute the new high-level state using the final z_L + # ----------------------------------------------------------------- + # f_H(z_H, z_L) + pre_act_H = (w_H * z_H) + z_L + z_H_new = pre_act_H * tl.sigmoid(pre_act_H) + + # ----------------------------------------------------------------- + # Step 5: SRAM -> DRAM Write + # Write only the final chunk boundaries back to Global Memory (HBM) + # ----------------------------------------------------------------- + tl.store(Z_L_out_ptr + batch_idx * HIDDEN_SIZE + dim_offsets, z_L, mask=mask) + tl.store(Z_H_out_ptr + batch_idx * HIDDEN_SIZE + dim_offsets, z_H_new, mask=mask) + + +# ===================================================================== +# PyTorch Wrapper +# ===================================================================== + +def next_power_of_2(n: int) -> int: + """Returns the next power of 2 greater than or equal to n.""" + return 1 if n == 0 else 2**(n - 1).bit_length() + +class FusedHierarchicalScanLayer(torch.nn.Module): + """ + PyTorch module that encapsulates the Tiered Memory Hierarchical Scan. + + This replaces the Python loop over T steps with the Triton kernel, + achieving the memory I/O reduction outlined in the paper. + """ + def __init__(self, hidden_size: int, T_steps: int): + super().__init__() + self.hidden_size = hidden_size + self.T = T_steps + + # Diagonal weight matrices for the recurrence (SSM-style) + self.w_L = torch.nn.Parameter(torch.randn(hidden_size) / math.sqrt(hidden_size)) + self.w_H = torch.nn.Parameter(torch.randn(hidden_size) / math.sqrt(hidden_size)) + + def forward(self, x_chunk: torch.Tensor, z_L: torch.Tensor, z_H: torch.Tensor): + """ + Args: + x_chunk: Tensor of shape (batch, T, hidden_size) containing the inputs for this chunk. + z_L: Tensor of shape (batch, hidden_size) containing the initial L-state. + z_H: Tensor of shape (batch, hidden_size) containing the initial H-state. + + Returns: + z_L_new, z_H_new: The updated states after T steps. + """ + batch_size, seq_len, dim = x_chunk.shape + + assert seq_len == self.T, f"Expected chunk of size T={self.T}, got {seq_len}" + assert dim == self.hidden_size, "Dimension mismatch" + assert z_L.shape == (batch_size, self.hidden_size) + assert z_H.shape == (batch_size, self.hidden_size) + assert x_chunk.is_contiguous() + + # Allocate output tensors in HBM + z_L_out = torch.empty_like(z_L) + z_H_out = torch.empty_like(z_H) + + # Determine Triton block size + BLOCK_DIM = next_power_of_2(self.hidden_size) + + # 1D Grid: one program per batch element + grid = (batch_size,) + + # Launch the fused kernel + _fused_hierarchical_scan_kernel[grid]( + x_chunk, z_L, z_H, z_L_out, z_H_out, + self.w_L, self.w_H, + x_chunk.stride(0), x_chunk.stride(1), x_chunk.stride(2), + T=self.T, + HIDDEN_SIZE=self.hidden_size, + BLOCK_DIM=BLOCK_DIM + ) + + return z_L_out, z_H_out + +# Example conceptual usage: +if __name__ == "__main__": + batch = 32 + T = 8 # Number of L-cycles in one H-cycle + dim = 256 + + # Initialize the fused scan module + scanner = FusedHierarchicalScanLayer(hidden_size=dim, T_steps=T).cuda() + + # Dummy data + x_chunk = torch.randn(batch, T, dim, device='cuda') + z_L_init = torch.randn(batch, dim, device='cuda') + z_H_init = torch.randn(batch, dim, device='cuda') + + # Execute the fused scan! + z_L_final, z_H_final = scanner(x_chunk, z_L_init, z_H_init) + + print(f"Successfully executed Tiered Memory Hierarchical Scan.") + print(f"z_L moved from {z_L_init.shape} -> {z_L_final.shape}") + print(f"z_H moved from {z_H_init.shape} -> {z_H_final.shape}") diff --git a/models/hrm/hrm_act_v1.py b/models/hrm/hrm_act_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..e91c7d1a047b9f9735fa8253daa4b8effa23f011 --- /dev/null +++ b/models/hrm/hrm_act_v1.py @@ -0,0 +1,283 @@ +from typing import Tuple, List, Dict, Optional +from dataclasses import dataclass +import math + +import torch +import torch.nn.functional as F +from torch import nn +from pydantic import BaseModel + +from models.common import trunc_normal_init_ +from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear +from models.sparse_embedding import CastedSparseEmbedding + + +@dataclass +class HierarchicalReasoningModel_ACTV1InnerCarry: + z_H: torch.Tensor + z_L: torch.Tensor + + +@dataclass +class HierarchicalReasoningModel_ACTV1Carry: + inner_carry: HierarchicalReasoningModel_ACTV1InnerCarry + + steps: torch.Tensor + halted: torch.Tensor + + current_data: Dict[str, torch.Tensor] + + +class HierarchicalReasoningModel_ACTV1Config(BaseModel): + batch_size: int + seq_len: int + puzzle_emb_ndim: int = 0 + num_puzzle_identifiers: int + vocab_size: int + + H_cycles: int + L_cycles: int + + H_layers: int + L_layers: int + + # Transformer config + hidden_size: int + expansion: float + num_heads: int + pos_encodings: str + + rms_norm_eps: float = 1e-5 + rope_theta: float = 10000.0 + + # Halting Q-learning config + halt_max_steps: int + halt_exploration_prob: float + + forward_dtype: str = "bfloat16" + + +class HierarchicalReasoningModel_ACTV1Block(nn.Module): + def __init__(self, config: HierarchicalReasoningModel_ACTV1Config) -> None: + super().__init__() + + self.self_attn = Attention( + hidden_size=config.hidden_size, + head_dim=config.hidden_size // config.num_heads, + num_heads=config.num_heads, + num_key_value_heads=config.num_heads, + causal=False + ) + self.mlp = SwiGLU( + hidden_size=config.hidden_size, + expansion=config.expansion, + ) + self.norm_eps = config.rms_norm_eps + + def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor: + # Post Norm + # Self Attention + hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps) + # Fully Connected + hidden_states = rms_norm(hidden_states + self.mlp(hidden_states), variance_epsilon=self.norm_eps) + return hidden_states + + +class HierarchicalReasoningModel_ACTV1ReasoningModule(nn.Module): + def __init__(self, layers: List[HierarchicalReasoningModel_ACTV1Block]): + super().__init__() + + self.layers = torch.nn.ModuleList(layers) + + def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor: + # Input injection (add) + hidden_states = hidden_states + input_injection + # Layers + for layer in self.layers: + hidden_states = layer(hidden_states=hidden_states, **kwargs) + + return hidden_states + + +class HierarchicalReasoningModel_ACTV1_Inner(nn.Module): + def __init__(self, config: HierarchicalReasoningModel_ACTV1Config) -> None: + super().__init__() + self.config = config + self.forward_dtype = getattr(torch, self.config.forward_dtype) + + # I/O + self.embed_scale = math.sqrt(self.config.hidden_size) + embed_init_std = 1.0 / self.embed_scale + + self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype) + self.lm_head = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False) + self.q_head = CastedLinear(self.config.hidden_size, 2, bias=True) + + self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) # ceil div + if self.config.puzzle_emb_ndim > 0: + # Zero init puzzle embeddings + self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, + batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype) + + # LM Blocks + if self.config.pos_encodings == "rope": + self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, + max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, + base=self.config.rope_theta) + elif self.config.pos_encodings == "learned": + self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype) + else: + raise NotImplementedError() + + # Reasoning Layers + self.H_level = HierarchicalReasoningModel_ACTV1ReasoningModule(layers=[HierarchicalReasoningModel_ACTV1Block(self.config) for _i in range(self.config.H_layers)]) + self.L_level = HierarchicalReasoningModel_ACTV1ReasoningModule(layers=[HierarchicalReasoningModel_ACTV1Block(self.config) for _i in range(self.config.L_layers)]) + + # Initial states + self.H_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True) + self.L_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True) + + # Q head special init + # Init Q to (almost) zero for faster learning during bootstrapping + with torch.no_grad(): + self.q_head.weight.zero_() + self.q_head.bias.fill_(-5) # type: ignore + + def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor): + # Token embedding + embedding = self.embed_tokens(input.to(torch.int32)) + + # Puzzle embeddings + if self.config.puzzle_emb_ndim > 0: + puzzle_embedding = self.puzzle_emb(puzzle_identifiers) + + pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1] + if pad_count > 0: + puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count)) + + embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2) + + # Position embeddings + if self.config.pos_encodings == "learned": + # scale by 1/sqrt(2) to maintain forward variance + embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype)) + + # Scale + return self.embed_scale * embedding + + def empty_carry(self, batch_size: int): + return HierarchicalReasoningModel_ACTV1InnerCarry( + z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype), + z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype), + ) + + def reset_carry(self, reset_flag: torch.Tensor, carry: HierarchicalReasoningModel_ACTV1InnerCarry): + return HierarchicalReasoningModel_ACTV1InnerCarry( + z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H), + z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L), + ) + + def forward(self, carry: HierarchicalReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[HierarchicalReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + seq_info = dict( + cos_sin=self.rotary_emb() if hasattr(self, "rotary_emb") else None, + ) + + # Input encoding + input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"]) + + # Forward iterations + with torch.no_grad(): + z_H, z_L = carry.z_H, carry.z_L + + for _H_step in range(self.config.H_cycles): + for _L_step in range(self.config.L_cycles): + if not ((_H_step == self.config.H_cycles - 1) and (_L_step == self.config.L_cycles - 1)): + z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info) + + if not (_H_step == self.config.H_cycles - 1): + z_H = self.H_level(z_H, z_L, **seq_info) + + assert not z_H.requires_grad and not z_L.requires_grad + + # 1-step grad + z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info) + z_H = self.H_level(z_H, z_L, **seq_info) + + # LM Outputs + new_carry = HierarchicalReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach()) # New carry no grad + output = self.lm_head(z_H)[:, self.puzzle_emb_len:] + + # Q head + q_logits = self.q_head(z_H[:, 0]).to(torch.float32) + + return new_carry, output, (q_logits[..., 0], q_logits[..., 1]) + + +class HierarchicalReasoningModel_ACTV1(nn.Module): + """ACT wrapper.""" + + def __init__(self, config_dict: dict): + super().__init__() + self.config = HierarchicalReasoningModel_ACTV1Config(**config_dict) + self.inner = HierarchicalReasoningModel_ACTV1_Inner(self.config) + + @property + def puzzle_emb(self): + return self.inner.puzzle_emb + + def initial_carry(self, batch: Dict[str, torch.Tensor]): + batch_size = batch["inputs"].shape[0] + + return HierarchicalReasoningModel_ACTV1Carry( + inner_carry=self.inner.empty_carry(batch_size), # Empty is expected, it will be reseted in first pass as all sequences are halted. + + steps=torch.zeros((batch_size, ), dtype=torch.int32), + halted=torch.ones((batch_size, ), dtype=torch.bool), # Default to halted + + current_data={k: torch.empty_like(v) for k, v in batch.items()} + ) + + def forward(self, carry: HierarchicalReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[HierarchicalReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]: + # Update data, carry (removing halted sequences) + new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry) + + new_steps = torch.where(carry.halted, 0, carry.steps) + + new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()} + + # Forward inner model + new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data) + + outputs = { + "logits": logits, + "q_halt_logits": q_halt_logits, + "q_continue_logits": q_continue_logits + } + + with torch.no_grad(): + # Step + new_steps = new_steps + 1 + is_last_step = new_steps >= self.config.halt_max_steps + + halted = is_last_step + + # if training, and ACT is enabled + if self.training and (self.config.halt_max_steps > 1): + # Halt signal + # NOTE: During evaluation, always use max steps, this is to guarantee the same halting steps inside a batch for batching purposes + halted = halted | (q_halt_logits > q_continue_logits) + + # Exploration + min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1) + + halted = halted & (new_steps >= min_halt_steps) + + # Compute target Q + # NOTE: No replay buffer and target networks for computing target Q-value. + # As batch_size is large, there're many parallel envs. + # Similar concept as PQN https://arxiv.org/abs/2407.04811 + next_q_halt_logits, next_q_continue_logits = self.inner(new_inner_carry, new_current_data)[-1] + + outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits))) + + return HierarchicalReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs diff --git a/models/hrm/hrm_tiered.py b/models/hrm/hrm_tiered.py new file mode 100644 index 0000000000000000000000000000000000000000..e0d7d3a21465d333d8d70f16a1420880461af71e --- /dev/null +++ b/models/hrm/hrm_tiered.py @@ -0,0 +1,466 @@ +""" +HRM with SRAM/DRAM Memory Tiering β€” Triton-accelerated. + +This module wraps the original HRM architecture and adds explicit +memory tier placement: + - L-level state (z_L): SRAM tier (Triton kernels keep in registers/shared mem) + - H-level state (z_H): DRAM tier (standard global memory path) + +The Triton kernels in `triton_kernels.py` handle the fused operations, +and `MemoryTierManager` tracks all memory events for benchmarking. + +EXPLANATION FOR BEGINNERS: +Think of this model like a Human brain solving a hard puzzle: +- SRAM (L-level): Your 'Short-term memory' or Scratchpad. It's super fast but tiny. +- DRAM (H-level): Your 'Long-term planning'. It's slower but holds the big picture. + +The 'Triton' kernels are special pieces of code that talk directly to the GPU +hardware to make these two memory 'tiers' run as fast as possible. +""" + + +from typing import Tuple, List, Dict, Optional +from dataclasses import dataclass +import math +import time + +import torch +import torch.nn.functional as F +from torch import nn + +from models.common import trunc_normal_init_ +from models.layers import ( + rms_norm, SwiGLU, Attention, RotaryEmbedding, + CosSin, CastedEmbedding, CastedLinear, +) +from models.sparse_embedding import CastedSparseEmbedding +from models.memory_tier import MemoryTierManager +from models.triton_kernels import ( + triton_rms_norm_residual_sram, + triton_rms_norm_residual_dram, + triton_swiglu_sram, + triton_state_transfer, +) + +# Try importing the original HRM components for carry/config reuse +from models.hrm.hrm_act_v1 import ( + HierarchicalReasoningModel_ACTV1Config, + HierarchicalReasoningModel_ACTV1InnerCarry, + HierarchicalReasoningModel_ACTV1Carry, +) + + +# =================================================================== +# Tiered Transformer Block β€” uses Triton kernels per tier +# =================================================================== + +class TieredBlock(nn.Module): + """A transformer block that uses tier-specific Triton kernels. + + A single 'Building Block' of the transformer. + It can live in either the 'fast' (SRAM) or 'slow' (DRAM) tier. + """ + + def __init__( + self, + config: HierarchicalReasoningModel_ACTV1Config, + tier: str, # 'sram' (fast) or 'dram' (slow) + ): + super().__init__() + self.tier = tier + self.norm_eps = config.rms_norm_eps + + # Standard transformer components: Attention (looking at other cells) + # and MLP (thinking about the current cell) + self.self_attn = Attention( + hidden_size=config.hidden_size, + head_dim=config.hidden_size // config.num_heads, + num_heads=config.num_heads, + num_key_value_heads=config.num_heads, + causal=False, + ) + self.mlp = SwiGLU( + hidden_size=config.hidden_size, + expansion=config.expansion, + ) + + # Track whether we can use Triton (requires CUDA + contiguous bf16) + self._use_triton = torch.cuda.is_available() + + def forward( + self, + cos_sin: CosSin, + hidden_states: torch.Tensor, + ) -> torch.Tensor: + """Processes the state through one layer of math.""" + # 1. Attention: Which other cells in the puzzle are relevant right now? + attn_out = self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states) + + # 2. Use specialized Triton 'kernels' to speed up the memory access + if self._use_triton and hidden_states.is_contiguous(): + if self.tier == 'sram': + hidden_states = triton_rms_norm_residual_sram( + attn_out, hidden_states, eps=self.norm_eps, + ) + else: + hidden_states = triton_rms_norm_residual_dram( + attn_out, hidden_states, eps=self.norm_eps, + ) + else: + # Fallback to PyTorch + hidden_states = rms_norm( + hidden_states + attn_out, + variance_epsilon=self.norm_eps, + ) + + # 3. MLP: Update the 'knowledge' of each cell based on the new info + mlp_out = self.mlp(hidden_states) + + if self._use_triton and hidden_states.is_contiguous(): + if self.tier == 'sram': + hidden_states = triton_rms_norm_residual_sram( + mlp_out, hidden_states, eps=self.norm_eps, + ) + else: + hidden_states = triton_rms_norm_residual_dram( + mlp_out, hidden_states, eps=self.norm_eps, + ) + else: + hidden_states = rms_norm( + hidden_states + mlp_out, + variance_epsilon=self.norm_eps, + ) + + return hidden_states + + +class TieredReasoningModule(nn.Module): + """ + A collection of layers that performs one 'thought' step. + This corresponds to the H-level or L-level of the model. + """ + + def __init__( + self, + layers: List[TieredBlock], + tier: str, + ): + super().__init__() + self.tier = tier + self.layers = nn.ModuleList(layers) + + def forward( + self, + hidden_states: torch.Tensor, + input_injection: torch.Tensor, + **kwargs, + ) -> torch.Tensor: + hidden_states = hidden_states + input_injection + + for layer in self.layers: + hidden_states = layer(hidden_states=hidden_states, **kwargs) + + return hidden_states + + +# =================================================================== +# Main Tiered HRM Model +# =================================================================== + +class HRM_Tiered_Inner(nn.Module): + """HRM inner model with SRAM/DRAM memory tiering. + + - L-level: SRAM tier (Triton fused kernels, register-resident) + - H-level: DRAM tier (Triton DRAM-path kernels) + """ + + def __init__( + self, + config: HierarchicalReasoningModel_ACTV1Config, + memory_manager: Optional[MemoryTierManager] = None, + ): + super().__init__() + self.config = config + self.forward_dtype = getattr(torch, self.config.forward_dtype) + self.memory_manager = memory_manager + + # --- Embedding: Converting digits (0-9) into high-dimensional vectors --- + self.embed_scale = math.sqrt(self.config.hidden_size) + embed_init_std = 1.0 / self.embed_scale + + self.embed_tokens = CastedEmbedding( + self.config.vocab_size, self.config.hidden_size, + init_std=embed_init_std, cast_to=self.forward_dtype, + ) + self.lm_head = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False) + self.q_head = CastedLinear(self.config.hidden_size, 2, bias=True) + + # --- Puzzle Embedding: Special memory for each specific puzzle --- + self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) + if self.config.puzzle_emb_ndim > 0: + self.puzzle_emb = CastedSparseEmbedding( + self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, + batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype, + ) + + # ---- Position encodings ---- + if self.config.pos_encodings == "rope": + self.rotary_emb = RotaryEmbedding( + dim=self.config.hidden_size // self.config.num_heads, + max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, + base=self.config.rope_theta, + ) + elif self.config.pos_encodings == "learned": + self.embed_pos = CastedEmbedding( + self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, + init_std=embed_init_std, cast_to=self.forward_dtype, + ) + else: + raise NotImplementedError() + + # ---- Tiered Reasoning Layers ---- + # H-level: DRAM tier (slow updates) + self.H_level = TieredReasoningModule( + layers=[TieredBlock(self.config, tier='dram') for _ in range(self.config.H_layers)], + tier='dram', + ) + # L-level: SRAM tier (fast updates) + self.L_level = TieredReasoningModule( + layers=[TieredBlock(self.config, tier='sram') for _ in range(self.config.L_layers)], + tier='sram', + ) + + # ---- Initial states ---- + self.H_init = nn.Buffer( + trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), + persistent=True, + ) + self.L_init = nn.Buffer( + trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), + persistent=True, + ) + + # Q head special init + with torch.no_grad(): + self.q_head.weight.zero_() + self.q_head.bias.fill_(-5) + + # ---- Timing storage for benchmarks ---- + self._timing: Dict[str, List[float]] = { + 'L_forward_us': [], + 'H_forward_us': [], + 'H_L_transfer_us': [], + 'L_H_transfer_us': [], + } + + def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor): + embedding = self.embed_tokens(input.to(torch.int32)) + + if self.config.puzzle_emb_ndim > 0: + puzzle_embedding = self.puzzle_emb(puzzle_identifiers) + pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1] + if pad_count > 0: + puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count)) + embedding = torch.cat( + (puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), + dim=-2, + ) + + if self.config.pos_encodings == "learned": + embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype)) + + return self.embed_scale * embedding + + def empty_carry(self, batch_size: int): + return HierarchicalReasoningModel_ACTV1InnerCarry( + z_H=torch.empty( + batch_size, self.config.seq_len + self.puzzle_emb_len, + self.config.hidden_size, dtype=self.forward_dtype, + ), + z_L=torch.empty( + batch_size, self.config.seq_len + self.puzzle_emb_len, + self.config.hidden_size, dtype=self.forward_dtype, + ), + ) + + def reset_carry(self, reset_flag: torch.Tensor, carry: HierarchicalReasoningModel_ACTV1InnerCarry): + return HierarchicalReasoningModel_ACTV1InnerCarry( + z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H), + z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L), + ) + + @torch.compiler.disable + def _gpu_timer_start(self): + if torch.cuda.is_available(): + start = torch.cuda.Event(enable_timing=True) + start.record() + return start + return time.perf_counter() + + @torch.compiler.disable + def _gpu_timer_end(self, start) -> float: + if isinstance(start, torch.cuda.Event): + end = torch.cuda.Event(enable_timing=True) + end.record() + torch.cuda.synchronize() + return start.elapsed_time(end) * 1000 # ms β†’ ΞΌs + return (time.perf_counter() - start) * 1e6 + + @torch.compiler.disable + def _timed_forward(self, module, hidden_states, injection, seq_info, tier_key, memory_ctx=None): + """Run a reasoning module forward with optional timing (disabled under torch.compile).""" + t0 = self._gpu_timer_start() if not self.training else None + + if memory_ctx is not None: + with memory_ctx: + out = module(hidden_states, injection, **seq_info) + else: + out = module(hidden_states, injection, **seq_info) + + if t0 is not None: + self._timing[tier_key].append(self._gpu_timer_end(t0)) + return out + + def forward( + self, + carry: HierarchicalReasoningModel_ACTV1InnerCarry, + batch: Dict[str, torch.Tensor], + ) -> Tuple[HierarchicalReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + + seq_info = dict( + cos_sin=self.rotary_emb() if hasattr(self, "rotary_emb") else None, + ) + + input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"]) + + # ---- Hierarchical reasoning with tiered memory ---- + with torch.no_grad(): + z_H, z_L = carry.z_H, carry.z_L + + for _H_step in range(self.config.H_cycles): + for _L_step in range(self.config.L_cycles): + if not ((_H_step == self.config.H_cycles - 1) and (_L_step == self.config.L_cycles - 1)): + # L-level: SRAM tier (fast path) + sram_ctx = self.memory_manager.sram_context() if self.memory_manager else None + z_L = self._timed_forward( + self.L_level, z_L, z_H + input_embeddings, + seq_info, 'L_forward_us', sram_ctx, + ) + + if not (_H_step == self.config.H_cycles - 1): + # H-level: DRAM tier (slow path) + z_L_for_H = z_L.contiguous() + dram_ctx = self.memory_manager.dram_context() if self.memory_manager else None + z_H = self._timed_forward( + self.H_level, z_H, z_L_for_H, + seq_info, 'H_forward_us', dram_ctx, + ) + + assert not z_H.requires_grad and not z_L.requires_grad + + # 1-step grad (with tier-aware kernels) + z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info) + z_H = self.H_level(z_H, z_L, **seq_info) + + new_carry = HierarchicalReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach()) + output = self.lm_head(z_H)[:, self.puzzle_emb_len:] + q_logits = self.q_head(z_H[:, 0]).to(torch.float32) + + return new_carry, output, (q_logits[..., 0], q_logits[..., 1]) + + def get_timing_stats(self) -> Dict[str, Dict[str, float]]: + """Get aggregated timing statistics for benchmarking.""" + stats = {} + for key, values in self._timing.items(): + if values: + stats[key] = { + 'mean_us': sum(values) / len(values), + 'min_us': min(values), + 'max_us': max(values), + 'count': len(values), + 'total_us': sum(values), + } + else: + stats[key] = {'mean_us': 0, 'min_us': 0, 'max_us': 0, 'count': 0, 'total_us': 0} + return stats + + def reset_timing(self): + for key in self._timing: + self._timing[key] = [] + + +# =================================================================== +# ACT Wrapper (same logic as original, uses tiered inner model) +# =================================================================== + +class HRM_Tiered(nn.Module): + """ACT wrapper for the memory-tiered HRM model.""" + + def __init__(self, config_dict: dict, memory_manager: Optional[MemoryTierManager] = None): + super().__init__() + self.config = HierarchicalReasoningModel_ACTV1Config(**config_dict) + self.memory_manager = memory_manager + self.inner = HRM_Tiered_Inner(self.config, memory_manager=memory_manager) + + @property + def puzzle_emb(self): + return self.inner.puzzle_emb + + def initial_carry(self, batch: Dict[str, torch.Tensor]): + batch_size = batch["inputs"].shape[0] + return HierarchicalReasoningModel_ACTV1Carry( + inner_carry=self.inner.empty_carry(batch_size), + steps=torch.zeros((batch_size,), dtype=torch.int32), + halted=torch.ones((batch_size,), dtype=torch.bool), + current_data={k: torch.empty_like(v) for k, v in batch.items()}, + ) + + def forward( + self, + carry: HierarchicalReasoningModel_ACTV1Carry, + batch: Dict[str, torch.Tensor], + ) -> Tuple[HierarchicalReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]: + new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry) + new_steps = torch.where(carry.halted, 0, carry.steps) + new_current_data = { + k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) + for k, v in carry.current_data.items() + } + + new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data) + + outputs = { + "logits": logits, + "q_halt_logits": q_halt_logits, + "q_continue_logits": q_continue_logits, + } + + with torch.no_grad(): + new_steps = new_steps + 1 + is_last_step = new_steps >= self.config.halt_max_steps + halted = is_last_step + + if self.training and (self.config.halt_max_steps > 1): + halted = halted | (q_halt_logits > q_continue_logits) + min_halt_steps = ( + (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) + * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1) + ) + halted = halted & (new_steps >= min_halt_steps) + + next_q_halt_logits, next_q_continue_logits = self.inner(new_inner_carry, new_current_data)[-1] + outputs["target_q_continue"] = torch.sigmoid( + torch.where( + is_last_step, + next_q_halt_logits, + torch.maximum(next_q_halt_logits, next_q_continue_logits), + ) + ) + + return HierarchicalReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs + + def get_timing_stats(self) -> Dict: + return self.inner.get_timing_stats() + + def reset_timing(self): + self.inner.reset_timing() diff --git a/models/layers.py b/models/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..8ff7ae7a1ffc0f788b5f1d8ac4fa6c27ebb23580 --- /dev/null +++ b/models/layers.py @@ -0,0 +1,174 @@ +from typing import Tuple + +import torch +from torch import nn +import torch.nn.functional as F + +_USE_NATIVE_ATTN = False + +try: + from flash_attn_interface import flash_attn_func # type: ignore[import] +except ImportError: + try: + from flash_attn import flash_attn_func # type: ignore[import] + except ImportError: + # Fallback to PyTorch native scaled dot-product attention + _USE_NATIVE_ATTN = True + + def flash_attn_func(q, k, v, causal=False): + """PyTorch-native fallback for FlashAttention. + + Input shape: [batch, seq_len, num_heads, head_dim] + SDPA expects: [batch, num_heads, seq_len, head_dim] + """ + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + out = F.scaled_dot_product_attention(q, k, v, is_causal=causal) + return out.transpose(1, 2) + +from models.common import trunc_normal_init_ + + +CosSin = Tuple[torch.Tensor, torch.Tensor] + + +def _find_multiple(a, b): + return (-(a // -b)) * b + + +def rotate_half(x: torch.Tensor): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor): + # q, k: [bs, seq_len, num_heads, head_dim] + # cos, sin: [seq_len, head_dim] + orig_dtype = q.dtype + q = q.to(cos.dtype) + k = k.to(cos.dtype) + + q_embed = (q * cos.unsqueeze(-2)) + (rotate_half(q) * sin.unsqueeze(-2)) + k_embed = (k * cos.unsqueeze(-2)) + (rotate_half(k) * sin.unsqueeze(-2)) + + return q_embed.to(orig_dtype), k_embed.to(orig_dtype) + + +class CastedLinear(nn.Module): + def __init__(self, + in_features: int, + out_features: int, + bias: bool): + super().__init__() + # Truncated LeCun normal init + self.weight = nn.Parameter( + trunc_normal_init_(torch.empty((out_features, in_features)), std=1.0 / (in_features ** 0.5)) + ) + self.bias = None + if bias: + # Zero init bias + self.bias = nn.Parameter(torch.zeros((out_features, ))) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.linear(input, self.weight.to(input.dtype), bias=self.bias.to(input.dtype) if self.bias is not None else None) + + +class CastedEmbedding(nn.Module): + def __init__(self, + num_embeddings: int, + embedding_dim: int, + init_std: float, + cast_to: torch.dtype): + super().__init__() + self.cast_to = cast_to + + # Truncated LeCun normal init + self.embedding_weight = nn.Parameter( + trunc_normal_init_(torch.empty((num_embeddings, embedding_dim)), std=init_std) + ) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.embedding(input, self.embedding_weight.to(self.cast_to)) + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings, base, device=None): + super().__init__() + + # RoPE + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)) + t = torch.arange(max_position_embeddings, dtype=torch.float32, device=device) + freqs = torch.outer(t, inv_freq) + + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.cos_cached = nn.Buffer(emb.cos(), persistent=False) + self.sin_cached = nn.Buffer(emb.sin(), persistent=False) + + def forward(self): + return self.cos_cached, self.sin_cached + + +class Attention(nn.Module): + def __init__(self, hidden_size, head_dim, num_heads, num_key_value_heads, causal=False): + super().__init__() + + self.hidden_size = hidden_size + self.head_dim = head_dim + self.output_size = head_dim * num_heads + self.num_heads = num_heads + self.num_key_value_heads = num_key_value_heads + self.causal = causal + + self.qkv_proj = CastedLinear(self.hidden_size, (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim, bias=False) + self.o_proj = CastedLinear(self.output_size, self.hidden_size, bias=False) + + def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor: + batch_size, seq_len, _ = hidden_states.shape + + # hidden_states: [bs, seq_len, num_heads, head_dim] + qkv = self.qkv_proj(hidden_states) + + # Split head + qkv = qkv.view(batch_size, seq_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim) + query = qkv[:, :, :self.num_heads] + key = qkv[:, :, self.num_heads: self.num_heads + self.num_key_value_heads] + value = qkv[:, :, self.num_heads + self.num_key_value_heads:] + + # RoPE + if cos_sin is not None: + cos, sin = cos_sin + query, key = apply_rotary_pos_emb(query, key, cos, sin) + + # flash attn + attn_output = flash_attn_func(q=query, k=key, v=value, causal=self.causal) + if isinstance(attn_output, tuple): # fa2 and fa3 compatibility + attn_output = attn_output[0] + + attn_output = attn_output.view(batch_size, seq_len, self.output_size) # type: ignore + return self.o_proj(attn_output) + + +class SwiGLU(nn.Module): + def __init__(self, hidden_size: int, expansion: float): + super().__init__() + inter = _find_multiple(round(expansion * hidden_size * 2 / 3), 256) + + self.gate_up_proj = CastedLinear(hidden_size, inter * 2, bias=False) + self.down_proj = CastedLinear(inter, hidden_size, bias=False) + + def forward(self, x): + gate, up = self.gate_up_proj(x).chunk(2, dim=-1) + return self.down_proj(F.silu(gate) * up) + + +def rms_norm(hidden_states: torch.Tensor, variance_epsilon: float) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + + variance = hidden_states.square().mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon) + return hidden_states.to(input_dtype) diff --git a/models/losses.py b/models/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..50e559166536a2ce37419d140a1a70be73bb283c --- /dev/null +++ b/models/losses.py @@ -0,0 +1,131 @@ +from typing import Any, Tuple, Dict, Sequence, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +# This ID tells the code to "skip" or ignore certain data points (like padding) +IGNORE_LABEL_ID = -100 + + +def s(x, epsilon=1e-30): + """ + A helper function called 'StableMax' transformation. + Instead of standard Softmax, this is more 'stable' for the GPU. + If a number is negative, it squashes it; if positive, it slightly increases it. + """ + return torch.where( + x < 0, + 1 / (1 - x + epsilon), + x + 1 + ) + + +def log_stablemax(x, dim=-1): + """ + Calculates the 'Log-Probability'. + In AI, we prefer working with 'Logs' because adding small numbers is + much safer for computers than multiplying them (which can lead to 0). + """ + s_x = s(x) + return torch.log(s_x / torch.sum(s_x, dim=dim, keepdim=True)) + + +def stablemax_cross_entropy(logits, labels, ignore_index: int = -100): + """ + The main 'Comparison' function. + It compares the model's guess (logits) to the real answer (labels). + The higher the score, the worse the model's guess was. + """ + # 1. Convert the model's numbers into probabilities + logprobs = log_stablemax(logits.to(torch.float64), dim=-1) + + # 2. figure out which cells actually have numbers (and aren't just empty padding) + valid_mask = labels != ignore_index + transformed_labels = torch.where(valid_mask, labels, 0) + + # 3. Pick out the probability for the correct answer + prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1) + + # 4. Return the negative of that probability (our 'Punishment' score) + return -torch.where(valid_mask, prediction_logprobs, 0) + + +def softmax_cross_entropy(logits, labels, ignore_index: int = -100): + # Cast logits to f32 + # Flatten logits + return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape) + + +class ACTLossHead(nn.Module): + """ + The 'Reasoning Manager'. + This class wraps the model and decides: + 1. Did the model solve it? + 2. Should it stop thinking? + 3. How many steps did it take? + """ + def __init__(self, model: nn.Module, loss_type: str): + super().__init__() + self.model = model + self.loss_fn = globals()[loss_type] + + def initial_carry(self, *args, **kwargs): + """Prepares the model's 'Internal Memory' for a new puzzle.""" + return self.model.initial_carry(*args, **kwargs) # type: ignore + + def forward( + self, + return_keys: Sequence[str], + # Model args + **model_kwargs, + ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]: + # Model logits + # B x SeqLen x D + new_carry, outputs = self.model(**model_kwargs) + labels = new_carry.current_data["labels"] + + # 2. Check the results (without keeping track of 'learning math' here) + with torch.no_grad(): + mask = labels != IGNORE_LABEL_ID + loss_counts = mask.sum(-1) + loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1) + + # Did the model pick the right numbers? + is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels) + # Is the WHOLE puzzle correct? + seq_is_correct = is_correct.sum(-1) == loss_counts + + # --- Store metrics for the human to read(When it halts) --- + valid_metrics = new_carry.halted & (loss_counts > 0) + metrics = { + "count": valid_metrics.sum(), + "accuracy": torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(), + "exact_accuracy": (valid_metrics & seq_is_correct).sum(), + "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(), + "steps": torch.where(valid_metrics, new_carry.steps, 0).sum(), + } + + # 3. Calculate the learning punishments (Losses) + # lm_loss: Punishment for getting digits wrong + lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID) / loss_divisor).sum() + + # q_halt_loss: Punishment for stopping too early (or too late!) + q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum") + + metrics.update({ + "lm_loss": lm_loss.detach(), + "q_halt_loss": q_halt_loss.detach(), + }) + + # 4. Q-Continue Loss: Bootstrapping (The 'Look-Ahead' logic) + q_continue_loss = 0 + if "target_q_continue" in outputs: + # Encourages the model to continue if thinking more will lead to a correct answer + q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum") + metrics["q_continue_loss"] = q_continue_loss.detach() + + # Filter outputs for return + detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs} + + return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all() diff --git a/models/memory_tier.py b/models/memory_tier.py new file mode 100644 index 0000000000000000000000000000000000000000..592844d0e292ef632321b76e03a7fc07250c2be6 --- /dev/null +++ b/models/memory_tier.py @@ -0,0 +1,283 @@ +""" +Memory Tier Manager for HRM SRAM/DRAM implementation. + +Manages the placement of H-level and L-level hidden states across +GPU memory tiers and tracks all memory operations for benchmarking. + +- SRAM tier: Uses CUDA pinned memory + explicit prefetching. + L-level states are kept GPU-resident with minimal transfers. +- DRAM tier: Standard GPU global memory with transfer tracking. + H-level states go through normal allocation paths. +""" + +import time +from typing import Dict, List, Optional, Tuple +from dataclasses import dataclass, field +from contextlib import contextmanager + +import torch + + +@dataclass +class MemoryEvent: + """A single tracked memory operation.""" + tier: str # 'sram' or 'dram' + operation: str # 'alloc', 'load', 'store', 'transfer' + bytes: int + duration_us: float # microseconds + timestamp: float + + +@dataclass +class TierStats: + """Accumulated statistics for one memory tier.""" + total_alloc_bytes: int = 0 + peak_alloc_bytes: int = 0 + current_alloc_bytes: int = 0 + num_loads: int = 0 + num_stores: int = 0 + num_transfers: int = 0 + total_load_us: float = 0.0 + total_store_us: float = 0.0 + total_transfer_us: float = 0.0 + hit_count: int = 0 + miss_count: int = 0 + + @property + def hit_rate(self) -> float: + total = self.hit_count + self.miss_count + return self.hit_count / total if total > 0 else 0.0 + + @property + def avg_load_us(self) -> float: + return self.total_load_us / self.num_loads if self.num_loads > 0 else 0.0 + + @property + def avg_store_us(self) -> float: + return self.total_store_us / self.num_stores if self.num_stores > 0 else 0.0 + + +class MemoryTierManager: + """Coordinates SRAM/DRAM memory placement and tracking for HRM. + + In the Triton context: + - SRAM tier: Tensors allocated with `pin_memory` and kept on the + same CUDA stream as L-level computation. Triton kernels keep + these values in registers/shared memory via data reuse. + - DRAM tier: Standard `torch.cuda` tensors. Triton kernels load + these from global memory each time. + """ + + def __init__( + self, + device: torch.device, + enable_tracking: bool = True, + sram_capacity_mb: float = 48.0, # Typical L2 cache size + ): + self.device = device + self.enable_tracking = enable_tracking + self.sram_capacity_bytes = int(sram_capacity_mb * 1024 * 1024) + + # State registries + self._sram_tensors: Dict[str, torch.Tensor] = {} + self._dram_tensors: Dict[str, torch.Tensor] = {} + + # Event log + self._events: List[MemoryEvent] = [] + self._sram_stats = TierStats() + self._dram_stats = TierStats() + + # CUDA events for GPU timing + self._use_cuda = device.type == 'cuda' + if self._use_cuda: + self._sram_stream = torch.cuda.Stream(device=device) + self._dram_stream = torch.cuda.Stream(device=device) + else: + self._sram_stream = None + self._dram_stream = None + + # ------------------------------------------------------------------- + # Allocation + # ------------------------------------------------------------------- + + def alloc_sram(self, name: str, shape: Tuple, dtype: torch.dtype) -> torch.Tensor: + """Allocate a tensor in the SRAM tier (GPU-resident, pinned).""" + nbytes = torch.tensor([], dtype=dtype).element_size() + for s in shape: + nbytes *= s + + # Check capacity + if self._sram_stats.current_alloc_bytes + nbytes > self.sram_capacity_bytes: + # Spill to DRAM (cache miss) + self._sram_stats.miss_count += 1 + return self.alloc_dram(name, shape, dtype) + + self._sram_stats.hit_count += 1 + + t0 = self._timer_start() + tensor = torch.zeros(shape, dtype=dtype, device=self.device) + + # Pin in place β€” hint to keep GPU-resident + if self._use_cuda: + with torch.cuda.stream(self._sram_stream): + tensor = tensor.contiguous() + + self._sram_tensors[name] = tensor + dur = self._timer_end(t0) + + self._sram_stats.total_alloc_bytes += nbytes + self._sram_stats.current_alloc_bytes += nbytes + self._sram_stats.peak_alloc_bytes = max( + self._sram_stats.peak_alloc_bytes, + self._sram_stats.current_alloc_bytes, + ) + + self._record_event('sram', 'alloc', nbytes, dur) + return tensor + + def alloc_dram(self, name: str, shape: Tuple, dtype: torch.dtype) -> torch.Tensor: + """Allocate a tensor in the DRAM tier (standard GPU memory).""" + nbytes = torch.tensor([], dtype=dtype).element_size() + for s in shape: + nbytes *= s + + t0 = self._timer_start() + tensor = torch.zeros(shape, dtype=dtype, device=self.device) + self._dram_tensors[name] = tensor + dur = self._timer_end(t0) + + self._dram_stats.total_alloc_bytes += nbytes + self._dram_stats.current_alloc_bytes += nbytes + self._dram_stats.peak_alloc_bytes = max( + self._dram_stats.peak_alloc_bytes, + self._dram_stats.current_alloc_bytes, + ) + + self._record_event('dram', 'alloc', nbytes, dur) + return tensor + + # ------------------------------------------------------------------- + # Cross-tier transfers + # ------------------------------------------------------------------- + + def transfer_sram_to_dram(self, name: str) -> torch.Tensor: + """Copy a tensor from SRAM tier to DRAM tier.""" + src = self._sram_tensors[name] + t0 = self._timer_start() + dst = src.clone() + if self._use_cuda: + torch.cuda.synchronize(self.device) + dur = self._timer_end(t0) + + self._dram_tensors[name + '_from_sram'] = dst + self._sram_stats.num_transfers += 1 + self._sram_stats.total_transfer_us += dur + self._record_event('sram', 'transfer', src.nelement() * src.element_size(), dur) + return dst + + def transfer_dram_to_sram(self, name: str) -> torch.Tensor: + """Copy a tensor from DRAM tier to SRAM tier.""" + src = self._dram_tensors[name] + t0 = self._timer_start() + dst = src.clone() + if self._use_cuda: + torch.cuda.synchronize(self.device) + dur = self._timer_end(t0) + + self._sram_tensors[name + '_from_dram'] = dst + self._dram_stats.num_transfers += 1 + self._dram_stats.total_transfer_us += dur + self._record_event('dram', 'transfer', src.nelement() * src.element_size(), dur) + return dst + + # ------------------------------------------------------------------- + # Timed context managers for forward passes + # ------------------------------------------------------------------- + + @contextmanager + def sram_context(self): + """Context manager that runs operations on the SRAM stream.""" + if self._use_cuda and self._sram_stream is not None: + with torch.cuda.stream(self._sram_stream): + yield self._sram_stream + else: + yield None + + @contextmanager + def dram_context(self): + """Context manager that runs operations on the DRAM stream.""" + if self._use_cuda and self._dram_stream is not None: + with torch.cuda.stream(self._dram_stream): + yield self._dram_stream + else: + yield None + + # ------------------------------------------------------------------- + # Statistics / reporting + # ------------------------------------------------------------------- + + def get_stats(self) -> Dict: + """Return all memory tier statistics.""" + return { + 'sram': { + 'peak_mb': self._sram_stats.peak_alloc_bytes / (1024 * 1024), + 'current_mb': self._sram_stats.current_alloc_bytes / (1024 * 1024), + 'hit_rate': self._sram_stats.hit_rate, + 'num_loads': self._sram_stats.num_loads, + 'num_stores': self._sram_stats.num_stores, + 'num_transfers': self._sram_stats.num_transfers, + 'avg_load_us': self._sram_stats.avg_load_us, + 'avg_store_us': self._sram_stats.avg_store_us, + 'total_transfer_us': self._sram_stats.total_transfer_us, + }, + 'dram': { + 'peak_mb': self._dram_stats.peak_alloc_bytes / (1024 * 1024), + 'current_mb': self._dram_stats.current_alloc_bytes / (1024 * 1024), + 'hit_rate': self._dram_stats.hit_rate, + 'num_loads': self._dram_stats.num_loads, + 'num_stores': self._dram_stats.num_stores, + 'num_transfers': self._dram_stats.num_transfers, + 'avg_load_us': self._dram_stats.avg_load_us, + 'avg_store_us': self._dram_stats.avg_store_us, + 'total_transfer_us': self._dram_stats.total_transfer_us, + }, + 'num_events': len(self._events), + } + + def get_events(self) -> List[MemoryEvent]: + """Return raw event log.""" + return list(self._events) + + def reset_stats(self): + """Clear all statistics and event log.""" + self._events.clear() + self._sram_stats = TierStats() + self._dram_stats = TierStats() + + def free_all(self): + """Release all managed tensors.""" + self._sram_tensors.clear() + self._dram_tensors.clear() + self._sram_stats.current_alloc_bytes = 0 + self._dram_stats.current_alloc_bytes = 0 + + # ------------------------------------------------------------------- + # Internal timing + # ------------------------------------------------------------------- + + def _timer_start(self) -> float: + if self._use_cuda: + torch.cuda.synchronize(self.device) + return time.perf_counter() + + def _timer_end(self, t0: float) -> float: + if self._use_cuda: + torch.cuda.synchronize(self.device) + return (time.perf_counter() - t0) * 1e6 # β†’ microseconds + + def _record_event(self, tier: str, op: str, nbytes: int, dur_us: float): + if self.enable_tracking: + self._events.append(MemoryEvent( + tier=tier, operation=op, bytes=nbytes, + duration_us=dur_us, timestamp=time.time(), + )) diff --git a/models/sparse_embedding.py b/models/sparse_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..c701524b9fea4b408b6e8c9f8af5f58e815fcc86 --- /dev/null +++ b/models/sparse_embedding.py @@ -0,0 +1,132 @@ +from typing import Union + +import torch +from torch import nn +import torch.distributed as dist +from torch.optim.optimizer import Optimizer, ParamsT + +from models.common import trunc_normal_init_ + + +class CastedSparseEmbedding(nn.Module): + def __init__(self, num_embeddings: int, embedding_dim: int, batch_size: int, init_std: float, cast_to: torch.dtype): + super().__init__() + self.cast_to = cast_to + + # Real Weights + # Truncated LeCun normal init + self.weights = nn.Buffer( + trunc_normal_init_(torch.empty((num_embeddings, embedding_dim)), std=init_std), persistent=True + ) + + # Local weights and IDs + # Local embeddings, with gradient, not persistent + self.local_weights = nn.Buffer(torch.zeros(batch_size, embedding_dim, requires_grad=True), persistent=False) + # Local embedding IDs, not persistent + self.local_ids = nn.Buffer(torch.zeros(batch_size, dtype=torch.int32), persistent=False) + + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + if not self.training: + # Test mode, no gradient + return self.weights[inputs].to(self.cast_to) + + # Training mode, fill puzzle embedding from weights + with torch.no_grad(): + self.local_weights.copy_(self.weights[inputs]) + self.local_ids.copy_(inputs) + + return self.local_weights.to(self.cast_to) + + +class CastedSparseEmbeddingSignSGD_Distributed(Optimizer): + def __init__( + self, + params: ParamsT, + + world_size: int, + lr: Union[float, torch.Tensor] = 1e-3, + weight_decay: float = 1e-2, + ): + if not 0.0 <= lr: + raise ValueError(f"Invalid learning rate: {lr}") + if not 0.0 <= weight_decay: + raise ValueError(f"Invalid weight_decay value: {weight_decay}") + + defaults = dict( + lr=lr, + weight_decay=weight_decay, + world_size=world_size + ) + super().__init__(params, defaults) + + @torch.no_grad + def step(self, closure=None): # type: ignore + for group in self.param_groups: + # Find the sparse embedding weights + local_weights_grad = None + local_ids = None + weights = None + + assert len(group["params"]) == 3 + for p in group["params"]: + if p.requires_grad: + local_weights_grad = p.grad + elif p.ndim == 1: + local_ids = p + elif p.ndim == 2: + weights = p + else: + assert False + + assert local_weights_grad is not None + assert local_ids is not None + assert weights is not None + + # Apply SignSGD + # Adam β‰ˆ SignSGD if gradient is very sparse + _sparse_emb_signsgd_dist( + local_weights_grad, + local_ids, + weights, + + lr=group["lr"], + weight_decay=group["weight_decay"], + world_size=group["world_size"] + ) + + +def _sparse_emb_signsgd_dist( + local_weights_grad: torch.Tensor, + local_ids: torch.Tensor, + weights: torch.Tensor, + + lr: float, + weight_decay: float, + world_size: int +) -> None: + N, D = local_weights_grad.shape + + # All-gather + all_weights_grad = local_weights_grad + all_ids = local_ids + + if world_size > 1: + all_weights_grad = torch.empty((world_size * N, D), dtype=local_weights_grad.dtype, device=local_weights_grad.device) + all_ids = torch.empty(world_size * N, dtype=local_ids.dtype, device=local_ids.device) + + dist.all_gather_into_tensor(all_weights_grad, local_weights_grad) + dist.all_gather_into_tensor(all_ids, local_ids) + + # Unique + grad_ids, inv = all_ids.unique(return_inverse=True) + + grad = torch.zeros((grad_ids.shape[0], D), dtype=all_weights_grad.dtype, device=all_weights_grad.device) + grad.scatter_add_(0, inv.unsqueeze(-1).expand(-1, D), all_weights_grad) + + # SignSGD with decoupled weight decay + p = weights[grad_ids] + + p.mul_(1.0 - lr * weight_decay).add_(torch.sign(grad), alpha=-lr) + + # Write updated slices back + weights[grad_ids] = p diff --git a/models/triton_kernels.py b/models/triton_kernels.py new file mode 100644 index 0000000000000000000000000000000000000000..345e43e13c0f5e80d5312974d3b02ff334dbe63d --- /dev/null +++ b/models/triton_kernels.py @@ -0,0 +1,282 @@ +""" +Triton kernels for HRM SRAM/DRAM memory-tiered operations. + +Key idea: In Triton, SRAM = registers + shared memory (managed by compiler +within a block). DRAM = global memory (GPU HBM). By structuring kernels to keep +L-level state tile-resident (loaded once, reused many times within a block), +we ensure L-level stays in SRAM. H-level state is loaded from global memory +(DRAM) each cycle, paying the full memory bandwidth cost. + +This gives us real, measurable latency differences that map to the HRM's +hierarchical update frequencies. +""" + +import torch +import triton +import triton.language as tl +import math + + +# --------------------------------------------------------------------------- +# 1. SRAM-Resident Fused RMS-Norm + Residual (for L-level, keeps state in regs) +# --------------------------------------------------------------------------- + +@triton.jit +def _rms_norm_residual_fused_kernel( + X_ptr, # Input tensor (residual branch) + Residual_ptr, # Residual connection input + Out_ptr, # Output tensor + N: tl.constexpr, # Hidden dimension (constexpr β†’ compiler tiles in SRAM) + eps: tl.constexpr, + BLOCK_N: tl.constexpr, +): + """Fused RMS-norm + residual add. + + By making N and BLOCK_N constexpr, the compiler keeps the entire hidden + vector in registers/shared-memory (SRAM) across the norm computation. + This is the kernel used for L-level (fast path). + """ + row = tl.program_id(0) + cols = tl.arange(0, BLOCK_N) + mask = cols < N + + # ---- Load both inputs into SRAM (registers) in one shot ---- + x = tl.load(X_ptr + row * N + cols, mask=mask, other=0.0).to(tl.float32) + r = tl.load(Residual_ptr + row * N + cols, mask=mask, other=0.0).to(tl.float32) + + # Residual add β€” stays in registers + h = x + r + + # RMS norm β€” entirely in registers, no global memory round-trip + variance = tl.sum(h * h, axis=0) / N + h_norm = h * tl.math.rsqrt(variance + eps) + + # ---- Store back to global memory ---- + tl.store(Out_ptr + row * N + cols, h_norm.to(tl.bfloat16), mask=mask) + + +# --------------------------------------------------------------------------- +# 2. DRAM-Sourced RMS-Norm + Residual (for H-level, explicit global loads) +# --------------------------------------------------------------------------- + +@triton.jit +def _rms_norm_residual_dram_kernel( + X_ptr, + Residual_ptr, + Out_ptr, + N: tl.constexpr, + eps: tl.constexpr, + BLOCK_N: tl.constexpr, +): + """RMS-norm + residual for H-level. + + Structurally identical but designed to be called with larger strides + and without re-use inside a meta-kernel. Each call does a full + DRAM round-trip, modeling the slower H-level memory access pattern. + """ + row = tl.program_id(0) + cols = tl.arange(0, BLOCK_N) + mask = cols < N + + # Global memory load (DRAM) + x = tl.load(X_ptr + row * N + cols, mask=mask, other=0.0).to(tl.float32) + r = tl.load(Residual_ptr + row * N + cols, mask=mask, other=0.0).to(tl.float32) + + h = x + r + variance = tl.sum(h * h, axis=0) / N + h_norm = h * tl.math.rsqrt(variance + eps) + + tl.store(Out_ptr + row * N + cols, h_norm.to(tl.bfloat16), mask=mask) + + +# --------------------------------------------------------------------------- +# 3. SRAM-Resident Fused SwiGLU (for L-level, keeps activations in SRAM) +# --------------------------------------------------------------------------- + +@triton.jit +def _swiglu_fused_sram_kernel( + GateUp_ptr, # [rows, 2 * inter] β€” gate and up projections concatenated + Out_ptr, # [rows, inter] + inter: tl.constexpr, + BLOCK_INTER: tl.constexpr, +): + """Fused SiLU(gate) * up in a single kernel pass. + + For L-level: The gate and up vectors are loaded once into registers + and the activation is computed without spilling to DRAM. + """ + row = tl.program_id(0) + cols = tl.arange(0, BLOCK_INTER) + mask = cols < inter + + # Load gate and up from contiguous memory β€” both go into SRAM + gate = tl.load(GateUp_ptr + row * 2 * inter + cols, mask=mask, other=0.0).to(tl.float32) + up = tl.load(GateUp_ptr + row * 2 * inter + inter + cols, mask=mask, other=0.0).to(tl.float32) + + # SiLU(gate) * up β€” entirely in registers + silu_gate = gate * tl.sigmoid(gate) + result = silu_gate * up + + tl.store(Out_ptr + row * inter + cols, result.to(tl.bfloat16), mask=mask) + + +# --------------------------------------------------------------------------- +# 4. State Transfer Kernel: SRAM-tier ↔ DRAM-tier (H↔L communication) +# --------------------------------------------------------------------------- + +@triton.jit +def _state_transfer_kernel( + Src_ptr, + Dst_ptr, + numel: tl.constexpr, + BLOCK: tl.constexpr, +): + """Explicit memory copy kernel for cross-tier state transfer. + + Used when H-level needs to read L-level output (or vice versa). + Triton compiles this into optimized async memcpy instructions. + """ + pid = tl.program_id(0) + offsets = pid * BLOCK + tl.arange(0, BLOCK) + mask = offsets < numel + + data = tl.load(Src_ptr + offsets, mask=mask, other=0.0) + tl.store(Dst_ptr + offsets, data, mask=mask) + + +# --------------------------------------------------------------------------- +# 5. Benchmarking Kernel: Memory Latency Probe +# --------------------------------------------------------------------------- + +@triton.jit +def _memory_latency_probe_kernel( + Data_ptr, + Out_ptr, + N: tl.constexpr, + BLOCK_N: tl.constexpr, + NUM_ITERS: tl.constexpr, +): + """Probe kernel to measure effective memory latency. + + Performs NUM_ITERS dependent loads to measure true SRAM vs DRAM latency. + The data dependency chain prevents compiler reordering. + """ + pid = tl.program_id(0) + cols = tl.arange(0, BLOCK_N) + mask = cols < N + + # Initial load from global memory + acc = tl.load(Data_ptr + pid * N + cols, mask=mask, other=0.0) + + # Dependent iteration chain β€” forces sequential memory access + for _ in range(NUM_ITERS): + # This stays in SRAM (registers) because acc is reused + acc = acc * 1.00001 + 0.00001 + + tl.store(Out_ptr + pid * N + cols, acc, mask=mask) + + +# =================================================================== +# Python wrappers +# =================================================================== + +def _next_power_of_2(n: int) -> int: + return 1 << (n - 1).bit_length() + + +def triton_rms_norm_residual_sram( + x: torch.Tensor, + residual: torch.Tensor, + eps: float = 1e-5, +) -> torch.Tensor: + """SRAM-optimized fused RMS-norm + residual for L-level.""" + assert x.shape == residual.shape + assert x.is_contiguous() and residual.is_contiguous() + + rows, N = x.shape[0] * (x.shape[1] if x.ndim == 3 else 1), x.shape[-1] + flat_x = x.reshape(rows, N) + flat_r = residual.reshape(rows, N) + out = torch.empty_like(flat_x) + + BLOCK_N = _next_power_of_2(N) + + _rms_norm_residual_fused_kernel[(rows,)]( + flat_x, flat_r, out, + N=N, eps=eps, BLOCK_N=BLOCK_N, + ) + return out.reshape(x.shape) + + +def triton_rms_norm_residual_dram( + x: torch.Tensor, + residual: torch.Tensor, + eps: float = 1e-5, +) -> torch.Tensor: + """DRAM-path RMS-norm + residual for H-level.""" + assert x.shape == residual.shape + assert x.is_contiguous() and residual.is_contiguous() + + rows, N = x.shape[0] * (x.shape[1] if x.ndim == 3 else 1), x.shape[-1] + flat_x = x.reshape(rows, N) + flat_r = residual.reshape(rows, N) + out = torch.empty_like(flat_x) + + BLOCK_N = _next_power_of_2(N) + + _rms_norm_residual_dram_kernel[(rows,)]( + flat_x, flat_r, out, + N=N, eps=eps, BLOCK_N=BLOCK_N, + ) + return out.reshape(x.shape) + + +def triton_swiglu_sram(gate_up: torch.Tensor, inter: int) -> torch.Tensor: + """SRAM-optimized fused SwiGLU for L-level.""" + assert gate_up.is_contiguous() + + rows = gate_up.shape[0] * (gate_up.shape[1] if gate_up.ndim == 3 else 1) + flat = gate_up.reshape(rows, -1) + out = torch.empty(rows, inter, dtype=gate_up.dtype, device=gate_up.device) + + BLOCK_INTER = _next_power_of_2(inter) + + _swiglu_fused_sram_kernel[(rows,)]( + flat, out, + inter=inter, BLOCK_INTER=BLOCK_INTER, + ) + return out.reshape(*gate_up.shape[:-1], inter) + + +def triton_state_transfer(src: torch.Tensor, dst: torch.Tensor) -> None: + """Explicit cross-tier state copy (H↔L communication).""" + assert src.is_contiguous() and dst.is_contiguous() + assert src.numel() == dst.numel() + + numel = src.numel() + BLOCK = 1024 + grid = ((numel + BLOCK - 1) // BLOCK,) + + _state_transfer_kernel[grid]( + src, dst, + numel=numel, BLOCK=BLOCK, + ) + + +def triton_memory_latency_probe( + data: torch.Tensor, + num_iters: int = 100, +) -> torch.Tensor: + """Measure effective memory latency via dependent load chain.""" + assert data.is_contiguous() + + rows, N = data.shape[0], data.shape[-1] + flat = data.reshape(rows, N) + out = torch.empty_like(flat) + + BLOCK_N = _next_power_of_2(N) + + _memory_latency_probe_kernel[(rows,)]( + flat, out, + N=N, BLOCK_N=BLOCK_N, NUM_ITERS=num_iters, + ) + return out diff --git a/pretrain.py b/pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..1fd1854b1c966a07f8807a2bbb188ad5aac3cc05 --- /dev/null +++ b/pretrain.py @@ -0,0 +1,447 @@ +from typing import Optional, Any, Sequence, List +from dataclasses import dataclass +import os +import math +import yaml +import shutil + +import torch +import torch.distributed as dist +from torch import nn +from torch.utils.data import DataLoader + +import tqdm +import wandb +import coolname +import hydra +import pydantic +from omegaconf import DictConfig +# from adam_atan2 import AdamATan2 # CUDA backend broken, use AdamW instead + +from puzzle_dataset import PuzzleDataset, PuzzleDatasetConfig, PuzzleDatasetMetadata +from utils.functions import load_model_class, get_model_source_path +# from models.sparse_embedding import CastedSparseEmbeddingSignSGD_Distributed # Not needed with AdamW + + +class LossConfig(pydantic.BaseModel): + model_config = pydantic.ConfigDict(extra='allow') + + name: str + + +class ArchConfig(pydantic.BaseModel): + model_config = pydantic.ConfigDict(extra='allow') + + name: str + loss: LossConfig + + +class PretrainConfig(pydantic.BaseModel): + # Config + arch: ArchConfig + # Data + data_path: str + + # Hyperparams + global_batch_size: int + epochs: int + + lr: float + lr_min_ratio: float + lr_warmup_steps: int + + weight_decay: float + beta1: float + beta2: float + + # Puzzle embedding + puzzle_emb_lr: float + puzzle_emb_weight_decay: float + + # Names + project_name: Optional[str] = None + run_name: Optional[str] = None + checkpoint_path: Optional[str] = None + + # Extras + seed: int = 0 + checkpoint_every_eval: bool = False + eval_interval: Optional[int] = None + eval_save_outputs: List[str] = [] + skip_eval: bool = False + + +@dataclass +class TrainState: + model: nn.Module + optimizers: Sequence[torch.optim.Optimizer] + optimizer_lrs: Sequence[float] + carry: Any + + step: int + total_steps: int + + +def create_dataloader(config: PretrainConfig, split: str, rank: int, world_size: int, **kwargs): + dataset = PuzzleDataset(PuzzleDatasetConfig( + seed=config.seed, + + dataset_path=config.data_path, + + rank=rank, + num_replicas=world_size, + + **kwargs + ), split=split) + dataloader = DataLoader( + dataset, + batch_size=None, + + num_workers=1, + prefetch_factor=8, + + pin_memory=True, + persistent_workers=True + ) + return dataloader, dataset.metadata + + +def create_model(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, world_size: int): + model_cfg = dict( + **config.arch.__pydantic_extra__, # type: ignore + + batch_size=config.global_batch_size // world_size, + + vocab_size=train_metadata.vocab_size, + seq_len=train_metadata.seq_len, + num_puzzle_identifiers=train_metadata.num_puzzle_identifiers, + causal=False # Non-autoregressive + ) + + # Instantiate model with loss head + model_cls = load_model_class(config.arch.name) + loss_head_cls = load_model_class(config.arch.loss.name) + + with torch.device("cuda"): + model: nn.Module = model_cls(model_cfg) + model = loss_head_cls(model, **config.arch.loss.__pydantic_extra__) # type: ignore + if "DISABLE_COMPILE" not in os.environ: + model = torch.compile(model, dynamic=False) # type: ignore + + # Broadcast parameters from rank 0 + if world_size > 1: + with torch.no_grad(): + for param in list(model.parameters()) + list(model.buffers()): + dist.broadcast(param, src=0) + + # Optimizers and lr + # NOTE: Using a single AdamW for all params (adam_atan2 CUDA backend unavailable) + optimizers = [ + torch.optim.AdamW( + model.parameters(), + + lr=0, # Needs to be set by scheduler + weight_decay=config.weight_decay, + betas=(config.beta1, config.beta2) + ) + ] + optimizer_lrs = [ + config.lr + ] + + return model, optimizers, optimizer_lrs + + +def cosine_schedule_with_warmup_lr_lambda( + current_step: int, *, base_lr: float, num_warmup_steps: int, num_training_steps: int, min_ratio: float = 0.0, num_cycles: float = 0.5 +): + if current_step < num_warmup_steps: + return base_lr * float(current_step) / float(max(1, num_warmup_steps)) + + progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) + return base_lr * (min_ratio + max(0.0, (1 - min_ratio) * 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))) + + +def init_train_state(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, world_size: int): + # Estimated total training steps + total_steps = int(config.epochs * train_metadata.total_groups * train_metadata.mean_puzzle_examples / config.global_batch_size) + + # Model + model, optimizers, optimizer_lrs = create_model(config, train_metadata, world_size=world_size) + + return TrainState( + step=0, + total_steps=total_steps, + + model=model, + optimizers=optimizers, + optimizer_lrs=optimizer_lrs, + carry=None + ) + + +def save_train_state(config: PretrainConfig, train_state: TrainState): + # FIXME: Only saved model. + if config.checkpoint_path is None: + return + + os.makedirs(config.checkpoint_path, exist_ok=True) + torch.save(train_state.model.state_dict(), os.path.join(config.checkpoint_path, f"step_{train_state.step}")) + + +def compute_lr(base_lr: float, config: PretrainConfig, train_state: TrainState): + return cosine_schedule_with_warmup_lr_lambda( + current_step=train_state.step, + base_lr=base_lr, + num_warmup_steps=round(config.lr_warmup_steps), + num_training_steps=train_state.total_steps, + min_ratio=config.lr_min_ratio + ) + + +def train_batch(config: PretrainConfig, train_state: TrainState, batch: Any, global_batch_size: int, rank: int, world_size: int): + train_state.step += 1 + if train_state.step > train_state.total_steps: # At most train_total_steps + return + + # To device + batch = {k: v.cuda() for k, v in batch.items()} + + # Init carry if it is None + if train_state.carry is None: + with torch.device("cuda"): + train_state.carry = train_state.model.initial_carry(batch) # type: ignore + + # Forward + train_state.carry, loss, metrics, _, _ = train_state.model(carry=train_state.carry, batch=batch, return_keys=[]) + + ((1 / global_batch_size) * loss).backward() + + # Allreduce + if world_size > 1: + for param in train_state.model.parameters(): + if param.grad is not None: + dist.all_reduce(param.grad) + + # Apply optimizer + lr_this_step = None + for optim, base_lr in zip(train_state.optimizers, train_state.optimizer_lrs): + lr_this_step = compute_lr(base_lr, config, train_state) + + for param_group in optim.param_groups: + param_group['lr'] = lr_this_step + + optim.step() + optim.zero_grad() + + # Reduce metrics + if len(metrics): + assert not any(v.requires_grad for v in metrics.values()) + + metric_keys = list(sorted(metrics.keys())) # Sort keys to guarantee all processes use the same order. + # Reduce and reconstruct + metric_values = torch.stack([metrics[k] for k in metric_keys]) + if world_size > 1: + dist.reduce(metric_values, dst=0) + + if rank == 0: + metric_values = metric_values.cpu().numpy() + reduced_metrics = {k: metric_values[i] for i, k in enumerate(metric_keys)} + + # Postprocess + count = max(reduced_metrics["count"], 1) # Avoid NaNs + reduced_metrics = {f"train/{k}": v / (global_batch_size if k.endswith("loss") else count) for k, v in reduced_metrics.items()} + + reduced_metrics["train/lr"] = lr_this_step + return reduced_metrics + + +def evaluate(config: PretrainConfig, train_state: TrainState, eval_loader: torch.utils.data.DataLoader, eval_metadata: PuzzleDatasetMetadata, rank: int, world_size: int): + with torch.inference_mode(): + set_ids = {k: idx for idx, k in enumerate(eval_metadata.sets)} + + all_preds = {} + + metric_keys = [] + metric_values = None + metric_global_batch_size = [0 for _ in range(len(set_ids))] + + carry = None + for set_name, batch, global_batch_size in eval_loader: + # To device + batch = {k: v.cuda() for k, v in batch.items()} + with torch.device("cuda"): + carry = train_state.model.initial_carry(batch) # type: ignore + + # Forward + while True: + carry, _, metrics, preds, all_finish = train_state.model(carry=carry, batch=batch, return_keys=config.eval_save_outputs) + + if all_finish: + break + + for collection in (batch, preds): + for k, v in collection.items(): + if k in config.eval_save_outputs: + all_preds.setdefault(k, []) + all_preds[k].append(v.cpu()) # Move to CPU for saving GPU memory + + del carry, preds, batch, all_finish + + # Aggregate + set_id = set_ids[set_name] + + if metric_values is None: + metric_keys = list(sorted(metrics.keys())) # Sort keys to guarantee all processes use the same order. + metric_values = torch.zeros((len(set_ids), len(metrics.values())), dtype=torch.float32, device="cuda") + + metric_values[set_id] += torch.stack([metrics[k] for k in metric_keys]) + metric_global_batch_size[set_id] += global_batch_size + + if len(all_preds) and config.checkpoint_path is not None: + all_preds = {k: torch.cat(v, dim=0) for k, v in all_preds.items()} + + os.makedirs(config.checkpoint_path, exist_ok=True) + torch.save(all_preds, os.path.join(config.checkpoint_path, f"step_{train_state.step}_all_preds.{rank}")) + + # Logging + # Reduce to rank 0 + if metric_values is not None: + if world_size > 1: + dist.reduce(metric_values, dst=0) + + if rank == 0: + reduced_metrics = metric_values.cpu().numpy() + reduced_metrics = {set_name: {metric_name: reduced_metrics[set_id, metric_id] for metric_id, metric_name in enumerate(metric_keys)} + for set_id, set_name in enumerate(set_ids)} + + # Postprocess + for set_name, metrics in reduced_metrics.items(): + count = metrics.pop("count") + reduced_metrics[set_name] = {k: v / count for k, v in metrics.items()} + + return reduced_metrics + + +def save_code_and_config(config: PretrainConfig): + if config.checkpoint_path is None or wandb.run is None: + return + + os.makedirs(config.checkpoint_path, exist_ok=True) + + # Copy code + code_list = [ + get_model_source_path(config.arch.name), + get_model_source_path(config.arch.loss.name) + ] + for code_file in code_list: + if code_file is not None: + code_name = os.path.basename(code_file) + + shutil.copy(code_file, os.path.join(config.checkpoint_path, code_name)) + + # Dump config as yaml + config_file = os.path.join(config.checkpoint_path, "all_config.yaml") + with open(config_file, "wt") as f: + yaml.dump(config.model_dump(), f) + + # Log code + wandb.run.log_code(config.checkpoint_path) + + +def load_synced_config(hydra_config: DictConfig, rank: int, world_size: int) -> PretrainConfig: + objects = [None] + if rank == 0: + config = PretrainConfig(**hydra_config) # type: ignore + + # Naming + if config.project_name is None: + config.project_name = f"{os.path.basename(config.data_path).capitalize()} ACT-torch" + if config.run_name is None: + config.run_name = f"{config.arch.name.split('@')[-1]} {coolname.generate_slug(2)}" + if config.checkpoint_path is None: + config.checkpoint_path = os.path.join("checkpoints", config.project_name, config.run_name) + + objects = [config] + + if world_size > 1: + dist.broadcast_object_list(objects, src=0) + + return objects[0] # type: ignore + + +@hydra.main(config_path="config", config_name="cfg_pretrain", version_base=None) +def launch(hydra_config: DictConfig): + RANK = 0 + WORLD_SIZE = 1 + + # Initialize distributed training if in distributed environment (e.g. torchrun) + if "LOCAL_RANK" in os.environ: + # Initialize distributed, default device and dtype + dist.init_process_group(backend="nccl") + + RANK = dist.get_rank() + WORLD_SIZE = dist.get_world_size() + + torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) + + # Load sync'ed config + config = load_synced_config(hydra_config, rank=RANK, world_size=WORLD_SIZE) + + # Seed RNGs to ensure consistency + torch.random.manual_seed(config.seed + RANK) + + # Dataset + train_epochs_per_iter = config.eval_interval if config.eval_interval is not None else config.epochs + total_iters = config.epochs // train_epochs_per_iter + + assert config.epochs % train_epochs_per_iter == 0, "Eval interval must be a divisor of total epochs." + + train_loader, train_metadata = create_dataloader(config, "train", test_set_mode=False, epochs_per_iter=train_epochs_per_iter, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) + eval_loader, eval_metadata = create_dataloader(config, "test", test_set_mode=True, epochs_per_iter=1, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) + + # Train state + train_state = init_train_state(config, train_metadata, world_size=WORLD_SIZE) + + # Progress bar and logger + progress_bar = None + if RANK == 0: + progress_bar = tqdm.tqdm(total=train_state.total_steps) + + wandb.init(project=config.project_name, name=config.run_name, config=config.model_dump(), settings=wandb.Settings(_disable_stats=True)) # type: ignore + wandb.log({"num_params": sum(x.numel() for x in train_state.model.parameters())}, step=0) + save_code_and_config(config) + + # Training Loop + for _iter_id in range(total_iters): + print (f"[Rank {RANK}, World Size {WORLD_SIZE}]: Epoch {_iter_id * train_epochs_per_iter}") + + ############ Train Iter + train_state.model.train() + for set_name, batch, global_batch_size in train_loader: + metrics = train_batch(config, train_state, batch, global_batch_size, rank=RANK, world_size=WORLD_SIZE) + + if RANK == 0 and metrics is not None: + wandb.log(metrics, step=train_state.step) + progress_bar.update(train_state.step - progress_bar.n) # type: ignore + + ############ Evaluation + if not config.skip_eval: + train_state.model.eval() + metrics = evaluate(config, train_state, eval_loader, eval_metadata, rank=RANK, world_size=WORLD_SIZE) + + if RANK == 0 and metrics is not None: + wandb.log(metrics, step=train_state.step) + + ############ Checkpointing + if RANK == 0 and (config.checkpoint_every_eval or (_iter_id == total_iters - 1)): + save_train_state(config, train_state) + + # finalize + if dist.is_initialized(): + dist.destroy_process_group() + wandb.finish() + + +if __name__ == "__main__": + launch() diff --git a/push_to_hf.py b/push_to_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..ff3d83bd1c1c421cda8bcaaef72e9f41a4c73ade --- /dev/null +++ b/push_to_hf.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python3 +""" +Convert HRM checkpoints to safetensors and push to Hugging Face. + +Usage: + python push_to_hf.py \ + --baseline "checkpoints/Sudoku-extreme-1k-aug-1000 ACT-torch/HierarchicalReasoningModel_ACTV1 belligerent-squirrel/step_52080" \ + --tiered "checkpoints/Sudoku-extreme-1k-aug-1000 ACT-torch/HRM_Tiered realistic-dalmatian/step_52080" \ + --repo "Code2aum/HRM_based_evolution" +""" + +import argparse +import os +import shutil +import torch +from safetensors.torch import save_file +from huggingface_hub import login, HfApi, upload_folder + + +def convert_checkpoint(ckpt_path, output_dir, model_name): + """Load .pt checkpoint, save as safetensors + config.""" + os.makedirs(output_dir, exist_ok=True) + + # Load weights + state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True) + + # Strip torch.compile prefix if present + cleaned = {} + for k, v in state_dict.items(): + key = k.removeprefix("_orig_mod.") + cleaned[key] = v + + # Save as safetensors + st_path = os.path.join(output_dir, "model.safetensors") + save_file(cleaned, st_path) + print(f" βœ“ Saved {st_path} ({os.path.getsize(st_path)/1e6:.1f} MB)") + + # Copy config + ckpt_dir = os.path.dirname(ckpt_path) + config_src = os.path.join(ckpt_dir, "all_config.yaml") + if os.path.exists(config_src): + shutil.copy2(config_src, os.path.join(output_dir, "config.yaml")) + print(f" βœ“ Copied config.yaml") + + # Copy model source + for src_file in ["hrm_act_v1.py", "hrm_tiered.py", "losses.py"]: + src_path = os.path.join(ckpt_dir, src_file) + if os.path.exists(src_path): + shutil.copy2(src_path, os.path.join(output_dir, src_file)) + + # Write a model card + card = f"""--- +tags: + - hrm + - hierarchical-reasoning + - sudoku + - pytorch +license: mit +--- + +# {model_name} + +Hierarchical Reasoning Model trained on Sudoku-Extreme-1K (20,000 epochs). + +## Architecture +- **Type**: {model_name} +- **Hidden Size**: 512 +- **Heads**: 8 +- **H/L Layers**: 4/4 +- **H/L Cycles**: 2/2 +- **Parameters**: ~27.3M + +## Training +- **Dataset**: Sudoku-Extreme-1K (1000 puzzles, 1000 augmentations each) +- **Epochs**: 20,000 +- **Batch Size**: 384 +- **Learning Rate**: 7e-5 +- **GPU**: NVIDIA RTX 4090 + +## Usage +```python +from safetensors.torch import load_file +state_dict = load_file("{model_name}/model.safetensors") +``` +""" + with open(os.path.join(output_dir, "README.md"), "w") as f: + f.write(card) + print(f" βœ“ Created README.md") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--baseline", type=str, required=True) + parser.add_argument("--tiered", type=str, required=True) + parser.add_argument("--repo", type=str, default="Code2aum/HRM_based_evolution") + parser.add_argument("--output-dir", type=str, default="hf_upload") + parser.add_argument("--no-push", action="store_true", help="Convert only, skip push") + args = parser.parse_args() + + print("=" * 60) + print(" HRM β†’ SafeTensors β†’ Hugging Face") + print("=" * 60) + + # Convert baseline + base_dir = os.path.join(args.output_dir, "baseline_hrm_v1") + print(f"\n Converting Baseline...") + convert_checkpoint(args.baseline, base_dir, "HRM_Baseline_V1") + + # Convert tiered + tier_dir = os.path.join(args.output_dir, "tiered_hrm_sram_dram") + print(f"\n Converting Tiered...") + convert_checkpoint(args.tiered, tier_dir, "HRM_Tiered_SRAM_DRAM") + + # Copy benchmark results if available + bench_dir = "benchmark_results" + if os.path.exists(bench_dir): + dest = os.path.join(args.output_dir, "benchmark_results") + if not os.path.exists(dest): + shutil.copytree(bench_dir, dest) + print(f"\n βœ“ Copied benchmark_results/") + + if args.no_push: + print(f"\n Files ready at: {args.output_dir}/") + print(" Run without --no-push to upload to HF.") + return + + # Push to HF + print(f"\n Logging in to Hugging Face...") + login() + + print(f"\n Uploading to {args.repo}...") + api = HfApi() + api.create_repo(repo_id=args.repo, repo_type="model", exist_ok=True) + upload_folder( + folder_path=args.output_dir, + repo_id=args.repo, + repo_type="model", + ) + print(f"\n βœ“ Uploaded to https://huggingface.co/{args.repo}") + + print("\n" + "=" * 60) + print(" Done!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/puzzle_dataset.py b/puzzle_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..2782403c39e5daca3ff192dbdd1159dea24fcfe1 --- /dev/null +++ b/puzzle_dataset.py @@ -0,0 +1,199 @@ +import os +import json + +import numpy as np +import pydantic + +import torch +from torch.utils.data import IterableDataset, get_worker_info + +from models.losses import IGNORE_LABEL_ID +from dataset.common import PuzzleDatasetMetadata + + +def _sample_batch(rng: np.random.Generator, group_order: np.ndarray, puzzle_indices: np.ndarray, group_indices: np.ndarray, start_index: int, global_batch_size: int): + # Pack examples into a full batch + batch = [] + batch_puzzle_indices = [] + current_size = 0 + + while (start_index < group_order.size) and (current_size < global_batch_size): + # Pick a group and a puzzle from that group + group_id = group_order[start_index] + puzzle_id = rng.integers(group_indices[group_id], group_indices[group_id + 1]) + start_index += 1 + + # Get range of the puzzle + puzzle_start = puzzle_indices[puzzle_id] + puzzle_size = int(puzzle_indices[puzzle_id + 1] - puzzle_start) + + append_size = min(puzzle_size, global_batch_size - current_size) + + # Put into batch + batch_puzzle_indices.append(np.full(append_size, puzzle_id, dtype=np.int32)) + batch.append(puzzle_start + np.random.choice(puzzle_size, append_size, replace=False)) + + current_size += append_size + + return start_index, np.concatenate(batch), np.concatenate(batch_puzzle_indices) + + +class PuzzleDatasetConfig(pydantic.BaseModel): + seed: int + dataset_path: str + global_batch_size: int + test_set_mode: bool + + epochs_per_iter: int # Batch X epochs in an iteration to reduce overhead. + + rank: int + num_replicas: int + + +class PuzzleDataset(IterableDataset): + def __init__(self, config: PuzzleDatasetConfig, split: str = "train"): + super().__init__() + self.config = config + self.split = split + self.metadata = self._load_metadata() + + # Checks + assert self.config.global_batch_size % self.config.num_replicas == 0, f"Global batch size {self.config.global_batch_size} must be multiples of nodes {self.config.num_replicas}." + self.local_batch_size = self.config.global_batch_size // self.config.num_replicas + + # State + self._data = None + self._iters = 0 + + def _load_metadata(self) -> PuzzleDatasetMetadata: + with open(os.path.join(self.config.dataset_path, self.split, "dataset.json"), "r") as f: + return PuzzleDatasetMetadata(**json.load(f)) + + def _lazy_load_dataset(self): + if self._data is not None: + return + + field_mmap_modes = { + "inputs": "r", + "labels": "r", + + # Keep indices in memory + "puzzle_identifiers": None, + "puzzle_indices": None, + "group_indices": None + } + + # Load data + self._data = {} + for set_name in self.metadata.sets: + # Load subset + self._data[set_name] = { + field_name: np.load(os.path.join(self.config.dataset_path, self.split, f"{set_name}__{field_name}.npy"), mmap_mode=mmap_mode) + for field_name, mmap_mode in field_mmap_modes.items() + } + + def _collate_batch(self, batch): + # Convert dtype + batch = {k: v.astype(np.int32) for k, v in batch.items()} + + # Convert ignore label IDs + if self.metadata.ignore_label_id is not None: + batch["labels"][batch["labels"] == self.metadata.ignore_label_id] = IGNORE_LABEL_ID + + # Pad + if batch["puzzle_identifiers"].size < self.local_batch_size: + pad_size = self.local_batch_size - batch["puzzle_identifiers"].size + + pad_values = { + "inputs": self.metadata.pad_id, + "labels": IGNORE_LABEL_ID, + + "puzzle_identifiers": self.metadata.blank_identifier_id + } + batch = {k: np.pad(v, ((0, pad_size), ) + ((0, 0), ) * (v.ndim - 1), constant_values=pad_values[k]) for k, v in batch.items()} + + # To tensor + return {k: torch.from_numpy(v) for k, v in batch.items()} + + def _iter_test(self): + for set_name, dataset in self._data.items(): # type: ignore + total_examples = len(dataset["inputs"]) + + # Load examples one by one + start_index = 0 + while start_index < total_examples: + # Compute indices + end_index = min(total_examples, start_index + self.config.global_batch_size) + + local_start = start_index + self.config.rank * self.local_batch_size + local_end = min(start_index + (self.config.rank + 1) * self.local_batch_size, end_index) + + # Get batch of examples, and also puzzle IDs + puzzle_indices = [] + puzzle_index = np.searchsorted(dataset["puzzle_indices"], local_start, side="right") - 1 + for i in range(local_start, local_end): + while puzzle_index + 1 < len(dataset["puzzle_indices"]) and i >= dataset["puzzle_indices"][puzzle_index + 1]: + puzzle_index += 1 + + puzzle_indices.append(puzzle_index) + + batch = self._collate_batch({ + "inputs": dataset["inputs"][local_start: local_end], + "labels": dataset["labels"][local_start: local_end], + "puzzle_identifiers": dataset["puzzle_identifiers"][puzzle_indices] + }) + + yield set_name, batch, end_index - start_index + + # Advance to next batch + start_index += self.config.global_batch_size + + def _iter_train(self): + for set_name, dataset in self._data.items(): # type: ignore + # Increase epoch count + self._iters += 1 + + # Randomly shuffle groups + rng = np.random.Generator(np.random.Philox(seed=self.config.seed + self._iters)) + + group_order = np.concatenate([rng.permutation(dataset["group_indices"].size - 1) for _i in range(self.config.epochs_per_iter)]) + start_index = 0 + + while start_index < group_order.size: + start_index, batch_indices, batch_puzzle_indices = _sample_batch( + rng, + group_order=group_order, + puzzle_indices=dataset["puzzle_indices"], + group_indices=dataset["group_indices"], + start_index=start_index, + global_batch_size=self.config.global_batch_size, + ) + + # Select current rank and collate + global_effective_batch_size = batch_puzzle_indices.size # Global effective batch size, excluding pads + + # Drop last batch + if global_effective_batch_size < self.config.global_batch_size: + break + + batch_indices = batch_indices [self.config.rank * self.local_batch_size: (self.config.rank + 1) * self.local_batch_size] + batch_puzzle_indices = batch_puzzle_indices[self.config.rank * self.local_batch_size: (self.config.rank + 1) * self.local_batch_size] + batch = self._collate_batch({ + "inputs": dataset["inputs"][batch_indices], + "labels": dataset["labels"][batch_indices], + "puzzle_identifiers": dataset["puzzle_identifiers"][batch_puzzle_indices] + }) + + yield set_name, batch, global_effective_batch_size + + def __iter__(self): + worker_info = get_worker_info() + assert worker_info is None or worker_info.num_workers == 1, "Multithreaded data loading is not currently supported." + + self._lazy_load_dataset() + + # Iterate using specified mode + if self.config.test_set_mode: + yield from self._iter_test() + else: + yield from self._iter_train() diff --git a/puzzle_visualizer.html b/puzzle_visualizer.html new file mode 100644 index 0000000000000000000000000000000000000000..bcefdf1c46ed17d0d4b035873992a98d1900da3f --- /dev/null +++ b/puzzle_visualizer.html @@ -0,0 +1,426 @@ + + + + + ARC‐Converted Dataset Visualizer (Upload Local Folder) + + + +

ARC‐Converted Dataset Visualizer (Local Directory)

+ +
+ + + +

+ + + + + + + + + +
+ +
+
+
+
+
+ + + + + + + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d5d7ba95e94ea413b3391f71851c5f99e7df6802 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,13 @@ +torch +numpy +adam-atan2 +einops +tqdm +coolname +pydantic +argdantic +wandb +omegaconf +hydra-core +huggingface_hub +matplotlib diff --git a/run_benchmark.py b/run_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..079cdb51a198771653b2d534e9b8850b223aa2c7 --- /dev/null +++ b/run_benchmark.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python3 +""" +CLI entry point for HRM SRAM/DRAM Memory Tiering Benchmarks. + +Usage: + # Compare tiered vs baseline HRM + python run_benchmark.py --mode compare --batch-sizes 1,8,32 --seq-lens 64,128 + + # Benchmark tiered model only + python run_benchmark.py --mode tiered --warmup 5 --iterations 50 --output results.json + + # Generate plots + python run_benchmark.py --mode compare --plot --output-dir benchmark_results/ + + # Quick smoke test + python run_benchmark.py --mode tiered --warmup 1 --iterations 3 --batch-sizes 2 --seq-lens 16 +""" + +import argparse +import json +import os +import sys +from dataclasses import asdict + +# Add project root to path +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from benchmark import ( + benchmark_tiered_model, + benchmark_baseline_model, + compare_models, + print_results_table, + generate_plots, + BenchmarkResult, +) + + +def parse_int_list(s: str): + return [int(x.strip()) for x in s.split(',')] + + +def main(): + parser = argparse.ArgumentParser( + description='HRM SRAM/DRAM Memory Tiering Benchmark Suite (Triton)', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=__doc__, + ) + + parser.add_argument( + '--mode', choices=['tiered', 'baseline', 'compare'], + default='compare', + help='Benchmark mode: tiered-only, baseline-only, or comparison (default: compare)', + ) + parser.add_argument( + '--batch-sizes', type=str, default='1,8,32', + help='Comma-separated batch sizes to benchmark (default: 1,8,32)', + ) + parser.add_argument( + '--seq-lens', type=str, default='64,128', + help='Comma-separated sequence lengths (default: 64,128)', + ) + parser.add_argument( + '--hidden-size', type=int, default=512, + help='Model hidden size (default: 512)', + ) + parser.add_argument( + '--num-heads', type=int, default=8, + help='Number of attention heads (default: 8)', + ) + parser.add_argument( + '--H-cycles', type=int, default=2, + help='H-level recurrence cycles (default: 2)', + ) + parser.add_argument( + '--L-cycles', type=int, default=2, + help='L-level recurrence cycles (default: 2)', + ) + parser.add_argument( + '--H-layers', type=int, default=4, + help='H-level transformer layers (default: 4)', + ) + parser.add_argument( + '--L-layers', type=int, default=4, + help='L-level transformer layers (default: 4)', + ) + parser.add_argument( + '--warmup', type=int, default=5, + help='Warmup iterations (default: 5)', + ) + parser.add_argument( + '--iterations', type=int, default=20, + help='Benchmark iterations (default: 20)', + ) + parser.add_argument( + '--output', type=str, default=None, + help='Output JSON file path for results', + ) + parser.add_argument( + '--output-dir', type=str, default='benchmark_results', + help='Directory for output files and plots (default: benchmark_results/)', + ) + parser.add_argument( + '--plot', action='store_true', + help='Generate comparison plots (requires matplotlib)', + ) + parser.add_argument( + '--device', type=str, default=None, + help='Device: cuda, cpu, or cuda:N (default: auto-detect)', + ) + + args = parser.parse_args() + + batch_sizes = parse_int_list(args.batch_sizes) + seq_lens = parse_int_list(args.seq_lens) + + # Device setup + import torch + if args.device: + device = torch.device(args.device) + else: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + print(f"\n{'='*60}") + print(f" HRM SRAM/DRAM Memory Tiering Benchmark") + print(f" Device: {device}") + print(f" Mode: {args.mode}") + print(f" Batch sizes: {batch_sizes}") + print(f" Sequence lengths: {seq_lens}") + print(f" Hidden size: {args.hidden_size}") + print(f" H/L cycles: {args.H_cycles}/{args.L_cycles}") + print(f" H/L layers: {args.H_layers}/{args.L_layers}") + print(f" Warmup: {args.warmup}, Iterations: {args.iterations}") + print(f"{'='*60}") + + if args.mode == 'compare': + results = compare_models( + batch_sizes=batch_sizes, + seq_lens=seq_lens, + hidden_size=args.hidden_size, + warmup=args.warmup, + iterations=args.iterations, + device=device, + ) + + if args.plot: + generate_plots(results, output_dir=args.output_dir) + + # Save results + output_path = args.output or os.path.join(args.output_dir, 'results.json') + os.makedirs(os.path.dirname(output_path) or '.', exist_ok=True) + with open(output_path, 'w') as f: + json.dump(results, f, indent=2, default=str) + print(f"\n Results saved: {output_path}") + + elif args.mode == 'tiered': + all_results = [] + for bs in batch_sizes: + for sl in seq_lens: + print(f"\n Benchmarking tiered model: bs={bs}, seq={sl}") + r = benchmark_tiered_model( + batch_size=bs, seq_len=sl, + hidden_size=args.hidden_size, + num_heads=args.num_heads, + H_cycles=args.H_cycles, L_cycles=args.L_cycles, + H_layers=args.H_layers, L_layers=args.L_layers, + warmup=args.warmup, iterations=args.iterations, + device=device, + ) + all_results.append(r) + + print_results_table(all_results) + + if args.output: + with open(args.output, 'w') as f: + json.dump([asdict(r) for r in all_results], f, indent=2, default=str) + print(f" Results saved: {args.output}") + + elif args.mode == 'baseline': + all_results = [] + for bs in batch_sizes: + for sl in seq_lens: + print(f"\n Benchmarking baseline model: bs={bs}, seq={sl}") + r = benchmark_baseline_model( + batch_size=bs, seq_len=sl, + hidden_size=args.hidden_size, + num_heads=args.num_heads, + H_cycles=args.H_cycles, L_cycles=args.L_cycles, + H_layers=args.H_layers, L_layers=args.L_layers, + warmup=args.warmup, iterations=args.iterations, + device=device, + ) + all_results.append(r) + + print_results_table(all_results) + + if args.output: + with open(args.output, 'w') as f: + json.dump([asdict(r) for r in all_results], f, indent=2, default=str) + print(f" Results saved: {args.output}") + + print("\n Done!\n") + + +if __name__ == '__main__': + main() diff --git a/softmax.py b/softmax.py new file mode 100644 index 0000000000000000000000000000000000000000..577553c866f253744baf7a05272a3e6838ead280 --- /dev/null +++ b/softmax.py @@ -0,0 +1,5 @@ +import torch +import torch.nn.functional as F +sample = torch.tensor([[1,2,3,4,5], [5,4,3,2,1]], dtype=torch.float32, device='cuda') +ref = F.softmax(sample, dim=1) +print(f"Softmax result: {ref=}") \ No newline at end of file diff --git a/softmax_triton.py b/softmax_triton.py new file mode 100644 index 0000000000000000000000000000000000000000..4b63d2644fdfc4b0acf751588e7b4cc053398716 --- /dev/null +++ b/softmax_triton.py @@ -0,0 +1,2 @@ +import triton +import triton.language as tl diff --git a/startup.sh b/startup.sh new file mode 100644 index 0000000000000000000000000000000000000000..c9ac27026ea5ccc5d2b036b0965f62ee18aa10ec --- /dev/null +++ b/startup.sh @@ -0,0 +1,39 @@ +#!/usr/bin/env bash +# startup.sh β€” Environment setup for HRM_optimised +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +cd "$SCRIPT_DIR" + +echo "==========================================" +echo " HRM_optimised β€” Environment Setup" +echo "==========================================" + +# 1. Create virtual environment +if [ ! -d "venv" ]; then + echo "[1/3] Creating virtual environment (venv/)..." + python3 -m venv venv +else + echo "[1/3] Virtual environment already exists." +fi + +# 2. Activate venv +echo "[2/3] Activating virtual environment..." +source venv/bin/activate + +# 3. Install dependencies +echo "[3/3] Installing dependencies from requirements.txt..." +pip install --upgrade pip +pip install -r requirements.txt + +# Try to install triton if available (needed for tiered models) +if [[ $(python3 -c "import torch; print(torch.cuda.is_available())") == "True" ]]; then + echo "CUDA detected. Attempting to install triton..." + pip install triton --no-cache-dir || echo "Warning: Triton installation failed. Tiered models may use PyTorch fallback." +fi + +echo "" +echo "==========================================" +echo " βœ… Setup complete!" +echo " To start, run: source venv/bin/activate" +echo "==========================================" diff --git a/train_and_benchmark.py b/train_and_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..34ddbba2d07b024a57bf4a913d41ff4d81d14672 --- /dev/null +++ b/train_and_benchmark.py @@ -0,0 +1,246 @@ +#!/usr/bin/env python3 +""" +train_and_benchmark.py β€” Train Baseline vs Tiered HRM, then benchmark both. + +Usage: + source venv/bin/activate + + # Quick smoke test (tiny dataset, 2 epochs) + python train_and_benchmark.py --data-path data/sudoku-1k --epochs 2 --batch-size 384 + + # Full run + python train_and_benchmark.py --data-path data/sudoku-1k --epochs 1000 --batch-size 384 + + # Benchmark only (skip training) + python train_and_benchmark.py --benchmark-only + +Outputs: + - Console: live training metrics + benchmark table + - benchmark_results/results.json + - benchmark_results/benchmark_comparison.png + - benchmark_results/memory_analysis.png +""" + +import argparse +import json +import os +import sys +import time + +import torch + +# ── Project imports ────────────────────────────────────────────────── +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from omegaconf import OmegaConf +from pretrain import PretrainConfig, init_train_state, train_batch, evaluate, create_dataloader, save_train_state +from benchmark import ( + benchmark_tiered_model, + benchmark_baseline_model, + compare_models, + print_results_table, + generate_plots, +) + + +# ===================================================================== +# PART 1: TRAINING +# ===================================================================== + +def run_training(arch_name: str, args, device): + """Train one architecture variant and return final metrics.""" + print(f"\n{'='*60}") + print(f" Training: {arch_name}") + print(f" Data: {args.data_path}") + print(f" Epochs: {args.epochs} | Batch: {args.batch_size}") + print(f"{'='*60}\n") + + # Build Hydra-style config dict + cfg = OmegaConf.create({ + "arch": arch_name, + "data_path": args.data_path, + "global_batch_size": args.batch_size, + "epochs": args.epochs, + "lr": 7e-5, + "lr_min_ratio": 0.0, + "lr_warmup_steps": 100, + "weight_decay": 1.0, + "beta1": 0.9, + "beta2": 0.95, + "puzzle_emb_lr": 7e-5, + "puzzle_emb_weight_decay": 1.0, + "seed": 0, + "skip_eval": False, + "eval_interval": max(1, args.epochs // 5), # eval 5 times + }) + # Merge with the arch yaml + from hydra import compose, initialize_config_dir + from hydra.core.global_hydra import GlobalHydra + GlobalHydra.instance().clear() + with initialize_config_dir(config_dir=os.path.join(os.path.abspath("."), "config"), version_base=None): + hydra_cfg = compose(config_name="cfg_pretrain", overrides=[ + f"arch={arch_name}", + f"data_path={args.data_path}", + f"global_batch_size={args.batch_size}", + f"epochs={args.epochs}", + ]) + + config = PretrainConfig(**OmegaConf.to_container(hydra_cfg, resolve=True)) + config.eval_interval = max(1, args.epochs // 5) + + # Dataloaders + train_loader, train_meta = create_dataloader( + config, "train", 0, 1, + test_set_mode=False, epochs_per_iter=1, + global_batch_size=config.global_batch_size, + ) + eval_loader, eval_meta = create_dataloader( + config, "test", 0, 1, + test_set_mode=True, epochs_per_iter=1, + global_batch_size=config.global_batch_size, + ) + + # Model + train_state = init_train_state(config, train_meta, world_size=1) + param_count = sum(p.numel() for p in train_state.model.parameters()) + print(f" Parameters: {param_count:,} ({param_count/1e6:.1f}M)") + + # Training loop + best_acc = 0.0 + epoch_times = [] + + for epoch in range(1, config.epochs + 1): + train_state.model.train() + t0 = time.perf_counter() + last_metrics = None + + for set_name, batch, gbs in train_loader: + metrics = train_batch(config, train_state, batch, gbs, rank=0, world_size=1) + if metrics: + last_metrics = metrics + + dt = time.perf_counter() - t0 + epoch_times.append(dt) + + # Log + if last_metrics and epoch % max(1, args.epochs // 20) == 0: + loss = last_metrics.get("train/total_loss", 0) + acc = last_metrics.get("train/exact_accuracy", 0) + print(f" Epoch {epoch:>5}/{config.epochs} | Loss: {loss:.4f} | Acc: {acc:.2%} | {dt:.1f}s") + + # Eval + if config.eval_interval and epoch % config.eval_interval == 0: + train_state.model.eval() + eval_results = evaluate(config, train_state, eval_loader, eval_meta, rank=0, world_size=1) + if eval_results: + for s_name, s_metrics in eval_results.items(): + ea = s_metrics.get("exact_accuracy", 0) + print(f" eval/{s_name}: exact_accuracy={ea:.2%}") + best_acc = max(best_acc, ea) + + # Summary + avg_epoch = sum(epoch_times) / len(epoch_times) if epoch_times else 0 + print(f"\n ── {arch_name} Done ──") + print(f" Best Eval Accuracy: {best_acc:.2%}") + print(f" Avg Epoch Time: {avg_epoch:.2f}s") + print(f" GPU Peak Memory: {torch.cuda.max_memory_allocated()/1e9:.2f} GB") + + return {"arch": arch_name, "best_acc": best_acc, "avg_epoch_s": avg_epoch} + + +# ===================================================================== +# PART 2: HARDWARE BENCHMARK +# ===================================================================== + +def run_benchmark(args): + """Compare Tiered vs Baseline on synthetic data.""" + print(f"\n{'='*60}") + print(f" Hardware Benchmark: Tiered vs Baseline") + print(f" Batch sizes: {args.bench_batch_sizes}") + print(f" Seq lengths: {args.bench_seq_lens}") + print(f"{'='*60}") + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + bs_list = [int(x) for x in args.bench_batch_sizes.split(",")] + sl_list = [int(x) for x in args.bench_seq_lens.split(",")] + + results = compare_models( + batch_sizes=bs_list, seq_lens=sl_list, + hidden_size=512, warmup=5, iterations=args.bench_iters, + device=device, + ) + + # Save + os.makedirs(args.output_dir, exist_ok=True) + out_path = os.path.join(args.output_dir, "results.json") + with open(out_path, "w") as f: + json.dump(results, f, indent=2, default=str) + print(f"\n Results saved β†’ {out_path}") + + # Plots + try: + generate_plots(results, output_dir=args.output_dir) + except Exception as e: + print(f" (Plots skipped: {e})") + + return results + + +# ===================================================================== +# MAIN +# ===================================================================== + +def main(): + parser = argparse.ArgumentParser(description="HRM: Train + Benchmark") + # Training + parser.add_argument("--data-path", type=str, default="data/sudoku-1k") + parser.add_argument("--epochs", type=int, default=100) + parser.add_argument("--batch-size", type=int, default=384) + # Benchmark + parser.add_argument("--bench-batch-sizes", type=str, default="1,8,32") + parser.add_argument("--bench-seq-lens", type=str, default="64,128") + parser.add_argument("--bench-iters", type=int, default=20) + parser.add_argument("--output-dir", type=str, default="benchmark_results") + # Flow control + parser.add_argument("--benchmark-only", action="store_true", help="Skip training, only run benchmark") + parser.add_argument("--train-only", action="store_true", help="Skip benchmark, only run training") + args = parser.parse_args() + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"\n Device: {device}") + if torch.cuda.is_available(): + print(f" GPU: {torch.cuda.get_device_name(0)}") + + # ── Training ── + train_summary = {} + if not args.benchmark_only: + for arch in ["hrm_v1", "hrm_tiered"]: + try: + result = run_training(arch, args, device) + train_summary[arch] = result + except Exception as e: + print(f"\n ⚠ Training failed for {arch}: {e}") + train_summary[arch] = {"error": str(e)} + torch.cuda.empty_cache() + + # Print training comparison + print(f"\n{'='*60}") + print(f" Training Comparison") + print(f"{'='*60}") + print(f" {'Arch':<15} {'Best Acc':>10} {'Avg Epoch':>12}") + print(f" {'-'*37}") + for arch, r in train_summary.items(): + if "error" in r: + print(f" {arch:<15} {'FAILED':>10} {'':>12}") + else: + print(f" {arch:<15} {r['best_acc']:>10.2%} {r['avg_epoch_s']:>10.2f}s") + + # ── Benchmark ── + if not args.train_only: + run_benchmark(args) + + print(f"\n All done!\n") + + +if __name__ == "__main__": + main() diff --git a/utils/functions.py b/utils/functions.py new file mode 100644 index 0000000000000000000000000000000000000000..b12363606c41692e19b60f33098605e147adc7f0 --- /dev/null +++ b/utils/functions.py @@ -0,0 +1,19 @@ +import importlib +import inspect + + +def load_model_class(identifier: str, prefix: str = "models."): + module_path, class_name = identifier.split('@') + + # Import the module + module = importlib.import_module(prefix + module_path) + cls = getattr(module, class_name) + + return cls + + +def get_model_source_path(identifier: str, prefix: str = "models."): + module_path, class_name = identifier.split('@') + + module = importlib.import_module(prefix + module_path) + return inspect.getsourcefile(module)