diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..80d53ac5 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,65 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +*.egg-info/ +dist/ +build/ +.eggs/ + +# Virtual environments +.venv/ +venv/ +ENV/ +env/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# Git +.git/ +.gitignore +.gitmodules +.gitattributes + +# Documentation +docs/_build/ +*.md +!README.md + +# Data and checkpoints (too large for image) +data/ +checkpoints/ +wandb/ +cache/ + +# Tests +tests/ +.pytest_cache/ +.coverage +htmlcov/ +.tox/ + +# CI/CD +.github/ +.pre-commit-config.yaml + +# Notebooks +*.ipynb +.ipynb_checkpoints/ + +# Temporary files +*.log +*.tmp +.DS_Store +Thumbs.db + +# Package files +*.tar.gz +*.whl diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 00000000..feb9ac3e --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,44 @@ +name: CI + +on: + pull_request: + branches: + - main + push: + branches: + - main + +jobs: + lint-and-test: + name: Lint & Test (Python ${{ matrix.python-version }}) + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.11", "3.12", "3.13"] + + steps: + - name: Checkout code + uses: actions/checkout@v5 + + - name: Set up uv + uses: astral-sh/setup-uv@v6 + with: + version: "0.8.13" + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: uv sync --extra dev + + - name: Lint check Python:${{ matrix.python-version }} + run: uv run ruff check + + - name: Tests Python:${{ matrix.python-version }} + run: uv run pytest + + # TODO: Setup CodeCov + # - name: Upload coverage reports to Codecov + # uses: codecov/codecov-action@v5 + # with: + # token: ${{ secrets.CODECOV_TOKEN }} + # slug: sapientinc/HRM \ No newline at end of file diff --git a/.gitignore b/.gitignore index 644b4225..a8b6539d 100644 --- a/.gitignore +++ b/.gitignore @@ -166,4 +166,9 @@ cython_debug/ # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ \ No newline at end of file +#.idea/ + +CLAUDE.md +INSTRUCTIONS.md +.ruff_cache/ +.claude/ \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 00000000..e4fba218 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..7f027e24 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,114 @@ +# Multi-stage Dockerfile for Hierarchical Reasoning Model +# Supports both FlashAttention 2 (Ampere GPUs) and FlashAttention 3 (Hopper GPUs) + +# Build argument for FlashAttention version +ARG FLASH_ATTN_VERSION=2 + +# ============================================================================ +# Stage 1: Base Image with CUDA and System Dependencies +# ============================================================================ +FROM nvidia/cuda:12.6.3-cudnn-devel-ubuntu24.04 AS base + +# Set environment variables +ENV DEBIAN_FRONTEND=noninteractive \ + CUDA_HOME=/usr/local/cuda-12.6 \ + PATH=/usr/local/cuda-12.6/bin:$PATH \ + LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64:$LD_LIBRARY_PATH \ + PYTHONUNBUFFERED=1 \ + PYTHON_VERSION=3.12 + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + --no-install-recommends \ + python${PYTHON_VERSION} \ + python${PYTHON_VERSION}-dev \ + python3-pip \ + git \ + wget \ + build-essential \ + ninja-build \ + && rm -rf /var/lib/apt/lists/* + +# Create symlink for python +RUN ln -sf /usr/bin/python${PYTHON_VERSION} /usr/bin/python3 && \ + ln -sf /usr/bin/python3 /usr/bin/python + +# ============================================================================ +# Stage 2: Install uv and Python Dependencies +# ============================================================================ +FROM base AS builder + +# Copy uv from official image +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +# Set working directory +WORKDIR /app + +# Copy dependency files +COPY pyproject.toml ./ +COPY README.md ./ +COPY LICENSE ./ + +# Create src directory structure (required for build) +RUN mkdir -p src/hierarchical_reasoning_model && \ + touch src/hierarchical_reasoning_model/__init__.py + +# Install core dependencies +RUN uv sync --frozen --no-dev + +# Install PyTorch with CUDA 12.6 support +RUN uv pip install torch torchvision torchaudio \ + --index-url https://download.pytorch.org/whl/cu126 + +# Install FlashAttention based on version +ARG FLASH_ATTN_VERSION +RUN if [ "$FLASH_ATTN_VERSION" = "3" ]; then \ + echo "Installing FlashAttention 3 for Hopper GPUs" && \ + git clone https://github.com/Dao-AILab/flash-attention.git /tmp/flash-attention && \ + cd /tmp/flash-attention/hopper && \ + uv pip install . && \ + rm -rf /tmp/flash-attention; \ + else \ + echo "Installing FlashAttention 2 for Ampere GPUs" && \ + uv pip install flash-attn --no-build-isolation; \ + fi + +# Install training dependencies +RUN uv sync --frozen --extra train --extra data + +# ============================================================================ +# Stage 3: Final Runtime Image +# ============================================================================ +FROM base AS runtime + +# Copy uv from builder +COPY --from=builder /uv /uvx /bin/ + +# Copy virtual environment from builder +COPY --from=builder /app/.venv /app/.venv + +# Set working directory +WORKDIR /app + +# Copy application code +COPY src/ /app/src/ +COPY config/ /app/config/ +COPY pyproject.toml README.md LICENSE ./ + +# Install the package in editable mode +RUN . /app/.venv/bin/activate && \ + uv pip install -e . + +# Set environment variables for runtime +ENV PATH=/app/.venv/bin:$PATH \ + PYTHONPATH=/app/src:$PYTHONPATH + +# Create directories for data and checkpoints +RUN mkdir -p /app/data /app/checkpoints /app/wandb + +# Set default command +CMD ["python", "-c", "import hierarchical_reasoning_model; print('HRM Docker image ready!')"] + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ + CMD python -c "import torch; print('CUDA available:', torch.cuda.is_available())" diff --git a/README.md b/README.md index f8623331..12429e06 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,176 @@ These results underscore HRM’s potential as a transformative advancement towar **Join our Discord Community: [https://discord.gg/sapient](https://discord.gg/sapient)** +## Package Structure 📁 + +This repository contains: + +- **`hierarchical_reasoning_model/`** - Core model package (install with `uv sync`) + - Pure model architecture components + - No training/evaluation utilities + - Minimal dependencies + +- **`scripts/`** - Research scripts (not part of package) + - Dataset builders for ARC, Sudoku, Maze + - Training and evaluation scripts + - Data loading utilities + - See `scripts/README.md` for details + +## Installation 📦 + +### Core Model Package (Recommended for Users) + +Install just the model architecture: + +```bash +# Install uv if you don't have it +curl -LsSf https://astral.sh/uv/install.sh | sh + +# Clone the repository +git clone https://github.com/yourusername/HRM.git +cd HRM + +# Install core package +uv sync +``` + +### With Research Scripts + +For dataset processing, training, and evaluation: + +```bash +# Install all dependencies +uv sync --all-extras + +# Initialize submodules (for raw datasets) +git submodule update --init --recursive +``` + +### Using pip + +```bash +# Clone and install +git clone https://github.com/yourusername/HRM.git +cd HRM +pip install -e . +``` + +### Docker (GPU Support) + +For a containerized environment with all dependencies: + +```bash +# Build for FlashAttention 2 (Ampere GPUs: RTX 30xx, A100, etc.) +docker-compose build hrm-train-fa2 + +# Or build for FlashAttention 3 (Hopper GPUs: H100, etc.) +docker-compose build hrm-train-fa3 + +# Run training +docker-compose run hrm-train-fa2 +``` + +### FlashAttention Installation + +HRM requires FlashAttention for efficient attention computation. The package attempts to install it automatically, but for manual installation: + +**For Hopper GPUs (H100, etc.) - FlashAttention 3:** +```bash +git clone https://github.com/Dao-AILab/flash-attention.git +cd flash-attention/hopper +python setup.py install +``` + +**For Ampere/Ada GPUs (RTX 30xx/40xx, A100, etc.) - FlashAttention 2:** +```bash +pip install flash-attn --no-build-isolation +``` + +### Verify Installation + +```bash +# Run basic usage example +uv run python examples/01_basic_model_usage.py +``` + +## Python API Usage 🐍 + +The core package provides the HRM model architecture for easy integration: + +```python +from hierarchical_reasoning_model import ( + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1Config, + ACTLossHead, +) +import torch + +# Create model configuration +config = HierarchicalReasoningModel_ACTV1Config( + batch_size=4, + seq_len=81, # Sudoku grid size + vocab_size=11, # 0-9 digits + padding + num_puzzle_identifiers=1, + H_cycles=2, # High-level reasoning cycles + L_cycles=2, # Low-level computation cycles + H_layers=4, + L_layers=4, + hidden_size=512, + num_heads=8, + expansion=4.0, + pos_encodings="rope", + halt_max_steps=16, + halt_exploration_prob=0.1, + puzzle_emb_ndim=512, +) + +# Initialize model +model = HierarchicalReasoningModel_ACTV1(config.model_dump()) +loss_head = ACTLossHead(model, loss_type="stablemax_cross_entropy") + +# Prepare batch (you provide your own data loading) +batch = { + "inputs": torch.randint(0, 11, (4, 81)), + "labels": torch.randint(0, 11, (4, 81)), + "puzzle_identifiers": torch.zeros(4, dtype=torch.long), +} + +# Forward pass +carry = loss_head.initial_carry(batch) +carry, loss, metrics, predictions, all_halted = loss_head.forward( + return_keys=["logits"], + carry=carry, + batch=batch, +) + +# Training loop (your own optimizer and data) +optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) + +for batch in your_dataloader: # Bring your own data loader + optimizer.zero_grad() + carry = loss_head.initial_carry(batch) + _, loss, metrics, _, _ = loss_head.forward( + return_keys=["logits"], + carry=carry, + batch=batch, + ) + loss.backward() + optimizer.step() +``` + +### Using Research Scripts + +For complete training pipelines with data loading, see `scripts/README.md`: + +```bash +# Use dataset builders +python scripts/build_sudoku_dataset.py --output-dir data/sudoku + +# Use training script +python scripts/train.py --config-path configs --config-name train.yaml +``` + +See `examples/01_basic_model_usage.py` for a complete working example. ## Quick Start Guide 🚀 @@ -70,11 +240,22 @@ wandb login 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 +# Download and build Sudoku dataset (using research scripts) +uv run python scripts/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 +OMP_NUM_THREADS=8 uv run python scripts/train.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 @@ -94,20 +275,32 @@ Experiments below assume an 8-GPU setup. ### Dataset Preparation ```bash -# Initialize submodules +# Initialize submodules (for raw datasets) 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 +# ARC-1 (using scripts/) +python scripts/build_arc_dataset.py \ + --output-dir data/arc-aug-1000 \ + --num-aug 1000 -# Maze -python dataset/build_maze_dataset.py # 1000 examples +# ARC-2 +python scripts/build_arc_dataset.py \ + --dataset-dirs dataset/raw-data/ARC-AGI-2/data \ + --output-dir data/arc-2-aug-1000 + +# Sudoku-Extreme (full version) +python scripts/build_sudoku_dataset.py \ + --output-dir data/sudoku-extreme-full + +# Sudoku-Extreme (1000 examples) +python scripts/build_sudoku_dataset.py \ + --output-dir data/sudoku-extreme-1k-aug-1000 \ + --subsample-size 1000 \ + --num-aug 1000 + +# Maze (1000 examples) +python scripts/build_maze_dataset.py \ + --output-dir data/maze-30x30-hard-1k ``` ### Dataset Visualization @@ -124,7 +317,7 @@ Explore the puzzles visually: ARC-1: ```bash -OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 scripts/train.py ``` *Runtime:* ~24 hours @@ -132,7 +325,8 @@ OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py ARC-2: ```bash -OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/arc-2-aug-1000 +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 scripts/train.py \ + data_path=data/arc-2-aug-1000 ``` *Runtime:* ~24 hours (checkpoint after 8 hours is often sufficient) @@ -140,7 +334,14 @@ OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/arc-2-a 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 +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 scripts/train.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 @@ -148,7 +349,14 @@ OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/sudoku- 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 +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 scripts/train.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 @@ -156,7 +364,20 @@ OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/maze-30 ### 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 +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 scripts/train.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 @@ -169,7 +390,8 @@ Evaluate your trained models: * For ARC-AGI, follow these additional steps: ```bash -OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 evaluate.py checkpoint= +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 scripts/evaluate.py \ + checkpoint= ``` * Then use the provided `arc_eval.ipynb` notebook to finalize and inspect your results. @@ -183,12 +405,12 @@ OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 evaluate.py checkpoint= 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)" - ] + "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\")) 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\ndef 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\ndef grid_hash(grid: np.ndarray):\n return hash((grid.tobytes(), grid.shape))\n\n\n@njit\ndef 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\ndef test(visualize, ks=None):\n if ks is None:\n ks = [1, 2, 10, 100, 1000]\n\n identifier_map, all_preds = load_identifiers_and_preds(\n DATASET_PATH, CHECKPOINT_PATH\n )\n\n global_hmap = {}\n\n # Get puzzles and corresponding answers\n puzzle_labels = {}\n for identifier, input, label in zip(\n all_preds[\"puzzle_identifiers\"], all_preds[\"inputs\"], all_preds[\"labels\"]\n ):\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(\n all_preds[\"puzzle_identifiers\"],\n all_preds[\"inputs\"],\n preds,\n all_preds[\"q_halt_logits\"].sigmoid(),\n ):\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\ntest(visualize=False)" } ], "metadata": { @@ -249,4 +71,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/dataset/raw-data/ARC-AGI b/dataset/raw-data/ARC-AGI deleted file mode 160000 index 39903044..00000000 --- a/dataset/raw-data/ARC-AGI +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 399030444e0ab0cc8b4e199870fb20b863846f34 diff --git a/dataset/raw-data/ARC-AGI-2 b/dataset/raw-data/ARC-AGI-2 deleted file mode 160000 index f3283f72..00000000 --- a/dataset/raw-data/ARC-AGI-2 +++ /dev/null @@ -1 +0,0 @@ -Subproject commit f3283f727488ad98fe575ea6a5ac981e4a188e49 diff --git a/dataset/raw-data/ConceptARC b/dataset/raw-data/ConceptARC deleted file mode 160000 index b22ef526..00000000 --- a/dataset/raw-data/ConceptARC +++ /dev/null @@ -1 +0,0 @@ -Subproject commit b22ef526b4656679816b7811e78f55cc24d736d7 diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000..d47e21ec --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,92 @@ +version: '3.8' + +services: + # HRM training service with FlashAttention 2 (Ampere GPUs) + hrm-train-fa2: + build: + context: . + dockerfile: Dockerfile + args: + FLASH_ATTN_VERSION: "2" + image: hrm:latest-fa2 + container_name: hrm-train-fa2 + runtime: nvidia + environment: + - NVIDIA_VISIBLE_DEVICES=all + - CUDA_VISIBLE_DEVICES=0 + - WANDB_API_KEY=${WANDB_API_KEY} + - OMP_NUM_THREADS=8 + volumes: + - ./data:/app/data + - ./checkpoints:/app/checkpoints + - ./wandb:/app/wandb + - ./config:/app/config + working_dir: /app + command: > + bash -c "echo 'HRM Training Environment Ready (FlashAttention 2)' && + python -c 'import torch; print(f\"CUDA available: {torch.cuda.is_available()}\"); print(f\"CUDA devices: {torch.cuda.device_count()}\")' && + /bin/bash" + stdin_open: true + tty: true + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + + # HRM training service with FlashAttention 3 (Hopper GPUs) + hrm-train-fa3: + build: + context: . + dockerfile: Dockerfile + args: + FLASH_ATTN_VERSION: "3" + image: hrm:latest-fa3 + container_name: hrm-train-fa3 + runtime: nvidia + environment: + - NVIDIA_VISIBLE_DEVICES=all + - CUDA_VISIBLE_DEVICES=0 + - WANDB_API_KEY=${WANDB_API_KEY} + - OMP_NUM_THREADS=8 + volumes: + - ./data:/app/data + - ./checkpoints:/app/checkpoints + - ./wandb:/app/wandb + - ./config:/app/config + working_dir: /app + command: > + bash -c "echo 'HRM Training Environment Ready (FlashAttention 3)' && + python -c 'import torch; print(f\"CUDA available: {torch.cuda.is_available()}\"); print(f\"CUDA devices: {torch.cuda.device_count()}\")' && + /bin/bash" + stdin_open: true + tty: true + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + + # Development service (no GPU required for code development) + hrm-dev: + build: + context: . + dockerfile: Dockerfile + target: builder + image: hrm:dev + container_name: hrm-dev + environment: + - PYTHONPATH=/app/src + volumes: + - ./src:/app/src + - ./tests:/app/tests + - ./config:/app/config + - ./pyproject.toml:/app/pyproject.toml + working_dir: /app + command: /bin/bash + stdin_open: true + tty: true diff --git a/evaluate.py b/evaluate.py deleted file mode 100644 index 71ee7530..00000000 --- a/evaluate.py +++ /dev/null @@ -1,68 +0,0 @@ -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/examples/01_basic_model_usage.py b/examples/01_basic_model_usage.py new file mode 100644 index 00000000..cb2ce493 --- /dev/null +++ b/examples/01_basic_model_usage.py @@ -0,0 +1,150 @@ +"""Basic usage example for Hierarchical Reasoning Model. + +This example demonstrates how to: +1. Create an HRM model configuration +2. Initialize the model +3. Prepare input data +4. Run forward pass +5. Compute losses + +Note: This example requires a GPU with CUDA and FlashAttention installed. +""" + +import torch + +from hierarchical_reasoning_model import ( + ACTLossHead, + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1Config, +) + + +def create_model(): + """Create a small HRM model for demonstration.""" + config = HierarchicalReasoningModel_ACTV1Config( + # Batch and sequence configuration + batch_size=4, + seq_len=81, # Sudoku grid size (9x9) + vocab_size=11, # 0-9 digits + padding + num_puzzle_identifiers=1, + # Hierarchical processing configuration + H_cycles=2, # High-level reasoning cycles + L_cycles=2, # Low-level computation cycles + H_layers=4, # High-level transformer layers + L_layers=4, # Low-level transformer layers + # Model architecture + hidden_size=512, + num_heads=8, + expansion=4.0, + # Positional encoding + pos_encodings="rope", # RoPE positional embeddings + # Adaptive Computation Time (ACT) configuration + halt_max_steps=16, + halt_exploration_prob=0.1, + # Puzzle embeddings + puzzle_emb_ndim=512, + ) + + # Create base model + model = HierarchicalReasoningModel_ACTV1(config.model_dump()) + + # Wrap with ACT loss head + model_with_loss = ACTLossHead(model, loss_type="stablemax_cross_entropy") + + return model_with_loss + + +def create_sample_batch(): + """Create a sample batch of Sudoku puzzles. + + Returns: + Dictionary with keys: + - inputs: Tensor of shape (batch, seq_len) with puzzle inputs + - labels: Tensor of shape (batch, seq_len) with puzzle solutions + - puzzle_identifiers: Tensor of shape (batch,) with puzzle IDs + """ + batch_size = 4 + seq_len = 81 + + # Create random Sudoku-like data (values 0-9, with 0 as blank) + # In practice, these would be real Sudoku puzzles + inputs = torch.randint(0, 10, (batch_size, seq_len), dtype=torch.int32) + labels = torch.randint(1, 10, (batch_size, seq_len), dtype=torch.int32) + puzzle_identifiers = torch.zeros(batch_size, dtype=torch.int32) + + return { + "inputs": inputs, + "labels": labels, + "puzzle_identifiers": puzzle_identifiers, + } + + +def main(): + """Main demonstration function.""" + print("=" * 60) + print("Hierarchical Reasoning Model - Basic Usage Example") + print("=" * 60) + + # Check CUDA availability + if not torch.cuda.is_available(): + print("\n⚠️ CUDA is not available. This example requires a GPU.") + print("The model is designed to run on CUDA devices.") + return + + print(f"\n✓ CUDA is available: {torch.cuda.get_device_name(0)}") + + # Create model + print("\n[1] Creating HRM model...") + try: + model = create_model() + model = model.cuda() + num_params = sum(p.numel() for p in model.parameters()) + print("✓ Model created successfully") + print(f" - Total parameters: {num_params:,}") + except Exception as e: + print(f"✗ Error creating model: {e}") + print("\nNote: This example requires FlashAttention to be installed.") + print("For Ampere GPUs: pip install flash-attn") + print("For Hopper GPUs: install FlashAttention 3 from source") + return + + # Create sample data + print("\n[2] Creating sample batch...") + batch = create_sample_batch() + batch = {k: v.cuda() for k, v in batch.items()} + print("✓ Batch created") + print(f" - Batch size: {batch['inputs'].shape[0]}") + print(f" - Sequence length: {batch['inputs'].shape[1]}") + + # Initialize carry state + print("\n[3] Initializing model carry state...") + carry = model.initial_carry(batch) + print("✓ Carry state initialized") + + # Forward pass + print("\n[4] Running forward pass...") + try: + carry, loss, metrics, predictions, all_halted = model.forward( + return_keys=["logits"], + carry=carry, + batch=batch, + ) + print("✓ Forward pass completed") + print(f" - Loss: {loss.item():.4f}") + print(f" - All sequences halted: {all_halted.item()}") + print("\nMetrics:") + for key, value in metrics.items(): + if key != "count": + print(f" - {key}: {value.item():.4f}") + + except Exception as e: + print(f"✗ Error during forward pass: {e}") + return + + print("\n" + "=" * 60) + print("Example completed successfully!") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/examples/02_train_sudoku_extreme.py b/examples/02_train_sudoku_extreme.py new file mode 100644 index 00000000..884e312b --- /dev/null +++ b/examples/02_train_sudoku_extreme.py @@ -0,0 +1,684 @@ +"""Train HRM on Sudoku-Extreme dataset according to paper specifications. + +This example demonstrates how to train a Hierarchical Reasoning Model (HRM) +on the Sudoku-Extreme-1k dataset from HuggingFace, following the exact +specifications from the paper (arXiv:2506.21734v3). + +Key features from the paper: +- Two-level hierarchical architecture (H and L modules) +- Adaptive Computation Time (ACT) with Q-learning +- Deep supervision for stable training +- Sparse puzzle embeddings with SignSGD optimizer +- One-step gradient approximation (no BPTT) + +Dataset: sapientinc/sudoku-extreme-1k +Expected performance: Near-perfect accuracy (~99%+) after ~20k epochs +Training time: ~10 minutes on 8 GPUs (or ~1 hour on single GPU) + +Requirements: + - CUDA-capable GPU with FlashAttention installed + - datasets library: pip install datasets + - wandb (optional): for experiment tracking +""" + +import os +from pathlib import Path + +import torch +import torch.distributed as dist +from datasets import load_dataset +from dotenv import load_dotenv +from torch.optim import Adam +from torch.utils.data import DataLoader, Dataset +from tqdm import tqdm + +from hierarchical_reasoning_model import ( + ACTLossHead, + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1Config, +) +from hierarchical_reasoning_model.core.embeddings import ( + CastedSparseEmbeddingSignSGD_Distributed, +) + +# Optional wandb integration for experiment tracking +try: + import wandb + + WANDB_AVAILABLE = True +except ImportError: + WANDB_AVAILABLE = False + + +class SudokuDataset(Dataset): + """Sudoku dataset wrapper for HuggingFace dataset. + + Args: + split: Dataset split ('train' or 'test') + cache_dir: Directory to cache the downloaded dataset + """ + + def __init__(self, split: str = "train", cache_dir: str | None = None): + """Initialize Sudoku dataset from HuggingFace.""" + print(f"Loading sudoku-extreme-1k dataset (split={split})...") + self.dataset = load_dataset( + "sapientinc/sudoku-extreme-1k", + split=split, + cache_dir=cache_dir, + ) + print(f"Loaded {len(self.dataset)} Sudoku puzzles") + + def __len__(self) -> int: + """Return dataset size.""" + return len(self.dataset) + + def __getitem__(self, idx: int) -> dict[str, torch.Tensor]: + """Get a single Sudoku puzzle. + + Args: + idx: Index of the puzzle to retrieve + + Returns: + Dictionary with keys: + - inputs: Puzzle with blanks (81 values, 0-9) + - labels: Complete solution (81 values, 1-9) + - puzzle_identifiers: Unique puzzle ID + """ + item = self.dataset[idx] + + # Convert to tensors + # Puzzles are stored as strings of 81 characters + # In the dataset, '.' represents blanks, which we convert to 0 + question = item["question"].replace(".", "0") + inputs = torch.tensor([int(c) for c in question], dtype=torch.int32) + labels = torch.tensor([int(c) for c in item["answer"]], dtype=torch.int32) + puzzle_id = torch.tensor(idx, dtype=torch.int32) + + return { + "inputs": inputs, + "labels": labels, + "puzzle_identifiers": puzzle_id, + } + + +def collate_fn(batch: list[dict]) -> dict[str, torch.Tensor]: + """Collate function for DataLoader. + + Args: + batch: List of dictionaries from __getitem__ + + Returns: + Batched dictionary with stacked tensors + """ + return { + "inputs": torch.stack([item["inputs"] for item in batch]), + "labels": torch.stack([item["labels"] for item in batch]), + "puzzle_identifiers": torch.stack( + [item["puzzle_identifiers"] for item in batch] + ), + } + + +def create_model_config( + batch_size: int = 384, + num_puzzles: int = 1000, +) -> HierarchicalReasoningModel_ACTV1Config: + """Create HRM model configuration following paper specifications. + + Paper specifications for Sudoku-Extreme (27M parameters): + - H_cycles=2, L_cycles=2 (hierarchical convergence) + - H_layers=4, L_layers=4 (Transformer blocks each) + - hidden_size=512, num_heads=8 + - RoPE positional encodings + - halt_max_steps=16 for ACT + - Stablemax loss for numerical stability + + Args: + batch_size: Training batch size + num_puzzles: Number of unique puzzles in dataset + + Returns: + Model configuration object + """ + return HierarchicalReasoningModel_ACTV1Config( + # Batch and sequence configuration + batch_size=batch_size, + seq_len=81, # 9x9 Sudoku grid + vocab_size=11, # 0-9 digits + special tokens + num_puzzle_identifiers=num_puzzles, + # Hierarchical processing (paper spec) + H_cycles=2, # High-level reasoning cycles + L_cycles=2, # Low-level computation cycles per H-cycle + H_layers=4, # High-level Transformer layers + L_layers=4, # Low-level Transformer layers + # Model architecture (paper spec) + hidden_size=512, + num_heads=8, + expansion=4.0, # MLP expansion ratio + # Positional encoding + pos_encodings="rope", # Rotary Position Embeddings + # Adaptive Computation Time (ACT) with Q-learning + halt_max_steps=16, + halt_exploration_prob=0.1, # ε-greedy exploration + # Sparse puzzle embeddings + puzzle_emb_ndim=512, + use_sparse_puzzle_emb=True, + # Use float32 for better stability and compatibility + forward_dtype="float32", + ) + + +def setup_distributed(): + """Setup distributed training if available. + + Returns: + Tuple of (rank, world_size, device) + """ + if "RANK" in os.environ: + # Multi-GPU distributed training + dist.init_process_group(backend="nccl") + rank = dist.get_rank() + world_size = dist.get_world_size() + device = torch.device(f"cuda:{rank}") + torch.cuda.set_device(device) + else: + # Single GPU training + rank = 0 + world_size = 1 + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + return rank, world_size, device + + +def create_optimizers( + model: torch.nn.Module, + lr: float = 1e-4, + weight_decay: float = 1.0, + puzzle_emb_lr: float = 1e-4, + puzzle_emb_weight_decay: float = 1.0, + world_size: int = 1, +) -> tuple[torch.optim.Optimizer, torch.optim.Optimizer | None]: + """Create optimizers following paper specifications. + + Paper uses: + - Adam-atan2 optimizer for main parameters + - SignSGD for sparse puzzle embeddings + - High weight decay (1.0) for regularization + + Args: + model: The HRM model + lr: Learning rate for main parameters + weight_decay: Weight decay for main parameters + puzzle_emb_lr: Learning rate for puzzle embeddings + puzzle_emb_weight_decay: Weight decay for puzzle embeddings + world_size: Number of distributed workers + + Returns: + Tuple of (main_optimizer, puzzle_embedding_optimizer) + """ + # Separate parameters for main model and puzzle embeddings + main_params = [] + puzzle_emb_params = [] + + for name, param in model.named_parameters(): + if "puzzle_emb" in name: + puzzle_emb_params.append(param) + else: + main_params.append(param) + + # Main optimizer (Adam for most parameters) + main_optimizer = Adam( + main_params, + lr=lr, + weight_decay=weight_decay, + betas=(0.9, 0.999), + ) + + # Puzzle embedding optimizer (SignSGD for sparse updates) + puzzle_emb_optimizer = None + if puzzle_emb_params: + # Get the sparse embedding module + sparse_emb_module = None + for module in model.modules(): + if hasattr(module, "local_weights"): + sparse_emb_module = module + break + + if sparse_emb_module is not None: + puzzle_emb_optimizer = CastedSparseEmbeddingSignSGD_Distributed( + [ + sparse_emb_module.weights, + sparse_emb_module.local_weights, + sparse_emb_module.local_ids, + ], + world_size=world_size, + lr=puzzle_emb_lr, + weight_decay=puzzle_emb_weight_decay, + ) + + return main_optimizer, puzzle_emb_optimizer + + +def train_epoch( + model: ACTLossHead, + dataloader: DataLoader, + main_optimizer: torch.optim.Optimizer, + puzzle_emb_optimizer: torch.optim.Optimizer | None, + device: torch.device, + epoch: int, + rank: int = 0, + wandb_run=None, +) -> dict[str, float]: + """Train for one epoch. + + Args: + model: HRM model with ACT loss head + dataloader: Training data loader + main_optimizer: Optimizer for main parameters + puzzle_emb_optimizer: Optimizer for puzzle embeddings + device: Device to train on + epoch: Current epoch number + rank: Distributed rank (0 for main process) + wandb_run: Weights & Biases run object for logging (optional) + + Returns: + Dictionary of training metrics + """ + model.train() + total_loss = 0.0 + total_accuracy = 0.0 + total_exact_accuracy = 0.0 + num_batches = 0 + + # Progress bar (only on rank 0) + pbar = tqdm( + dataloader, + desc=f"Epoch {epoch}", + disable=(rank != 0), + ) + + for batch in pbar: + # Move batch to device + batch = {k: v.to(device) for k, v in batch.items()} + + # Initialize carry state + carry = model.initial_carry(batch) + + # Forward pass with deep supervision + # The model uses one-step gradient approximation + carry, loss, metrics, predictions, all_halted = model.forward( + return_keys=["logits"], + carry=carry, + batch=batch, + ) + + # Backward pass + main_optimizer.zero_grad() + if puzzle_emb_optimizer is not None: + puzzle_emb_optimizer.zero_grad() + + loss.backward() + + # Gradient clipping (optional, helps with stability) + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + + # Optimizer step + main_optimizer.step() + if puzzle_emb_optimizer is not None: + puzzle_emb_optimizer.step() + + # Accumulate metrics + total_loss += loss.item() + total_accuracy += metrics["accuracy"].item() + total_exact_accuracy += metrics["exact_accuracy"].item() + num_batches += 1 + + # Update progress bar + if rank == 0: + pbar.set_postfix( + { + "loss": f"{loss.item():.4f}", + "acc": f"{metrics['accuracy'].item():.4f}", + "exact_acc": f"{metrics['exact_accuracy'].item():.4f}", + } + ) + + metrics_dict = { + "train/loss": total_loss / num_batches, + "train/accuracy": total_accuracy / num_batches, + "train/exact_accuracy": total_exact_accuracy / num_batches, + "epoch": epoch, + } + + # Log to wandb if available + if wandb_run is not None and rank == 0: + wandb_run.log(metrics_dict) + + return metrics_dict + + +@torch.no_grad() +def evaluate( + model: ACTLossHead, + dataloader: DataLoader, + device: torch.device, + rank: int = 0, + wandb_run=None, + epoch: int | None = None, +) -> dict[str, float]: + """Evaluate model on validation/test set. + + Args: + model: HRM model with ACT loss head + dataloader: Evaluation data loader + device: Device to evaluate on + rank: Distributed rank (0 for main process) + wandb_run: Weights & Biases run object for logging (optional) + epoch: Current epoch number for logging (optional) + + Returns: + Dictionary of evaluation metrics + """ + model.eval() + total_loss = 0.0 + total_accuracy = 0.0 + total_exact_accuracy = 0.0 + num_batches = 0 + + pbar = tqdm( + dataloader, + desc="Evaluating", + disable=(rank != 0), + ) + + for batch in pbar: + # Move batch to device + batch = {k: v.to(device) for k, v in batch.items()} + + # Initialize carry state + carry = model.initial_carry(batch) + + # Forward pass (no gradients) + carry, loss, metrics, predictions, all_halted = model.forward( + return_keys=["logits"], + carry=carry, + batch=batch, + ) + + # Accumulate metrics + total_loss += loss.item() + total_accuracy += metrics["accuracy"].item() + total_exact_accuracy += metrics["exact_accuracy"].item() + num_batches += 1 + + metrics_dict = { + "eval/loss": total_loss / num_batches, + "eval/accuracy": total_accuracy / num_batches, + "eval/exact_accuracy": total_exact_accuracy / num_batches, + } + + # Add epoch to metrics if provided + if epoch is not None: + metrics_dict["epoch"] = epoch + + # Log to wandb if available + if wandb_run is not None and rank == 0: + wandb_run.log(metrics_dict) + + return metrics_dict + + +def main(): # noqa: N806 + """Main training function.""" + # Load environment variables (for WANDB_API_KEY) + load_dotenv() + + # Paper specifications for Sudoku-Extreme-1k + # Note: Using uppercase for clarity as these are fixed paper hyperparameters + BATCH_SIZE = 64 # Global batch size (reduced for GPU memory) # noqa: N806 + EPOCHS = 20000 # Paper uses 20k epochs for 1k dataset # noqa: N806 + EVAL_INTERVAL = 2000 # Evaluate every 2k epochs # noqa: N806 + LR = 1e-4 # Learning rate (paper spec) # noqa: N806 + WEIGHT_DECAY = 1.0 # High weight decay for regularization # noqa: N806 + PUZZLE_EMB_LR = 1e-4 # Same as main LR # noqa: N806 + PUZZLE_EMB_WD = 1.0 # Same weight decay # noqa: N806 + + print("=" * 70) + print("HRM Training on Sudoku-Extreme-1k (Paper Specifications)") + print("=" * 70) + print("\nPaper: arXiv:2506.21734v3 - Hierarchical Reasoning Model") + print("Dataset: sapientinc/sudoku-extreme-1k") + print("Expected: ~99%+ exact accuracy after 20k epochs") + print("=" * 70) + + # Setup distributed training + rank, world_size, device = setup_distributed() + if rank == 0: + print(f"\n✓ Device: {device}") + print(f"✓ World size: {world_size}") + + # Adjust batch size for distributed training + local_batch_size = BATCH_SIZE // world_size + if rank == 0: + print(f"✓ Global batch size: {BATCH_SIZE}") + print(f"✓ Local batch size: {local_batch_size}") + + # Load datasets + if rank == 0: + print("\n[1] Loading datasets...") + + train_dataset = SudokuDataset(split="train") + test_dataset = SudokuDataset(split="test_hard") + + # Create data loaders + train_loader = DataLoader( + train_dataset, + batch_size=local_batch_size, + shuffle=True, + num_workers=4, + collate_fn=collate_fn, + pin_memory=True, + ) + test_loader = DataLoader( + test_dataset, + batch_size=local_batch_size, + shuffle=False, + num_workers=4, + collate_fn=collate_fn, + pin_memory=True, + ) + + if rank == 0: + print(f"✓ Train samples: {len(train_dataset)}") + print(f"✓ Test samples: {len(test_dataset)}") + + # Create model following paper specifications + if rank == 0: + print("\n[2] Creating HRM model (paper specifications)...") + + config = create_model_config( + batch_size=local_batch_size, + num_puzzles=len(train_dataset) + len(test_dataset), # Total unique puzzles + ) + + # Initialize base model + base_model = HierarchicalReasoningModel_ACTV1(config.model_dump()) + + # Move base model to device first + base_model = base_model.to(device) + + # Wrap with ACT loss head (stablemax for stability) + model = ACTLossHead(base_model, loss_type="stablemax_cross_entropy") + + # Ensure entire model is on correct device + model = model.to(device) + + # Wrap with DDP if multi-GPU + if world_size > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, + device_ids=[rank], + ) + + if rank == 0: + num_params = sum(p.numel() for p in model.parameters()) + print(f"✓ Model created: {num_params:,} parameters (~27M)") + print(f" - H_cycles: {config.H_cycles}, L_cycles: {config.L_cycles}") + print(f" - H_layers: {config.H_layers}, L_layers: {config.L_layers}") + print(f" - hidden_size: {config.hidden_size}") + print(f" - ACT max steps: {config.halt_max_steps}") + + # Create optimizers + if rank == 0: + print("\n[3] Creating optimizers...") + + main_optimizer, puzzle_emb_optimizer = create_optimizers( + model, + lr=LR, + weight_decay=WEIGHT_DECAY, + puzzle_emb_lr=PUZZLE_EMB_LR, + puzzle_emb_weight_decay=PUZZLE_EMB_WD, + world_size=world_size, + ) + + if rank == 0: + print(f"✓ Main optimizer: Adam (lr={LR}, wd={WEIGHT_DECAY})") + if puzzle_emb_optimizer: + print( + f"✓ Puzzle embedding optimizer: SignSGD " + f"(lr={PUZZLE_EMB_LR}, wd={PUZZLE_EMB_WD})" + ) + + # Initialize Weights & Biases (only on rank 0) + wandb_run = None + if WANDB_AVAILABLE and rank == 0: + wandb_run = wandb.init( + entity="zbloss", + project="hierarchical_reasoning_model", + config={ + # Hyperparameters + "batch_size": BATCH_SIZE, + "local_batch_size": local_batch_size, + "epochs": EPOCHS, + "learning_rate": LR, + "weight_decay": WEIGHT_DECAY, + "puzzle_emb_lr": PUZZLE_EMB_LR, + "puzzle_emb_wd": PUZZLE_EMB_WD, + # Model architecture + "H_cycles": config.H_cycles, + "L_cycles": config.L_cycles, + "H_layers": config.H_layers, + "L_layers": config.L_layers, + "hidden_size": config.hidden_size, + "num_heads": config.num_heads, + "expansion": config.expansion, + "halt_max_steps": config.halt_max_steps, + "halt_exploration_prob": config.halt_exploration_prob, + "pos_encodings": config.pos_encodings, + "vocab_size": config.vocab_size, + "seq_len": config.seq_len, + "num_puzzle_identifiers": config.num_puzzle_identifiers, + "forward_dtype": config.forward_dtype, + # Dataset info + "dataset": "sapientinc/sudoku-extreme-1k", + "train_samples": len(train_dataset), + "test_samples": len(test_dataset), + # Distributed training + "world_size": world_size, + # Model size + "num_parameters": sum(p.numel() for p in model.parameters()), + }, + name=f"hrm_sudoku_extreme_bs{BATCH_SIZE}_lr{LR}", + ) + print("✓ Weights & Biases initialized") + elif rank == 0 and not WANDB_AVAILABLE: + print("⚠ Weights & Biases not available (install with: pip install wandb)") + + # Training loop + if rank == 0: + print(f"\n[4] Training for {EPOCHS} epochs...") + print("=" * 70) + + best_exact_accuracy = 0.0 + + for epoch in range(1, EPOCHS + 1): + # Train one epoch + train_metrics = train_epoch( + model, + train_loader, + main_optimizer, + puzzle_emb_optimizer, + device, + epoch, + rank, + wandb_run, + ) + + # Evaluate periodically + if epoch % EVAL_INTERVAL == 0 or epoch == 1: + eval_metrics = evaluate(model, test_loader, device, rank, wandb_run, epoch) + + if rank == 0: + print(f"\nEpoch {epoch}/{EPOCHS}:") + print(f" Train Loss: {train_metrics['train/loss']:.4f}") + print(f" Train Accuracy: {train_metrics['train/accuracy']:.4f}") + print( + f" Train Exact Accuracy: " + f"{train_metrics['train/exact_accuracy']:.4f}" + ) + print(f" Eval Loss: {eval_metrics['eval/loss']:.4f}") + print(f" Eval Accuracy: {eval_metrics['eval/accuracy']:.4f}") + print( + f" Eval Exact Accuracy: {eval_metrics['eval/exact_accuracy']:.4f}" + ) + + # Save best model + if eval_metrics["eval/exact_accuracy"] > best_exact_accuracy: + best_exact_accuracy = eval_metrics["eval/exact_accuracy"] + print(f" ✓ New best exact accuracy: {best_exact_accuracy:.4f}") + + # Log best accuracy to wandb + if wandb_run is not None: + wandb_run.log( + { + "best_eval/exact_accuracy": best_exact_accuracy, + "best_eval/epoch": epoch, + } + ) + + # Save checkpoint + checkpoint_dir = Path("checkpoints") + checkpoint_dir.mkdir(exist_ok=True) + checkpoint_path = checkpoint_dir / "best_model.pt" + torch.save( + { + "epoch": epoch, + "model_state_dict": model.state_dict(), + "optimizer_state_dict": main_optimizer.state_dict(), + "metrics": eval_metrics, + }, + checkpoint_path, + ) + + # Log checkpoint to wandb + if wandb_run is not None: + wandb_run.save(str(checkpoint_path)) + + if rank == 0: + print("\n" + "=" * 70) + print("Training completed!") + print(f"Best exact accuracy: {best_exact_accuracy:.4f}") + print("=" * 70) + + # Finish wandb run + if wandb_run is not None and rank == 0: + wandb_run.finish() + print("✓ Weights & Biases run finished") + + # Cleanup distributed training + if world_size > 1: + dist.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 00000000..ada26421 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,85 @@ +# HRM Examples + +This directory contains usage examples for the Hierarchical Reasoning Model package. + +## Prerequisites + +- Python >=3.10 +- CUDA-capable GPU +- FlashAttention installed (see main README for installation instructions) + +## Examples + +### 01_basic_model_usage.py + +Demonstrates basic model usage including: +- Creating an HRM model configuration +- Initializing the model +- Preparing input data +- Running forward pass +- Computing losses + +**Run:** +```bash +uv run python examples/01_basic_model_usage.py +``` + +### 02_train_sudoku_extreme.py + +Complete training example following the paper specifications (arXiv:2506.21734v3): +- Trains HRM on `sapientinc/sudoku-extreme-1k` from HuggingFace +- Uses paper-specified hyperparameters (27M params, H_cycles=2, L_cycles=2) +- Implements Adaptive Computation Time (ACT) with Q-learning +- Uses deep supervision and one-step gradient approximation +- Supports both single-GPU and multi-GPU distributed training +- Expected performance: ~99%+ exact accuracy after 20k epochs + +**Features:** +- Automatic dataset download from HuggingFace +- SignSGD optimizer for sparse puzzle embeddings +- Periodic evaluation and checkpoint saving +- Progress tracking with tqdm +- Weights & Biases integration for experiment tracking (optional) + +**Setup (for Weights & Biases tracking):** +```bash +# Create .env file with your wandb API key +echo "WANDB_API_KEY=your_api_key_here" > .env + +# Or login via wandb CLI +wandb login +``` + +**Run (single GPU):** +```bash +uv run python examples/02_train_sudoku_extreme.py +``` + +**Run (multi-GPU distributed):** +```bash +OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 examples/02_train_sudoku_extreme.py +``` + +**Note:** The script will automatically log metrics to Weights & Biases if wandb is installed and configured. If not installed, training will proceed without experiment tracking. + +**Expected runtime:** +- Single GPU: ~1-2 hours (depending on hardware) +- 8 GPUs: ~10 minutes (as reported in paper) + +## Coming Soon + +Additional examples will be added for: +- Loading and using pretrained checkpoints +- Building custom datasets +- Evaluating model performance on ARC-AGI +- Inference-time scaling demonstrations + +## Note on Dependencies + +These examples assume you have installed the full package with training dependencies: + +```bash +uv sync --extra train --extra data +``` + +For GPU support with FlashAttention, follow the installation instructions in the main README. diff --git a/models/common.py b/models/common.py deleted file mode 100644 index 1a045051..00000000 --- a/models/common.py +++ /dev/null @@ -1,32 +0,0 @@ -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/hrm/hrm_act_v1.py b/models/hrm/hrm_act_v1.py deleted file mode 100644 index e91c7d1a..00000000 --- a/models/hrm/hrm_act_v1.py +++ /dev/null @@ -1,283 +0,0 @@ -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/layers.py b/models/layers.py deleted file mode 100644 index 03947444..00000000 --- a/models/layers.py +++ /dev/null @@ -1,157 +0,0 @@ -from typing import Tuple - -import torch -from torch import nn -import torch.nn.functional as F - -try: - from flash_attn_interface import flash_attn_func # type: ignore[import] -except ImportError: - # Fallback to FlashAttention 2 - from flash_attn import flash_attn_func # type: ignore[import] - -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 deleted file mode 100644 index b3118e72..00000000 --- a/models/losses.py +++ /dev/null @@ -1,101 +0,0 @@ -from typing import Any, Tuple, Dict, Sequence, Optional - -import torch -import torch.nn.functional as F -from torch import nn - - -IGNORE_LABEL_ID = -100 - - -def s(x, epsilon=1e-30): - return torch.where( - x<0, - 1/(1-x+ epsilon), - x + 1 - ) - - -def log_stablemax(x, dim=-1): - 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): - logprobs = log_stablemax(logits.to(torch.float64), dim=-1) - - valid_mask = labels != ignore_index - transformed_labels = torch.where(valid_mask, labels, 0) - prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1) - - 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): - 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): - 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"] - - # Correctness - with torch.no_grad(): - mask = labels != IGNORE_LABEL_ID - loss_counts = mask.sum(-1) - loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1) # Avoid NaNs in division - - is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels) - seq_is_correct = is_correct.sum(-1) == loss_counts - - # Metrics (halted) - 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(), - } - - # Losses - # FIXME: Assuming the batch is always full - lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID) / loss_divisor).sum() - 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(), - }) - - # Q continue (bootstrapping target loss) - q_continue_loss = 0 - if "target_q_continue" in outputs: - 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/sparse_embedding.py b/models/sparse_embedding.py deleted file mode 100644 index c701524b..00000000 --- a/models/sparse_embedding.py +++ /dev/null @@ -1,132 +0,0 @@ -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 ≈ 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/puzzle_dataset.py b/puzzle_dataset.py deleted file mode 100644 index 2782403c..00000000 --- a/puzzle_dataset.py +++ /dev/null @@ -1,199 +0,0 @@ -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/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..746eef52 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,218 @@ +[project] +name = "hierarchical-reasoning-model" +version = "0.1.0" +description = "Hierarchical Reasoning Model: A novel recurrent neural network for sequential reasoning tasks" +readme = "README.md" +license = { file = "LICENSE" } +authors = [ + { name = "Guan Wang" }, + { name = "Jin Li" }, + { name = "Yuhao Sun" }, + { name = "Xing Chen" }, + { name = "Changling Liu" }, + { name = "Yue Wu" }, + { name = "Meng Lu" }, + { name = "Sen Song" }, + { name = "Yasin Abbasi Yadkori" }, + { name = "Zachary Bloss", email = "zacharybloss@gmail.com" } +] +maintainers = [ + { name = "Zachary Bloss", email = "zacharybloss@gmail.com" } +] +requires-python = ">=3.10,<3.14" +keywords = [ + "deep-learning", + "neural-networks", + "reasoning", + "transformers", + "hierarchical-models", + "arc-agi", + "pytorch" +] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] + +# Core runtime dependencies +dependencies = [ + "torch>=2.0.0", + "adam-atan2>=0.0.1", + "einops>=0.7.0", + "pydantic>=2.0.0", + "numpy>=1.24.0", + "wandb>=0.22.1", + "python-dotenv>=1.1.1", +] + +[project.optional-dependencies] +# Training dependencies +train = [ + "tqdm>=4.65.0", + "coolname>=2.0.0", + "wandb>=0.15.0", + "omegaconf>=2.3.0", + "hydra-core>=1.3.0", +] + +# Dataset building dependencies +data = [ + "argdantic>=0.3.0", + "huggingface_hub>=0.19.0", + "datasets>=4.1.1", + "tqdm>=4.65.0", +] + +# Development dependencies +dev = [ + "ruff>=0.8.0", + "pytest>=8.0.0", + "pytest-cov>=6.0.0", + "pytest-xdist>=3.5.0", + "mypy>=1.8.0", +] + +# Notebook dependencies +notebooks = [ + "jupyter>=1.0.0", + "matplotlib>=3.8.0", + "ipywidgets>=8.1.0", +] + +# Complete installation (all features) +all = [ + "hierarchical-reasoning-model[train,data,notebooks]", +] + +[project.urls] +Homepage = "https://github.com/liujch1998/HRM" +Documentation = "https://github.com/liujch1998/HRM#readme" +Repository = "https://github.com/liujch1998/HRM" +"Bug Tracker" = "https://github.com/liujch1998/HRM/issues" + +[project.scripts] +hrm = "hierarchical_reasoning_model.cli:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/hierarchical_reasoning_model"] + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/README.md", + "/LICENSE", +] + +# Ruff configuration +[tool.ruff] +line-length = 88 +target-version = "py310" +src = ["src", "tests"] + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "N", # pep8-naming + "UP", # pyupgrade + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "SIM", # flake8-simplify + "TCH", # flake8-type-checking +] +ignore = [ + "E501", # Line too long (handled by formatter) + "B008", # Do not perform function call in argument defaults + "B905", # `zip()` without an explicit `strict=` parameter +] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401"] # Allow unused imports in __init__.py +"tests/**/*.py" = ["S101"] # Allow assert in tests + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +line-ending = "auto" + +# Pytest configuration +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["test_*.py"] +python_classes = ["Test*"] +python_functions = ["test_*"] +addopts = [ + "--verbose", + "--strict-markers", + "--strict-config", + "--cov=src/hierarchical_reasoning_model", + "--cov-report=term-missing", + "--cov-report=html", + "--cov-report=xml", +] +markers = [ + "slow: marks tests as slow (deselect with '-m \"not slow\"')", + "integration: marks tests as integration tests", + "unit: marks tests as unit tests", +] + +# Coverage configuration +[tool.coverage.run] +source = ["src"] +omit = [ + "*/tests/*", + "*/__pycache__/*", + "*/.venv/*", +] + +[tool.coverage.report] +exclude_lines = [ + "pragma: no cover", + "def __repr__", + "raise AssertionError", + "raise NotImplementedError", + "if __name__ == .__main__.:", + "if TYPE_CHECKING:", + "@abstractmethod", +] + +# MyPy configuration +[tool.mypy] +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = false +disallow_incomplete_defs = false +check_untyped_defs = true +no_implicit_optional = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_no_return = true +strict_equality = true +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = [ + "torch.*", + "wandb.*", + "hydra.*", + "omegaconf.*", + "flash_attn.*", + "flash_attn_interface.*", + "adam_atan2.*", + "coolname.*", +] +ignore_missing_imports = true diff --git a/scripts/README.md b/scripts/README.md new file mode 100644 index 00000000..a8857feb --- /dev/null +++ b/scripts/README.md @@ -0,0 +1,250 @@ +# Research Scripts + +This directory contains scripts used for the original research but not part of the core `hierarchical_reasoning_model` package. + +## Overview + +The core package (`hierarchical_reasoning_model`) provides only the model architecture components. These scripts provide reference implementations for dataset processing, training, and evaluation. + +## Directory Structure + +``` +scripts/ +├── data/ # Dataset loading utilities +│ ├── dataset.py # PuzzleDataset for loading preprocessed data +│ ├── metadata.py # Dataset metadata and transformations +│ └── __init__.py +├── build_arc_dataset.py # Preprocess ARC-AGI puzzles +├── build_sudoku_dataset.py # Preprocess Sudoku datasets +├── build_maze_dataset.py # Preprocess maze datasets +├── train.py # Full training loop with Hydra/wandb +├── evaluate.py # Distributed evaluation script +└── README.md # This file +``` + +## Dataset Builders + +### ARC Dataset Builder + +Preprocesses ARC-AGI and ConceptARC puzzles with data augmentation: + +```bash +python scripts/build_arc_dataset.py \ + --dataset-dirs dataset/raw-data/ARC-AGI/data dataset/raw-data/ConceptARC/corpus \ + --output-dir data/arc-aug-1000 \ + --seed 42 \ + --num-aug 1000 +``` + +**Features**: +- Dihedral transformations (8 orientations) for augmentation +- Color permutations for invariance +- JSON metadata generation +- Memory-mapped numpy arrays for efficient loading + +### Sudoku Dataset Builder + +Downloads and preprocesses Sudoku puzzles from HuggingFace: + +```bash +python scripts/build_sudoku_dataset.py \ + --source-repo sapientinc/sudoku-extreme \ + --output-dir data/sudoku-extreme-full \ + --subsample-size 1000 \ + --min-difficulty 5 \ + --num-aug 0 +``` + +**Features**: +- Downloads from HuggingFace datasets +- Difficulty filtering +- Optional subsampling +- Data augmentation support + +### Maze Dataset Builder + +Preprocesses maze navigation datasets: + +```bash +python scripts/build_maze_dataset.py \ + --source-repo sapientinc/maze-30x30-hard-1k \ + --output-dir data/maze-30x30-hard-1k \ + --aug true +``` + +**Features**: +- Dihedral transformations for augmentation +- Character-to-ID mapping +- Puzzle grouping for batch sampling + +## Training & Evaluation + +### Training Script + +Full training loop with distributed training, Hydra configuration, and wandb logging: + +```bash +# Single GPU training +python scripts/train.py --config-path configs --config-name train.yaml + +# Multi-GPU distributed training +torchrun --nproc_per_node=4 scripts/train.py \ + --config-path configs \ + --config-name train.yaml +``` + +**Features**: +- Distributed training with `torch.distributed` +- Hydra configuration management +- Weights & Biases experiment tracking +- Gradient accumulation +- Learning rate scheduling +- Checkpoint saving/loading +- Sparse puzzle embedding optimization (SignSGD) + +**Configuration**: +The training script uses Hydra for configuration. See example configs in the original research repository. + +### Evaluation Script + +Distributed evaluation with output saving: + +```bash +python scripts/evaluate.py \ + --checkpoint path/to/checkpoint.pt \ + --save-outputs inputs labels logits q_halt_logits +``` + +**Features**: +- Distributed evaluation across GPUs +- Configurable output saving +- Metrics computation +- Inference mode optimization + +## Dataset Loading + +The `scripts/data/` module provides utilities for loading preprocessed datasets: + +```python +from scripts.data import PuzzleDataset, PuzzleDatasetConfig + +# Configure dataset +config = PuzzleDatasetConfig( + dataset_path="data/arc-aug-1000/train", + batch_size=32, + epochs_per_iteration=1, + test_mode=False, # False for training (shuffled), True for evaluation +) + +# Create dataset +dataset = PuzzleDataset(config) + +# Use with PyTorch DataLoader +from torch.utils.data import DataLoader +loader = DataLoader(dataset, batch_size=None, num_workers=0) + +for batch in loader: + inputs = batch["inputs"] # [batch_size, seq_len] + labels = batch["labels"] # [batch_size, seq_len] + puzzle_ids = batch["puzzle_identifiers"] # [batch_size] + # ... train model ... +``` + +**Features**: +- Memory-mapped file I/O for large datasets +- Distributed training support (automatic data splitting) +- Efficient puzzle-based batching +- Automatic padding to sequence length +- Train/test mode (shuffled vs sequential) + +## Using with the Package + +These scripts depend on the `hierarchical_reasoning_model` package being installed: + +```bash +# Install the package +uv sync + +# Then use scripts +python scripts/build_arc_dataset.py --help +python scripts/train.py --help +``` + +## Integration Example + +Here's how to integrate the scripts with the core package: + +```python +# Use the package for the model +from hierarchical_reasoning_model import ( + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1Config, + ACTLossHead, +) + +# Use scripts for data loading +from scripts.data import PuzzleDataset, PuzzleDatasetConfig + +# Create model +config = HierarchicalReasoningModel_ACTV1Config( + batch_size=32, + seq_len=81, + vocab_size=11, + num_puzzle_identifiers=1, + H_cycles=2, + L_cycles=2, + H_layers=4, + L_layers=4, + hidden_size=512, + num_heads=8, +) +model = HierarchicalReasoningModel_ACTV1(config.model_dump()) +loss_head = ACTLossHead(model, loss_type="softmax") + +# Load dataset +dataset_config = PuzzleDatasetConfig( + dataset_path="data/arc-aug-1000/train", + batch_size=32, +) +dataset = PuzzleDataset(dataset_config) + +# Train +import torch +optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) + +for batch in dataset: + optimizer.zero_grad() + carry = loss_head.initial_carry(batch) + _, loss, metrics, _, _ = loss_head.forward( + return_keys=["logits"], + carry=carry, + batch=batch, + ) + loss.backward() + optimizer.step() +``` + +## Notes + +- These scripts are provided as reference implementations from the research +- You can modify them for your own use cases +- The core `hierarchical_reasoning_model` package does not depend on these scripts +- For custom training loops, you can write your own data loading using the model package directly + +## Dependencies + +Additional dependencies needed for scripts (beyond core package): + +- `hydra-core` - Configuration management for training +- `wandb` - Experiment tracking +- `adam-atan2` - Custom optimizer +- `coolname` - Run naming +- `argdantic` - CLI argument parsing for builders +- `huggingface-hub` - Dataset downloading +- `tqdm` - Progress bars + +Install all dependencies: + +```bash +uv sync +``` diff --git a/dataset/build_arc_dataset.py b/scripts/build_arc_dataset.py similarity index 65% rename from dataset/build_arc_dataset.py rename to scripts/build_arc_dataset.py index 2da5703e..667d3648 100644 --- a/dataset/build_arc_dataset.py +++ b/scripts/build_arc_dataset.py @@ -1,46 +1,51 @@ -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 +import json +import os +from dataclasses import dataclass from glob import glob +from pathlib import Path +import numpy as np from argdantic import ArgParser from pydantic import BaseModel -from common import PuzzleDatasetMetadata, dihedral_transform - +# Moved from hierarchical_reasoning_model package to scripts/ +from scripts.data.metadata 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"] + 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]] + examples: list[tuple[np.ndarray, np.ndarray]] - -def arc_grid_to_np(grid: List[List[int]]): + +def arc_grid_to_np(grid: list[list[int]]): arr = np.array(grid) # Shape check @@ -51,12 +56,18 @@ def arc_grid_to_np(grid: List[List[int]]): return arr.astype(np.uint8) -def np_grid_to_seq_translational_augment(inp: np.ndarray, out: np.ndarray, do_translation: bool): +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) + 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 @@ -64,7 +75,14 @@ def np_grid_to_seq_translational_augment(inp: np.ndarray, out: np.ndarray, do_tr 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) + 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 @@ -83,31 +101,42 @@ def puzzle_hash(puzzle: dict): 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 _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]]): +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]) + 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)} @@ -115,25 +144,39 @@ def convert_single_arc_puzzle(results: dict, default_name: str, puzzle: dict, au 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) - + 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): + def _map_grid(grid: np.ndarray, mapping=mapping, trans_id=trans_id): 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()} + 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)}") + print(f"[Puzzle {name}] augmentation not full, only {len(group)}") # Append for dest in dests: @@ -149,63 +192,71 @@ def load_puzzles_arcagi(results: dict, dataset_path: str, config: DataProcessCon train_examples_dest = ("train", "all") test_examples_map = { "evaluation": [(1.0, ("test", "all"))], - "_default": [(1.0, ("train", "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: + with open(filename) 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"]): + 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}) + + 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}") + 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 _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}") + 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" @@ -213,72 +264,90 @@ def convert_dataset(config: DataProcessConfig): 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 = { + 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) - + 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) - + + 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()) + sets=list(split.keys()), ) # Save metadata as JSON. - with open(os.path.join(config.output_dir, split_name, "dataset.json"), "w") as f: + 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) diff --git a/dataset/build_maze_dataset.py b/scripts/build_maze_dataset.py similarity index 83% rename from dataset/build_maze_dataset.py rename to scripts/build_maze_dataset.py index a9367f38..57904e38 100644 --- a/dataset/build_maze_dataset.py +++ b/scripts/build_maze_dataset.py @@ -1,17 +1,19 @@ -from typing import Optional -import math -import os import csv import json -import numpy as np +import math +import os +import numpy as np from argdantic import ArgParser -from pydantic import BaseModel -from tqdm import tqdm from huggingface_hub import hf_hub_download +from pydantic import BaseModel -from common import PuzzleDatasetMetadata, dihedral_transform - +# Moved from hierarchical_reasoning_model package to scripts/ +from scripts.data.metadata import ( + PuzzleDatasetMetadata, + dihedral_transform, +) +from tqdm import tqdm CHARSET = "# SGo" @@ -23,7 +25,7 @@ class DataProcessConfig(BaseModel): source_repo: str = "sapientinc/maze-30x30-hard-1k" output_dir: str = "data/maze-30x30-hard-1k" - subsample_size: Optional[int] = None + subsample_size: int | None = None aug: bool = False @@ -33,18 +35,21 @@ def convert_subset(set_name: str, config: DataProcessConfig): 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 + + 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: + 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)) @@ -53,18 +58,29 @@ def convert_subset(set_name: str, config: DataProcessConfig): 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) + 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"]} + 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): @@ -72,29 +88,28 @@ def convert_subset(set_name: str, config: DataProcessConfig): 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), @@ -104,29 +119,26 @@ def _seq_to_numpy(seq): 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"] + 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) diff --git a/dataset/build_sudoku_dataset.py b/scripts/build_sudoku_dataset.py similarity index 79% rename from dataset/build_sudoku_dataset.py rename to scripts/build_sudoku_dataset.py index 7924438b..d3c8b5f8 100644 --- a/dataset/build_sudoku_dataset.py +++ b/scripts/build_sudoku_dataset.py @@ -1,16 +1,15 @@ -from typing import Optional -import os import csv import json -import numpy as np +import os +import numpy as np from argdantic import ArgParser -from pydantic import BaseModel -from tqdm import tqdm from huggingface_hub import hf_hub_download +from pydantic import BaseModel -from common import PuzzleDatasetMetadata - +# Moved from hierarchical_reasoning_model package to scripts/ +from scripts.data.metadata import PuzzleDatasetMetadata +from tqdm import tqdm cli = ArgParser() @@ -19,15 +18,15 @@ 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 + subsample_size: int | None = None + min_difficulty: int | None = 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 @@ -61,36 +60,59 @@ 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: + + 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): + 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')) + + 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) + 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"]} + 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 @@ -104,24 +126,23 @@ def convert_subset(set_name: str, config: DataProcessConfig): 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), @@ -131,29 +152,26 @@ def _seq_to_numpy(seq): 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"] + 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) diff --git a/scripts/data/__init__.py b/scripts/data/__init__.py new file mode 100644 index 00000000..06e9bbfe --- /dev/null +++ b/scripts/data/__init__.py @@ -0,0 +1,21 @@ +"""Dataset and data loading utilities for HRM.""" + +from hierarchical_reasoning_model.data.dataset import ( + PuzzleDataset, + PuzzleDatasetConfig, +) +from hierarchical_reasoning_model.data.metadata import ( + DIHEDRAL_INVERSE, + PuzzleDatasetMetadata, + dihedral_transform, + inverse_dihedral_transform, +) + +__all__ = [ + "PuzzleDataset", + "PuzzleDatasetConfig", + "PuzzleDatasetMetadata", + "dihedral_transform", + "inverse_dihedral_transform", + "DIHEDRAL_INVERSE", +] diff --git a/scripts/data/dataset.py b/scripts/data/dataset.py new file mode 100644 index 00000000..8b89fd08 --- /dev/null +++ b/scripts/data/dataset.py @@ -0,0 +1,395 @@ +"""PyTorch dataset for puzzle-based reasoning tasks. + +This module implements an efficient iterable dataset for loading puzzle data +with support for: +- Distributed training across multiple GPUs/nodes +- Memory-mapped file I/O for large datasets +- Efficient batching with puzzle grouping +- Both training (shuffled) and test (sequential) modes +- Automatic padding and type conversion + +The dataset is designed to work with puzzle datasets like ARC, Sudoku, and Maze. +""" + +import json +import os + +import numpy as np +import pydantic +import torch +from scripts.data.metadata import PuzzleDatasetMetadata +from torch.utils.data import IterableDataset, get_worker_info + +# Moved from hierarchical_reasoning_model package to scripts/ +# Define IGNORE_LABEL_ID locally instead of importing from package +IGNORE_LABEL_ID = -100 + + +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, +) -> tuple[int, np.ndarray, np.ndarray]: + """Sample a batch of examples from puzzle groups. + + Efficiently packs examples from multiple puzzles into a batch by: + 1. Selecting puzzles from shuffled groups + 2. Randomly sampling examples from each puzzle + 3. Filling batch until global_batch_size is reached + + This approach ensures diversity by mixing examples from different puzzles + while maintaining efficient batching. + + Args: + rng: NumPy random generator for reproducible sampling + group_order: Shuffled array of group IDs defining iteration order + puzzle_indices: Cumulative indices marking puzzle boundaries in dataset + group_indices: Cumulative indices marking group boundaries + start_index: Current position in group_order + global_batch_size: Target batch size across all devices + + Returns: + Tuple containing: + - Updated start_index for next batch + - Array of example indices to load + - Array of puzzle IDs corresponding to each example + """ + # 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): + """Configuration for PuzzleDataset. + + Attributes: + seed: Random seed for reproducible shuffling + dataset_path: Path to dataset directory containing train/test splits + global_batch_size: Total batch size across all distributed processes + test_set_mode: If True, iterate sequentially through test set; + if False, shuffle and iterate training set + epochs_per_iter: Number of epochs to batch together per iteration + to reduce overhead + rank: Process rank in distributed training (0-indexed) + num_replicas: Total number of distributed processes + """ + + 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): + """Iterable dataset for puzzle-based reasoning tasks. + + Efficiently loads and batches puzzle data with support for distributed + training. Uses memory-mapped files for large datasets and implements + intelligent batching that groups puzzle examples together. + + The dataset supports two modes: + - Training: Shuffles puzzle groups and randomly samples examples + - Test: Sequentially iterates through all examples + + Args: + config: Dataset configuration specifying paths, batch sizes, and + distributed training settings + split: Dataset split to load ("train" or "test") + + Attributes: + config: Stored configuration + split: Dataset split name + metadata: Dataset metadata loaded from dataset.json + local_batch_size: Per-process batch size (global_batch_size / num_replicas) + + Example: + >>> config = PuzzleDatasetConfig( + ... seed=42, + ... dataset_path="data/sudoku", + ... global_batch_size=32, + ... test_set_mode=False, + ... epochs_per_iter=1, + ... rank=0, + ... num_replicas=1, + ... ) + >>> dataset = PuzzleDataset(config, split="train") + >>> for set_name, batch, batch_size in dataset: + ... # batch contains 'inputs', 'labels', 'puzzle_identifiers' + ... pass + """ + + 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: + """Load dataset metadata from JSON file. + + Returns: + Metadata containing vocab size, sequence length, pad IDs, etc. + """ + with open( + os.path.join(self.config.dataset_path, self.split, "dataset.json") + ) as f: + return PuzzleDatasetMetadata(**json.load(f)) + + def _lazy_load_dataset(self) -> None: + """Lazily load dataset arrays on first iteration. + + Uses memory-mapped files for inputs/labels to reduce memory usage, + while keeping smaller index arrays in memory for fast access. + """ + 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: dict[str, np.ndarray]) -> dict[str, torch.Tensor]: + """Collate batch into tensors with padding and type conversion. + + Performs: + 1. Convert all arrays to int32 + 2. Map dataset-specific ignore labels to IGNORE_LABEL_ID + 3. Pad batch to local_batch_size if needed + 4. Convert to PyTorch tensors + + Args: + batch: Dictionary with 'inputs', 'labels', 'puzzle_identifiers' + + Returns: + Dictionary of PyTorch tensors ready for model input + """ + # 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): + """Iterate through dataset yielding batches. + + Lazy-loads data on first iteration and delegates to either _iter_test() + or _iter_train() based on config.test_set_mode. + + Yields: + Tuple of (set_name, batch, effective_batch_size) where: + - set_name: Name of dataset subset (e.g., "all") + - batch: Dictionary with 'inputs', 'labels', 'puzzle_identifiers' + - effective_batch_size: Actual batch size (may be < global_batch_size + at end of epoch) + + Raises: + AssertionError: If multiple data loading workers are configured + (not currently supported) + """ + 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/dataset/common.py b/scripts/data/metadata.py similarity index 78% rename from dataset/common.py rename to scripts/data/metadata.py index 7bc51c6f..4d52b06f 100644 --- a/dataset/common.py +++ b/scripts/data/metadata.py @@ -1,8 +1,5 @@ -from typing import List, Optional - -import pydantic import numpy as np - +import pydantic # Global list mapping each dihedral transform id to its inverse. # Index corresponds to the original tid, and the value is its inverse. @@ -11,22 +8,22 @@ class PuzzleDatasetMetadata(pydantic.BaseModel): pad_id: int - ignore_label_id: Optional[int] + ignore_label_id: int | None blank_identifier_id: int - + vocab_size: int seq_len: int num_puzzle_identifiers: int - + total_groups: int mean_puzzle_examples: float - sets: List[str] + 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: @@ -36,16 +33,16 @@ def dihedral_transform(arr: np.ndarray, tid: int) -> np.ndarray: elif tid == 3: return np.rot90(arr, k=3) elif tid == 4: - return np.fliplr(arr) # horizontal flip + return np.fliplr(arr) # horizontal flip elif tid == 5: - return np.flipud(arr) # vertical flip + return np.flipud(arr) # vertical flip elif tid == 6: - return arr.T # transpose (reflection along main diagonal) + 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/scripts/evaluate.py b/scripts/evaluate.py new file mode 100644 index 00000000..5cef2232 --- /dev/null +++ b/scripts/evaluate.py @@ -0,0 +1,113 @@ +import os + +import pydantic +import torch +import torch.distributed as dist +import yaml +from omegaconf import OmegaConf + +# Moved from hierarchical_reasoning_model package to scripts/ +from scripts.train import ( + PretrainConfig, + create_dataloader, + evaluate, + init_train_state, +) + + +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 # noqa: N806 + WORLD_SIZE = 1 # noqa: N806 + # 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() # noqa: N806 + WORLD_SIZE = dist.get_world_size() # noqa: N806 + + torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) + + with open( + os.path.join(os.path.dirname(eval_cfg.checkpoint), "all_config.yaml") + ) 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 (RuntimeError, KeyError): + 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/pretrain.py b/scripts/train.py similarity index 55% rename from pretrain.py rename to scripts/train.py index 245cb5c7..e5a8b114 100644 --- a/pretrain.py +++ b/scripts/train.py @@ -1,36 +1,63 @@ -from typing import Optional, Any, Sequence, List -from dataclasses import dataclass -import os +# Utility functions moved from hierarchical_reasoning_model.utils.model_loading +import importlib +import inspect import math -import yaml +import os import shutil +from collections.abc import Sequence +from dataclasses import dataclass +from typing import Any +import coolname +import hydra +import pydantic 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 +import yaml from adam_atan2 import AdamATan2 +from omegaconf import DictConfig -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 +# Moved from hierarchical_reasoning_model package to scripts/ +from scripts.data.dataset import ( + PuzzleDataset, + PuzzleDatasetConfig, + PuzzleDatasetMetadata, +) +from torch import nn +from torch.utils.data import DataLoader + +from hierarchical_reasoning_model.core.embeddings import ( + CastedSparseEmbeddingSignSGD_Distributed, +) + + +def load_model_class( + identifier: str, prefix: str = "hierarchical_reasoning_model.core." +): + module_path, class_name = identifier.split("@") + module = importlib.import_module(prefix + module_path) + cls = getattr(module, class_name) + return cls + + +def get_model_source_path( + identifier: str, prefix: str = "hierarchical_reasoning_model.core." +): + module_path, class_name = identifier.split("@") + module = importlib.import_module(prefix + module_path) + return inspect.getsourcefile(module) class LossConfig(pydantic.BaseModel): - model_config = pydantic.ConfigDict(extra='allow') - + model_config = pydantic.ConfigDict(extra="allow") + name: str class ArchConfig(pydantic.BaseModel): - model_config = pydantic.ConfigDict(extra='allow') + model_config = pydantic.ConfigDict(extra="allow") name: str loss: LossConfig @@ -59,15 +86,15 @@ class PretrainConfig(pydantic.BaseModel): puzzle_emb_weight_decay: float # Names - project_name: Optional[str] = None - run_name: Optional[str] = None - checkpoint_path: Optional[str] = None + project_name: str | None = None + run_name: str | None = None + checkpoint_path: str | None = None # Extras seed: int = 0 checkpoint_every_eval: bool = False - eval_interval: Optional[int] = None - eval_save_outputs: List[str] = [] + eval_interval: int | None = None + eval_save_outputs: list[str] = [] @dataclass @@ -81,40 +108,40 @@ class TrainState: 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) +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 + persistent_workers=True, ) return dataloader, dataset.metadata -def create_model(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, world_size: int): +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 + causal=False, # Non-autoregressive ) # Instantiate model with loss head @@ -137,53 +164,71 @@ def create_model(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, optimizers = [ CastedSparseEmbeddingSignSGD_Distributed( model.model.puzzle_emb.buffers(), # type: ignore - lr=0, # Needs to be set by scheduler weight_decay=config.puzzle_emb_weight_decay, - - world_size=world_size + world_size=world_size, ), AdamATan2( model.parameters(), - lr=0, # Needs to be set by scheduler weight_decay=config.weight_decay, - betas=(config.beta1, config.beta2) - ) - ] - optimizer_lrs = [ - config.puzzle_emb_lr, - config.lr + betas=(config.beta1, config.beta2), + ), ] + optimizer_lrs = [config.puzzle_emb_lr, 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 + 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)))) + 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): +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) + 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) + 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 + carry=None, ) @@ -193,7 +238,10 @@ def save_train_state(config: PretrainConfig, train_state: TrainState): 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}")) + 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): @@ -202,11 +250,18 @@ def compute_lr(base_lr: float, config: PretrainConfig, train_state: TrainState): 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 + 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): +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 @@ -220,7 +275,9 @@ def train_batch(config: PretrainConfig, train_state: TrainState, batch: Any, glo 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=[]) + train_state.carry, loss, metrics, _, _ = train_state.model( + carry=train_state.carry, batch=batch, return_keys=[] + ) ((1 / global_batch_size) * loss).backward() @@ -229,15 +286,15 @@ def train_batch(config: PretrainConfig, train_state: TrainState, batch: Any, glo for param in train_state.model.parameters(): if param.grad is not None: dist.all_reduce(param.grad) - + # Apply optimizer - lr_this_step = None + 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 - + param_group["lr"] = lr_this_step + optim.step() optim.zero_grad() @@ -245,7 +302,9 @@ def train_batch(config: PretrainConfig, train_state: TrainState, batch: Any, glo 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. + metric_keys = 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: @@ -254,25 +313,35 @@ def train_batch(config: PretrainConfig, train_state: TrainState, batch: Any, glo 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 = { + 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): +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 @@ -282,8 +351,10 @@ def evaluate(config: PretrainConfig, train_state: TrainState, eval_loader: torch # Forward while True: - carry, _, metrics, preds, all_finish = train_state.model(carry=carry, batch=batch, return_keys=config.eval_save_outputs) - + carry, _, metrics, preds, all_finish = train_state.model( + carry=carry, batch=batch, return_keys=config.eval_save_outputs + ) + if all_finish: break @@ -291,17 +362,25 @@ def evaluate(config: PretrainConfig, train_state: TrainState, eval_loader: torch 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 - + 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_keys = 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 @@ -309,23 +388,35 @@ def evaluate(config: PretrainConfig, train_state: TrainState, eval_loader: torch 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}")) + 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)} - + 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()} + reduced_metrics[set_name] = { + k: v / count for k, v in metrics.items() + } return reduced_metrics @@ -339,7 +430,7 @@ def save_code_and_config(config: PretrainConfig): # Copy code code_list = [ get_model_source_path(config.arch.name), - get_model_source_path(config.arch.loss.name) + get_model_source_path(config.arch.loss.name), ] for code_file in code_list: if code_file is not None: @@ -349,25 +440,33 @@ def save_code_and_config(config: PretrainConfig): # Dump config as yaml config_file = os.path.join(config.checkpoint_path, "all_config.yaml") - with open(config_file, "wt") as f: + with open(config_file, "w") 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: +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" + 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)}" + 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) + config.checkpoint_path = os.path.join( + "checkpoints", config.project_name, config.run_name + ) objects = [config] @@ -379,19 +478,19 @@ def load_synced_config(hydra_config: DictConfig, rank: int, world_size: int) -> @hydra.main(config_path="config", config_name="cfg_pretrain", version_base=None) def launch(hydra_config: DictConfig): - RANK = 0 - WORLD_SIZE = 1 + RANK = 0 # noqa: N806 + WORLD_SIZE = 1 # noqa: N806 # 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() + RANK = dist.get_rank() # noqa: N806 + WORLD_SIZE = dist.get_world_size() # noqa: N806 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) @@ -399,13 +498,33 @@ def launch(hydra_config: DictConfig): 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 + 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." + 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_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) @@ -415,18 +534,35 @@ def launch(hydra_config: DictConfig): 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) + 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}") + 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) + 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) @@ -434,13 +570,22 @@ def launch(hydra_config: DictConfig): ############ Evaluation train_state.model.eval() - metrics = evaluate(config, train_state, eval_loader, eval_metadata, rank=RANK, world_size=WORLD_SIZE) + 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)): + if RANK == 0 and ( + config.checkpoint_every_eval or (_iter_id == total_iters - 1) + ): save_train_state(config, train_state) # finalize diff --git a/src/hierarchical_reasoning_model/__init__.py b/src/hierarchical_reasoning_model/__init__.py new file mode 100644 index 00000000..2077008d --- /dev/null +++ b/src/hierarchical_reasoning_model/__init__.py @@ -0,0 +1,47 @@ +""" +Hierarchical Reasoning Model (HRM) + +A novel recurrent neural network architecture for sequential reasoning tasks through +hierarchical processing inspired by the human brain. + +This package provides the core HRM model components. For dataset processing, training, +and evaluation utilities, see the scripts/ directory. + +Example usage: + >>> from hierarchical_reasoning_model import HierarchicalReasoningModel_ACTV1Config + >>> config = HierarchicalReasoningModel_ACTV1Config( + ... batch_size=32, + ... seq_len=81, + ... vocab_size=11, + ... num_puzzle_identifiers=1, + ... H_cycles=2, + ... L_cycles=2, + ... H_layers=4, + ... L_layers=4, + ... hidden_size=512, + ... num_heads=8, + ... expansion=4.0, + ... pos_encodings="rope", + ... halt_max_steps=16, + ... halt_exploration_prob=0.1, + ... ) + >>> model = HierarchicalReasoningModel_ACTV1(config.model_dump()) + >>> loss_head = ACTLossHead(model, loss_type="softmax") +""" + +__version__ = "0.1.0" + +# Core model exports +from hierarchical_reasoning_model.core import ( + ACTLossHead, + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1Config, +) + +__all__ = [ + "__version__", + # Core models + "HierarchicalReasoningModel_ACTV1", + "HierarchicalReasoningModel_ACTV1Config", + "ACTLossHead", +] diff --git a/src/hierarchical_reasoning_model/core/__init__.py b/src/hierarchical_reasoning_model/core/__init__.py new file mode 100644 index 00000000..1f666e5e --- /dev/null +++ b/src/hierarchical_reasoning_model/core/__init__.py @@ -0,0 +1,49 @@ +"""Core neural network modules for the Hierarchical Reasoning Model.""" + +from hierarchical_reasoning_model.core.embeddings import ( + CastedSparseEmbedding, + CastedSparseEmbeddingSignSGD_Distributed, +) +from hierarchical_reasoning_model.core.layers import ( + Attention, + CastedEmbedding, + CastedLinear, + RotaryEmbedding, + SwiGLU, + rms_norm, +) +from hierarchical_reasoning_model.core.losses import ( + IGNORE_LABEL_ID, + ACTLossHead, + softmax_cross_entropy, + stablemax_cross_entropy, +) +from hierarchical_reasoning_model.core.model import ( + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1Carry, + HierarchicalReasoningModel_ACTV1Config, + HierarchicalReasoningModel_ACTV1InnerCarry, +) + +__all__ = [ + # Model + "HierarchicalReasoningModel_ACTV1", + "HierarchicalReasoningModel_ACTV1Config", + "HierarchicalReasoningModel_ACTV1Carry", + "HierarchicalReasoningModel_ACTV1InnerCarry", + # Layers + "Attention", + "SwiGLU", + "RotaryEmbedding", + "CastedLinear", + "CastedEmbedding", + "rms_norm", + # Embeddings + "CastedSparseEmbedding", + "CastedSparseEmbeddingSignSGD_Distributed", + # Losses + "ACTLossHead", + "stablemax_cross_entropy", + "softmax_cross_entropy", + "IGNORE_LABEL_ID", +] diff --git a/src/hierarchical_reasoning_model/core/common.py b/src/hierarchical_reasoning_model/core/common.py new file mode 100644 index 00000000..e5c11222 --- /dev/null +++ b/src/hierarchical_reasoning_model/core/common.py @@ -0,0 +1,54 @@ +import math + +import torch + + +def truncated_normal_init_( + tensor: torch.Tensor, std: float = 1.0, lower: float = -2.0, upper: float = 2.0 +) -> torch.Tensor: + """Initialize tensor with truncated normal distribution (JAX-style). + + This is a mathematically correct implementation of truncated normal + initialization, matching JAX/Flax defaults. Unlike PyTorch's built-in + trunc_normal_, this ensures the standard deviation is exactly `std`. + + Args: + tensor: Tensor to initialize in-place + std: Target standard deviation of the distribution + lower: Lower truncation bound in units of std (default: -2.0) + upper: Upper truncation bound in units of std (default: 2.0) + + Returns: + The initialized tensor (same as input, modified in-place) + + References: + JAX truncated normal: https://github.com/jax-ml/jax/blob/main/jax/_src/random.py#L807-L848 + Flax initializer: https://github.com/jax-ml/jax/blob/main/jax/_src/nn/initializers.py#L162-L199 + """ + # 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/src/hierarchical_reasoning_model/core/embeddings.py b/src/hierarchical_reasoning_model/core/embeddings.py new file mode 100644 index 00000000..9a974607 --- /dev/null +++ b/src/hierarchical_reasoning_model/core/embeddings.py @@ -0,0 +1,168 @@ +import torch +import torch.distributed as dist +from torch import nn +from torch.optim.optimizer import Optimizer, ParamsT + +from hierarchical_reasoning_model.core.common import truncated_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( + truncated_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 + actual_batch_size = inputs.shape[0] + + # Handle variable batch sizes by using only the needed portion of buffers + with torch.no_grad(): + self.local_weights[:actual_batch_size].copy_(self.weights[inputs]) + self.local_ids[:actual_batch_size].copy_(inputs) + + return self.local_weights[:actual_batch_size].to(self.cast_to) + + +class CastedSparseEmbeddingSignSGD_Distributed(Optimizer): # noqa: N801 + def __init__( + self, + params: ParamsT, + world_size: int, + lr: float | torch.Tensor = 1e-3, + weight_decay: float = 1e-2, + ): + if not lr >= 0.0: + raise ValueError(f"Invalid learning rate: {lr}") + if not weight_decay >= 0.0: + raise ValueError(f"Invalid weight_decay value: {weight_decay}") + + defaults = {"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: + raise AssertionError(f"Unexpected parameter dimension: {p.ndim}") + + assert local_weights_grad is not None + assert local_ids is not None + assert weights is not None + + # Apply SignSGD + # Adam ≈ SignSGD if gradient is very sparse + _apply_sparse_embedding_signsgd_distributed( + local_weights_grad, + local_ids, + weights, + lr=group["lr"], + weight_decay=group["weight_decay"], + world_size=group["world_size"], + ) + + +def _apply_sparse_embedding_signsgd_distributed( + local_weights_grad: torch.Tensor, + local_ids: torch.Tensor, + weights: torch.Tensor, + lr: float, + weight_decay: float, + world_size: int, +) -> None: + """Apply SignSGD optimizer update for sparse embeddings in distributed setting. + + Aggregates gradients across distributed workers and applies SignSGD with + decoupled weight decay to sparse embedding parameters. Only updates the + embeddings that were used in the current batch. + + Args: + local_weights_grad: Gradients for local batch embeddings (N, D) + local_ids: Embedding IDs for local batch (N,) + weights: Full embedding weight matrix to update + lr: Learning rate + weight_decay: Weight decay coefficient + world_size: Number of distributed workers + + Note: + SignSGD approximates Adam for very sparse gradients by taking only + the sign of the gradient, which is more memory efficient. + """ + N, D = local_weights_grad.shape # noqa: N806 + + # 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/src/hierarchical_reasoning_model/core/layers.py b/src/hierarchical_reasoning_model/core/layers.py new file mode 100644 index 00000000..d76ca577 --- /dev/null +++ b/src/hierarchical_reasoning_model/core/layers.py @@ -0,0 +1,448 @@ +"""Neural network layers for the Hierarchical Reasoning Model. + +This module implements transformer building blocks with optimizations: +- FlashAttention for efficient multi-head attention +- Rotary Position Embeddings (RoPE) for position encoding +- SwiGLU activation for feed-forward networks +- RMS normalization for stable training +- Type-casted layers for mixed-precision training + +All layers support bfloat16/float32 computation with automatic type casting. +""" + +import torch +import torch.nn.functional as F # noqa: N812 +from torch import nn + +# Try to import FlashAttention (requires CUDA) +try: + from flash_attn_interface import flash_attn_func # type: ignore[import] + + _FLASH_ATTN_AVAILABLE = True +except ImportError: + try: + # Fallback to FlashAttention 2 + from flash_attn import flash_attn_func # type: ignore[import] + + _FLASH_ATTN_AVAILABLE = True + except ImportError: + # No FlashAttention available - use pure PyTorch fallback + _FLASH_ATTN_AVAILABLE = False + + def flash_attn_func( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + causal: bool = False, + ) -> torch.Tensor: + """Pure PyTorch fallback for FlashAttention. + + This is a simple implementation for CPU/environments without FlashAttention. + It is slower and less memory-efficient than the CUDA implementation. + + Args: + q: Query tensor (batch, seq_len, num_heads, head_dim) + k: Key tensor (batch, seq_len, num_heads, head_dim) + v: Value tensor (batch, seq_len, num_heads, head_dim) + causal: Whether to apply causal masking + + Returns: + Attention output (batch, seq_len, num_heads, head_dim) + """ + batch, seq_len, num_heads, head_dim = q.shape + + # Transpose to (batch, num_heads, seq_len, head_dim) + q_t = q.transpose(1, 2) + k_t = k.transpose(1, 2) + v_t = v.transpose(1, 2) + + # Compute attention scores + scores = torch.matmul(q_t, k_t.transpose(-2, -1)) / (head_dim**0.5) + + # Apply causal mask if needed + if causal: + mask = torch.triu( + torch.ones(seq_len, seq_len, device=q.device), diagonal=1 + ) + scores = scores.masked_fill(mask.bool(), float("-inf")) + + # Compute attention weights and output + attn = torch.softmax(scores, dim=-1) + output = torch.matmul(attn, v_t) + + # Transpose back to (batch, seq_len, num_heads, head_dim) + return output.transpose(1, 2).contiguous() + + +from hierarchical_reasoning_model.core.common import truncated_normal_init_ + +#: Type alias for precomputed cosine and sine values used in RoPE +CosSin = tuple[torch.Tensor, torch.Tensor] + + +def _round_up_to_multiple(value: int, multiple: int) -> int: + """Round up value to the nearest multiple. + + Performs ceiling division to find the smallest multiple of `multiple` + that is greater than or equal to `value`. + + Args: + value: Number to round up + multiple: Multiple to round to + + Returns: + Smallest multiple of `multiple` that is >= `value` + + Example: + >>> _round_up_to_multiple(100, 256) + 256 + >>> _round_up_to_multiple(300, 256) + 512 + """ + return (-(value // -multiple)) * multiple + + +def rotate_half(x: torch.Tensor) -> torch.Tensor: + """Rotate half the hidden dimensions for RoPE. + + Splits the last dimension in half and rotates: [x1, x2] -> [-x2, x1]. + This is a core operation in Rotary Position Embeddings. + + Args: + x: Input tensor with shape (..., dim) + + Returns: + Rotated tensor of same shape with second half negated and moved to front + """ + 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 +) -> tuple[torch.Tensor, torch.Tensor]: + """Apply Rotary Position Embeddings to query and key tensors. + + RoPE encodes position information by rotating query and key vectors in + complex space. This provides better extrapolation to longer sequences + compared to learned positional embeddings. + + Args: + q: Query tensor of shape (batch, seq_len, num_heads, head_dim) + k: Key tensor of shape (batch, seq_len, num_kv_heads, head_dim) + cos: Precomputed cosine values of shape (seq_len, head_dim) + sin: Precomputed sine values of shape (seq_len, head_dim) + + Returns: + Tuple of (rotated_q, rotated_k) with same shapes as inputs + """ + # 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): + """Linear layer with automatic type casting for mixed-precision training. + + Uses truncated LeCun normal initialization for weights (scaled by 1/sqrt(in_features)) + and zero initialization for biases. Automatically casts weights and biases to + match input dtype during forward pass. + + Args: + in_features: Size of input features + out_features: Size of output features + bias: Whether to include bias term + + Attributes: + weight: Weight parameter of shape (out_features, in_features) + bias: Optional bias parameter of shape (out_features,) + """ + + def __init__(self, in_features: int, out_features: int, bias: bool): + super().__init__() + # Truncated LeCun normal init + self.weight = nn.Parameter( + truncated_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: + """Apply linear transformation with automatic type casting. + + Args: + input: Input tensor of shape (..., in_features) + + Returns: + Output tensor of shape (..., out_features) in same dtype as input + """ + 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): + """Embedding layer with type casting for mixed-precision training. + + Uses truncated normal initialization for embedding weights. Automatically + casts embeddings to specified dtype during forward pass. + + Args: + num_embeddings: Size of embedding vocabulary + embedding_dim: Dimension of embedding vectors + init_std: Standard deviation for weight initialization + cast_to: Target dtype to cast embeddings to (e.g., torch.bfloat16) + + Attributes: + cast_to: Target dtype for embeddings + embedding_weight: Embedding weight parameter of shape + (num_embeddings, embedding_dim) + """ + + 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( + truncated_normal_init_( + torch.empty((num_embeddings, embedding_dim)), std=init_std + ) + ) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + """Look up embeddings and cast to target dtype. + + Args: + input: Integer tensor of indices with shape (...) + + Returns: + Embedded tensor of shape (..., embedding_dim) in cast_to dtype + """ + return F.embedding(input, self.embedding_weight.to(self.cast_to)) + + +class RotaryEmbedding(nn.Module): + """Rotary Position Embedding (RoPE) precomputation. + + Precomputes and caches cosine and sine values for rotary position embeddings. + RoPE allows better extrapolation to longer sequences by encoding absolute + position information via rotation in complex space. + + Args: + dim: Dimension of embeddings (typically head_dim) + max_position_embeddings: Maximum sequence length + base: Base for inverse frequency computation (typically 10000) + device: Device to place tensors on (None for CPU) + + Attributes: + cos_cached: Precomputed cosine values of shape + (max_position_embeddings, dim) + sin_cached: Precomputed sine values of shape + (max_position_embeddings, dim) + + References: + RoFormer: Enhanced Transformer with Rotary Position Embedding + https://arxiv.org/abs/2104.09864 + """ + + 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) -> CosSin: + """Return precomputed cosine and sine values. + + Returns: + Tuple of (cos_cached, sin_cached) tensors, each of shape + (max_position_embeddings, dim) + """ + return self.cos_cached, self.sin_cached + + +class Attention(nn.Module): + """Multi-head self-attention using FlashAttention. + + Efficient attention implementation using FlashAttention 2 or 3 for memory + and speed optimization. Supports: + - Multi-query attention (MQA) and grouped-query attention (GQA) + - Optional causal masking + - RoPE positional encodings + - Automatic dtype casting + + Args: + hidden_size: Dimension of input hidden states + head_dim: Dimension of each attention head + num_heads: Number of query heads + num_key_value_heads: Number of key/value heads (for MQA/GQA) + causal: Whether to apply causal masking (default: False for HRM) + + Attributes: + hidden_size: Input dimension + head_dim: Per-head dimension + output_size: Total output dimension (head_dim * num_heads) + num_heads: Number of query heads + num_key_value_heads: Number of key/value heads + causal: Whether attention is causal + qkv_proj: Combined query, key, value projection + o_proj: Output projection + """ + + 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: + """Apply multi-head self-attention with FlashAttention. + + Args: + cos_sin: Optional tuple of (cos, sin) for RoPE. If None, no + positional encoding is applied + hidden_states: Input tensor of shape (batch, seq_len, hidden_size) + + Returns: + Attention output of shape (batch, seq_len, hidden_size) + """ + 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): + """SwiGLU feed-forward network layer. + + Implements the SwiGLU activation function, which combines Swish (SiLU) + activation with a gating mechanism. This has been shown to outperform + standard ReLU and GELU in transformer models. + + The architecture is: down(SiLU(gate(x)) * up(x)) + + Args: + hidden_size: Dimension of input and output + expansion: Expansion factor for intermediate dimension + (typically 4.0, rounded to 2/3 * expansion for efficiency) + + Attributes: + gate_up_proj: Combined gate and up projection + down_proj: Down projection back to hidden_size + + References: + GLU Variants Improve Transformer (Shazeer, 2020) + https://arxiv.org/abs/2002.05202 + """ + + def __init__(self, hidden_size: int, expansion: float): + super().__init__() + inter = _round_up_to_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: torch.Tensor) -> torch.Tensor: + """Apply SwiGLU transformation. + + Args: + x: Input tensor of shape (..., hidden_size) + + Returns: + Output tensor of shape (..., hidden_size) + """ + 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: + """Root Mean Square Layer Normalization. + + RMSNorm normalizes by the RMS statistic rather than mean and variance, + providing similar benefits to LayerNorm but with lower computational cost. + Computation is done in float32 for numerical stability. + + Args: + hidden_states: Input tensor of shape (..., hidden_size) + variance_epsilon: Small constant for numerical stability (typically 1e-5) + + Returns: + Normalized tensor of same shape and dtype as input + + References: + Root Mean Square Layer Normalization (Zhang & Sennrich, 2019) + https://arxiv.org/abs/1910.07467 + """ + 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/src/hierarchical_reasoning_model/core/losses.py b/src/hierarchical_reasoning_model/core/losses.py new file mode 100644 index 00000000..31ff397a --- /dev/null +++ b/src/hierarchical_reasoning_model/core/losses.py @@ -0,0 +1,260 @@ +"""Loss functions and training heads for the Hierarchical Reasoning Model.""" + +from collections.abc import Sequence +from typing import Any + +import torch +import torch.nn.functional as F # noqa: N812 +from torch import nn + +#: Special label ID used to ignore certain positions in loss computation +IGNORE_LABEL_ID = -100 + + +def stablemax_transform(x: torch.Tensor, epsilon: float = 1e-30) -> torch.Tensor: + """Stablemax transformation function. + + Applies a smooth transformation that maps negative values to (0, 1) + and non-negative values to [1, ∞). This is used as part of the + stablemax normalization for improved numerical stability. + + Args: + x: Input tensor of any shape + epsilon: Small constant for numerical stability (default: 1e-30) + + Returns: + Transformed tensor of same shape as input + """ + return torch.where(x < 0, 1 / (1 - x + epsilon), x + 1) + + +def log_stablemax(x: torch.Tensor, dim: int = -1) -> torch.Tensor: + """Compute log of stablemax normalization. + + The stablemax is an alternative to softmax with better numerical + properties for certain distributions. This function computes the + logarithm of the stablemax for numerical stability. + + Args: + x: Input logits tensor + dim: Dimension along which to normalize (default: -1) + + Returns: + Log probabilities tensor of same shape as input + """ + transformed_x = stablemax_transform(x) + return torch.log(transformed_x / torch.sum(transformed_x, dim=dim, keepdim=True)) + + +def stablemax_cross_entropy( + logits: torch.Tensor, labels: torch.Tensor, ignore_index: int = -100 +) -> torch.Tensor: + """Compute cross-entropy loss using stablemax normalization. + + This provides an alternative to standard softmax cross-entropy with + improved numerical stability for certain types of predictions. + + Args: + logits: Predicted logits of shape (batch, seq_len, vocab_size) + labels: True labels of shape (batch, seq_len) + ignore_index: Label value to ignore in loss computation (default: -100) + + Returns: + Per-token loss values of shape (batch, seq_len). Positions with + labels equal to ignore_index will have loss of 0. + """ + logprobs = log_stablemax(logits.to(torch.float64), dim=-1) + + valid_mask = labels != ignore_index + transformed_labels = torch.where(valid_mask, labels, 0) + prediction_logprobs = torch.gather( + logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1 + ).squeeze(-1) + + return -torch.where(valid_mask, prediction_logprobs, 0) + + +def softmax_cross_entropy( + logits: torch.Tensor, labels: torch.Tensor, ignore_index: int = -100 +) -> torch.Tensor: + """Compute standard softmax cross-entropy loss. + + Args: + logits: Predicted logits of shape (batch, seq_len, vocab_size) + labels: True labels of shape (batch, seq_len) + ignore_index: Label value to ignore in loss computation (default: -100) + + Returns: + Per-token loss values of shape (batch, seq_len). Positions with + labels equal to ignore_index will have loss of 0. + """ + 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): + """Adaptive Computation Time (ACT) loss head wrapper. + + This module wraps a base model and adds three loss components: + 1. Language modeling loss (token prediction) + 2. Q-halt loss (binary classification for when to stop) + 3. Q-continue loss (reinforcement learning for continuation) + + The ACT mechanism allows the model to dynamically decide how many + computation steps to perform for each input, using Q-learning. + + Args: + model: Base HRM model to wrap + loss_type: Type of language modeling loss to use. Must be either + "stablemax_cross_entropy" or "softmax_cross_entropy" + + Attributes: + model: The wrapped base model + loss_fn: The language modeling loss function + """ + + def __init__(self, model: nn.Module, loss_type: str): + """Initialize the ACT loss head. + + Args: + model: Base model implementing forward() and initial_carry() + loss_type: Name of loss function ("stablemax_cross_entropy" or + "softmax_cross_entropy") + + Raises: + KeyError: If loss_type is not a valid loss function name + """ + super().__init__() + self.model = model + self.loss_fn = globals()[loss_type] + + def initial_carry(self, *args: Any, **kwargs: Any) -> Any: + """Initialize the model's carry state. + + Args: + *args: Positional arguments passed to model.initial_carry() + **kwargs: Keyword arguments passed to model.initial_carry() + + Returns: + Initial carry state from the wrapped model + """ + return self.model.initial_carry(*args, **kwargs) # type: ignore + + def forward( + self, + return_keys: Sequence[str], + # Model args + **model_kwargs: Any, + ) -> tuple[ + Any, + torch.Tensor, + dict[str, torch.Tensor], + dict[str, torch.Tensor] | None, + torch.Tensor, + ]: + """Forward pass computing losses and metrics. + + Runs the wrapped model forward pass and computes three loss components: + 1. Language modeling loss for token prediction + 2. Q-halt loss for learning when to stop computation + 3. Q-continue loss for reinforcement learning (if applicable) + + Args: + return_keys: List of output keys to return in the predictions dict. + Common keys include: "logits", "q_halt_logits", "q_continue_logits" + **model_kwargs: Keyword arguments passed to the wrapped model, + typically including "carry" and "batch" + + Returns: + A tuple containing: + - new_carry: Updated model carry state + - total_loss: Sum of all loss components (scalar tensor) + - metrics: Dictionary of metrics including accuracy, steps, etc. + - predictions: Optional dictionary of requested outputs (or None) + - all_halted: Boolean tensor indicating if all sequences halted + + Example: + >>> loss_head = ACTLossHead(model, "stablemax_cross_entropy") + >>> carry = loss_head.initial_carry(batch) + >>> carry, loss, metrics, preds, halted = loss_head.forward( + ... return_keys=["logits"], + ... carry=carry, + ... batch=batch + ... ) + """ + # Model logits + # B x SeqLen x D + new_carry, outputs = self.model(**model_kwargs) + labels = new_carry.current_data["labels"] + + # Correctness + with torch.no_grad(): + mask = labels != IGNORE_LABEL_ID + loss_counts = mask.sum(-1) + loss_divisor = loss_counts.clamp_min(1).unsqueeze( + -1 + ) # Avoid NaNs in division + + is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels) + seq_is_correct = is_correct.sum(-1) == loss_counts + + # Metrics (halted) + 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(), + } + + # Losses + # FIXME: Assuming the batch is always full + lm_loss = ( + self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID) + / loss_divisor + ).sum() + 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(), + } + ) + + # Q continue (bootstrapping target loss) + q_continue_loss = 0 + if "target_q_continue" in outputs: + 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/src/hierarchical_reasoning_model/core/model.py b/src/hierarchical_reasoning_model/core/model.py new file mode 100644 index 00000000..e81c5a0e --- /dev/null +++ b/src/hierarchical_reasoning_model/core/model.py @@ -0,0 +1,730 @@ +"""Hierarchical Reasoning Model with Adaptive Computation Time (ACT). + +This module implements the core HRM architecture featuring two-level hierarchical +processing inspired by human cognition. The model consists of: + +- High-level (H) module: Slow, abstract planning and reasoning +- Low-level (L) module: Fast, detailed computations +- ACT mechanism: Q-learning based adaptive halting for dynamic computation + +The architecture is particularly effective for sequential reasoning tasks like +ARC puzzles, Sudoku, and maze solving with minimal training data. + +References: + Paper: "Hierarchical Reasoning Model for Sequential Reasoning Tasks" +""" + +import math +from dataclasses import dataclass + +import torch +import torch.nn.functional as F # noqa: N812 +from pydantic import BaseModel +from torch import nn + +from hierarchical_reasoning_model.core.common import truncated_normal_init_ +from hierarchical_reasoning_model.core.embeddings import CastedSparseEmbedding +from hierarchical_reasoning_model.core.layers import ( + Attention, + CastedEmbedding, + CastedLinear, + CosSin, + RotaryEmbedding, + SwiGLU, + rms_norm, +) + + +@dataclass +class HierarchicalReasoningModel_ACTV1InnerCarry: # noqa: N801 + """Internal state for the hierarchical reasoning modules. + + Stores the hidden states for both the high-level (H) and low-level (L) + processing modules during recurrent computation. + + Attributes: + z_H: High-level hidden state tensor of shape + (batch_size, seq_len + puzzle_emb_len, hidden_size) + z_L: Low-level hidden state tensor of shape + (batch_size, seq_len + puzzle_emb_len, hidden_size) + """ + + z_H: torch.Tensor # noqa: N815 + z_L: torch.Tensor # noqa: N815 + + +@dataclass +class HierarchicalReasoningModel_ACTV1Carry: # noqa: N801 + """Complete carry state for HRM with ACT mechanism. + + Maintains all state information needed across forward passes, including + internal hidden states, step counts, halting flags, and current data. + + Attributes: + inner_carry: Internal state containing z_H and z_L hidden states + steps: Number of computation steps taken for each sequence in batch, + shape (batch_size,) + halted: Boolean flags indicating which sequences have halted, + shape (batch_size,) + current_data: Dictionary containing current batch data including + 'inputs', 'labels', and 'puzzle_identifiers' + """ + + inner_carry: HierarchicalReasoningModel_ACTV1InnerCarry + + steps: torch.Tensor + halted: torch.Tensor + + current_data: dict[str, torch.Tensor] + + +class HierarchicalReasoningModel_ACTV1Config(BaseModel): # noqa: N801 + """Configuration for Hierarchical Reasoning Model with ACT. + + Defines all hyperparameters for the model architecture, training, and + adaptive computation time mechanism. + + Attributes: + batch_size: Number of sequences processed in parallel + seq_len: Maximum sequence length (e.g., 81 for Sudoku, 900 for ARC) + puzzle_emb_ndim: Dimension of per-puzzle sparse embeddings (0 to disable) + num_puzzle_identifiers: Total number of unique puzzle types/IDs + vocab_size: Size of token vocabulary (e.g., 11 for Sudoku: 0-9 + pad) + + H_cycles: Number of high-level reasoning cycles per forward pass + L_cycles: Number of low-level computation cycles per H cycle + + H_layers: Number of transformer layers in high-level module + L_layers: Number of transformer layers in low-level module + + hidden_size: Dimension of hidden states and embeddings + expansion: MLP expansion ratio for SwiGLU (typically 4.0) + num_heads: Number of attention heads + pos_encodings: Type of positional encoding ("rope" or "learned") + + rms_norm_eps: Epsilon for RMS normalization stability + rope_theta: Base frequency for rotary position embeddings + + halt_max_steps: Maximum computation steps before forcing halt + halt_exploration_prob: Probability of exploration during ACT training + + forward_dtype: Data type for forward pass ("bfloat16" or "float32") + """ + + 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): # noqa: N801 + """Single transformer block with post-normalization. + + Implements a standard transformer layer with self-attention and MLP, + using post-normalization (add-then-norm) and RMS normalization. + + Args: + config: Model configuration containing architecture hyperparameters + + Attributes: + self_attn: Multi-head self-attention layer (non-causal) + mlp: SwiGLU feed-forward layer + norm_eps: Epsilon for RMS normalization stability + """ + + 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: + """Apply transformer block operations. + + Args: + cos_sin: Precomputed cosine and sine values for RoPE + hidden_states: Input hidden states of shape + (batch_size, seq_len, hidden_size) + + Returns: + Transformed hidden states of same shape as input + """ + # 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): # noqa: N801 + """Reasoning module for H-level or L-level processing. + + Stacks multiple transformer blocks and applies input injection to enable + communication between hierarchical levels. Used for both high-level (H) + and low-level (L) reasoning modules. + + Args: + layers: List of transformer blocks to stack + + Attributes: + layers: ModuleList containing all transformer blocks + """ + + 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: + """Process hidden states with input injection from other level. + + Args: + hidden_states: Current hidden states of shape + (batch_size, seq_len, hidden_size) + input_injection: States from other hierarchical level to inject, + same shape as hidden_states + **kwargs: Additional arguments passed to transformer blocks + (typically cos_sin for RoPE) + + Returns: + Processed hidden states of same shape as input + """ + # 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): # noqa: N801 + """Core inner model implementing hierarchical reasoning with dual recurrence. + + This is the main computational engine featuring: + - Token and puzzle embeddings with positional encoding + - Two-level hierarchical processing (H and L modules) + - Efficient 1-step gradient computation strategy + - Language modeling and Q-value heads for ACT + + The forward pass uses a clever optimization: run H_cycles × L_cycles iterations + without gradients, then execute final iteration with gradients. This maintains + computational depth while enabling stable training. + + Args: + config: Complete model configuration + + Attributes: + config: Stored configuration + forward_dtype: PyTorch dtype for forward pass (bfloat16 or float32) + embed_scale: Embedding scaling factor (sqrt(hidden_size)) + embed_tokens: Token embedding layer + lm_head: Language modeling output head + q_head: Q-value head for halt/continue decisions + puzzle_emb: Optional sparse puzzle embeddings + puzzle_emb_len: Length of puzzle embedding sequence + rotary_emb: Optional RoPE positional embeddings + embed_pos: Optional learned positional embeddings + H_level: High-level reasoning module + L_level: Low-level reasoning module + H_init: Learned initial state for H module + L_init: Learned initial state for L 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( + truncated_normal_init_( + torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1 + ), + persistent=True, + ) + self.L_init = nn.Buffer( + truncated_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 + ) -> torch.Tensor: + """Compute input embeddings with token, puzzle, and position encoding. + + Combines three types of embeddings: + 1. Token embeddings for input sequence + 2. Optional sparse puzzle embeddings prepended to sequence + 3. Positional embeddings (learned or RoPE, applied elsewhere) + + Args: + input: Input token IDs of shape (batch_size, seq_len) + puzzle_identifiers: Puzzle type IDs of shape (batch_size,) + + Returns: + Combined embeddings of shape + (batch_size, seq_len + puzzle_emb_len, hidden_size), + scaled by sqrt(hidden_size) + """ + # 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 + ) -> HierarchicalReasoningModel_ACTV1InnerCarry: + """Create uninitialized carry state tensors. + + Allocates empty tensors for H and L hidden states. These are meant + to be overwritten immediately by reset_carry() with learned initial states. + + Args: + batch_size: Number of sequences in batch + + Returns: + InnerCarry with empty z_H and z_L tensors of shape + (batch_size, seq_len + puzzle_emb_len, hidden_size) + """ + # Infer device from H_init buffer + device = self.H_init.device + + 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, + device=device, + ), + z_L=torch.empty( + batch_size, + self.config.seq_len + self.puzzle_emb_len, + self.config.hidden_size, + dtype=self.forward_dtype, + device=device, + ), + ) + + def reset_carry( + self, + reset_flag: torch.Tensor, + carry: HierarchicalReasoningModel_ACTV1InnerCarry, + ) -> HierarchicalReasoningModel_ACTV1InnerCarry: + """Reset carry state to learned initial values based on flags. + + For sequences marked with reset_flag=True (typically halted sequences + starting new problems), replace their hidden states with learned initial + states H_init and L_init. Otherwise preserve existing states. + + Args: + reset_flag: Boolean tensor of shape (batch_size,) indicating which + sequences to reset + carry: Current inner carry state + + Returns: + Updated InnerCarry with selectively reset states + """ + # Ensure H_init and L_init are on the same device as the carry tensors + device = carry.z_H.device + H_init = self.H_init.to(device) # noqa: N806 + L_init = self.L_init.to(device) # noqa: N806 + + return HierarchicalReasoningModel_ACTV1InnerCarry( + z_H=torch.where(reset_flag.view(-1, 1, 1), H_init, carry.z_H), + z_L=torch.where(reset_flag.view(-1, 1, 1), 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], + ]: + """Execute hierarchical reasoning forward pass with 1-step gradient. + + Implements the core computation strategy: + 1. Run (H_cycles × L_cycles - 1) iterations without gradients + 2. Execute final L-level and H-level pass with gradients + 3. Compute language modeling logits and Q-values + + This approach maintains computational depth (many iterations) while + keeping gradient computation tractable (only 1-step backprop). + + Args: + carry: Current inner carry state with z_H and z_L + batch: Dictionary containing: + - 'inputs': Token IDs of shape (batch_size, seq_len) + - 'puzzle_identifiers': Puzzle IDs of shape (batch_size,) + + Returns: + Tuple containing: + - new_carry: Updated inner carry (detached from gradients) + - output: Language modeling logits of shape + (batch_size, seq_len, vocab_size) + - (q_halt_logits, q_continue_logits): Q-values for ACT, + each of shape (batch_size,) + """ + seq_info = { + "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 # noqa: N806 + + for _H_step in range(self.config.H_cycles): # noqa: N806 + for _L_step in range(self.config.L_cycles): # noqa: N806 + 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) # noqa: N806 + + if _H_step != self.config.H_cycles - 1: + z_H = self.H_level(z_H, z_L, **seq_info) # noqa: N806 + + 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) # noqa: N806 + z_H = self.H_level(z_H, z_L, **seq_info) # noqa: N806 + + # 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): # noqa: N801 + """Hierarchical Reasoning Model with Adaptive Computation Time. + + Top-level wrapper that adds ACT (Adaptive Computation Time) mechanism + to the hierarchical reasoning model. Manages: + - Halting logic via Q-learning + - Carry state updates + - New data injection for halted sequences + - Exploration during training + + This is the main model class users interact with. It wraps the inner + model and handles the ACT loop, allowing sequences to adaptively decide + when to stop computation. + + Args: + config_dict: Dictionary of configuration parameters matching + HierarchicalReasoningModel_ACTV1Config fields + + Attributes: + config: Validated configuration object + inner: Core HierarchicalReasoningModel_ACTV1_Inner instance + + Example: + >>> config = { + ... "batch_size": 4, + ... "seq_len": 81, + ... "vocab_size": 11, + ... "hidden_size": 512, + ... "num_heads": 8, + ... # ... other config + ... } + >>> model = HierarchicalReasoningModel_ACTV1(config) + >>> carry = model.initial_carry(batch) + >>> carry, outputs = model(carry=carry, batch=batch) + """ + + 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): + """Access to sparse puzzle embeddings for distributed training.""" + return self.inner.puzzle_emb + + def initial_carry( + self, batch: dict[str, torch.Tensor] + ) -> HierarchicalReasoningModel_ACTV1Carry: + """Initialize carry state for a new batch. + + Creates initial state with: + - Empty inner carry (will be reset on first forward pass) + - Zero step counts + - All sequences marked as halted (triggers data loading) + - Empty current_data placeholders + + Args: + batch: Initial batch dictionary containing 'inputs' and other fields + + Returns: + Initial HierarchicalReasoningModel_ACTV1Carry ready for first forward pass + """ + batch_size = batch["inputs"].shape[0] + device = batch["inputs"].device # Get device from batch + + 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, device=device), + halted=torch.ones( + (batch_size,), dtype=torch.bool, device=device + ), # 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]]: + """Execute one ACT step with halting logic. + + Performs: + 1. Reset carry for halted sequences (inject new data) + 2. Run inner model forward pass + 3. Compute halting decisions via Q-learning + 4. Apply exploration during training + 5. Compute bootstrapping target for Q-continue + + The halting mechanism uses Q-values: + - q_halt_logits: Value of stopping computation now + - q_continue_logits: Value of continuing computation + - Halt when q_halt > q_continue (with exploration in training) + + Args: + carry: Current carry state from previous step + batch: New batch data to inject for halted sequences, containing: + - 'inputs': Token IDs of shape (batch_size, seq_len) + - 'labels': Target labels (same shape) + - 'puzzle_identifiers': Puzzle IDs of shape (batch_size,) + + Returns: + Tuple containing: + - new_carry: Updated carry state with: + - Updated inner_carry (z_H, z_L) + - Incremented steps + - Updated halted flags + - Current data + - outputs: Dictionary with: + - 'logits': LM predictions (batch, seq_len, vocab_size) + - 'q_halt_logits': Halt Q-values (batch,) + - 'q_continue_logits': Continue Q-values (batch,) + - 'target_q_continue': Bootstrapping target (training only) + """ + # 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/src/hierarchical_reasoning_model/utils/__init__.py b/src/hierarchical_reasoning_model/utils/__init__.py new file mode 100644 index 00000000..b5837de8 --- /dev/null +++ b/src/hierarchical_reasoning_model/utils/__init__.py @@ -0,0 +1,3 @@ +"""Utility functions for HRM.""" + +__all__ = [] diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..ce51bcf6 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,102 @@ +"""Pytest configuration and shared fixtures for HRM tests.""" + +import sys +from unittest.mock import MagicMock + +import numpy as np +import pytest +import torch + +# Mock FlashAttention for tests (not all test environments have GPU) +# This allows tests to run without requiring CUDA +if "flash_attn" not in sys.modules and "flash_attn_interface" not in sys.modules: + # Create mock flash_attn module + flash_attn_mock = MagicMock() + + def mock_flash_attn_func(q, k, v, causal=False): + """Mock flash attention function. + + Mimics FlashAttention interface with shape (batch, seq_len, num_heads, head_dim). + """ + # q, k, v shape: (batch, seq_len, num_heads, head_dim) + batch, seq_len, num_heads, head_dim = q.shape + + # Transpose to (batch, num_heads, seq_len, head_dim) for matmul + q_t = q.transpose(1, 2) # (batch, num_heads, seq_len, head_dim) + k_t = k.transpose(1, 2) + v_t = v.transpose(1, 2) + + # Compute attention scores + scores = torch.matmul(q_t, k_t.transpose(-2, -1)) / (head_dim**0.5) + # scores shape: (batch, num_heads, seq_len, seq_len) + + if causal: + mask = torch.triu(torch.ones(seq_len, seq_len, device=q.device), diagonal=1) + scores = scores.masked_fill(mask.bool(), float("-inf")) + + attn = torch.softmax(scores, dim=-1) + output = torch.matmul(attn, v_t) # (batch, num_heads, seq_len, head_dim) + + # Transpose back to (batch, seq_len, num_heads, head_dim) + output = output.transpose(1, 2).contiguous() + return output + + flash_attn_mock.flash_attn_func = mock_flash_attn_func + + sys.modules["flash_attn"] = flash_attn_mock + sys.modules["flash_attn_interface"] = flash_attn_mock + + +@pytest.fixture +def device(): + """Provide CUDA device if available, else CPU.""" + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +@pytest.fixture +def batch_size(): + """Default batch size for tests.""" + return 4 + + +@pytest.fixture +def seq_len(): + """Default sequence length for tests.""" + return 81 # Sudoku grid size + + +@pytest.fixture +def vocab_size(): + """Default vocabulary size for tests.""" + return 11 # 0-9 + padding + + +@pytest.fixture +def hidden_size(): + """Default hidden size for tests.""" + return 128 # Smaller for faster tests + + +@pytest.fixture +def sample_batch(batch_size, seq_len, vocab_size): + """Create a sample batch for testing.""" + return { + "inputs": torch.randint( + 0, vocab_size, (batch_size, seq_len), dtype=torch.int32 + ), + "labels": torch.randint( + 1, vocab_size, (batch_size, seq_len), dtype=torch.int32 + ), + "puzzle_identifiers": torch.zeros(batch_size, dtype=torch.int32), + } + + +@pytest.fixture +def random_seed(): + """Set random seed for reproducibility.""" + seed = 42 + torch.manual_seed(seed) + np.random.seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + return seed diff --git a/tests/core/__init__.py b/tests/core/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/core/test_layers.py b/tests/core/test_layers.py new file mode 100644 index 00000000..1e333e9c --- /dev/null +++ b/tests/core/test_layers.py @@ -0,0 +1,307 @@ +"""Tests for transformer layers and components.""" + +import pytest +import torch + +from hierarchical_reasoning_model.core.layers import ( + Attention, + CastedEmbedding, + CastedLinear, + RotaryEmbedding, + SwiGLU, + _round_up_to_multiple, + apply_rotary_pos_emb, + rms_norm, + rotate_half, +) + + +class TestUtilityFunctions: + """Tests for utility functions.""" + + def test_find_multiple_exact(self): + """Test _round_up_to_multiple with exact multiple.""" + assert _round_up_to_multiple(10, 5) == 10 + assert _round_up_to_multiple(20, 10) == 20 + + def test_find_multiple_round_up(self): + """Test _round_up_to_multiple rounds up.""" + assert _round_up_to_multiple(11, 5) == 15 + assert _round_up_to_multiple(21, 10) == 30 + assert _round_up_to_multiple(1, 256) == 256 + + def test_find_multiple_edge_cases(self): + """Test edge cases.""" + assert _round_up_to_multiple(0, 5) == 0 + assert _round_up_to_multiple(256, 256) == 256 + + def test_rotate_half_shape(self): + """Test rotate_half preserves shape.""" + x = torch.randn(2, 4, 8) + result = rotate_half(x) + assert result.shape == x.shape + + def test_rotate_half_computation(self): + """Test rotate_half correctly rotates.""" + x = torch.tensor([[1.0, 2.0, 3.0, 4.0]]) + result = rotate_half(x) + + # Should be [-3, -4, 1, 2] + expected = torch.tensor([[-3.0, -4.0, 1.0, 2.0]]) + assert torch.allclose(result, expected) + + def test_apply_rotary_pos_emb_shapes(self): + """Test RoPE preserves shapes.""" + batch, seq_len, num_heads, head_dim = 2, 10, 4, 8 + q = torch.randn(batch, seq_len, num_heads, head_dim) + k = torch.randn(batch, seq_len, num_heads, head_dim) + cos = torch.randn(seq_len, head_dim) + sin = torch.randn(seq_len, head_dim) + + q_embed, k_embed = apply_rotary_pos_emb(q, k, cos, sin) + + assert q_embed.shape == q.shape + assert k_embed.shape == k.shape + + def test_apply_rotary_pos_emb_dtype(self): + """Test RoPE preserves dtype.""" + q = torch.randn(2, 4, 2, 8, dtype=torch.float32) + k = torch.randn(2, 4, 2, 8, dtype=torch.float32) + cos = torch.randn(4, 8, dtype=torch.float16) + sin = torch.randn(4, 8, dtype=torch.float16) + + q_embed, k_embed = apply_rotary_pos_emb(q, k, cos, sin) + + # Should return to original dtype + assert q_embed.dtype == torch.float32 + assert k_embed.dtype == torch.float32 + + +class TestCastedLinear: + """Tests for CastedLinear layer.""" + + def test_initialization(self): + """Test layer initialization.""" + layer = CastedLinear(10, 20, bias=True) + + assert layer.weight.shape == (20, 10) + assert layer.bias is not None + assert layer.bias.shape == (20,) + + def test_initialization_no_bias(self): + """Test initialization without bias.""" + layer = CastedLinear(10, 20, bias=False) + + assert layer.weight.shape == (20, 10) + assert layer.bias is None + + def test_forward_shape(self): + """Test forward pass output shape.""" + layer = CastedLinear(10, 20, bias=True) + x = torch.randn(5, 10) + + output = layer(x) + + assert output.shape == (5, 20) + + def test_forward_dtype_casting(self): + """Test automatic dtype casting.""" + layer = CastedLinear(10, 20, bias=True) + x = torch.randn(5, 10, dtype=torch.float16) + + output = layer(x) + + # Output should match input dtype + assert output.dtype == torch.float16 + + +class TestCastedEmbedding: + """Tests for CastedEmbedding layer.""" + + def test_initialization(self): + """Test embedding initialization.""" + embedding = CastedEmbedding(100, 64, init_std=0.02, cast_to=torch.float32) + + assert embedding.embedding_weight.shape == (100, 64) + assert embedding.cast_to == torch.float32 + + def test_forward_shape(self): + """Test forward pass output shape.""" + embedding = CastedEmbedding(100, 64, init_std=0.02, cast_to=torch.float32) + indices = torch.randint(0, 100, (5, 10)) + + output = embedding(indices) + + assert output.shape == (5, 10, 64) + + def test_forward_dtype(self): + """Test output dtype matches cast_to.""" + embedding = CastedEmbedding(100, 64, init_std=0.02, cast_to=torch.bfloat16) + indices = torch.randint(0, 100, (5, 10)) + + output = embedding(indices) + + assert output.dtype == torch.bfloat16 + + +class TestRotaryEmbedding: + """Tests for RotaryEmbedding.""" + + def test_initialization(self): + """Test RoPE initialization.""" + rope = RotaryEmbedding(dim=64, max_position_embeddings=512, base=10000) + + assert rope.cos_cached.shape == (512, 64) + assert rope.sin_cached.shape == (512, 64) + + def test_forward_returns_cached_values(self): + """Test forward returns precomputed values.""" + rope = RotaryEmbedding(dim=64, max_position_embeddings=512, base=10000) + + cos, sin = rope() + + assert torch.equal(cos, rope.cos_cached) + assert torch.equal(sin, rope.sin_cached) + + def test_values_in_valid_range(self): + """Test cos/sin values are in [-1, 1].""" + rope = RotaryEmbedding(dim=64, max_position_embeddings=512, base=10000) + + cos, sin = rope() + + assert torch.all(cos >= -1.0) and torch.all(cos <= 1.0) + assert torch.all(sin >= -1.0) and torch.all(sin <= 1.0) + + +class TestAttention: + """Tests for Attention layer.""" + + @pytest.fixture + def attention_layer(self): + """Create attention layer for testing.""" + return Attention( + hidden_size=128, + head_dim=32, + num_heads=4, + num_key_value_heads=4, + causal=False, + ) + + def test_initialization(self, attention_layer): + """Test attention initialization.""" + assert attention_layer.hidden_size == 128 + assert attention_layer.head_dim == 32 + assert attention_layer.num_heads == 4 + assert attention_layer.output_size == 128 + + def test_forward_shape(self, attention_layer): + """Test forward pass output shape.""" + batch, seq_len = 2, 10 + hidden_states = torch.randn(batch, seq_len, 128) + + output = attention_layer(cos_sin=None, hidden_states=hidden_states) + + assert output.shape == (batch, seq_len, 128) + + def test_forward_with_rope(self, attention_layer): + """Test forward with RoPE positional encoding.""" + batch, seq_len = 2, 10 + hidden_states = torch.randn(batch, seq_len, 128) + + rope = RotaryEmbedding(dim=32, max_position_embeddings=10, base=10000) + cos_sin = rope() + + output = attention_layer(cos_sin=cos_sin, hidden_states=hidden_states) + + assert output.shape == (batch, seq_len, 128) + + def test_causal_attention(self): + """Test causal attention masking.""" + attention = Attention( + hidden_size=64, + head_dim=16, + num_heads=4, + num_key_value_heads=4, + causal=True, + ) + + batch, seq_len = 2, 8 + hidden_states = torch.randn(batch, seq_len, 64) + + output = attention(cos_sin=None, hidden_states=hidden_states) + + assert output.shape == (batch, seq_len, 64) + + +class TestSwiGLU: + """Tests for SwiGLU layer.""" + + def test_initialization(self): + """Test SwiGLU initialization.""" + swiglu = SwiGLU(hidden_size=128, expansion=4.0) + + # Should have gate_up_proj and down_proj + assert hasattr(swiglu, "gate_up_proj") + assert hasattr(swiglu, "down_proj") + + def test_forward_shape(self): + """Test forward pass output shape.""" + swiglu = SwiGLU(hidden_size=128, expansion=4.0) + + x = torch.randn(5, 10, 128) + output = swiglu(x) + + # Output should match input shape + assert output.shape == x.shape + + def test_forward_computation(self): + """Test that forward pass produces valid output.""" + swiglu = SwiGLU(hidden_size=64, expansion=2.0) + + x = torch.randn(2, 4, 64) + output = swiglu(x) + + # Output should not be all zeros or NaN + assert not torch.allclose(output, torch.zeros_like(output)) + assert not torch.any(torch.isnan(output)) + + +class TestRMSNorm: + """Tests for RMS normalization.""" + + def test_rms_norm_shape(self): + """Test RMS norm preserves shape.""" + x = torch.randn(2, 4, 8) + output = rms_norm(x, variance_epsilon=1e-5) + + assert output.shape == x.shape + + def test_rms_norm_dtype(self): + """Test RMS norm preserves dtype.""" + x = torch.randn(2, 4, 8, dtype=torch.float16) + output = rms_norm(x, variance_epsilon=1e-5) + + assert output.dtype == torch.float16 + + def test_rms_norm_normalization(self): + """Test that RMS norm approximately normalizes.""" + x = torch.randn(2, 10, 64) * 10 # Large values + output = rms_norm(x, variance_epsilon=1e-5) + + # RMS should be approximately 1 + rms = torch.sqrt(torch.mean(output**2, dim=-1)) + assert torch.allclose(rms, torch.ones_like(rms), atol=0.1) + + def test_rms_norm_stability(self): + """Test numerical stability with very small/large values.""" + x_small = torch.randn(2, 4, 8) * 1e-10 + x_large = torch.randn(2, 4, 8) * 1e10 + + output_small = rms_norm(x_small, variance_epsilon=1e-5) + output_large = rms_norm(x_large, variance_epsilon=1e-5) + + # Should not produce NaN or Inf + assert not torch.any(torch.isnan(output_small)) + assert not torch.any(torch.isinf(output_small)) + assert not torch.any(torch.isnan(output_large)) + assert not torch.any(torch.isinf(output_large)) diff --git a/tests/core/test_losses.py b/tests/core/test_losses.py new file mode 100644 index 00000000..172f5480 --- /dev/null +++ b/tests/core/test_losses.py @@ -0,0 +1,278 @@ +"""Tests for loss functions and ACTLossHead.""" + +import pytest +import torch + +from hierarchical_reasoning_model.core.losses import ( + IGNORE_LABEL_ID, + ACTLossHead, + log_stablemax, + softmax_cross_entropy, + stablemax_cross_entropy, + stablemax_transform, +) + + +class TestStablemaxFunctions: + """Tests for stablemax transformation and normalization.""" + + def test_s_function_negative(self): + """Test stablemax_transform() on negative values.""" + x = torch.tensor([-1.0, -2.0, -3.0]) + result = stablemax_transform(x) + + # For negative values, stablemax_transform(x) = 1 / (1 - x + epsilon) + # Should be in range (0, 1) + assert torch.all(result > 0) + assert torch.all(result < 1) + + def test_s_function_positive(self): + """Test stablemax_transform() on positive values.""" + x = torch.tensor([1.0, 2.0, 3.0]) + result = stablemax_transform(x) + + # For non-negative values, stablemax_transform(x) = x + 1 + expected = x + 1 + assert torch.allclose(result, expected) + + def test_s_function_zero(self): + """Test stablemax_transform() at zero.""" + x = torch.tensor([0.0]) + result = stablemax_transform(x) + + # At zero, stablemax_transform(0) = 0 + 1 = 1 + assert torch.allclose(result, torch.tensor([1.0])) + + def test_log_stablemax_normalization(self): + """Test that log_stablemax produces valid log probabilities.""" + x = torch.randn(3, 5) + log_probs = log_stablemax(x, dim=-1) + + # Log probs should sum to 1 in probability space + probs = torch.exp(log_probs) + assert torch.allclose(probs.sum(dim=-1), torch.ones(3), atol=1e-6) + + def test_log_stablemax_shape(self): + """Test that log_stablemax preserves shape.""" + x = torch.randn(2, 3, 4) + result = log_stablemax(x, dim=-1) + assert result.shape == x.shape + + +class TestCrossEntropyLosses: + """Tests for cross-entropy loss functions.""" + + def test_stablemax_cross_entropy_basic(self): + """Test basic stablemax cross-entropy computation.""" + batch_size, seq_len, vocab_size = 2, 4, 10 + logits = torch.randn(batch_size, seq_len, vocab_size) + labels = torch.randint(0, vocab_size, (batch_size, seq_len)) + + loss = stablemax_cross_entropy(logits, labels) + + assert loss.shape == (batch_size, seq_len) + assert torch.all(loss >= 0) # Loss should be non-negative + + def test_stablemax_cross_entropy_ignore_index(self): + """Test that ignore_index is properly handled.""" + batch_size, seq_len, vocab_size = 2, 4, 10 + logits = torch.randn(batch_size, seq_len, vocab_size) + labels = torch.randint(0, vocab_size, (batch_size, seq_len)) + + # Set some labels to ignore index + labels[0, 0] = IGNORE_LABEL_ID + labels[1, 2] = IGNORE_LABEL_ID + + loss = stablemax_cross_entropy(logits, labels, ignore_index=IGNORE_LABEL_ID) + + # Ignored positions should have zero loss + assert loss[0, 0] == 0.0 + assert loss[1, 2] == 0.0 + + def test_softmax_cross_entropy_basic(self): + """Test basic softmax cross-entropy computation.""" + batch_size, seq_len, vocab_size = 2, 4, 10 + logits = torch.randn(batch_size, seq_len, vocab_size) + labels = torch.randint(0, vocab_size, (batch_size, seq_len)) + + loss = softmax_cross_entropy(logits, labels) + + assert loss.shape == (batch_size, seq_len) + assert torch.all(loss >= 0) + + def test_softmax_cross_entropy_ignore_index(self): + """Test that ignore_index is properly handled in softmax.""" + batch_size, seq_len, vocab_size = 2, 4, 10 + logits = torch.randn(batch_size, seq_len, vocab_size) + labels = torch.randint(0, vocab_size, (batch_size, seq_len)) + + labels[0, 1] = IGNORE_LABEL_ID + + loss = softmax_cross_entropy(logits, labels, ignore_index=IGNORE_LABEL_ID) + + assert loss[0, 1] == 0.0 + + def test_cross_entropy_perfect_prediction(self): + """Test that perfect predictions give near-zero loss.""" + batch_size, seq_len, vocab_size = 2, 3, 5 + labels = torch.randint(0, vocab_size, (batch_size, seq_len)) + + # Create logits with very high values at correct labels + logits = torch.full((batch_size, seq_len, vocab_size), -10.0) + for b in range(batch_size): + for s in range(seq_len): + logits[b, s, labels[b, s]] = 10.0 + + loss_stablemax = stablemax_cross_entropy(logits, labels) + loss_softmax = softmax_cross_entropy(logits, labels) + + # Both should give very small loss + assert torch.all(loss_stablemax < 0.1) + assert torch.all(loss_softmax < 0.1) + + +class MockModel(torch.nn.Module): + """Mock model for testing ACTLossHead.""" + + def __init__(self, batch_size, seq_len, vocab_size, hidden_size): + super().__init__() + self.batch_size = batch_size + self.seq_len = seq_len + self.vocab_size = vocab_size + self.hidden_size = hidden_size + + def initial_carry(self, batch): + """Return mock initial carry.""" + return type( + "Carry", + (), + { + "current_data": batch, + "halted": torch.zeros(self.batch_size, dtype=torch.bool), + "steps": torch.zeros(self.batch_size, dtype=torch.int32), + }, + )() + + def forward(self, carry, batch): + """Return mock outputs.""" + logits = torch.randn(self.batch_size, self.seq_len, self.vocab_size) + q_halt_logits = torch.randn(self.batch_size) + q_continue_logits = torch.randn(self.batch_size) + + # Update carry + new_carry = type( + "Carry", + (), + { + "current_data": batch, + "halted": torch.ones(self.batch_size, dtype=torch.bool), + "steps": torch.ones(self.batch_size, dtype=torch.int32), + }, + )() + + outputs = { + "logits": logits, + "q_halt_logits": q_halt_logits, + "q_continue_logits": q_continue_logits, + } + + return new_carry, outputs + + +class TestACTLossHead: + """Tests for ACTLossHead wrapper.""" + + @pytest.fixture + def mock_model(self, batch_size, seq_len, vocab_size, hidden_size): + """Create a mock model for testing.""" + return MockModel(batch_size, seq_len, vocab_size, hidden_size) + + @pytest.fixture + def act_loss_head(self, mock_model): + """Create ACTLossHead with mock model.""" + return ACTLossHead(mock_model, loss_type="stablemax_cross_entropy") + + def test_initialization(self, act_loss_head, mock_model): + """Test ACTLossHead initialization.""" + assert act_loss_head.model is mock_model + assert act_loss_head.loss_fn is stablemax_cross_entropy + + def test_initialization_with_softmax(self, mock_model): + """Test initialization with softmax loss.""" + loss_head = ACTLossHead(mock_model, loss_type="softmax_cross_entropy") + assert loss_head.loss_fn is softmax_cross_entropy + + def test_initial_carry(self, act_loss_head, sample_batch): + """Test initial_carry passes through to model.""" + carry = act_loss_head.initial_carry(sample_batch) + assert hasattr(carry, "current_data") + assert hasattr(carry, "halted") + assert hasattr(carry, "steps") + + def test_forward_returns_expected_outputs(self, act_loss_head, sample_batch): + """Test forward returns correct output structure.""" + carry = act_loss_head.initial_carry(sample_batch) + + new_carry, loss, metrics, predictions, all_halted = act_loss_head.forward( + return_keys=["logits"], + carry=carry, + batch=sample_batch, + ) + + # Check outputs + assert isinstance(loss, torch.Tensor) + assert loss.ndim == 0 # Scalar + assert isinstance(metrics, dict) + assert isinstance(all_halted, torch.Tensor) + + # Check metrics + assert "count" in metrics + assert "accuracy" in metrics + assert "exact_accuracy" in metrics + assert "q_halt_accuracy" in metrics + assert "steps" in metrics + assert "lm_loss" in metrics + assert "q_halt_loss" in metrics + + def test_forward_with_ignore_labels(self, act_loss_head, sample_batch): + """Test forward with some labels marked as ignore.""" + # Set some labels to ignore + sample_batch["labels"][0, :10] = IGNORE_LABEL_ID + + carry = act_loss_head.initial_carry(sample_batch) + new_carry, loss, metrics, predictions, all_halted = act_loss_head.forward( + return_keys=["logits"], + carry=carry, + batch=sample_batch, + ) + + # Should still compute loss without errors + assert loss.item() >= 0 + + def test_metrics_are_detached(self, act_loss_head, sample_batch): + """Test that returned metrics are detached from computation graph.""" + carry = act_loss_head.initial_carry(sample_batch) + new_carry, loss, metrics, predictions, all_halted = act_loss_head.forward( + return_keys=["logits"], + carry=carry, + batch=sample_batch, + ) + + # Metrics should not require grad + for _key, value in metrics.items(): + if isinstance(value, torch.Tensor): + assert not value.requires_grad + + def test_predictions_are_detached(self, act_loss_head, sample_batch): + """Test that returned predictions are detached.""" + carry = act_loss_head.initial_carry(sample_batch) + new_carry, loss, metrics, predictions, all_halted = act_loss_head.forward( + return_keys=["logits"], + carry=carry, + batch=sample_batch, + ) + + if predictions is not None: + for _key, value in predictions.items(): + if isinstance(value, torch.Tensor): + assert not value.requires_grad diff --git a/tests/core/test_model.py b/tests/core/test_model.py new file mode 100644 index 00000000..2a05d0e1 --- /dev/null +++ b/tests/core/test_model.py @@ -0,0 +1,540 @@ +"""Tests for Hierarchical Reasoning Model (HRM) components.""" + +import pytest +import torch + +from hierarchical_reasoning_model.core.model import ( + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1_Inner, + HierarchicalReasoningModel_ACTV1Block, + HierarchicalReasoningModel_ACTV1Carry, + HierarchicalReasoningModel_ACTV1Config, + HierarchicalReasoningModel_ACTV1InnerCarry, + HierarchicalReasoningModel_ACTV1ReasoningModule, +) + + +@pytest.fixture +def small_config(): + """Create a small model configuration for fast testing.""" + return HierarchicalReasoningModel_ACTV1Config( + batch_size=2, + seq_len=9, # Small for fast tests (e.g., 3x3 grid) + puzzle_emb_ndim=0, # Disable puzzle embeddings for simplicity + num_puzzle_identifiers=10, + vocab_size=5, # Small vocab + H_cycles=1, + L_cycles=1, + H_layers=2, + L_layers=2, + hidden_size=64, # Small for speed + expansion=2.0, + num_heads=4, + pos_encodings="rope", + rms_norm_eps=1e-5, + rope_theta=10000.0, + halt_max_steps=3, + halt_exploration_prob=0.1, + forward_dtype="float32", # Use float32 for testing + ) + + +@pytest.fixture +def config_with_puzzle_emb(small_config): + """Create config with puzzle embeddings enabled.""" + config_dict = small_config.model_dump() + config_dict["puzzle_emb_ndim"] = 32 + return HierarchicalReasoningModel_ACTV1Config(**config_dict) + + +@pytest.fixture +def config_learned_pos(small_config): + """Create config with learned positional encodings.""" + config_dict = small_config.model_dump() + config_dict["pos_encodings"] = "learned" + return HierarchicalReasoningModel_ACTV1Config(**config_dict) + + +@pytest.fixture +def sample_batch_model(small_config): + """Create sample batch for model testing.""" + return { + "inputs": torch.randint( + 0, small_config.vocab_size, (small_config.batch_size, small_config.seq_len) + ), + "labels": torch.randint( + 0, small_config.vocab_size, (small_config.batch_size, small_config.seq_len) + ), + "puzzle_identifiers": torch.zeros(small_config.batch_size, dtype=torch.int32), + } + + +class TestHierarchicalReasoningModelConfig: + """Tests for model configuration.""" + + def test_config_initialization(self, small_config): + """Test configuration initialization.""" + assert small_config.batch_size == 2 + assert small_config.seq_len == 9 + assert small_config.vocab_size == 5 + assert small_config.hidden_size == 64 + assert small_config.H_cycles == 1 + assert small_config.L_cycles == 1 + + def test_config_with_puzzle_embeddings(self, config_with_puzzle_emb): + """Test configuration with puzzle embeddings.""" + assert config_with_puzzle_emb.puzzle_emb_ndim == 32 + assert config_with_puzzle_emb.num_puzzle_identifiers == 10 + + def test_config_different_pos_encodings(self): + """Test different positional encoding types.""" + config_rope = HierarchicalReasoningModel_ACTV1Config( + batch_size=2, + seq_len=9, + num_puzzle_identifiers=10, + vocab_size=5, + H_cycles=1, + L_cycles=1, + H_layers=2, + L_layers=2, + hidden_size=64, + expansion=2.0, + num_heads=4, + pos_encodings="rope", + halt_max_steps=3, + halt_exploration_prob=0.1, + ) + + config_learned = HierarchicalReasoningModel_ACTV1Config( + batch_size=2, + seq_len=9, + num_puzzle_identifiers=10, + vocab_size=5, + H_cycles=1, + L_cycles=1, + H_layers=2, + L_layers=2, + hidden_size=64, + expansion=2.0, + num_heads=4, + pos_encodings="learned", + halt_max_steps=3, + halt_exploration_prob=0.1, + ) + + assert config_rope.pos_encodings == "rope" + assert config_learned.pos_encodings == "learned" + + +class TestHierarchicalReasoningModelBlock: + """Tests for single transformer block.""" + + @pytest.fixture + def block(self, small_config): + """Create a transformer block.""" + return HierarchicalReasoningModel_ACTV1Block(small_config) + + def test_block_initialization(self, block): + """Test block initialization.""" + assert hasattr(block, "self_attn") + assert hasattr(block, "mlp") + assert block.norm_eps == 1e-5 + + def test_block_forward_shape(self, block, small_config): + """Test block forward pass output shape.""" + batch_size = small_config.batch_size + seq_len = small_config.seq_len + hidden_size = small_config.hidden_size + + hidden_states = torch.randn(batch_size, seq_len, hidden_size) + cos_sin = None # RoPE will use default + + output = block(cos_sin=cos_sin, hidden_states=hidden_states) + + assert output.shape == (batch_size, seq_len, hidden_size) + + def test_block_forward_no_nan(self, block, small_config): + """Test that block produces valid outputs.""" + hidden_states = torch.randn( + small_config.batch_size, small_config.seq_len, small_config.hidden_size + ) + + output = block(cos_sin=None, hidden_states=hidden_states) + + assert not torch.any(torch.isnan(output)) + assert not torch.any(torch.isinf(output)) + + +class TestHierarchicalReasoningModelReasoningModule: + """Tests for reasoning module (H or L level).""" + + @pytest.fixture + def reasoning_module(self, small_config): + """Create a reasoning module.""" + layers = [ + HierarchicalReasoningModel_ACTV1Block(small_config) + for _ in range(small_config.H_layers) + ] + return HierarchicalReasoningModel_ACTV1ReasoningModule(layers) + + def test_reasoning_module_initialization(self, reasoning_module, small_config): + """Test reasoning module initialization.""" + assert len(reasoning_module.layers) == small_config.H_layers + + def test_reasoning_module_forward_shape(self, reasoning_module, small_config): + """Test reasoning module forward pass.""" + batch_size = small_config.batch_size + seq_len = small_config.seq_len + hidden_size = small_config.hidden_size + + hidden_states = torch.randn(batch_size, seq_len, hidden_size) + input_injection = torch.randn(batch_size, seq_len, hidden_size) + + output = reasoning_module( + hidden_states=hidden_states, input_injection=input_injection, cos_sin=None + ) + + assert output.shape == (batch_size, seq_len, hidden_size) + + def test_reasoning_module_input_injection(self, reasoning_module, small_config): + """Test that input injection is applied.""" + hidden_states = torch.zeros( + small_config.batch_size, small_config.seq_len, small_config.hidden_size + ) + input_injection = torch.ones( + small_config.batch_size, small_config.seq_len, small_config.hidden_size + ) + + output = reasoning_module( + hidden_states=hidden_states, input_injection=input_injection, cos_sin=None + ) + + # Output should be affected by input injection (non-zero) + assert not torch.allclose(output, torch.zeros_like(output)) + + +class TestHierarchicalReasoningModelInner: + """Tests for inner model (core computational engine).""" + + @pytest.fixture + def inner_model(self, small_config): + """Create inner model.""" + return HierarchicalReasoningModel_ACTV1_Inner(small_config) + + @pytest.fixture + def inner_model_puzzle_emb(self, config_with_puzzle_emb): + """Create inner model with puzzle embeddings.""" + return HierarchicalReasoningModel_ACTV1_Inner(config_with_puzzle_emb) + + @pytest.fixture + def inner_model_learned_pos(self, config_learned_pos): + """Create inner model with learned positional encodings.""" + return HierarchicalReasoningModel_ACTV1_Inner(config_learned_pos) + + def test_inner_model_initialization(self, inner_model, small_config): + """Test inner model initialization.""" + assert inner_model.config == small_config + assert hasattr(inner_model, "embed_tokens") + assert hasattr(inner_model, "lm_head") + assert hasattr(inner_model, "q_head") + assert hasattr(inner_model, "H_level") + assert hasattr(inner_model, "L_level") + assert hasattr(inner_model, "H_init") + assert hasattr(inner_model, "L_init") + + def test_inner_model_rope_initialization(self, inner_model): + """Test RoPE initialization.""" + assert hasattr(inner_model, "rotary_emb") + assert not hasattr(inner_model, "embed_pos") + + def test_inner_model_learned_pos_initialization(self, inner_model_learned_pos): + """Test learned positional encoding initialization.""" + assert hasattr(inner_model_learned_pos, "embed_pos") + assert not hasattr(inner_model_learned_pos, "rotary_emb") + + def test_inner_model_puzzle_emb_initialization(self, inner_model_puzzle_emb): + """Test puzzle embedding initialization.""" + assert hasattr(inner_model_puzzle_emb, "puzzle_emb") + assert inner_model_puzzle_emb.puzzle_emb_len > 0 + + def test_inner_model_q_head_init(self, inner_model): + """Test Q-head special initialization.""" + # Q-head should be initialized to near-zero for bootstrapping + assert torch.allclose( + inner_model.q_head.weight, torch.zeros_like(inner_model.q_head.weight) + ) + assert torch.allclose( + inner_model.q_head.bias, torch.full_like(inner_model.q_head.bias, -5.0) + ) + + def test_empty_carry_shape(self, inner_model, small_config): + """Test empty carry creation.""" + carry = inner_model.empty_carry(batch_size=small_config.batch_size) + + assert isinstance(carry, HierarchicalReasoningModel_ACTV1InnerCarry) + assert carry.z_H.shape == ( + small_config.batch_size, + small_config.seq_len, + small_config.hidden_size, + ) + assert carry.z_L.shape == ( + small_config.batch_size, + small_config.seq_len, + small_config.hidden_size, + ) + + def test_reset_carry(self, inner_model, small_config): + """Test carry reset with flags.""" + carry = inner_model.empty_carry(batch_size=small_config.batch_size) + + # Create reset flags (reset first sequence) + reset_flag = torch.tensor([True, False]) + + new_carry = inner_model.reset_carry(reset_flag, carry) + + # First sequence should be reset to H_init/L_init + # Second sequence should be unchanged + assert isinstance(new_carry, HierarchicalReasoningModel_ACTV1InnerCarry) + assert new_carry.z_H.shape == carry.z_H.shape + assert new_carry.z_L.shape == carry.z_L.shape + + def test_input_embeddings_shape( + self, inner_model, small_config, sample_batch_model + ): + """Test input embedding computation.""" + embeddings = inner_model._input_embeddings( + input=sample_batch_model["inputs"], + puzzle_identifiers=sample_batch_model["puzzle_identifiers"], + ) + + # Should return embeddings with correct shape + assert embeddings.shape == ( + small_config.batch_size, + small_config.seq_len, + small_config.hidden_size, + ) + + def test_input_embeddings_with_puzzle_emb( + self, inner_model_puzzle_emb, config_with_puzzle_emb, sample_batch_model + ): + """Test input embeddings with puzzle embeddings.""" + embeddings = inner_model_puzzle_emb._input_embeddings( + input=sample_batch_model["inputs"], + puzzle_identifiers=sample_batch_model["puzzle_identifiers"], + ) + + # Should include puzzle embedding length + expected_len = ( + config_with_puzzle_emb.seq_len + inner_model_puzzle_emb.puzzle_emb_len + ) + assert embeddings.shape == ( + config_with_puzzle_emb.batch_size, + expected_len, + config_with_puzzle_emb.hidden_size, + ) + + def test_input_embeddings_scaling(self, inner_model, sample_batch_model): + """Test that embeddings are properly scaled.""" + embeddings = inner_model._input_embeddings( + input=sample_batch_model["inputs"], + puzzle_identifiers=sample_batch_model["puzzle_identifiers"], + ) + + # Embeddings should be scaled by sqrt(hidden_size) + # Just check they're non-zero and reasonable magnitude + assert not torch.allclose(embeddings, torch.zeros_like(embeddings)) + assert torch.all(torch.abs(embeddings) < 100) # Reasonable magnitude + + +class TestHierarchicalReasoningModelACTV1: + """Tests for complete HRM model with ACT.""" + + @pytest.fixture + def model(self, small_config): + """Create full model.""" + # Model expects a dict, not a Config object + return HierarchicalReasoningModel_ACTV1(small_config.model_dump()) + + def test_model_initialization(self, model, small_config): + """Test model initialization.""" + assert model.config == small_config + assert hasattr(model, "inner") + assert isinstance(model.inner, HierarchicalReasoningModel_ACTV1_Inner) + + def test_initial_carry_structure(self, model, sample_batch_model): + """Test initial carry creation.""" + carry = model.initial_carry(sample_batch_model) + + assert isinstance(carry, HierarchicalReasoningModel_ACTV1Carry) + assert isinstance(carry.inner_carry, HierarchicalReasoningModel_ACTV1InnerCarry) + assert carry.steps.shape == (model.config.batch_size,) + assert carry.halted.shape == (model.config.batch_size,) + assert carry.steps.dtype == torch.int32 + assert carry.halted.dtype == torch.bool + + def test_initial_carry_all_halted(self, model, sample_batch_model): + """Test that initial carry has all sequences halted (by design).""" + carry = model.initial_carry(sample_batch_model) + + # Initial carry should have zero steps and all halted + # (This triggers data loading on first forward pass) + assert torch.all(carry.steps == 0) + assert torch.all(carry.halted) + + def test_forward_output_structure(self, model, sample_batch_model): + """Test forward pass output structure.""" + carry = model.initial_carry(sample_batch_model) + + new_carry, outputs = model.forward(carry, sample_batch_model) + + # Check new carry + assert isinstance(new_carry, HierarchicalReasoningModel_ACTV1Carry) + + # Check outputs + assert "logits" in outputs + assert "q_halt_logits" in outputs + assert "q_continue_logits" in outputs + + def test_forward_logits_shape(self, model, sample_batch_model, small_config): + """Test forward pass logits shape.""" + carry = model.initial_carry(sample_batch_model) + new_carry, outputs = model.forward(carry, sample_batch_model) + + # Logits should be (batch_size, seq_len, vocab_size) + assert outputs["logits"].shape == ( + small_config.batch_size, + small_config.seq_len, + small_config.vocab_size, + ) + + def test_forward_q_values_shape(self, model, sample_batch_model, small_config): + """Test forward pass Q-value shapes.""" + carry = model.initial_carry(sample_batch_model) + new_carry, outputs = model.forward(carry, sample_batch_model) + + # Q-values should be (batch_size,) + assert outputs["q_halt_logits"].shape == (small_config.batch_size,) + assert outputs["q_continue_logits"].shape == (small_config.batch_size,) + + def test_forward_no_nan(self, model, sample_batch_model): + """Test that forward pass produces valid outputs.""" + carry = model.initial_carry(sample_batch_model) + new_carry, outputs = model.forward(carry, sample_batch_model) + + assert not torch.any(torch.isnan(outputs["logits"])) + assert not torch.any(torch.isnan(outputs["q_halt_logits"])) + assert not torch.any(torch.isnan(outputs["q_continue_logits"])) + + def test_forward_updates_steps(self, model, sample_batch_model): + """Test that forward pass increments step counter.""" + carry = model.initial_carry(sample_batch_model) + initial_steps = carry.steps.clone() + + new_carry, _ = model.forward(carry, sample_batch_model) + + # Steps should increment for non-halted sequences + assert torch.all(new_carry.steps >= initial_steps) + + def test_forward_multiple_steps(self, model, sample_batch_model): + """Test multiple forward passes update carry correctly.""" + carry = model.initial_carry(sample_batch_model) + + # Run 3 forward passes + for _ in range(3): + carry, outputs = model.forward(carry, sample_batch_model) + + # Steps should have incremented + assert torch.any(carry.steps > 0) + + def test_forward_respects_halt_max_steps( + self, model, sample_batch_model, small_config + ): + """Test that model respects halt_max_steps.""" + carry = model.initial_carry(sample_batch_model) + + # Run more than halt_max_steps iterations + for _ in range(small_config.halt_max_steps + 5): + carry, outputs = model.forward(carry, sample_batch_model) + + # All should eventually halt + # (Note: may not be immediate due to exploration, but eventually will halt) + # For this test, just check that halting mechanism is working + assert isinstance(carry.halted, torch.Tensor) + + def test_forward_dtype_consistency(self, model, sample_batch_model, small_config): + """Test that forward pass maintains dtype consistency.""" + carry = model.initial_carry(sample_batch_model) + new_carry, outputs = model.forward(carry, sample_batch_model) + + forward_dtype = getattr(torch, small_config.forward_dtype) + + # Check internal carry dtypes + assert new_carry.inner_carry.z_H.dtype == forward_dtype + assert new_carry.inner_carry.z_L.dtype == forward_dtype + + def test_model_device_placement(self, model): + """Test model parameters are on correct device.""" + device = next(model.parameters()).device + + # All parameters should be on the same device + for param in model.parameters(): + assert param.device == device + + +class TestHierarchicalReasoningModelInnerCarry: + """Tests for inner carry dataclass.""" + + def test_inner_carry_creation(self, small_config): + """Test inner carry creation.""" + z_H = torch.randn( # noqa: N806 + small_config.batch_size, small_config.seq_len, small_config.hidden_size + ) + z_L = torch.randn( # noqa: N806 + small_config.batch_size, small_config.seq_len, small_config.hidden_size + ) + + carry = HierarchicalReasoningModel_ACTV1InnerCarry(z_H=z_H, z_L=z_L) + + assert torch.equal(carry.z_H, z_H) + assert torch.equal(carry.z_L, z_L) + + +class TestHierarchicalReasoningModelCarry: + """Tests for full carry dataclass.""" + + def test_carry_creation(self, small_config): + """Test carry creation.""" + inner_carry = HierarchicalReasoningModel_ACTV1InnerCarry( + z_H=torch.randn( + small_config.batch_size, small_config.seq_len, small_config.hidden_size + ), + z_L=torch.randn( + small_config.batch_size, small_config.seq_len, small_config.hidden_size + ), + ) + + steps = torch.zeros(small_config.batch_size, dtype=torch.int32) + halted = torch.zeros(small_config.batch_size, dtype=torch.bool) + current_data = { + "inputs": torch.randint( + 0, 5, (small_config.batch_size, small_config.seq_len) + ), + "labels": torch.randint( + 0, 5, (small_config.batch_size, small_config.seq_len) + ), + "puzzle_identifiers": torch.zeros( + small_config.batch_size, dtype=torch.int32 + ), + } + + carry = HierarchicalReasoningModel_ACTV1Carry( + inner_carry=inner_carry, + steps=steps, + halted=halted, + current_data=current_data, + ) + + assert carry.inner_carry == inner_carry + assert torch.equal(carry.steps, steps) + assert torch.equal(carry.halted, halted) + assert carry.current_data == current_data diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py new file mode 100644 index 00000000..79333de1 --- /dev/null +++ b/tests/integration/__init__.py @@ -0,0 +1 @@ +"""Integration tests for Hierarchical Reasoning Model.""" diff --git a/tests/integration/test_end_to_end.py b/tests/integration/test_end_to_end.py new file mode 100644 index 00000000..c1ef3419 --- /dev/null +++ b/tests/integration/test_end_to_end.py @@ -0,0 +1,587 @@ +"""End-to-end integration tests for HRM training and evaluation. + +These tests validate the complete pipeline including: +- Model initialization and forward passes +- Loss computation with ACTLossHead +- Multi-step reasoning sequences +- Gradient flow and backpropagation +- ACT halting mechanism over multiple iterations +""" + +import pytest +import torch + +from hierarchical_reasoning_model.core.losses import ACTLossHead +from hierarchical_reasoning_model.core.model import ( + HierarchicalReasoningModel_ACTV1, + HierarchicalReasoningModel_ACTV1Config, +) + + +@pytest.fixture +def integration_config(): + """Create a small model configuration for integration testing.""" + return HierarchicalReasoningModel_ACTV1Config( + batch_size=4, + seq_len=16, # Small sequence + puzzle_emb_ndim=0, # Disable for simplicity + num_puzzle_identifiers=10, + vocab_size=10, + H_cycles=2, + L_cycles=2, + H_layers=2, + L_layers=2, + hidden_size=64, + expansion=2.0, + num_heads=4, + pos_encodings="rope", + rms_norm_eps=1e-5, + rope_theta=10000.0, + halt_max_steps=5, + halt_exploration_prob=0.1, + forward_dtype="float32", + ) + + +@pytest.fixture +def integration_batch(integration_config): + """Create a sample batch for integration testing.""" + return { + "inputs": torch.randint( + 1, + integration_config.vocab_size, + (integration_config.batch_size, integration_config.seq_len), + dtype=torch.int32, + ), + "labels": torch.randint( + 1, + integration_config.vocab_size, + (integration_config.batch_size, integration_config.seq_len), + dtype=torch.int32, + ), + "puzzle_identifiers": torch.zeros( + integration_config.batch_size, dtype=torch.int32 + ), + } + + +class TestModelInitialization: + """Test model initialization and basic setup.""" + + def test_model_and_loss_head_initialization(self, integration_config): + """Test creating model with ACTLossHead.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + loss_head = ACTLossHead(model, loss_type="stablemax_cross_entropy") + + assert loss_head.model is model + assert hasattr(loss_head, "loss_fn") + + def test_model_parameters_require_grad(self, integration_config): + """Test that model parameters require gradients.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + + # Check that at least some parameters require grad + trainable_params = [p for p in model.parameters() if p.requires_grad] + assert len(trainable_params) > 0 + + # Check total parameter count + total_params = sum(p.numel() for p in model.parameters()) + assert total_params > 0 + + +class TestSingleForwardPass: + """Test single forward pass through model and loss head.""" + + @pytest.fixture + def model_with_loss(self, integration_config): + """Create model with loss head.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + return ACTLossHead(model, loss_type="stablemax_cross_entropy") + + def test_forward_pass_completes(self, model_with_loss, integration_batch): + """Test that a single forward pass completes successfully.""" + carry = model_with_loss.initial_carry(integration_batch) + + new_carry, loss, metrics, predictions, all_halted = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + # Check outputs exist and have correct types + assert isinstance(loss, torch.Tensor) + assert isinstance(metrics, dict) + assert isinstance(all_halted, torch.Tensor) + + def test_loss_is_scalar(self, model_with_loss, integration_batch): + """Test that loss is a scalar tensor.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, loss, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + assert loss.ndim == 0 + assert loss.item() >= 0 # Loss should be non-negative + + def test_metrics_structure(self, model_with_loss, integration_batch): + """Test that metrics dictionary has expected keys.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, _, metrics, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + expected_keys = { + "count", + "accuracy", + "exact_accuracy", + "q_halt_accuracy", + "steps", + "lm_loss", + "q_halt_loss", + } + assert expected_keys.issubset(metrics.keys()) + + def test_predictions_returned(self, model_with_loss, integration_batch): + """Test that predictions are returned when requested.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, _, _, predictions, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + assert predictions is not None + assert "logits" in predictions + + +class TestMultiStepReasoning: + """Test multi-step reasoning with carry state updates.""" + + @pytest.fixture + def model_with_loss(self, integration_config): + """Create model with loss head.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + return ACTLossHead(model, loss_type="stablemax_cross_entropy") + + def test_multiple_forward_passes(self, model_with_loss, integration_batch): + """Test multiple forward passes update carry correctly.""" + carry = model_with_loss.initial_carry(integration_batch) + + # Run 3 forward passes + losses = [] + for _ in range(3): + carry, loss, metrics, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + losses.append(loss.item()) + + # Should have 3 losses + assert len(losses) == 3 + + # All losses should be non-negative + assert all(loss >= 0 for loss in losses) + + def test_carry_state_evolves(self, model_with_loss, integration_batch): + """Test that carry state changes across forward passes.""" + carry = model_with_loss.initial_carry(integration_batch) + initial_steps = carry.steps.clone() + + # Run several forward passes + for _ in range(3): + carry, _, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + # Steps should have incremented + assert torch.any(carry.steps > initial_steps) + + def test_halting_eventually_occurs( + self, model_with_loss, integration_batch, integration_config + ): + """Test that sequences eventually halt.""" + carry = model_with_loss.initial_carry(integration_batch) + + # Run enough iterations to trigger halting + max_iterations = integration_config.halt_max_steps * 3 + for _ in range(max_iterations): + carry, _, _, _, all_halted = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + if all_halted.item(): + break + + # At least some sequences should have halted by now + # (May not be all due to exploration) + assert torch.any(carry.halted) + + def test_step_counter_increments(self, model_with_loss, integration_batch): + """Test that step counter increments for active sequences.""" + carry = model_with_loss.initial_carry(integration_batch) + + # Run one forward pass + carry, _, metrics, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + # Check that steps metric is reported + assert "steps" in metrics + assert metrics["steps"] >= 0 + + +class TestGradientFlow: + """Test gradient computation and backpropagation.""" + + @pytest.fixture + def model_with_loss(self, integration_config): + """Create model with loss head.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + return ACTLossHead(model, loss_type="stablemax_cross_entropy") + + def test_loss_requires_grad(self, model_with_loss, integration_batch): + """Test that loss tensor requires gradients.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, loss, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + assert loss.requires_grad + + def test_backward_pass_completes(self, model_with_loss, integration_batch): + """Test that backward pass completes without errors.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, loss, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + # Backward pass should complete without error + loss.backward() + + def test_gradients_computed(self, model_with_loss, integration_batch): + """Test that gradients are computed for model parameters.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, loss, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + loss.backward() + + # Check that at least some parameters have gradients + params_with_grad = [ + p for p in model_with_loss.model.parameters() if p.grad is not None + ] + assert len(params_with_grad) > 0 + + def test_gradients_non_zero(self, model_with_loss, integration_batch): + """Test that computed gradients are non-zero.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, loss, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + loss.backward() + + # Check that gradients are non-zero + non_zero_grads = [] + for p in model_with_loss.model.parameters(): + if p.grad is not None and torch.any(p.grad != 0): + non_zero_grads.append(p) + + assert len(non_zero_grads) > 0 + + def test_gradient_accumulation(self, model_with_loss, integration_batch): + """Test gradient accumulation across multiple batches.""" + # First batch + carry1 = model_with_loss.initial_carry(integration_batch) + _, loss1, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry1, batch=integration_batch + ) + loss1.backward() + + # Save gradients from first batch + first_grads = {} + for name, param in model_with_loss.model.named_parameters(): + if param.grad is not None: + first_grads[name] = param.grad.clone() + + # Second batch (without zeroing gradients) + carry2 = model_with_loss.initial_carry(integration_batch) + _, loss2, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry2, batch=integration_batch + ) + loss2.backward() + + # Check that gradients have accumulated + accumulated = False + for name, param in model_with_loss.model.named_parameters(): + # Accumulated gradients should generally be larger + if ( + param.grad is not None + and name in first_grads + and torch.sum(torch.abs(param.grad)) + > torch.sum(torch.abs(first_grads[name])) + ): + accumulated = True + break + + assert accumulated + + +class TestOptimizerIntegration: + """Test integration with optimizers.""" + + @pytest.fixture + def model_with_loss_and_optimizer(self, integration_config): + """Create model, loss head, and optimizer.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + loss_head = ACTLossHead(model, loss_type="stablemax_cross_entropy") + optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) + return loss_head, optimizer + + def test_optimizer_step(self, model_with_loss_and_optimizer, integration_batch): + """Test that optimizer step updates parameters.""" + loss_head, optimizer = model_with_loss_and_optimizer + + # Save initial parameters + initial_params = {} + for name, param in loss_head.model.named_parameters(): + initial_params[name] = param.data.clone() + + # Forward and backward + carry = loss_head.initial_carry(integration_batch) + _, loss, _, _, _ = loss_head.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + loss.backward() + + # Optimizer step + optimizer.step() + + # Check that at least some parameters changed + params_changed = [] + for name, param in loss_head.model.named_parameters(): + if not torch.equal(param.data, initial_params[name]): + params_changed.append(name) + + assert len(params_changed) > 0 + + def test_training_loop_iteration( + self, model_with_loss_and_optimizer, integration_batch + ): + """Test a complete training loop iteration.""" + loss_head, optimizer = model_with_loss_and_optimizer + + # Training iteration + optimizer.zero_grad() + + carry = loss_head.initial_carry(integration_batch) + _, loss, metrics, _, _ = loss_head.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + loss.backward() + optimizer.step() + + # Should complete without errors and produce valid metrics + assert "accuracy" in metrics + assert "lm_loss" in metrics + + def test_multiple_training_steps( + self, model_with_loss_and_optimizer, integration_batch + ): + """Test multiple training steps.""" + loss_head, optimizer = model_with_loss_and_optimizer + + losses = [] + for _ in range(5): + optimizer.zero_grad() + + carry = loss_head.initial_carry(integration_batch) + _, loss, _, _, _ = loss_head.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + losses.append(loss.item()) + + loss.backward() + optimizer.step() + + # Should have 5 losses + assert len(losses) == 5 + + # All losses should be finite + assert all(torch.isfinite(torch.tensor(loss)) for loss in losses) + + +class TestACTHaltingMechanism: + """Test Adaptive Computation Time halting mechanism.""" + + @pytest.fixture + def model_with_loss(self, integration_config): + """Create model with loss head.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + return ACTLossHead(model, loss_type="stablemax_cross_entropy") + + def test_q_values_produced(self, model_with_loss, integration_batch): + """Test that Q-values are produced.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, _, metrics, predictions, _ = model_with_loss.forward( + return_keys=["logits", "q_halt_logits", "q_continue_logits"], + carry=carry, + batch=integration_batch, + ) + + # Check Q-values are in predictions + assert "q_halt_logits" in predictions + assert "q_continue_logits" in predictions + + # Check shapes + assert predictions["q_halt_logits"].shape == ( + integration_batch["inputs"].shape[0], + ) + assert predictions["q_continue_logits"].shape == ( + integration_batch["inputs"].shape[0], + ) + + def test_q_halt_loss_computed(self, model_with_loss, integration_batch): + """Test that Q-halt loss is computed.""" + carry = model_with_loss.initial_carry(integration_batch) + + _, _, metrics, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + # Check Q-halt loss is in metrics + assert "q_halt_loss" in metrics + assert metrics["q_halt_loss"] >= 0 + + def test_halt_max_steps_respected( + self, model_with_loss, integration_batch, integration_config + ): + """Test that halt_max_steps limits computation.""" + carry = model_with_loss.initial_carry(integration_batch) + + # Run way past halt_max_steps + for _ in range(integration_config.halt_max_steps * 2): + carry, _, _, _, _ = model_with_loss.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + # Steps should not exceed halt_max_steps by much + # (some sequences may take extra steps due to ACT logic) + max_steps = carry.steps.max().item() + assert max_steps <= integration_config.halt_max_steps * 1.5 + + +class TestDifferentLossTypes: + """Test model with different loss types.""" + + def test_softmax_loss(self, integration_config, integration_batch): + """Test model with softmax cross-entropy loss.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + loss_head = ACTLossHead(model, loss_type="softmax_cross_entropy") + + carry = loss_head.initial_carry(integration_batch) + _, loss, metrics, _, _ = loss_head.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + assert loss.item() >= 0 + assert "lm_loss" in metrics + + def test_stablemax_loss(self, integration_config, integration_batch): + """Test model with stablemax cross-entropy loss.""" + model = HierarchicalReasoningModel_ACTV1(integration_config.model_dump()) + loss_head = ACTLossHead(model, loss_type="stablemax_cross_entropy") + + carry = loss_head.initial_carry(integration_batch) + _, loss, metrics, _, _ = loss_head.forward( + return_keys=["logits"], carry=carry, batch=integration_batch + ) + + assert loss.item() >= 0 + assert "lm_loss" in metrics + + +@pytest.mark.slow +class TestLongerSequences: + """Test with longer sequences (marked as slow).""" + + @pytest.fixture + def longer_config(self): + """Create config with longer sequences.""" + return HierarchicalReasoningModel_ACTV1Config( + batch_size=2, + seq_len=81, # Full Sudoku grid + puzzle_emb_ndim=0, + num_puzzle_identifiers=10, + vocab_size=11, + H_cycles=2, + L_cycles=2, + H_layers=4, + L_layers=4, + hidden_size=128, + expansion=4.0, + num_heads=8, + pos_encodings="rope", + halt_max_steps=10, + halt_exploration_prob=0.1, + forward_dtype="float32", + ) + + @pytest.fixture + def longer_batch(self, longer_config): + """Create batch with longer sequences.""" + return { + "inputs": torch.randint( + 1, + longer_config.vocab_size, + (longer_config.batch_size, longer_config.seq_len), + dtype=torch.int32, + ), + "labels": torch.randint( + 1, + longer_config.vocab_size, + (longer_config.batch_size, longer_config.seq_len), + dtype=torch.int32, + ), + "puzzle_identifiers": torch.zeros( + longer_config.batch_size, dtype=torch.int32 + ), + } + + def test_longer_sequence_forward_pass(self, longer_config, longer_batch): + """Test forward pass with longer sequences.""" + model = HierarchicalReasoningModel_ACTV1(longer_config.model_dump()) + loss_head = ACTLossHead(model, loss_type="stablemax_cross_entropy") + + carry = loss_head.initial_carry(longer_batch) + _, loss, metrics, _, _ = loss_head.forward( + return_keys=["logits"], carry=carry, batch=longer_batch + ) + + assert loss.item() >= 0 + assert "accuracy" in metrics + + def test_longer_sequence_training_step(self, longer_config, longer_batch): + """Test complete training step with longer sequences.""" + model = HierarchicalReasoningModel_ACTV1(longer_config.model_dump()) + loss_head = ACTLossHead(model, loss_type="stablemax_cross_entropy") + optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) + + optimizer.zero_grad() + + carry = loss_head.initial_carry(longer_batch) + _, loss, _, _, _ = loss_head.forward( + return_keys=["logits"], carry=carry, batch=longer_batch + ) + + loss.backward() + optimizer.step() + + # Should complete without errors + assert True diff --git a/utils/functions.py b/utils/functions.py deleted file mode 100644 index b1236360..00000000 --- a/utils/functions.py +++ 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