pallas-forge 0.1.2

Lightweight auto-tuning framework for Pallas kernels on Google TPU

pallas-forge

A lightweight auto-tuning framework for Pallas kernels on Google TPU.

pallas-forge helps you systematically discover why some kernel configurations outperform others by 3-5×, producing performance heatmaps, XProf profiler traces, and roofline charts.

 

Measured on TPU v5e via Colab. Reproduce with notebooks/05_reproduce_figures.ipynb.

Features

• 3 reference kernels with progressive complexity:
  • Tiled MatMul (compute-bound, MXU-dominated)
  • Fused RMSNorm + Residual (memory-bound, VPU-dominated)
  • Fused SwiGLU / GeGLU (compute-bound, fused activation)
• Auto-tuning framework — grid search and random search over block sizes, grid dims, pipeline stages
• Performance heatmaps — visual proof of how parameter choices affect throughput
• XProf integration — capture and compare profiler traces for top configurations
• Roofline analysis — classify kernels as compute-bound or memory-bound
• CPU testing — all kernels run on CPU via Pallas interpret mode (no TPU required for development)

Architecture

Your script calls pallas-forge; the kernels use jax.experimental.pallas, which lowers to Mosaic on TPU (or Triton on GPU). The tuner wraps everything in warm-up + statistical timing + optional XProf trace capture.

Installation

From PyPI (once published)

pip install pallas-forge          # CPU interpret mode
pip install "pallas-forge[viz]"   # with matplotlib/seaborn for heatmaps
pip install "pallas-forge[tpu]"   # on a Linux TPU VM

From source

git clone https://github.com/nklinh91/pallas-forge
cd pallas-forge

# Basic install (CPU testing via interpret mode)
pip install -e .

# With development tools + visualization
pip install -e ".[dev,viz]"

# On a Linux TPU VM (libtpu is Linux-only)
pip install -e ".[all,tpu]"

Tip: Create a dedicated conda env first to avoid polluting your base environment:

conda create -n pallas-forge python=3.11 -y
conda activate pallas-forge
pip install -e ".[dev,viz]"

Quick Start

Auto-tune a kernel

import jax
import jax.numpy as jnp
from pallas_forge import tiled_matmul
from pallas_forge.tune import tune, TuneConfig

# Define config space
config = TuneConfig.from_dict({
    "block_m": [64, 128, 256],
    "block_k": [64, 128],
    "block_n": [64, 128, 256],
})

# Define inputs
M, K, N = 2048, 2048, 2048
def input_fn(cfg):
    key = jax.random.PRNGKey(0)
    x = jax.random.normal(key, (M, K), dtype=jnp.bfloat16)
    w = jax.random.normal(key, (K, N), dtype=jnp.bfloat16)
    return (x, w)

# Define kernel wrapper
def kernel_fn(x, w, *, block_m, block_k, block_n):
    return tiled_matmul(x, w, block_m=block_m, block_k=block_k, block_n=block_n)

# Run auto-tuning
report = tune(kernel_fn, input_fn, config)

# Export results
report.to_csv("results.csv")
report.heatmap("block_m", "block_n", save_path="heatmap.png")

# Best config
best = report.best(1)[0]
print(f"Best: {best.config} -> {best.median_ms:.3f} ms")
print(f"Speedup range: {report.speedup_range:.1f}x")

Use a kernel directly

from pallas_forge import tiled_matmul, fused_rmsnorm_residual, fused_swiglu

# MatMul
result = tiled_matmul(x, w, block_m=128, block_k=128, block_n=128)

# Fused RMSNorm + Residual
normed, new_residual = fused_rmsnorm_residual(x, residual, weight)

# Fused SwiGLU
output = fused_swiglu(x, w_gate, w_up, block_m=128, block_n=256)

Roofline analysis

from pallas_forge.profile import roofline_chart, TPU_SPECS

tpu = TPU_SPECS["v4"]  # or "v5e", "v5p"
roofline_chart(
    report.results,
    peak_tflops=tpu["peak_tflops_bf16"],
    peak_bandwidth_gb_s=tpu["peak_bandwidth_gb_s"],
    save_path="roofline.png",
)

Requires flops_fn and bytes_fn to be passed to tune() so TFLOPS and bandwidth get populated.

XProf trace capture for top configurations

report = tune(
    kernel_fn, input_fn, config,
    top_n_traces=3,
    trace_output_dir="./xprof_traces",
)
# Open traces in TensorBoard:  tensorboard --logdir ./xprof_traces

Speedup vs XLA baseline

Measured on TPU v5e (via Colab). The honest finding:

• Fused RMSNorm + Residual wins big: 3.78× — the fusion eliminates an HBM round-trip on an intermediate that XLA can't easily avoid.
• SwiGLU and Tiled MatMul lose to XLA (0.65× and 0.12×) — XLA's hand-tuned kernels for standard matmul / activation patterns are hard to beat with a teaching-quality implementation. That's a useful finding, not a failure: the blog series unpacks why.

Reproduce with notebooks/05_reproduce_figures.ipynb on a Colab TPU runtime (takes ~5-10 minutes).

Project Structure

pallas_forge/
  _compat.py          # CPU/TPU compatibility (interpret mode)
  kernels/
    matmul.py          # Tiled MatMul
    rmsnorm.py         # Fused RMSNorm + Residual
    swiglu.py          # Fused SwiGLU / GeGLU
  tune/
    __init__.py        # tune() entry point
    config.py          # TuneConfig search space
    search.py          # GridSearch, RandomSearch
    runner.py          # BenchmarkRunner with proper timing
    report.py          # JSON/CSV export + heatmaps
    trace.py           # XProf trace capture
  profile/
    roofline.py        # Roofline chart generation
    analysis.py        # Utilization analysis
benchmarks/            # Benchmark scripts per kernel
tests/                 # Correctness tests (run on CPU)

Running Tests

# All tests (run on CPU via Pallas interpret mode — no TPU required)
pytest tests/ -v

All 54 tests should pass on CPU. Tests marked @requires_tpu are skipped automatically when no TPU is detected.

Running Benchmarks (requires TPU)

# Individual kernel
python benchmarks/bench_matmul.py

# All benchmarks
python benchmarks/run_all.py

Outputs (CSV/JSON results + PNG heatmaps) land in results/.

Notebooks

Interactive walkthroughs in notebooks/ — each includes a Colab setup cell so you can run them on a free TPU runtime.

Notebook	Focus
01_tiled_matmul.ipynb	BlockSpec, grid, block sizes, bfloat16
02_fused_rmsnorm.ipynb	Kernel fusion, VPU vs MXU, HBM traffic
03_swiglu_geglu.ipynb	Compute-bound fusion, SwiGLU vs GeGLU
04_auto_tuning.ipynb	tune(), heatmaps, random search, YAML configs
05_reproduce_figures.ipynb	Regenerate all blog/README figures from real TPU measurements

Supported TPU generations

pallas_forge.profile.TPU_SPECS provides hardware presets for roofline analysis:

Generation	Peak bf16 TFLOPS	Peak HBM GB/s	VMEM (MB)
v4	275	1200	32
v5e	197	819	32
v5p	459	2765	95

Contributing & releases

• Contributing: open an issue or PR. Tests (pytest tests/) must pass on CPU.
• Pre-commit hooks: after pip install -e ".[dev]", run pre-commit install once. Ruff and basic hygiene checks will then run on every commit, so you won't push lint-failing code.
• Publishing: see PUBLISHING.md for the PyPI release workflow (automated via GitHub Actions + Trusted Publishing).
• Changelog: see CHANGELOG.md.

License

MIT