hip-quant 0.5.2

HIP/ROCm quantization library for AMD GPUs

🚀 hip-quant

Blazing Fast On-Device Tensor Quantization for AMD GPUs

hip-quant is a standalone Python library and highly optimized HIP C++ backend that quantizes tensors directly on AMD GPUs with no CPU round-trips. The offline GGUF path consumes float32; the PyTorch FP8 training extension accepts float32, float16, and bfloat16 tensors.

It ships two independent APIs that can be used together or separately:

API	Purpose	Requires
NumPy / ctypes (offline)	Offline GGUF-format quantization via packaged DLL	ROCm runtime, numpy
PyTorch extension (training)	GPU-resident FP8 training ops with full autograd	PyTorch 2.x + ROCm, built _C extension

Hardware Status

Runtime validation is currently on RDNA4. The PyTorch FP8 WMMA kernels target gfx1200 and gfx1201; gfx1200 is treated as the cut-down gfx1201 die with the same relevant FP8 WMMA capabilities.

CDNA support is included for the offline NumPy/DLL quantization path and compatibility tooling. The default DLL build now emits one all-target DLL for gfx90a, gfx942, RDNA3 gfx1100-gfx1103, and RDNA4 gfx1200/gfx1201. The gfx12 WMMA FP8 GEMM test is intentionally disabled on CDNA; CDNA can support FP8/BF16 through MFMA/rocBLASLt-style paths, but not this RDNA4-specific gfx12 WMMA builtin path.

⚠️ gfx12 FP8 WMMA Safety (Windows RDNA4)

The PyTorch FP8 training API (Fp8Linear, Fp8ScaledLinear, Fp8ShadowLinear) uses __builtin_amdgcn_wmma_f32_16x16x16_fp8_fp8_w32_gfx12 for the fused GEMM forward/backward pass. On ROCm 7.1 with Windows gfx1201, these WMMA intrinsics can trigger a GPU TDR (driver timeout) that corrupts GPU memory and may restart the PC.

Root cause: The ROCm 7.1 HIP runtime has a stability issue with gfx12 WMMA instructions on RDNA4. The kernel launch succeeds but the GPU can hang asynchronously, causing subsequent tensor.item() calls to read back corrupted memory — typically manifesting as a ZeroDivisionError at torch_api.py:495 (1.0 / weight_inv_scale with a zeroed GPU value).

Fix: Wheels now package a ROCm 7.2.1-built DLL named hip_quantize_rocm721.dll next to the legacy hip_quantize.dll. On Windows, HipQuant() prefers hip_quantize_rocm721.dll when present and searches the active Python environment's ROCm/PyTorch DLL directories before the system ROCm 7.1 path. The legacy DLL can still be forced with HIP_QUANT_DLL_VARIANT=legacy.

Default safety policy: gfx12 FP8/BF8 WMMA kernels are disabled by default because bad driver/compiler combinations can hang or reset the GPU. Enable them only for controlled testing:

$env:HIP_QUANT_ENABLE_GFX12_WMMA = "1"

Force-disable WMMA regardless of runtime/device:

$env:HIP_QUANT_DISABLE_WMMA = "1"

Validated local test system:

• GPU: AMD Radeon RX 9070 XT, gfx1201, 16 GB VRAM
• CPU: AMD Ryzen 7 7800X3D, 8 cores / 16 threads
• RAM: 32 GB system memory
• OS/toolchain: Windows, Visual Studio 2022 Build Tools, ROCm installed at C:\Program Files\AMD\ROCm\7.1
• PyTorch venv: C:\venvs\medusa_rocm\Scripts\python.exe
• PyTorch: 2.9.1+rocm7.2.1, HIP runtime: 7.2.53211-158bd99533
• FP8 WMMA microtest verified with the packaged ROCm 7.2.1 DLL: 50 bounded launches through fp8_gemm_test_wmma

Note: The offline NumPy/DLL quantization path does not use WMMA and is unaffected. It works with both ROCm 7.1 and 7.2 runtimes. The packaged ROCm 7.2.1 DLL is preferred on Windows to avoid ROCm 7.1 gfx12 WMMA hazards when optional FP8 GEMM tests are enabled.

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⚡ Supported Quantization Formats

🔢 Standard & K-Quants (offline API)

• Legacy: Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, Q8_1
• K-Quants: Q2_K, Q3_K, Q4_K, Q5_K, Q6_K

🧠 I-Quants (Importance Matrix)

Non-linear quants that preserve quality at extreme low bits:

• IQ1_S, IQ2_XXS, IQ2_XS, IQ3_XXS, IQ3_S, IQ4_NL, IQ4_XS

⚖️ Ternary Quants

For models trained to be ternary (BitNet, TriLM):

• TQ1_0 (1.69 bpw), TQ2_0 (2.06 bpw)

🧪 FP8 Formats (both APIs)

Format	Layout	Use case
F8_E4M3	1s·4e·3m, bias=7, max=±448, NaN only	Forward activations & weights
F8_E5M2	1s·5e·2m, bias=15, max=±57344, ±Inf+NaN	Backward gradients

Both use OCP standard semantics with round-to-nearest-even. Math is validated against ml_dtypes reference — 90/90 test cases pass.

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🛠️ Build

Offline DLL (NumPy API)

Default build emits one DLL for CDNA, RDNA3, and RDNA4 targets. By default it uses C:\Program Files\AMD\ROCm\7.1\bin\hipcc.exe; pass -RocmBin to use a ROCm/PyTorch venv toolchain:

.\build.ps1

# Build the packaged ROCm 7.2.1 DLL from a PyTorch ROCm venv
.\build.ps1 -Output hip_quantize_rocm721.dll -RocmBin "C:\venvs\medusa_rocm\Scripts"

# Custom target set
.\build.ps1 -Arch "gfx942,gfx1200,gfx1201"

The build script adds -mno-wavefrontsize64 so gfx12 w32 WMMA code is compiled as Wave32.

PyTorch Extension (_C)

Requires PyTorch with ROCm support (torch 2.x+rocm):

& "C:\venvs\medusa_rocm\Scripts\python.exe" setup_torch.py build_ext --inplace

To build a PyPI wheel that includes the compiled _C.pyd extension, build with the extension flag from the ROCm/PyTorch environment:

$env:HIP_QUANT_BUILD_TORCH_EXT = "1"
& "C:\venvs\medusa_rocm\Scripts\python.exe" -m build --wheel --no-isolation

Without HIP_QUANT_BUILD_TORCH_EXT=1, python -m build creates a Windows wheel that packages the ctypes DLLs but does not include _C.pyd. The PyTorch extension can still be built locally with setup_torch.py build_ext --inplace.

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📦 Installation

# Binary wheel with packaged ROCm 7.2.1 ctypes DLL
pip install dist/hip_quant-0.4.8-cp312-cp312-win_amd64.whl

# With PyTorch optional dependency declared
pip install "hip-quant[torch]"

On Windows, DLL resolution order is:

• HIP_QUANT_DLL or HIP_QUANT_DLL_PATH, if set
• hip_quantize_rocm721.dll
• hip_quantize.dll

Runtime DLL directories include HIP_QUANT_ROCM_BIN, HIP_QUANT_ROCM_HOME, ROCM_HOME, ROCM_PATH, HIP_PATH, the active venv's _rocm_sdk_core\bin, torch\lib, Scripts, then the system ROCm 7.1 path.

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🐍 Usage

Offline NumPy API

import numpy as np
from hip_quant import quantize

weights = np.random.randn(4096, 4096).astype(np.float32)

# Quantize directly to Q4_K on the GPU — byte-exact match to llama.cpp
q4k_bytes = quantize(weights, type_num=12)  # 12 = Q4_K

FP8 (offline)

from hip_quant import GGML_TYPE, get_hip_quant

hq = get_hip_quant()
x    = np.random.randn(4096, 4096).astype(np.float32)
grad = (np.random.randn(4096, 4096) * 128).astype(np.float32)

x_e4m3    = hq.quantize_numpy(x,    GGML_TYPE["F8_E4M3"])  # forward
grad_e5m2 = hq.quantize_numpy(grad, GGML_TYPE["F8_E5M2"])  # backward

CLI

hip-quant --help
python -m hip_quant --help

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PyTorch Training API

Requires: python setup_torch.py build_ext --inplace first.

Element-wise FP8 quant / dequant (Phase 1 & 2)

import torch
from hip_quant.torch_api import quantize_e4m3, dequantize_e4m3
from hip_quant.torch_api import quantize_e5m2, dequantize_e5m2

x = torch.randn(1024, 1024, device="cuda")  # stays on GPU the whole time

x_fp8  = quantize_e4m3(x)          # torch.uint8, same shape, same device
x_back = dequantize_e4m3(x_fp8)    # torch.float32, no CPU transfer

Fake-FP8 Linear (autograd-safe, Phase 3)

Fp8LinearFunction uses E4M3 for forward activations/weights and E5M2 for backward gradients. It accepts torch.float32, torch.float16, and torch.bfloat16 inputs/weights. It also implements Activation Compression, saving uint8 tensors in the autograd graph to cut activation VRAM by 4× versus FP32, and 2× versus FP16/BF16.

BF16/FP16 support applies to:

• quantize_e4m3() and quantize_e5m2() inputs
• Fp8LinearFunction forward/backward
• Fp8Linear, Fp8ScaledLinear, Fp8ShadowLinear, Fp8Conv1d, and Fp8Conv2d module parameters and gradients
• Fp8ShadowLinear master weights, so user-selected BF16/FP16 master weights reduce persistent parameter and gradient VRAM versus FP32

from hip_quant.torch_api import convert_to_fp8, Adafactor

# Drop-in replacement for all nn.Linear layers in a model
model = MySmallLM(...)

# shadow=True: replaces nn.Linear with Fp8ShadowLinear
# Weights are stored as uint8 in memory, forward pass decompresses on the fly
# Cuts weight VRAM by 4×
convert_to_fp8(model, shadow=True, skip_names={"lm_head"})
model.cuda()

# Adafactor optimizer: adaptive learning rates with sublinear memory cost
# Cuts optimizer state VRAM by ~1000× compared to AdamW
opt = Adafactor(model.parameters(), relative_step=True)

FP8 Conv1d / Conv2d

fp8_conv1d, fp8_conv2d, Fp8Conv1d, and Fp8Conv2d lower convolution to an unfold/im2col matrix multiply and reuse the same FP8 scaled linear backend. That means hipBLASLt via PyTorch torch._scaled_mm is used first when available, while the custom gfx12 WMMA path remains the fallback/testing path.

import torch
from hip_quant.torch_api import Fp8Conv1d, Fp8Conv2d, fp8_conv1d, fp8_conv2d

x1 = torch.randn(8, 16, 1024, device="cuda", dtype=torch.bfloat16)
conv1 = Fp8Conv1d(16, 32, kernel_size=3, padding=1,
                  device="cuda", dtype=torch.bfloat16)
y1 = conv1(x1)
y1_func = fp8_conv1d(x1, conv1.weight, conv1.bias, padding=1)

x = torch.randn(8, 3, 224, 224, device="cuda", dtype=torch.bfloat16)
conv = Fp8Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
                 device="cuda", dtype=torch.bfloat16)

y = conv(x)

# Functional form mirrors torch.nn.functional.conv2d for numeric parameters.
y2 = fp8_conv2d(x, conv.weight, conv.bias, stride=2, padding=1)

Supported convolution options: numeric stride, padding, dilation, and groups with zero padding mode. Inputs and weights must be CUDA/HIP tensors.

Combined VRAM savings for a 500M-param LLM: Before: ~7.6 GB (Weights 2GB, Acts 1.6GB, AdamW 4GB) After: ~0.9 GB (Weights 0.5GB, Acts 0.4GB, Adafactor 4MB)

Direct autograd.Function

from hip_quant.torch_api import Fp8LinearFunction

out = Fp8LinearFunction.apply(input, weight, bias)  # bias optional

Fused FP8 Linear Fallback (gfx12 WMMA kernels)

The high-level Fp8Linear, Fp8ScaledLinear, Fp8ShadowLinear, Fp8Conv1d, and Fp8Conv2d APIs try the hipBLASLt-backed PyTorch _scaled_mm route first. These direct custom WMMA entry points are the fallback/testing path.

These kernels are disabled by default. Enable only after validating your ROCm runtime and GPU stability:

$env:HIP_QUANT_ENABLE_GFX12_WMMA = "1"

from hip_quant import (
    fp8_linear_forward,
    fp8_linear_forward_scaled,
    fp8_linear_forward_fp8_weight,
    fp8_linear_backward_input,
    fp8_linear_backward_input_scaled,
    fp8_linear_backward_weight,
    fp8_linear_backward_weight_scaled,
)

# [M,K] @ [N,K].T = [M,N]
# forward: E4M3 x E4M3 WMMA, backward: E5M2/BF8 x E5M2/BF8 WMMA
out        = fp8_linear_forward(input, weight, bias=None)
grad_in    = fp8_linear_backward_input(grad_output, weight)
grad_wt    = fp8_linear_backward_weight(grad_output, input)

# Scaled path used by Fp8ScaledLinear and Fp8ShadowLinear
out_scaled = fp8_linear_forward_scaled(input, weight, bias, input_scale, weight_scale)
grad_in_s  = fp8_linear_backward_input_scaled(grad_output, weight, weight_scale)
grad_wt_s  = fp8_linear_backward_weight_scaled(grad_output, input, input_scale)

These functions are also used by Fp8Linear, Fp8ScaledLinear, and Fp8ShadowLinear after the extension is built.

gfx1201 FP8/BF16 Microbenchmark

Measured on the validated local RX 9070 XT gfx1201 system with PyTorch 2.9.1+rocm7.2.1 and HIP_QUANT_ENABLE_GFX12_WMMA=1:

$env:HIP_QUANT_ENABLE_GFX12_WMMA = "1"
& "C:\venvs\medusa_rocm\Scripts\python.exe" tests\torch\bench_fp8.py

Elementwise FP8 ops, shape=(4096, 4096), dtype=bf16
quantize_e4m3:   0.243 ms
quantize_e5m2:   0.205 ms
dequantize_e4m3: 0.218 ms
dequantize_e5m2: 0.206 ms
Fp8ShadowLinear, batch=32, in=4096, out=4096, dtype=bf16
forward:          2.553 ms
forward+backward: 5.622 ms
total wall time: 0.86 s

The benchmark is available at tests/torch/bench_fp8.py. Without HIP_QUANT_ENABLE_GFX12_WMMA=1, it reports only the elementwise FP8 timings and skips WMMA linear kernels.

The 0.4.8 FP8/BF16 optimization pass is primarily a speed and memory-bandwidth improvement: it reuses pre-quantized FP8 activations/gradients, skips redundant output zeroing, fuses bias stores, vectorizes elementwise FP8 kernels, and caches offline FP8 temporary buffers. Persistent VRAM savings are still mainly provided by Fp8ShadowLinear FP8 weight shadows and activation compression; this release reduces transient allocations and extra memory passes around those features.

RDNA3 (gfx11) and CDNA devices are rejected for this specific builtin path. CDNA FP8/BF16 GEMM should use an MFMA/rocBLASLt implementation instead.

Scale / amax tracking (Phase 4 scaffold)

from hip_quant.torch_api import Fp8TensorMeta

meta = Fp8TensorMeta(history_len=16, device="cuda")
meta.update(x)                   # records amax, updates scale/inv_scale

x_fp8  = meta.quantize_e4m3(x)  # scaled, then quantized
x_back = meta.dequantize_e4m3(x_fp8)  # dequantized, then rescaled

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🔒 Memory Safety

All PyTorch extension functions are guarded against:

• Non-CUDA tensors (TORCH_CHECK(is_cuda))
• Non-contiguous layout (TORCH_CHECK(is_contiguous))
• Wrong dtype (float32 / float16 / bfloat16 for floating inputs, uint8 for FP8 buffers)
• Dimension mismatch for GEMM
• int64 → int narrowing — explicit checked_int() with TORCH_CHECK
• Hardware grid limit — gridDim.y ≤ 65535 validated before launch
• Cross-device pointers — input.device() == weight.device() checked
• Empty tensors — numel == 0 early-return before dim3(0) (UB in HIP)

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🧪 Running Tests

Math tests (no GPU required)

python tests/torch/test_math_fp8.py
# 90/90 pass — validated against ml_dtypes reference

PyTorch GPU tests

# Build extension first
& "C:\venvs\medusa_rocm\Scripts\python.exe" setup_torch.py build_ext --inplace

& "C:\venvs\medusa_rocm\Scripts\python.exe" -m pytest tests/torch/test_fp8.py -v

Full Pipeline Tests (CPU Mock)

python tests/test_pipeline.py -v
# Tests full integration of Adafactor, Shadow Linear, and activation compression
# Pure-Python mock, 100% test coverage for the API

Compatibility Tests (CPU + DLL)

& "C:\venvs\medusa_rocm\Scripts\python.exe" -m pytest tests/test_compat.py -v

# Device/compat reports
& "C:\venvs\medusa_rocm\Scripts\python.exe" -m hip_quant --info
& "C:\venvs\medusa_rocm\Scripts\python.exe" -m hip_quant --compat

Optional gfx12 FP8 WMMA Stress Test

Only run this on a stable ROCm 7.2+ gfx12 system. It can still reset the GPU on bad driver/runtime combinations.

$env:PYTHONPATH = "C:\path\to\src"
$env:HIP_QUANT_ENABLE_GFX12_WMMA = "1"
& "C:\venvs\medusa_rocm\Scripts\python.exe" test_fp8_gemm.py

The release DLL was locally checked with 50 bounded fp8_gemm_test_wmma launches on gfx1201 and HIP runtime 70253211.

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📤 Release / PyPI Upload

Build the distributables:

$env:HIP_QUANT_BUILD_TORCH_EXT = "1"
& "C:\venvs\medusa_rocm\Scripts\python.exe" -m build --no-isolation

Check the artifacts:

& "C:\venvs\medusa_rocm\Scripts\python.exe" -m twine check `
  "dist\hip_quant-0.4.8-cp312-cp312-win_amd64.whl" `
  "dist\hip_quant-0.4.8.tar.gz"

Upload to PyPI:

& "C:\venvs\medusa_rocm\Scripts\python.exe" -m twine upload `
  "dist\hip_quant-0.4.8-cp312-cp312-win_amd64.whl" `
  "dist\hip_quant-0.4.8.tar.gz"

Do not upload stale universal wheels such as hip_quant-0.4.8-py3-none-any.whl. The Windows wheel is intentionally platform-tagged because it contains DLLs.

Suggested release order:

• Build and run twine check
• Upload to TestPyPI or PyPI
• Install the uploaded package in a clean venv and verify HipQuant().dll_path resolves to hip_quantize_rocm721.dll
• Commit/tag the exact source and DLL used for the PyPI upload

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🗂️ Project Structure

hip_quant/
├── __init__.py              # NumPy / ctypes offline API
├── __main__.py              # CLI entry point
├── torch_api.py             # PyTorch FP8 training API (Phases 1–4)
├── device_info.py           # GPU/DLL compatibility probe helpers
├── cdna_compat.py           # CDNA feature table, build configs, CPU refs
├── setup_torch.py           # PyTorch C++ extension build script
├── build.ps1                # DLL build script (hipcc)
├── hip_quantize.cpp         # Offline quantization kernels (DLL source)
├── hip_quant_util.h         # Shared FP8 / FP16 device helpers
├── hip_quant_types.h        # GGML block type definitions
├── kernels/                 # Per-format offline HIP kernels (.cu)
├── torch_ext/               # PyTorch extension source
│   ├── pytorch_bindings.cpp # C++ bindings (TORCH_CHECK, pybind11)
│   ├── fp8_quant_kernels.hip# Element-wise quant/dequant kernels
│   └── fp8_linear_kernels.hip# Tiled FP8 GEMM kernels
└── tests/torch/             # GPU test suite (pytest)

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📋 Architecture Notes

• RDNA4 PyTorch target — FP8 WMMA extension kernels are compiled with --offload-arch=gfx1200 and --offload-arch=gfx1201
• Default offline DLL target — build.ps1 compiles the portable DLL quantization kernels for gfx90a, gfx942, RDNA3 gfx1100-gfx1103, and RDNA4 gfx1200/gfx1201
• Current validation scope — runtime-tested locally on gfx1201 RX 9070 XT; gfx1200 and CDNA code objects are build-validated and need separate hardware runtime validation
• BF16/FP16 PyTorch support — FP8 quantization and linear kernels accept FP32, FP16, and BF16 tensors, accumulating in FP32 registers and storing results in the input/master dtype
• Device-resident kernels — FP8 tensor data stays on device through tensor.data_ptr(); scalar scale metadata is passed through legacy float launcher arguments. Non-MSVC builds use PyTorch's current stream, while Windows/MSVC ROCm builds currently fall back to the default HIP stream because the PyTorch HIP stream headers do not compile cleanly under MSVC.
• Phase 4 GEMM is a correctness-first tiled stub. Replace the inner loop with a rocBLASLt FP8 GEMM path for production throughput once validated on your ROCm stack
• Offline API unchanged — the NumPy/ctypes path is untouched; both APIs coexist cleanly