Rocm Strix Halo

rocm-strix-halo — gfx1151 / Ryzen AI Max+ 395

gfx1151 is not on AMD's official ROCm support matrix as of this writing and is not in upstream PyTorch ROCm wheels (download.pytorch.org/whl/rocm6.x). Those wheels detect the GPU but die at first compute call with HIP error: invalid device function. The supported path is AMD's TheRock nightly wheels.

Bootstrap

make env-strix-halo        # equivalent to:
# bash bootstrap.sh --accel=rocm --rocm-nightly --rocm-arch=gfx1151
source .venv/bin/activate
make doctor                # should print: backend=rocm  arch=gfx1151:xnack-

Under the hood bootstrap.sh installs from https://rocm.nightlies.amd.com/v2/gfx1151/ (PEP 503 simple index — published by AMD in github.com/ROCm/TheRock). Auto-detected if rocminfo shows gfx1151.

bootstrap.sh also writes one runtime hint to .venv/bin/activate:

export PYTORCH_HIP_ALLOC_CONF=expandable_segments:True

We deliberately do NOT export PYTORCH_ROCM_ARCH (build-time only — no runtime effect on prebuilt wheels) and NOT HSA_OVERRIDE_GFX_VERSION (TheRock has native gfx1151 kernels and needs no override). If you fall off TheRock onto a non-gfx1151 build (Ollama, llama.cpp, stock PyTorch ROCm), set HSA_OVERRIDE_GFX_VERSION=11.5.1 — gfx1151 is RDNA 3.5, not 3.0 (11.0.0 is wrong).

What works out of the box

• torch, torchvision, torchaudio, triton — from TheRock nightlies.
• transformers, trl, peft, accelerate, datasets — pure-Python, work unchanged.
• scripts/train_sft.py, scripts/train_dpo.py, scripts/train_grpo.py — the platform-overrides branch detects ROCm, sets TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1, forces attn_implementation="sdpa" (so SDPA picks the AOTriton flash kernel), and drops adamw_8bit → adamw_torch. bf16 stays on (gfx1151 supports it).
• AOTriton flash-attention via SDPA — libaotriton_v2.so.0.11.2 ships inside the TheRock torch wheel (torch/lib/). Verified ~20× speedup vs the math kernel on this exact host (0.65 ms vs 12.80 ms per (2, 16, 1024, 64) bf16 attention call), matching ROCm/ROCm#6034.

Two flash-attention paths — pick the one that works

There are two routes named "flash attention" in HF Transformers, and on gfx1151 only one of them is real:

Without TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 set, PyTorch silently falls back to the math SDPA kernel and prints Flash attention was not compiled for current AMD GPU architecture — the warning is misleading, the kernel IS compiled, the env-var gate is just closed. bootstrap.sh writes the export into .venv/bin/activate so it loads automatically; the trainer scripts also set it defensively.

Re: aule-attention (https://github.com/AuleTechnologies/Aule-Attention) — its Triton backend lists MI300X / MI300A / MI250X / MI250 / MI210 / MI100 and its Vulkan backend covers RDNA3 dGPUs / RDNA2; gfx1151 is on neither path. README claims <1e-3 fp16 deviation vs torch SDPA but ships no public training-loss reproduction or test-suite metrics. Not a recommended training path on this arch yet.

What still does NOT work out of the box (build-from-source paths)

bf16 numerical caveats on gfx1151 (ROCm/ROCm#6034 — still OPEN)

AMD acknowledged 5 distinct bf16-related bugs that hit training stability on gfx1151. None of these are AOTriton kernel bugs — they're in the broader HIP/torch.compile path. Plan around them:

• NaN within ~15 steps when total_batch_size <= 2**13 (8192 tokens)
• NaN at head_dim=32 (use 64+)
• NaN at network depth ≥ 12
• NaN with Adam beta2 < 0.97 (default is fine; check if you tweak)
• Cumulative bf16 drift causing crashes around step ~1000 on long runs

Mitigation: prefer larger device batches, head_dim 64+, depth ≤ 10, default betas. Checkpoint frequently. If you see drift, restart from the last good checkpoint or fall back to attn_implementation="eager" for diagnosis.

Build bitsandbytes from source on TheRock 7.11+

Confirmed recipe from TheRock #2945:

git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git
cd bitsandbytes

source .venv/bin/activate          # the venv with TheRock torch installed
export ROCM_HOME=/opt/rocm
export HIP_PATH=/opt/rocm
export PYTORCH_ROCM_ARCH=gfx1151    # build-time only — required here

cmake -DCOMPUTE_BACKEND=hip -S .
make -j$(nproc)
pip install -e . --no-build-isolation

python -c "import bitsandbytes; print('ok')"

Note that PYTORCH_ROCM_ARCH=gfx1151 IS load-bearing at build time (just not at runtime). The TheRock environment also still requires the TheRock-specific env vars from issue #1658 to be set in some setups; if cmake can't find ROCm headers, export ROCM_PATH=$(python -c "from rocm_sdk import path; print(path())") or fall back to /opt/rocm.

Enable AOTriton flash-attention on ROCm 7.1+

export TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1

Then attn_implementation="flash_attention_2" works inside the ROCm 7.1 PyTorch container. On bare-metal TheRock nightlies, AOTriton must be built locally (0.11 added gfx1151 support — see ROCm/aotriton commit 0e7d518).

Memory budget — the actual win on Strix Halo

Strix Halo ships with up to 128 GB unified LPDDR5X (96 GB allocatable to GPU under Linux, kernel-tunable). Treat this as one big pool — you do not need LoRA / 4-bit for ≤7B models the way you do on a 24 GB consumer NVIDIA card.

If you allocate <96 GB to GPU in BIOS / kernel cmdline, dial back accordingly. Check actual GPU memory with make doctor (it prints mem=NN GiB).

Sanity test after install

.venv/bin/python -c "
import torch
print('cuda_avail=', torch.cuda.is_available())
print('hip       =', torch.version.hip)
print('arch      =', torch.cuda.get_device_properties(0).gcnArchName)
x = torch.randn(4096, 4096, device='cuda', dtype=torch.bfloat16)
y = x @ x.T
print('matmul ok, dtype=', y.dtype, 'shape=', tuple(y.shape))
"

If the matmul works, full-stack training works.

When to fire

• make doctor shows arch=gfx1151:*.
• User says "Strix Halo", "Ryzen AI Max+ 395", "AI 395", "gfx1151", "128GB unified memory".
• User hits HIP error: invalid device function on stock PyTorch ROCm.

When NOT to use this skill

• gfx1100/1101/1102 (RX 7900/7800/7700 dGPU) — those ARE in upstream PyTorch ROCm wheels. Use make env-rocm (no nightly).
• gfx942 (MI300) — same: upstream wheels work; use make env-rocm.

References

• TheRock release notes: https://github.com/ROCm/TheRock/blob/main/RELEASES.md
• gfx1151 wheel discussion: https://github.com/ROCm/TheRock/discussions/655
• bitsandbytes source-build recipe (closed 2026-02-03): https://github.com/ROCm/TheRock/issues/2945
• AOTriton 0.11 + gfx1151 (closed 2025-11-01): https://github.com/ROCm/ROCm/issues/5404
• AOTriton gfx1151 commit: https://github.com/ROCm/aotriton/commit/0e7d518e83e6ada6a41cbb8b8b48f50c652fdeeb
• ROCm 7.1 PyTorch install (Radeon / Ryzen AI): https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/docs/install/installryz/native_linux/install-pytorch.html
• TheRock env-var blocker (still open): https://github.com/ROCm/TheRock/issues/1658
• Working community recipe: https://github.com/kyuz0/amd-strix-halo-pytorch-gfx1151-aotriton