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2 changes: 1 addition & 1 deletion DEPLOYMENT.md
Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,7 @@ Then install the attention and kernel dependencies:
git -C deploy/tmp/cutlass checkout dcf215af
# build only this machine's arch (the default is a 5-arch fat binary —
# sm_80..120a — which multiplies compile time ~5x); auto-detected:
export JOYOMNI_OPS_CUDA_ARCHS=$(python -c "import torch; cc = torch.cuda.get_device_capability(0); print(f'{cc[0]}{cc[1]}a' if cc[0] >= 10 else f'{cc[0]}{cc[1]}')")
export JOYOMNI_OPS_CUDA_ARCHS=$(python -c "import torch; cc = torch.cuda.get_device_capability(0); print(f'{cc[0]}{cc[1]}a' if cc[0] >= 9 else f'{cc[0]}{cc[1]}')")
echo "building joyomni_ops for sm_$JOYOMNI_OPS_CUDA_ARCHS"
JOYOMNI_OPS_CUTLASS_DIR=$(pwd)/deploy/tmp/cutlass \
python -m pip install --no-build-isolation ./deploy/joyomni_ops
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9 changes: 7 additions & 2 deletions deploy/joyomni_ops/setup.py
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
- fp8_scaled_mm : FP8 per-token x per-channel scaled GEMM (cutlass)

GPU arch coverage is chosen from the local nvcc version:
- always: sm_80, sm_89, sm_90
- always: sm_80, sm_89, sm_90a
- CUDA >= 12.8: also sm_100a (B200) and sm_120a (RTX PRO 6000 / RTX 5090)
So building on a CUDA 12.8+ toolchain automatically yields Blackwell support.
"""
Expand Down Expand Up @@ -57,7 +57,12 @@ def _gencodes():
flags = [
"-gencode=arch=compute_80,code=sm_80",
"-gencode=arch=compute_89,code=sm_89",
"-gencode=arch=compute_90,code=sm_90",
# Hopper must be sm_90a: fp8_scaled_mm selects cutlass'
# KernelTmaWarpSpecializedPingpongFP8FastAccum, whose WGMMA path is
# arch-conditional. Built as plain sm_90 it compiles and loads, then
# aborts every launch ("Arch conditional MMA instruction used without
# targeting appropriate compute capability").
"-gencode=arch=compute_90a,code=sm_90a",
]
if (mj, mn) >= (12, 8):
flags += [
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