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# downdload the issue1.sh by the following wget
# wget https://gist.githubusercontent.com/AmosLewis/00fdb4e9a96f29c188828e3ff4ea29ef/raw/8377ffd2f53e58e12a9adae7b92b3d5a7f35d98b/bisect-issue1.sh
(bisect.venv) ➜ bisect git:(main) ✗ python ./bisect_packages.py \
--good-ref=00e88733e6b8c8cdb351d4516509f56daebdf604 \
--bad-ref=4451b8ba42b1249eb3ed1b5031a46f51733984ed \
--test-script=/sharedfile/attn/bisect/issue1.sh
Welcome to bisect_packages.py!
# wget https://sharkpublic.blob.core.windows.net/sharkpublic/chi/llama/atten/fp8_attn.mlir
iree-compile \
/sharedfile/attn/128/fp8_attn.mlir \
--iree-hip-target=gfx942 \
-o=/sharedfile/attn/128/fp8_attn.vmfb \
--iree-hal-target-device=hip \
--iree-dispatch-creation-enable-aggressive-fusion=true \
--iree-global-opt-propagate-transposes=true \
--iree-opt-aggressively-propagate-transposes=true \
--iree-opt-data-tiling=false \
# downdload the issue1.sh by the following wget
# wget https://gist.githubusercontent.com/AmosLewis/00fdb4e9a96f29c188828e3ff4ea29ef/raw/8377ffd2f53e58e12a9adae7b92b3d5a7f35d98b/bisect-issue1.sh
(bisect.venv) ➜ bisect git:(main) ✗ python ./bisect_packages.py \
--good-ref=6eadf3df6f57a20283da8318f9178b0d92ae50eb \
--bad-ref=00e88733e6b8c8cdb351d4516509f56daebdf604 \
--test-script=/sharedfile/attn/bisect/issue1.sh
Welcome to bisect_packages.py!
------------------------------------------------------------------
/home/chi/src/iree-build-trace/tools/iree-compile \
/sharedfile/attn/128/fp8_attn.mlir \
--iree-hip-target=gfx942 \
-o=/sharedfile/attn/128/fp8_attn.vmfb \
--iree-hal-target-device=hip \
--iree-dispatch-creation-enable-aggressive-fusion=true \
--iree-global-opt-propagate-transposes=true \
--iree-opt-aggressively-propagate-transposes=true \
--iree-opt-data-tiling=false \
--iree-preprocessing-pass-pipeline='builtin.module(util.func(iree-preprocessing-generalize-linalg-matmul-experimental))' \
(.venv) ➜ 32 python3 -m sharktank.examples.export_paged_llm_v1 --irpa-file=/sharedfile/llama3_8b_fp8.irpa \
--output-mlir=/sharedfile/32/fp8_attn.mlir \
--output-config=/sharedfile/32/config_attn.json \
--bs=1 --attention-kernel sharktank \
--attention-dtype=bfloat16 \
--activation-dtype=bfloat16 \
--kv-cache-dtype=float8_e4m3fnuz \
--use-hf \
--use-attention-mask
iree-base-compiler 3.3.0rc20250215
iree-base-runtime 3.3.0rc20250215
iree-turbine 3.3.0rc20250215
iree-compile /sharedfile/128/fp8_128.mlir \
--iree-hip-target=gfx942 \
-o=/sharedfile/128/fp8_128_0224_ir0215.vmfb \
--iree-hal-target-device=hip \
--iree-dispatch-creation-enable-aggressive-fusion=true \
--iree-global-opt-propagate-transposes=true \
iree-base-compiler 3.2.0rc20250209
iree-base-runtime 3.2.0rc20250209
iree-compile /sharedfile/128/fp8_128_0212.mlir \
--iree-hip-target=gfx942 \
-o=/sharedfile/128/fp8_128_0212_ir0209.vmfb \
--iree-hal-target-device=hip \
--iree-dispatch-creation-enable-aggressive-fusion=true \
--iree-global-opt-propagate-transposes=true \
--iree-opt-aggressively-propagate-transposes=true \
This file has been truncated, but you can view the full file.
iree-base-compiler 3.3.0rc20250223
iree-base-runtime 3.3.0rc20250223
iree-turbine 3.3.0rc20250223
wget https://sharkpublic.blob.core.windows.net/sharkpublic/chi/llama/fp8_32_kv16.mlir
iree-compile /sharedfile/32/fp8_32_kv16.mlir \
--iree-hip-target=gfx942 \
-o=/sharedfile/32/fp8_32_kv16.vmfb \
--iree-hal-target-device=hip \
python3 -m sharktank.examples.export_paged_llm_v1 --irpa-file=/sharedfile/llama3_8b_fp8.irpa \
--output-mlir=/sharedfile/32/fp8_32.mlir \
--output-config=/sharedfile/32/config_32.json \
--bs=1 --attention-kernel torch \
--attention-dtype=float8_e4m3fnuz --activation-dtype=bfloat16 \
--use-hf \
--kv-cache-dtype=float8_e4m3fnuz
/home/chi/src/shark-ai/.venv/lib/python3.11/site-packages/iree/turbine/aot/params.py:163: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
return torch.from_numpy(wrapper)
Exporting prefill_bs1
This file has been truncated, but you can view the full file.
#map = affine_map<()[s0] -> (4, s0 * 32)>
#map1 = affine_map<()[s0] -> (4, s0)>
#map2 = affine_map<()[s0] -> (s0, 2097152)>
#map3 = affine_map<()[s0] -> (s0 * 32)>
#map4 = affine_map<()[s0] -> (1, 1, s0 * 32, 131072)>
#map5 = affine_map<()[s0] -> (1, 1, s0 * 32, s0 * 32)>
#map6 = affine_map<()[s0] -> (4, 1, s0 * 32)>
#map7 = affine_map<()[s0] -> (4, 1, 1, s0 * 32)>
#map8 = affine_map<()[s0] -> (4, 1, s0 * 32, s0 * 32)>
#map9 = affine_map<()[s0] -> (4, s0 * 32, 4096)>