Comments (8)
ZenDNN library is intended to be used in conjunction with the frameworks like TensorFlow, PyTorch, ONNXRT and cannot be used independently.
ZenDNN release v3.3 is available at https://developer.amd.com/zendnn/
We recommend to refer the official DLRM https://github.com/facebookresearch/dlrm.git for benchmarking.
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Thank you! It is really helpful. BTW, how about the suggest benchmark of Bert-Large?
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ZenDNN library is intended to be used in conjunction with the frameworks like TensorFlow, PyTorch, ONNXRT and cannot be used independently. ZenDNN release v3.3 is available at https://developer.amd.com/zendnn/
We recommend to refer the official DLRM https://github.com/facebookresearch/dlrm.git for benchmarking.
When I ran the official dlrm benchmark with ZenDNN v3.3 pytorch (python3.8), there is an issue:
RuntimeError: could not execute a primitive
And when I use previous ZenDNN version (v3.3), there is a core dumped issue:
Segmentation fault (core dumped)
The issue happened when min-batch-size equal and larger than 128 on my test vm (m6a.16xlarge).
These issues didn't happen when I didn't use ZenDNN.
So are there any tricks to avoid them?
The benchmark cmd:
export OMP_NUM_THREADS=8 && export GOMP_CPU_AFFINITY=0-7 && numactl --physcpubind=0-7 -m 0 python dlrm_s_pytorch.py --mini-batch-size=128 --test-num-workers=0 --num-batches=100 --data-generation=random --arch-mlp-bot=512-512-64 --arch-mlp-top=1024-1024-1024-1 --arch-sparse-feature-size=64 --arch-embedding-size=1000000-1000000-1000000-1000000-1000000-1000000-1000000-1000000 --num-indices-per-lookup=100 --arch-interaction-op=dot --numpy-rand-seed=727 --print-freq=100 --print-time --inference-only
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Segmentation fault (core dumped) occurred using ZenDNN3.2 pytorch when using criteo data benchmark on dlrm_s_pytorch.py (in https://github.com/facebookresearch/dlrm.git)
from zendnn.
Thank you! It is really helpful. BTW, how about the suggest benchmark of Bert-Large?
Hi, Please refer the Bert: https://github.com/google-research/bert
from zendnn.
Segmentation fault (core dumped) occurred using ZenDNN3.2 pytorch when using criteo data benchmark on dlrm_s_pytorch.py (in https://github.com/facebookresearch/dlrm.git)
Hi, we found the issue when running official dlrm benchmark and criteo data benchmark.
We will submit the fix very soon.
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Segmentation fault (core dumped) occurred using ZenDNN3.2 pytorch when using criteo data benchmark on dlrm_s_pytorch.py (in https://github.com/facebookresearch/dlrm.git)
Hi, we found the issue when running official dlrm benchmark and criteo data benchmark.
We will submit the fix very soon.
Thank you. And how about the "RuntimeError: could not execute a primitive" using zendnn3.3?
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Segmentation fault (core dumped) occurred using ZenDNN3.2 pytorch when using criteo data benchmark on dlrm_s_pytorch.py (in https://github.com/facebookresearch/dlrm.git)
Hi, we found the issue when running official dlrm benchmark and criteo data benchmark.
We will submit the fix very soon.Thank you. And how about the "RuntimeError: could not execute a primitive" using zendnn3.3?
That is also fixed. The fix will be submitted very soon.
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Related Issues (8)
- about zenMatMulSplit split strategy HOT 1
- Support Pytorch with python v3.11
- Issue while setting up ZenDNN in conda container HOT 1
- `libblis-mt.so.3` error while installing torch HOT 2
- Can I build PT_v1.12_ZenDNN_v4.0_source_code to obtain a pytorch with both zendnn cpu capability and cuda GPU capability at the same time?
- Support for Pytorch 2.x?
- Clarification on ZenDNN tuning for tensorflow HOT 2
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