About a big embedding table
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- Dominant language
- Cuda
- Stars
- 50
- Forks
- 9
- PR merge metrics
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Description
We run cuembed_test.py with k=5000000(embedding nums) and n=7000(indices nums), and the results are:
cuEmbed: 0.0005564689636230469
Pytorch: 0.00020170211791992188
fprop test pass = tensor(True, device='cuda:0')
bprop test pass = False
it seems that cuEmbed is much slower than pytorch.
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Research direction
Start by running cuembed_test.py with k=5000000 and n=7000, then compare the cuEmbed and PyTorch timings. Investigate the reported bprop test failure alongside the fprop result; done means the backward test passes and the performance difference is explained or addressed.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 45/100