Journey With Me Into The Mind of Large Language Models: Interesting Findings in AnthropicAI's Scaling Monosemanticity paper.
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Jaward Sesay
Jaward
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I like to train large deep neural nets too 🧠🤖💥 | First Paper (AutoAgents: A Framework for Automatic Agent Generation) Accepted @ IJCAI 2024 | Role Model Karpathy
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Triton nanoGPT now has a custom cross entropy loss kernel 🚀
Next: matmul, gradually overthrowing all major PyTorch ops:)
Simplified pseudo for parallel cross-entropy loss compute:
- init program: get pid, compute offsets, load targets.
- init row_max and row_sum.
- for-loop1 (find max logits): update row_max with max logits.
- for-loop2 (compute softmax and loss): compute row_sum, update loss.
- add log(row_sum) and store loss.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/triton_nanoGPT.ipynb
Next: matmul, gradually overthrowing all major PyTorch ops:)
Simplified pseudo for parallel cross-entropy loss compute:
- init program: get pid, compute offsets, load targets.
- init row_max and row_sum.
- for-loop1 (find max logits): update row_max with max logits.
- for-loop2 (compute softmax and loss): compute row_sum, update loss.
- add log(row_sum) and store loss.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/triton_nanoGPT.ipynb
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342
This has to be the first peak performance level use case of a non-autoregressive architecture for TTS. Flow matching for the win!!
Demo: mrfakename/E2-F5-TTS
Model: SWivid/E2-TTS
Demo: mrfakename/E2-F5-TTS
Model: SWivid/E2-TTS
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