Multiple updates and refactorings (#231)
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@@ -27,7 +27,7 @@ Despite its lightweight design, DeepGEMM's performance matches or exceeds expert
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- [x] Fix TMA multicast compatibility for indivisible shapes
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- [x] Skip useless computation on M
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- [x] NVRTC as a faster compiler
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- [ ] Sanitizer for testing
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- [x] Sanitizer for testing
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- [x] Weight gradient kernels for dense models
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- [x] Weight gradient kernels for MoE models
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- [ ] Better `get_best_configs` modeling
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@@ -69,9 +69,7 @@ cat develop.sh
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# Test all GEMM implements
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python tests/test_layout.py
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python tests/test_attention.py
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python tests/test_bf16.py
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python tests/test_fp8.py
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python tests/test_lazy_init.py
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python tests/test_core.py
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```
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### Installation
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