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lean-dojo
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TorchLean
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feat(autograd): reduce the TapeM surface to one lemma, and prove it
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#123:
Pull request
#31
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NicolasRouquette
1h 26m 32s
NicolasRouquette:tapem-run-lemmas
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feat(autograd): reduce the TapeM surface to one lemma, and prove it
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#73:
Pull request
#31
opened by
NicolasRouquette
1h 48m 38s
NicolasRouquette:tapem-run-lemmas
NicolasRouquette:tapem-run-lemmas
1h 48m 38s
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feat(cuda): fused scaledProdExp buffer op — exp((c·x)·y) in one kernel
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#122:
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NicolasRouquette:cuda-scaled-prod-exp
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feat(cuda): fused scaledProdExp buffer op — exp((c·x)·y) in one kernel
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#72:
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#23
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NicolasRouquette:cuda-scaled-prod-exp
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feat(cuda): TexTable — layered 1-D lookup-table textures with point/hardware filtering
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#121:
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NicolasRouquette:cuda-texture-table
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feat(cuda): TexTable — layered 1-D lookup-table textures with point/hardware filtering
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#71:
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feat(cuda): report device identity and compiled architecture
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#120:
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feat(cuda): report device identity and compiled architecture
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#70:
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NicolasRouquette:cuda-device-identity
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perf(ioloader): decode NPY payloads unboxed, and per leading-axis block concurrently
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#119:
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NicolasRouquette:npy-bulk-decode
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perf(ioloader): decode NPY payloads unboxed, and per leading-axis block concurrently
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#69:
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feat(cuda): TexTable — layered 1-D lookup-table textures with point/hardware filtering
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#118:
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#24
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1h 31m 50s
NicolasRouquette:cuda-texture-table
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feat(cuda): TexTable — layered 1-D lookup-table textures with point/hardware filtering
CI
#68:
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#24
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2m 44s
NicolasRouquette:cuda-texture-table
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feat(cuda): fused scaledProdExp buffer op — exp((c·x)·y) in one kernel
CI
#67:
Pull request
#23
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NicolasRouquette
2m 52s
NicolasRouquette:cuda-scaled-prod-exp
NicolasRouquette:cuda-scaled-prod-exp
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feat(cuda): fused scaledProdExp buffer op — exp((c·x)·y) in one kernel
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#117:
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#23
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1h 25m 33s
NicolasRouquette:cuda-scaled-prod-exp
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feat(cuda): scoped device-memory arena (withCudaArena) + use-after-free detector
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#116:
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1h 26m 53s
NicolasRouquette:cuda-arena-scope
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feat(cuda): scoped device-memory arena (withCudaArena) + use-after-free detector
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#66:
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feat(cuda): bundle + auto-link the glibc≥2.38 isoc23 link shim
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#65:
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feat(cuda): bundle + auto-link the glibc≥2.38 isoc23 link shim
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#115:
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#18
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NicolasRouquette:cuda-isoc23-shim
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feat(cuda): selectable compilation target via -K cuda_arch, traced by Lake
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#114:
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#27
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1h 29m 38s
NicolasRouquette:cuda-arch-target
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feat(cuda): selectable compilation target via -K cuda_arch, traced by Lake
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#64:
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Small complex fix
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#113:
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1h 34m 16s
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1h 34m 16s
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Small complex fix
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#63:
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Major upgrade
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#112:
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1h 29m 8s
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Major upgrade
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#62:
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cfbb01e
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1h 6m 45s
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1h 6m 45s
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Keep batched row scaling for recurrent models
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#111:
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