tender
@tenderizzation
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python test/test_matmul_cuda.py
South Silly Valley (南湾）
Joined July 2010
DM me "hey" I'll debug your CUDA error: an illegal memory access🍒 "hi" I'll debug your cuDNN error: CUDNN_STATUS_BAD_PARAM 🍑 "howdy" I'll debug your CUDA error: CUBLAS_STATUS_EXECUTION_FAILED 🍓
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DM me for the secret environment variables
This is the exact moment where torch compile now has max_autotune_us_gov backend opt in that wire transfer 1$ for executing every graph module
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I am ambivalent about missing college because this was what literally every day was
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the “now” doing some heavy lifting
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the model when you neglect document masking during pretraining and the space invaders speedrun video ends up next to someone’s instagram story
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picking the PCI-E cake over the SXM cake when there is ostensibly no cost difference is crazy
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imagine you’re a data guy at one of the big labs curating github repos for pretraining “maybe we can sort them by number of stars?” an entirely reasonable heuristic, one might conclude
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when @alyankovic drops the "It's All About the Blackwells" music video I'll start worrying until then,
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pytorch consulting the math library heuristics which say transposing the matrix will yield a 30% speedup over the non-transposed case, deciding to transpose the matrix, and immediately running out of memory https://t.co/5fodHFSwFp
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“hey, can you check if this fixes the problem?” <link to 30GiB docker container>
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“back in my day when people said ‘numerical stability’ they were referring to topics like machine epsilon and conditioning, not whether an implementation of an algorithm was grossly incorrect” “sure thing, let’s get you back to numpy where the default dtype is float64”
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we are probably 1-2 kitschy branding refresh cycles from returning to full blown 2010
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protip you don’t need unit tests you just need the one big is my loss still bitwise identical test
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rut roh
The chiefs are washed lmao
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