Viewing enriched Twitter post
✨ New blog post 👀: We have some very fast AI-generated kernels generated with a simple test-time only search. They are performing close to or in some cases even beating the standard expert-optimized production kernels shipped in PyTorch. (1/6) [🔗 link in final post] https://t.co/luNU5D71dK
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"text": "very cool work! A question about the Conv2d pytorch baseline in fp32 though: that wouldn’t use tensor cores right? (unless cudnn.allow_tf32 = True, but still ~0.5x flops of 16bit). But the generated kernel is casts down to fp16 IIUC. do the numerics end up as accurate as the baseline? would a fp16 pytorch impl be a better baseline?",
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"full_text": "@anneouyang very cool work! A question about the Conv2d pytorch baseline in fp32 though: that wouldn’t use tensor cores right? (unless cudnn.allow_tf32 = True, but still ~0.5x flops of 16bit). But the generated kernel is casts down to fp16 IIUC. do the numerics end up as accurate as the…",
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"full_text": "@anneouyang This is very interesting work! The FP32 matmul result uses a clever trick (decomposing into low-high fp16 pairs), but doesn't support full FP32 range (e.g. 65536 becomes inf)\n\nI guess that's allowed by the KernelBench input-range definition? But seems a little risky",
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