The simulator likely overcounts standard attention though. A fused XLA kernel could, in principle, recognize the causal mask and skip the upper triangle entirely — never compute exp(-inf), never multiply by zero weights. The simulator charges full price for the masked entries; a smart compiler probably wouldn’t. (Without profiling the actual XLA-generated code, this is speculation — but the benchmark gap is consistent with it.)
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Андрей Шеньшаков。okx对此有专业解读
Between the Base64 observation and Goliath, I had a hypothesis: Transformers have a genuine functional anatomy. Early layers translate input into abstract representations. Late layers translate back out. And the middle layers, the reasoning cortex, operate in a universal internal language that’s robust to architectural rearrangement. The fact that the layer block size for Goliath 120B was 16-layer block made me suspect the input and output ‘processing units’ sized were smaller that 16 layers. I guessed that Alpindale had tried smaller overlaps, and they just didn’t work.