CUDA op getrows fails for long sequences #11189
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T5 embeddings have a square input pos tensor which quickly exceeds the 65k limit of getrows Implemented only for _float, need other implementations
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| static const int64_t MAX_GRID_Y = 65535; | ||
| for (int64_t startY = 0; startY < ne10; startY += MAX_GRID_Y) { |
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This code is incorrect. The grid y dimension uses 16 bits and ranges from 0 to 65535 (inclusive). So the correct stride would be 65536. With this code two threads per grid write to the same address (though this should result in identical results). The correct way to fix this would be to modify the CUDA kernel and have it iterate with a stride of 65536 over the y dimension. This will also avoid issues with the number of nodes in a CUDA graph varying depending on input parameters.
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I have integrated the ProstT5 protein language into Foldseek. Thanks a lot for the great library! I am upstreaming a few fixes for issues I found in ggml during the integration. I hope that it's okay to push the changes here and that they get synced at some point to the main ggml repo.
The T5 encoder has a square input pos tensor (
llm_build_pos_bucket(cause = false)) which quickly exceeds the 65k limit (on most GPUs?) of the CUDA GET_ROWS op.I have implemented this only for the _float op and I don't feel very confident in CUDA programming. I tested this one specifically for my use-case against the reference implementation of my model, but don't have models ready to test for quantized versions.