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* WIP: absorb adding input into std_attn and std_ffn * WIP: NCCL infra * WIP: add reduce and fake_cpy ops * WIP * WIP: graph appears to work, layer is broken * WIP: Qwen3-MoE works with graph, layer still broken * WIP: GLM-4.5 graph works * WIP: fix sm layer (dense) * WIP: fix sm layer (MoE) * WIP: fast PP with bespoke 4-GPU NCCL I guess, I'm not using NCCL the right way as PP is very low with a single communicator group for 3 or more GPUs. But if I create 4 communicator groups for pairs of GPUs (0,1, 2,3, 0,2, 1,3) and use that, PP is fast: I'm hitting 1500 t/s for L3-70B on the 4x3090 system, which is ~20% better than the previous sm graph without NCCL. But that cannot be the solution (I cannot be creating pairwise communicators and associated logic for every possible number of GPUs). * WIP: Cohere2 * Explicitely set device * Bespoke 3-GPU case * WIP * Do not repeat get_rows multiple times * Fix 3 GPUs * OK, let's leave it in * Simple async * This sync seems enough * Only do async for 4 or more backends With 2 GPUs (so, 3 backends) not using async is slightly faster * Scheduler changes * Use OpenMP if available Surprisingly (at least to me), this is quite a bit faster than std::thread and std::barrier. GLM-4.5-AIR with 4 GPUs is now at 105 t/s at zero context! * Do not use OpenMP if there are tensor overrides * Set omp max active levels * Be more careful with having set the device before using a stream * Command line option to turn on async. Set to false by defualt for now --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>