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Model uye Pipeline Parallelism
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Nzira yekuparadzanisa masvomhu mukati meimwe neural-network layer kune akawanda maGPU saka modhi yakakurisa kune imwe mudziyo inogona kuramba ichimhanya.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Izvo zvine basa nekuti mamodheru emuganho ane mazana emabhiriyoni emaparamita ayo hapana imwechete GPU inogona kubata kana kuverenga nekukurumidza zvakakwana ega.
Tensor parallelism (inonziwo intra-layer model parallelism) shards uremu hwemunhu matrices paGPUs pane kuisa maturu akazara pamidziyo yakasiyana. Mushanduri, iyo hombe matrix kuwanda-kutarisisa fungidziro uye yekudyisa-mberi MLP-yakakamurwa: semuenzaniso, iyo MLP yekutanga huremu matrix inogovaniswa nemakoramu uye yechipiri nemitsara, saka yega yega GPU inoverengera chidimbu uye imwe chete-kuderedza inosanganisa mhedzisiro. Attention is split across heads, with each GPU handling a subset. Nekuti yega yega GPU inoita chikamu chega rega rega panguva imwe chete, tensor parallelism inoderedza per-GPU ndangariro uye inomhanyisa compute, asi inoda kazhinji, yakakwirira-bandwidth kutaurirana pakati peGPU yega yega. Ndokusaka ichiwanzovharirwa mukati menode yakabatana neNVLink, uye yakasanganiswa nepombi uye data parallelism yekudzidziswa kwakakura kwazvo uye kushumira mabasa.
Kudzidzira 175B-parameter modhi nekugovanisa huremu hwega hwega matrices kuyambuka 8 GPUs mune imwe NVLink-yakabatana node uchishandisa Megatron-LM.
Kushandira 70B-parameter chat modhi muvLLM ine tensor_parallel_size=4 kuti huremu hukwane pamaGPU mana uye pindura munguva chaiyo.
Kupatsanura kutarisisa kwekushandura kunotungamira muGPUs kuitira kuti mudziyo wega wega uverenge subset, wozobatanidza zvinobuda kune inotevera layer.
Kubatanidza tensor parallelism mukati me node uye pombi parallelism munzvimbo dzese kudzidzisa matrillion-parameter modhi pamasumbu makuru eGPU.
Tensor parallelism inoramba iine hwaro asi iri kuwedzera kusanganiswa kuita '3D parallelism' (tensor + pombi + data) uye yakasanganiswa nenyanzvi parallelism yeMisanganiswa-ye-Nyanzvi mhando. Frameworks like Megatron-LM, DeepSpeed, and vLLM automate the sharding. As GPU interconnects (NVLink, NVSwitch) and optical fabrics get faster, the node-boundary limit relaxes, allowing wider tensor-parallel groups. Expect smarter auto-parallelization that picks shard dimensions and group sizes to minimize communication for a given cluster topology.
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Nzira yekuparadzanisa masvomhu mukati meimwe neural-network layer kune akawanda maGPU saka modhi yakakurisa kune imwe mudziyo inogona kuramba ichimhanya. Izvo zvine basa nekuti mamodheru emuganho ane mazana emabhiriyoni emaparamita ayo hapana imwechete GPU inogona kubata kana kuverenga nekukurumidza zvakakwana ega.
Tensor (intra-layer) parallelism inoshatisa huremu hwematrices mukati mechikamu, saka GPU yega yega inoverengera chikamu cheiyo yakafanana-yakasiyana nepipeline parallelism, iyo inoisa yakazara maseru pamaGPU akasiyana.
Kupatsanura yekutanga matrix nemakoramu kunoita kuti GPU yega yega ishandise iyo isiri mutsara munharaunda; kupatsanura chechipiri nemitsara kunoreva chidimbu chimwe chete-chete chinosanganisa zvakabuda zvishoma-kuderedza kuwiriranisa.
Imwe neimwe layer inokonzeresa kutaurirana (zvese-zvinoderedza), saka inorema, latency-sensitive traffic inoda kukurumidza intra-node links seNVLink pane inononoka inter-node network.
Misoro yekutarisa yakazvimiririra, saka inogovaniswa pamaGPU ese - mudziyo wega wega unoverengera mimwe misoro uye zvinobuda zvinosanganiswa.
Yese-kuderedza inoverengera zvakabuda zvakapetwa paGPU yega yega kuti vabvumirane pane mhedzisiro yemusara; izvi zvinoitika kakawanda pane imwe layer mumashure ekumberi nekumashure.
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InoteveraGaidhi rinotevera
Model uye Pipeline Parallelism
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