| # Experiments and Results | |
| - [GPT2 Experiments](./gpt2.md) - main working doc with all the experiment results | |
| The following is mostly outdated and should be recycled/updated: | |
| Model | |
| * Basically GPT, but with slightly different attention/loss mask (see agenda above) | |
| * Targeting 175-200B parameters, seq length 512 or 1024, adam optimizer | |
| * Note: our nodes have 128GB total gpu memory each -> need sharding (model weights alone are 350GB in fp16!) | |
| * Note 2: may need to adjust expectations based on estimated training time (pending) | |
| * Subsequent runs: consider encoder-decoder arch? longer sequences? adafactor? | |
| Making it train: | |
| * Candidates for run1: deepspeed-gpt and megatron-gpt. Save fairscale/t5/custom for later. Use reference training code instead of integrating. | |
| * Only consider training nodes with 4x V100-32GB because there are too few 8x nodes in JZ | |
| * Current strategy: fully sharded data-parallel between nodes, arbitrary black magic within one node. | |
| For each framework, we need to measure (hackathon participants + Sidd + Laurel) | |
| * maximum number of parameters that we can train on 1 node | |
| * minimum number of nodes to fit 175B-parameter model (num_layers/dmodel/dhead: from gpt3 table 2.1) | |
| * training throughput (i.e. training tokens per second in both configurations) | |
| * Once we get through the initial debugging, ask microsoft/nvidia teams to sanity-check our configs for DS/Megatron respectively | |
| Investigate: | |
| * @Stas measured allreduce bandwidth between two nodes as ~135Gbps, now he's is investigating if we can get closer to the theoretical max = 400Gbps | |
| * Estimated training time for 175B parameters - based on training throughput in megatron/deepspeed tech reports; estimated time to "reasonable performance". (TODO @Yozh and @Max Ryabinin). Sanity-check whether the 8-node servers are indeed too few to train in reasonable time. | |
| * Figure out the how to introduce semi-autoregressive attention mask (and loss mask) to the code of deepspeed and megatron-LM training configs (looking for volunteers!) | |
| Next steps: | |
| * After Stas figures out the bandwidth, we need to measure how performance scales with #nodes using data-parallel training. | |
| * Ask/measure how many server nodes can we reliably provision with JZ. Find the optimal trade-off between #nodes vs training time. | |
| * a quick preliminary run with the same configuration, but fewer parameters ... and we can unleash the beast! | |