| # Big model inference benchmarks |
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| Running inference with Accelerate on big models. |
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| ## Setup |
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| These benchmarks use the `transformers` library: |
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| ```bash |
| pip install transformers |
| ``` |
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| To reproduce or test a new setup, run |
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| ```py |
| python inference_acc.py model_name |
| ``` |
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| This script supports `gpt-j-6b`, `gpt-neox`, `opt` (30B version) and `T0pp` out of the box, but you can specify any valid checkpoint for `model_name`. |
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| To force a different `torch_dtype` than the one in the config: `--torch_dtype xxx`. |
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| If you get an error linked to disk offload, you need to add the option `--disk-offload` |
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| ## Results |
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| On a setup with two Titan RTXs (24GB of RAM) and 32GB of RAM, we get the following benchmarks (T0pp does not run in float16, which is why it's not included). |
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| | Model | Model load time | Generation time | dtype | GPU 0 use | GPU 1 use | CPU use | Disk offload | |
| |:-----:|:---------------:|:---------------:|:-----:|:---------:|:---------:|:-------:|:------------:| |
| | GPT-J-6B | 8.7s | 0.05s per token | float16 | 11.7GB | 0GB | 0GB | no | |
| | GPT-J-6B | 12.4s | 0.06s per token | float32 | 21.9GB | 1.5GB | 0GB | no | |
| | GPT-Neo-X-20B | 30.9s | 0.08s per token | float16 | 21.5GB | 18GB | 0GB | no | |
| | GPT-Neo-X-20B | 78.2s | 10.72s per token | float32 | 20.3GB | 22.7 GB | 24.4GB | yes | |
| | T0pp (11B) | 29.4s | 0.05s per token | float32 | 21.1GB | 21.3GB | 0GB | no | |
| | OPT-30B | 34.5s | 2.37s per token | float16 | 20.7GB | 22.3GB | 14.1GB | no | |
| | OPT-30B | 112.3s | 33.9s per token | float32 | 20.2GB | 21.2GB | 23.5GB | yes | |
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| Note on the results: |
| - using two GPUs instead of one does not slow down generation |
| - using CPU offload slows down a bit (see OPT-30b) |
| - using disk offload slows down a lot (need to implement prefetching) |
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| You will also note that Accelerate does not use anymore GPU and CPU RAM than necessary: |
| - peak GPU memory is exactly the size of the model put on a given GPU |
| - peak CPU memory is either the size of the biggest checkpoint shard or the part of the model offloaded on CPU, whichever is bigger. |
