Before this PR, `model.generate()` returned one excess token when resuming generation with an existing (the last token of the previous session, `session.last_token_id`). This is an unexpected behavior not convenient for the downstream apps, so this PR changes it until it's too late.
This pull request adds an option to run Petals server on multiple local GPUs. It uses https://github.com/BlackSamorez/tensor_parallel
- 8bit approximation error same as in main (mean~=2% q0.9~=5%)
- TP=1, 2, 3 (see screenshots above)
- forward, grad w.r.t. input and inference exact match with main with TP=1
- `>=`80% GPU utilization with 3x 1080ti, batch = 8 tokens
- throughput measured with and without TP
- TP on 1080Tis has near-linear speedup comparable to the benchmarks (see first message)
Co-authored-by: Iaroslav Lisniak <yalisnyak@nes.ru>
Co-authored-by: Andrei Panferov <andrei@blacksamorez.ru>
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
This pullrequest removes custom speed_test code in favour of speedtest-cli module.
This is necessary to ensure that random warnings / print-outs do not mess with our outputs.
Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>
- latest accelerate, transformers, huggingface_hub
- rearrange attention caches to support https://github.com/huggingface/transformers/pull/18344
- remove unused code
- fix edge case where session crashes when receiving seq length 0
- assert transformer version when importing WrappedBloomBlock
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>
Summary:
```python
parser.add_argument('--attn_cache_size', type=str, default=None,
help='The size of GPU memory allocated for storing past attention keys/values between inference steps. '
'Examples: 500MB, 1.2GB, 1073741824 (bytes). Note that 1KB != 1KiB here. '
'Default: 0.5GiB * num_blocks * hidden_size / 14336. '
'The latter is the hidden size of the bigscience/bloom-petals model.')
parser.add_argument('--request_timeout', type=float, required=False, default=3 * 60,
help='Timeout (in seconds) for the whole rpc_forward/rpc_backward/rpc_forward_stream/rpc_backward_stream request')
parser.add_argument('--session_timeout', type=float, required=False, default=30 * 60,
help='Timeout (in seconds) for the whole inference session')
parser.add_argument('--step_timeout', type=float, required=False, default=60,
help="Timeout (in seconds) for waiting the next step's inputs inside an inference session")
parser.add_argument('--load_in_8bit', type=bool, default=None,
help="Convert the loaded model into mixed-8bit quantized model. Default: True if GPU is available")
```
Co-authored-by: justheuristic <justheuristic@gmail.com>
1. Petals can be now installed using `pip install git+https://github.com/bigscience-workshop/petals`
- In case if you already cloned the repo, you can do `pip install .` or `pip install .[dev]`
2. Moved `src` => `src/petals`
- Replaced `from src.smth import smth` with `from petals.smth import smth`
3. Moved `cli` => `src/petals/cli`
- Replaced `python -m cli.run_smth` with `python -m petals.cli.run_smth` (all utilities are now available right after pip installation)
4. Moved the `requirements*.txt` contents to `setup.cfg` (`requirements.txt` for packages is not supported well by modern packaging utils)
5. Increased the package version from `0.2` to `1.0alpha1`
Alexander Borzunov
justheuristic