This PR:
1. Shows the current Petals version and checks for updates on startup.
2. Reports the current version and DHT mode in `rpc_info()`, so it can be shown on http://health.petals.ml or used on clients for efficient routing.
Servers joining from behind NATs/firewalls usually take several minutes to join a libp2p relay before they become accessible from the outside Internet. Moreover, requests to such servers are slower and more likely to fail (e.g., if the server switches a relay at the moment). If such servers host certain DHT keys, the swarm may occasionally lose read/write access to these keys, which results in:
- Clients being unable to find any servers hosting a certain block.
- All servers starting rebalancing to the same place to close the alleged "gap" in the swarm.
This PRs modifies servers so that DHT keys are only hosted on **directly reachable** servers (the ones who aren't behind NAT/firewall). This way, DHT becomes more stable and works faster. Of course, trhe servers behind NATs/firewalls still accept requests for running inference/forward/backward for blocks they hold (it's more acceptable for this kind of requests to be slower or fail).
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
* Don't count open fds since it leads to AccessDenied crashes on some machines
* Use --load_in_8bit=False by default in case of tensor parallelism
* Install petals from PyPI in fine-tuning tutorials
- Added relay options to servers
- Enabled relay options by default
- Changed hivemind version to 1.1.5
- Moved reachability check to be performed after blocks are loaded
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
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>
1. If we connect to the **public swarm**, the server now **automatically checks its DHT's reachability** from the outside world using API at http://health.petals.ml This is important to disallow unreachable servers to proceed (they create issues for the clients, such as repetitive retries).
If http://health.petals.ml is down, the server proceeds without the check (so we don't depend on it). However, if health.petals.ml is up and explicitly tells us that we are unrechable, the server shows the reason of that and how to solve it.
The check may be disabled with the `--skip_reachability_check` option (though I can't imagine cases where someone needs to use it).
2. Added `--port` and `--public_ip` as **simplified convenience options** for users not familiar with `--host_maddrs` and `--announce_maddrs`.
- 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>
The cause of OOMs were the cyclic references `TransformerBackend <-> PrioritizedTaskPool` that could not have been garbage collected properly. Still, I've added explicit tensor removal just in case.
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>
Currently, the schemas use `torch.float32`, so all inputs and outputs converted to float32 before sending and after receiving on both servers and clients. This creates a huge slowdown for the system.
* This PR makes the schemas use the server's `--torch_dtype` argument (default is `torch.bloat16` for BLOOM-176B)
* an option for client to request a specific output compression. Use case 1: client sends quantized inputs and expects quantized inputs in return. Use case 2: client uses quantization for gradients w.r.t. activations, but keeps grads w.r.t. __prompts__ as is for greater precision.
* a comment explaining the purpose of NoSpendingPolicy - since we likely won't have it for the workshop
* a test with custom compression (janky implementation for testing purposes)
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`
justheuristic
Alexander Borzunov
Egiazarian Vage
Max Ryabinin