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.
Even if the swarm seems to have at least 2 servers for each block, turning off on one of the servers could break it. That's because once a server is turned off, others may move to a better position, creating a significant downtime on their way. This PR prohibits switching blocks if it would make the swarm disjoint along the way.
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>
This PR makes servers return their free cache (in tokens * layers to make it compression-agnostic)
To be used when calling make_sequence(optimize="inference")
This pull-request implements a simple (1) greedy (2) latency-agnostic routing optimization that should speed up both our use cases.
Why this exists: our effort to merge full routing (ping-aware, throughut-aware, dijkstra) is in a sorry state between several branches; merging it into main would take many days.
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
If all servers holding a certain block are blacklisted, we should display errors from them instead of raising `No peers holding blocks`.
Indeed, if the error is client-caused, the client should learn its reason from the latest error messages. In turn, if the error is server/network-caused and we only have a few servers, we'd better know the error instead of banning all the servers and making the user think that no servers are available.
* 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>
A handler's RPC code may be cancelled due to a request timeout or a client closing the connection. Before this PR:
- If `.cancel()` happens while waiting for `hivemind.utils.enter_asynchronously()`, the lock will never be released.
- If `.cancel()` happens while doing that before freeing memory, the memory will never be freed.
This PR fixes it by deferring the cancellation with [asyncio.shield()](https://docs.python.org/3/library/asyncio-task.html#asyncio.shield). Now, the cancellation will happen only when all locks are released and alloc/free has completed.
1. Added `from petals.client import *` to `petals/__init__.py`, so you can write just that:
```python
from petals import DistributedBloomForCausalLM
```
I didn't do the same with server, since its classes are supposed to by used by `petals.cli.run_server`, not end-users. Though it's still possible to do `from petals.server.smth import smth` if necessary.
2. Fixed one more logging issue: log lines from hivemind were shown twice due to a bug in #156.
3. Removed unused `runtime.py`, since the server actually uses `hivemind.moe.Runtime`, and `runtime.py` has no significant changes comparing to it.
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>
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`.
* Add missing methods for SamplingAlgorithm, fix docstrings
* Add SamplingAlgorithm to _choose_sample_algorithm
* Add test_sampling
* Add a warning if sampling options were passed, but do_sample=False
* Skip the sampling test for now
Co-authored-by: Alexander Borzunov <borzunov.alexander@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>
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.
Alexander Borzunov
Artem Chumachenko
justheuristic
Shuchang Zhou
Egiazarian Vage
Aleksandr Borzunov
Max Ryabinin