This PR updates multiple default arguments in clients and servers:
1. **The client defaults to `torch_dtype=torch.float32` instead of `torch_dtype="auto"`.**
The old default was to load weights in the dtype they are saved in (usually bfloat16/float16), which caused issues when the client was run on CPU (the default unless you call `.cuda()`). Specifically, bfloat16 is slow on most CPUs (unless a CPU supports AVX512) and float16 can't be run natively and leads to an exception. This default was a legacy of the earliest Petals versions designed to run BLOOM - its embeddings were so big that they didn't fit into RAM in float32 (e.g., in Colab). The newer models don't have this issue.
In contrast, the new default leads to good speed on all CPUs and is consistent with PyTorch and HF Transformers. Also, the client now shows "bfloat16 on non-AVX512 CPU" in all cases (previously this warning was shown only if the machine has enough RAM to fit float32 weights, which could hide the crucial reason of inference being slow).
**Note:** This change is backward-incompatible, so we have to increase at least the minor package version (2.2.0 -> 2.3.0.dev0).
2. **The server uses 2x smaller `--attn_cache_tokens`.**
The old default led to loading 39 (out of 80) or 78 (out of 80) blocks for popular models on some GPU types, which visibly slowed down inference due to an excess network hop. It was also leaving too much cache, so that inference slowed down much before the cache is used.
The new default leads to more efficient block layouts and makes the inference routing algorithm choose alternative paths through other servers when a particular server already has enough active inference sessions (= its cache is full).
3. **The client's max number of retries can be limited by the `PETALS_MAX_RETRIES` env var.**
This is to limit `ClientConfig.max_retries` in tests, so we see tracebacks instead of retrying indefinitely in case of errors.
This PR adds:
- Support for models based on `transformers.FalconModel` (the in-library format for Falcon). Tested on Falcon-40B.
- CI tests for Falcon-RW-1B.
- `--throughput dry_run` option to evaluate throughput and exit right away (implemented by @mryab).
Limitations:
- Backward pass support is broken for now, will be fixed in #500.
Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>
This PR makes both clients and servers work on macOS. Specifically, it:
- Follows https://github.com/learning-at-home/hivemind/pull/586 to run a macOS-compatible `p2pd` binary (both x86-64 and ARM64 are supported)
- Fixes forking issues and tests on macOS, Python 3.10+
- Introduces basic support for serving model blocks on Apple M1/M2 GPUs (torch.mps)
- Increases max number of open files by default (it's not enough on Linux and is really small on macOS)
- rpc_inference: server will now accept allocation timeout from user, defaults to no timeout
- bugfix: inference timeout is now measured from the moment the request is received
- previously, you would have to wait for your timeout plus the time it takes to sort through the queue (other users' timeout)
- now, you get AllocationFailed if you had to wait for over (timeout) seconds - regardless of other users
- a request for inference with no timeout will now fail instantly if there is not enough memory available
- dtype number of bytes is now correctly determined for int, bool & other types
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Co-authored-by: Your Name <you@example.com>
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: Aleksandr Borzunov <hxrussia@gmail.com>
This PR extends CI to:
1. Test Llama code using [TinyLlama-v0](https://huggingface.co/Maykeye/TinyLLama-v0).
2. Test rebalancing (sets up a situation where the 1st server needs to change its original position).
3. Check if benchmark scripts run (in case someone breaks its code). Note that the benchmark results are meaningless here (since they're measured on a tiny swarm of CPU servers, with low `--n_steps`).
4. Test `petals.cli.run_dht`.
5. Increase swap space and watch free RAM (a common issue is that actions are cancelled without explanation if there's not enough RAM - so it's a useful reminder + debug tool).
6. Fix flapping tests for bloom-560m by increasing tolerance.
Other minor changes: fix `--help` messages to show defaults, fix docs, tune rebalancing constants.
This prohibits passing `--initial_peers` without arguments, since it's likely to be a side-effect from `--initial_peers $INITIAL_PEERS` with the env var not set.
Users should use `--new_swarm` for that, as explained in the private swarm tutorial.
This PR is designed to avoid OOMs when processing long sequences that happen due to the huge attention logits matrices.
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
Since `petals.ml` DNS record is still unavailable, we're switching everything to https://petals.dev
Co-authored-by: Aleksandr Borzunov <hxrussia@gmail.com>
This PR:
1. **Adds shortest path routing for inference.** We build a graph with client-server and server-server latencies and compute costs, as well as empirically measured overheads. For client-server latencies, we ping possible first and last servers in a sequence in `SequenceManager.update()`. We penalize servers who may not have enough cache for our request. This uses info added to DHT in #355, #356, #358.
2. **Makes a server ping neighboring servers in addition to next ones.** This is to get an opportunity to change the server even before we use all its blocks (e.g., because a neighboring server is faster). This feature is not enabled though, since it increases graph size for N servers to O(N^2) - but we may enable it if needed.
3. **Fixes a `SequenceManager` bug with the first `update()`.** Previously, this update was likely to produce incorrect information and cause to `MissingBlocksErrors` until the next update happens.
Implement an option to deploy PEFT adapters to a server. Clients can set active_adapter=... to use these adapters.
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Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: justheuristic <justheuristic@gmail.com>
This PR:
- Adds benchmark scripts for inference, forward pass, and full training step (e.g. used for experiments in our paper).
- Fixes bug with dtypes in `petals.DistributedBloomForSequenceClassification`.
- (minor refactor) Moves `DTYPE_MAP` to `petals.constants` as a useful constant.
This PR:
1. **Abolishes the model conversion procedure.** Now, models are downloaded directly from original repositories like https://huggingface.co/bigscience/bloom. Servers download only shards with blocks to be hosted, and clients download only shards with input/output embeddings and layernorms.
- BLOOM is loaded from `bigscience/bloom`, but we use the DHT prefix `bigscience/bloom-petals` for backward compatibility. Same with smaller BLOOMs and BLOOMZ.
- LLaMA can be loaded from any repo like `username/llama-65b-hf`, but we use the DHT prefix `llama-65b-hf` (without the username) to accomodate blocks from different repos (there're a few of them with minor differences, such as `Llama` vs. `LLaMA` in the class name).
2. **Refactors the client to generalize it for multiple models.** Now, we have `petals.models` packages that contain model-specific code (e.g. `petals.models.bloom`, `petals.models.llama`). General code (e.g. CPU-efficient LM head, p-tuning) is kept in `petals.client`.
3. **Introduces** `WrappedLlamaBlock`, `DistributedLlamaConfig`, `DistributedLlamaForCausalLM`, `DistributedLlamaForSequenceClassification`, and `DistributedLlamaModel` compatible with Petals functionality (p-tuning, adapters, etc.).
4. **Introduces** `AutoDistributedConfig` that automatically chooses the correct config class (`DistributedLlamaConfig` or `DistributedBloomConfig`). The refactored configs contain all model-specific info for both clients and servers.
Upgrade instructions:
- Remove disk caches for blocks in old (converted) format to save disk space. That is, remove `~/.cache/petals/model--bigscience--bloom-petals` and `~/.cache/petals/model--bigscience--bloomz-petals` directories (if present).
For some reasons, right now 15 sec is not enough to connect to the bootstrap peers in the public swarm, as reported by multiple users and observed by me. Increasing it to 120 sec until we find the root cause of the issue.
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>
- 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>
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`.
- 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
Artem Chumachenko
Max Ryabinin
Egiazarian Vage
Aleksandr Borzunov