petals/tests/test_sequence_manager.py

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import threading
import time
import pytest
import torch
from hivemind import DHT, get_logger
from petals import AutoDistributedConfig
from petals.client import RemoteSequenceManager, RemoteSequential
from petals.data_structures import UID_DELIMITER
from test_utils import *
logger = get_logger(__name__)
@pytest.mark.forked
@pytest.mark.parametrize("mode", ["max_throughput", "min_latency"])
def test_sequence_manager_basics(mode: str):
config = AutoDistributedConfig.from_pretrained(MODEL_NAME, initial_peers=INITIAL_PEERS)
dht = DHT(initial_peers=config.initial_peers, client_mode=True, start=True)
Refactor RemoteSequenceManager (#309) This PR: 1. **Extracts `SequenceManagerConfig` and `SequenceManagerState` subclasses.** The config is provided by caller and never changed from inside `RemoteSequenceManager`. The state is a part of the `RemoteSequenceManager`'s state shared between the main manager and its slices. We fix some slicing bugs along the way. 2. **Removes `dht_prefix` and `p2p` arguments, makes `dht` argument optional.** `dht_prefix` can always be overridden using `config.dht_prefix`. `p2p` actually needed only under the hood of `RemoteSequenceManager`, so it can extract it by itself without exposing this low-level class to callers. If strictly necessary, a caller can provide `p2p` as a part of `SequenceManagerState`. `dht` is also needed only by `RemoteSequenceManager`, so we can make it optional in the parent classes and create it automatically when it's not provided. 3. **Simplifies retry logic.** Previously, we could have "nested" retry loops: one in `._update()`, another in inference/forward/backward steps. The loop in `._update()` could introduce issues to concurrent inference/forward/backward calls, since it blocks the entire class if its delay period becomes too high. Now this logic is simplified: `._update()` performs only one attempt to fetch the DHT info, any retries are triggered by the inference/forward/backward steps. 4. **Removes deprecated `RemoteTransformerBlock`.** `RemoteTransformerBlock` was deprecated a long time ago, before Petals 1.0.0. Its removal is long due. 5. **Removes `dht_utils.get_remote_module()`, `dht_utils.get_remote_sequence()`.** This functions duplicate the functionality of the `RemoteSequential` constructor. 6. (minor) **Removes `RemoteSequential.is_subsequence` flag.** This flag worked incorrectly and was never used. I am removing it for the sake of simplicity.
2023-05-07 09:41:13 +00:00
sequential = RemoteSequential(config, dht=dht)
shutdown_evt = threading.Event()
# test RemoteSequential with lossy compression
Add LLaMA support (#323) 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).
2023-06-23 11:46:10 +00:00
block_uids = [f"{config.dht_prefix}{UID_DELIMITER}{i}" for i in range(config.num_hidden_layers)]
sequential = RemoteSequential(
config,
sequence_manager=RemoteSequenceManagerWithChecks(config, block_uids, dht=dht, _was_shut_down=shutdown_evt),
)
sequence = sequential.sequence_manager.make_sequence(mode=mode)
assert all(sequence[i].peer_id != sequence[i + 1].peer_id for i in range(len(sequence) - 1))
assert sequential.sequence_manager.is_alive()
assert sequential.sequence_manager._thread.ready.is_set()
assert not shutdown_evt.is_set()
sequential(torch.randn(1, 2, config.hidden_size))
sequential.sequence_manager.shutdown()
del sequential
time.sleep(1)
assert shutdown_evt.is_set()
class RemoteSequenceManagerWithChecks(RemoteSequenceManager):
"""A sequence manager that signals if it was shut down"""
def __init__(self, *args, _was_shut_down: threading.Event, **kwargs):
super().__init__(*args, **kwargs)
self._was_shut_down = _was_shut_down
def shutdown(self):
super().shutdown()
assert not self.is_alive()
self._was_shut_down.set()