2022-12-01 07:25:55 +00:00
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import threading
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import time
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import pytest
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import torch
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2022-12-15 05:12:18 +00:00
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from hivemind import DHT, get_logger
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2022-12-01 07:25:55 +00:00
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2023-08-08 15:10:27 +00:00
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from petals import AutoDistributedConfig
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2022-12-01 07:25:55 +00:00
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from petals.client import RemoteSequenceManager, RemoteSequential
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from petals.data_structures import UID_DELIMITER
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2023-03-12 21:49:04 +00:00
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from test_utils import *
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2022-12-01 07:25:55 +00:00
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2023-02-19 01:46:17 +00:00
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logger = get_logger(__name__)
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2022-12-01 07:25:55 +00:00
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@pytest.mark.forked
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2023-05-09 18:38:20 +00:00
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@pytest.mark.parametrize("mode", ["max_throughput", "min_latency"])
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2023-01-11 20:26:09 +00:00
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def test_sequence_manager_basics(mode: str):
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2023-08-08 15:10:27 +00:00
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config = AutoDistributedConfig.from_pretrained(MODEL_NAME, initial_peers=INITIAL_PEERS)
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2022-12-01 07:25:55 +00:00
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dht = DHT(initial_peers=config.initial_peers, client_mode=True, start=True)
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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
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sequential = RemoteSequential(config, dht=dht)
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2022-12-01 07:25:55 +00:00
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shutdown_evt = threading.Event()
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# test RemoteSequential with lossy compression
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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
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block_uids = [f"{config.dht_prefix}{UID_DELIMITER}{i}" for i in range(config.num_hidden_layers)]
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2022-12-01 07:25:55 +00:00
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sequential = RemoteSequential(
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config,
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2023-07-14 14:40:47 +00:00
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sequence_manager=RemoteSequenceManagerWithChecks(config, block_uids, dht=dht, _was_shut_down=shutdown_evt),
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2022-12-01 07:25:55 +00:00
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)
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2023-01-11 20:26:09 +00:00
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sequence = sequential.sequence_manager.make_sequence(mode=mode)
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2022-12-01 08:21:10 +00:00
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assert all(sequence[i].peer_id != sequence[i + 1].peer_id for i in range(len(sequence) - 1))
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2022-12-01 07:25:55 +00:00
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assert sequential.sequence_manager.is_alive()
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assert sequential.sequence_manager._thread.ready.is_set()
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assert not shutdown_evt.is_set()
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sequential(torch.randn(1, 2, config.hidden_size))
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sequential.sequence_manager.shutdown()
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del sequential
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time.sleep(1)
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assert shutdown_evt.is_set()
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2023-07-14 14:40:47 +00:00
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class RemoteSequenceManagerWithChecks(RemoteSequenceManager):
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2022-12-01 07:25:55 +00:00
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"""A sequence manager that signals if it was shut down"""
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def __init__(self, *args, _was_shut_down: threading.Event, **kwargs):
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super().__init__(*args, **kwargs)
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self._was_shut_down = _was_shut_down
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def shutdown(self):
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super().shutdown()
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assert not self.is_alive()
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self._was_shut_down.set()
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