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103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
from __future__ import annotations
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from typing import Optional, Union
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import torch
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from hivemind import DHT, P2P, get_logger, use_hivemind_log_handler
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from hivemind.moe.client.remote_expert_worker import RemoteExpertWorker
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from torch import nn
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import petals.client
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from petals.client.inference_session import InferenceSession
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from petals.client.sequence_manager import RemoteSequenceManager
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from petals.client.sequential_autograd import _RemoteSequentialAutogradFunction
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from petals.data_structures import UID_DELIMITER
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from petals.utils.misc import DUMMY
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use_hivemind_log_handler("in_root_logger")
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logger = get_logger(__file__)
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class RemoteSequential(nn.Module):
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"""
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A sequence of transformer blocks hosted by the swarm.
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"""
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def __init__(
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self,
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config: petals.client.DistributedBloomConfig,
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dht: DHT,
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dht_prefix: Optional[str] = None,
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p2p: Optional[P2P] = None,
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sequence_manager: Optional[RemoteSequenceManager] = None,
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):
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super().__init__()
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self.config = config
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self.dht = dht
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self.dht_prefix = dht_prefix or config.dht_prefix
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self.p2p = RemoteExpertWorker.run_coroutine(dht.replicate_p2p()) if p2p is None else p2p
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num_blocks = self.config.n_layer if sequence_manager is None else len(sequence_manager)
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block_uids = [f"{config.dht_prefix}{UID_DELIMITER}{i}" for i in range(num_blocks)]
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if sequence_manager is None:
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logger.debug(f"Creating new sequence manager for block uids: {block_uids}")
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self.sequence_manager = RemoteSequenceManager(dht, block_uids, self.p2p)
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self.is_subsequence = False
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else:
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logger.debug(f"Reusing sequence manager with {len(sequence_manager)} modules")
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self.sequence_manager = sequence_manager
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assert isinstance(sequence_manager.block_uids, list)
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self.is_subsequence = self.sequence_manager.block_uids != block_uids
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def forward(self, inputs: torch.Tensor, prompts: torch.Tensor = DUMMY):
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outputs = _RemoteSequentialAutogradFunction.apply(inputs, prompts, self.sequence_manager)
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return outputs
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def __getitem__(self, ix: Union[int, slice]) -> RemoteSequential:
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assert isinstance(ix, (int, slice))
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if isinstance(ix, int):
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return RemoteTransformerBlock(
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self.config,
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self.dht,
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dht_prefix=self.dht_prefix,
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p2p=self.p2p,
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sequence_manager=self.sequence_manager[ix],
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)
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else:
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return RemoteSequential(
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self.config,
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self.dht,
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dht_prefix=self.dht_prefix,
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p2p=self.p2p,
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sequence_manager=self.sequence_manager[ix],
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)
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def __iter__(self):
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for block_index in range(len(self)):
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yield self[block_index]
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def __len__(self):
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return len(self.sequence_manager)
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def inference_session(self, **kwargs) -> InferenceSession:
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self.sequence_manager.update_()
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return InferenceSession(self.sequence_manager, self.p2p, **kwargs)
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def extra_repr(self) -> str:
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return f"modules={self.sequence_manager.block_uids[0]}..{self.sequence_manager.block_uids[-1]}"
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class RemoteTransformerBlock(RemoteSequential):
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"""Single transformer block hosted by swarm
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This class is deprecated and kept for backward compatibility.
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It will be removed soon in favor of using ``RemoteSequential`` directly.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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assert len(self) == 1, "Remote Block is a sequence size 1"
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def extra_repr(self):
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return f"{self.sequence_manager.block_uids[0]}"
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