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331 lines
14 KiB
Python
331 lines
14 KiB
Python
from __future__ import annotations
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import asyncio
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import itertools
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import logging
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import time
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from typing import AsyncIterator, List, Optional
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import torch
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from hivemind import (
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P2P,
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MSGPackSerializer,
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anext,
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deserialize_torch_tensor,
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get_logger,
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nested_flatten,
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serialize_torch_tensor,
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)
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from hivemind.moe.client.remote_expert_worker import RemoteExpertWorker
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from hivemind.p2p import StubBase
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from hivemind.proto import runtime_pb2
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from petals.client.sequence_manager import RemoteSequenceManager
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from petals.data_structures import CHAIN_DELIMITER, ModuleUID, RemoteSpanInfo, RPCInfo
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from petals.server.handler import TransformerConnectionHandler
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from petals.utils.misc import DUMMY, is_dummy
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logger = get_logger(__file__)
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class _ServerInferenceSession:
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"""
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An interface to a single multi-step *inference* session for a a set of blocks on a specific server.
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:note: This class is *not* fault-tolerant out of the box.
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"""
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def __init__(
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self,
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uid: ModuleUID,
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rpc_info: RPCInfo,
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inputs_queue: asyncio.Queue,
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outputs_aiter: AsyncIterator,
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*,
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timeout: float,
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max_length: int,
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points: int = 0,
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):
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self.uid, self.rpc_info = uid, rpc_info
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self.num_blocks = uid.count(CHAIN_DELIMITER) + 1
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self._inputs_queue: asyncio.Queue[runtime_pb2.ExpertRequest] = inputs_queue
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self._outputs_stream: AsyncIterator[runtime_pb2.ExpertResponse] = outputs_aiter
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self.timeout = timeout
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self._serialized_metadata = MSGPackSerializer.dumps(dict(max_length=max_length, points=points))
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self.stepped = False
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self.closed = False
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@classmethod
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async def create(
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cls, stub: StubBase, uid: ModuleUID, rpc_info: RPCInfo, timeout: float, **metadata
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) -> _ServerInferenceSession:
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"""Create a new session for a given remote module. This code is meant to be run inside RemoteExpertWorker"""
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inputs_queue = asyncio.Queue()
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outputs_stream = await asyncio.wait_for(
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stub.rpc_inference(cls._read_inputs_from_queue(inputs_queue)),
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timeout,
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)
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return cls(uid, rpc_info, inputs_queue, outputs_stream, timeout=timeout, **metadata)
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@staticmethod
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async def _read_inputs_from_queue(queue: asyncio.Queue, input_timeout: Optional[float] = None) -> AsyncIterator:
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while True:
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next_input_message = await asyncio.wait_for(queue.get(), input_timeout)
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yield next_input_message
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if not next_input_message.uid and not next_input_message.tensors:
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break # this message means "done sending"
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def step(
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self,
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new_hidden_states: torch.Tensor,
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prompts: Optional[torch.Tensor] = None,
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hypo_ids: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""
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Inference step: send a chunk of input tesors and receive a chunk of outputs
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:prompts: optional DEEP prompts, added to a prefix of each layer's outputs,
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if specified, deep promts should have shape [num_layers, batch_size, prefix_len, hid_size]
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"""
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if self.closed:
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raise Exception("Session is closed, cannot perform step")
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if prompts is None or is_dummy(prompts):
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prompts = DUMMY
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else:
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assert prompts.ndim == 4, "deep promts should have shape [num_layers, batch_size, prefix_len, hid_size]"
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assert prompts.shape[0] == self.num_blocks
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assert prompts.shape[1] in (new_hidden_states.shape[0], 1)
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assert prompts.shape[2] <= new_hidden_states.shape[1]
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assert prompts.shape[3] == new_hidden_states.shape[2]
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if hypo_ids is None or is_dummy(hypo_ids):
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hypo_ids = DUMMY
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else:
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assert len(hypo_ids) == len(new_hidden_states)
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assert hypo_ids.dtype == torch.int64
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# serialize inputs and put them into the queue
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inputs = (new_hidden_states, prompts, hypo_ids)
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outputs_serialized = RemoteExpertWorker.run_coroutine(
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self._step(
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runtime_pb2.ExpertRequest(
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uid=self.uid,
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tensors=[
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serialize_torch_tensor(tensor.to(proto.dtype), proto.compression)
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for tensor, proto in zip(inputs, nested_flatten(self.rpc_info["inference_schema"]))
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],
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metadata=self._serialized_metadata if not self.stepped else None,
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)
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)
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)
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outputs = list(map(deserialize_torch_tensor, outputs_serialized.tensors))
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assert outputs[0].shape == inputs[0].shape, f"expected outputs[0] to be hidden states but got {outputs[0]}"
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return outputs[0]
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async def _step(self, inputs_serialized: runtime_pb2.ExpertRequest) -> runtime_pb2.ExpertResponse:
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"""Inference step on serialized data. This code is meant to be run inside RemoteExpertWorker"""
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await self._inputs_queue.put(inputs_serialized)
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self.stepped = True
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return await asyncio.wait_for(anext(self._outputs_stream), self.timeout)
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def close(self):
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"""Finish a given inference session, close the underlying connection"""
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if self._outputs_stream is None:
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return # already closed
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RemoteExpertWorker.run_coroutine(self._aclose_stream())
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self._outputs_stream = self._inputs_queue = None
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self.closed = True
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async def _aclose_stream(self):
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"""Close the inference session. This code is meant to be run inside RemoteExpertWorker"""
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if self._outputs_stream is None:
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return # already closed
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if self.stepped:
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await self._inputs_queue.put(runtime_pb2.ExpertRequest()) # empty request will trigger end of session
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try:
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await anext(self._outputs_stream)
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except StopAsyncIteration:
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pass
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def __del__(self):
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self.close()
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def __enter__(self):
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assert not self.closed
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return self
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def __exit__(self, *exc_details):
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self.close()
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class InferenceSession:
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"""
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An interface to a multi-step *inference* session for a sequence of remote transformer blocks
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"""
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def __init__(self, sequence_manager: RemoteSequenceManager, p2p: P2P, max_length: int, **metadata):
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self._sequence_manager = sequence_manager
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self._p2p = p2p
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self._closed = False
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self._chosen_spans = []
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self._server_sessions = []
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self._server_inputs = [] # Used in case of server failures to regenerate attention caches on new servers
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self._position = 0
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self._max_length = max_length
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self._metadata = metadata
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def _enter_server_sessions(self, chosen_spans: List[RemoteSpanInfo]) -> List[_ServerInferenceSession]:
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server_sessions = []
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try:
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for span in chosen_spans:
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stub = TransformerConnectionHandler.get_stub(self._p2p, span.peer_id)
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span_uids = CHAIN_DELIMITER.join(self._sequence_manager.block_uids[span.start : span.end])
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session = RemoteExpertWorker.run_coroutine(
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_ServerInferenceSession.create(
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stub,
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span_uids,
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rpc_info=self._sequence_manager.rpc_info,
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timeout=self._sequence_manager.timeout,
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max_length=self._max_length,
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**self._metadata,
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)
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)
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server_sessions.append(session)
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session.__enter__()
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return server_sessions
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except:
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self._exit_server_sessions(server_sessions)
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raise
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def _exit_server_sessions(self, server_sessions: List[_ServerInferenceSession]) -> None:
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for session in reversed(server_sessions):
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try:
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session.__exit__(None, None, None)
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except Exception:
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logger.debug("Caught exception while closing connection to server:", exc_info=True)
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def __enter__(self) -> "InferenceSession":
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assert not self._closed and not self._chosen_spans
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return self
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def step(self, inputs: torch.Tensor, prompts: Optional[torch.Tensor] = None, **kwargs) -> torch.Tensor:
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assert not self._closed
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if torch.is_grad_enabled():
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logger.warning("Running inference session with grad enabled. Gradients will *not* be propagated correctly.")
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n_blocks = len(self._sequence_manager)
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if prompts is None or is_dummy(prompts):
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prompts = DUMMY
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else:
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assert prompts.ndim == 4 and prompts.shape[0] == n_blocks
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inputs_device = inputs.device
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inputs_dtype = inputs.dtype
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inputs = inputs.cpu()
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prompts = prompts.cpu()
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n_input_tokens = inputs.shape[1]
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if self._position + n_input_tokens > self._max_length:
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raise ValueError(
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f"Maximum length exceeded: prefix {self._position} + current {n_input_tokens} exceeds pre-allocated maximum {self._max_length}"
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)
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server_idx = 0
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block_idx = 0
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recovery_until = -1 # Recovery mode is disabled until a failure happens
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while block_idx < n_blocks:
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for attempt_no in itertools.count():
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logger.debug(f"Inference: block {block_idx}, attempt {attempt_no}")
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try:
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if attempt_no >= 1:
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self._sequence_manager.update_()
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if not self._chosen_spans or not self._server_sessions or attempt_no >= 1:
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# If there is a failed server session, this code closes it
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self._exit_server_sessions(self._server_sessions[server_idx : server_idx + 1])
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n_prev_spans = len(self._chosen_spans)
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update_end = self._chosen_spans[server_idx].end if server_idx < n_prev_spans else n_blocks
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if attempt_no >= 1 and update_end > recovery_until:
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logger.info(
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f"Due to a server failure, remote attention caches "
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f"from block {block_idx} to {update_end} will be regenerated"
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)
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recovery_until = max(recovery_until, update_end)
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updated_spans = self._sequence_manager.make_sequence(block_idx, update_end)
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# make_sequence() could return a longer sequence
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updated_spans[-1].end = min(updated_spans[-1].end, update_end)
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updated_sessions = self._enter_server_sessions(updated_spans)
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logger.debug(
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f"Found path from block {block_idx} to {update_end} via {len(updated_spans)} servers"
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)
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# If there is a failed span, this code replaces it, otherwise it just adds new ones
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self._chosen_spans[server_idx : server_idx + 1] = updated_spans
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self._server_sessions[server_idx : server_idx + 1] = updated_sessions
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recovery_inputs = self._server_inputs[server_idx] if server_idx < n_prev_spans else None
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self._server_inputs[server_idx : server_idx + 1] = [recovery_inputs] + [None] * (
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len(updated_spans) - 1
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)
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assert len(self._chosen_spans) == len(self._server_sessions) == len(self._server_inputs), (
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f"Broken state: {len(self._chosen_spans)} spans, {len(self._server_sessions)} sessions, "
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f"{len(self._server_inputs)} inputs"
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)
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session = self._server_sessions[server_idx]
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span = self._chosen_spans[server_idx]
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if self._server_inputs[server_idx] is None:
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self._server_inputs[server_idx] = inputs
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elif self._server_inputs[server_idx].shape[1] == self._position:
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self._server_inputs[server_idx] = torch.cat(
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[self._server_inputs[server_idx], inputs[:, -n_input_tokens:]], dim=1
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)
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assert self._server_inputs[server_idx].shape[1] == self._position + n_input_tokens, (
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f"Broken input cache: server_idx={server_idx} shape={self._server_inputs[server_idx].shape} "
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f"position={self._position} n_input_tokens={n_input_tokens}"
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)
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if not session.stepped:
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inputs = self._server_inputs[server_idx] # Pass full inputs including prefix
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else:
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inputs = inputs[:, -n_input_tokens:] # No need to pass prefix further
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outputs = session.step(inputs, prompts[span.start : span.end], **kwargs)
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assert (
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inputs.shape == outputs.shape
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), f"Shape mismatch: inputs.shape={inputs.shape}, outputs.shape={outputs.shape})"
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inputs = outputs
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server_idx += 1
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block_idx = span.end
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break
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except Exception as e:
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delay = self._sequence_manager.get_retry_delay(attempt_no)
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logger.warning(
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f"Caught exception when running inference from block {block_idx} "
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f"(retry in {delay:.0f} sec): {repr(e)}"
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)
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traceback_level = logging.DEBUG if str(e) else logging.WARNING
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logger.log(traceback_level, "See detailed traceback below:", exc_info=True)
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time.sleep(delay)
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self._position += n_input_tokens
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outputs = inputs.to(device=inputs_device, dtype=inputs_dtype)
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return outputs
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def close(self, *exc_details):
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"""Finish a given inference session, close the underlying connection"""
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if not self._closed:
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self._server_inputs.clear()
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self._exit_server_sessions(self._server_sessions)
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self._server_sessions.clear()
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self._chosen_spans.clear()
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self._closed = True
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def __exit__(self, *exc_details):
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self.close(*exc_details)
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def __del__(self):
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self.close()
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