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216 lines
8.8 KiB
Python
216 lines
8.8 KiB
Python
from __future__ import annotations
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import asyncio
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import contextlib
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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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use_hivemind_log_handler,
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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 src.client.sequence_manager import RemoteSequenceManager
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from src.data_structures import CHAIN_DELIMITER, ModuleUID, RemoteSpanInfo, RPCInfo
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from src.server.handler import TransformerConnectionHandler
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from src.utils.misc import DUMMY, is_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 RemoteTransformerBlockInferenceSession:
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"""
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An interface to a single multi-step *inference* session for a specific remote module on a specific server
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:note: this inference session 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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max_length: int,
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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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# warning: this code manages async objects that are only usable inside RemoteExpertWorker's background thread;
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# using them in any other EventLoop may cause side-effects including, headaches, diarrhea, and loss of sleep
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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._serialized_metadata = MSGPackSerializer.dumps(dict(max_length=max_length))
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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: Optional[float] = None, **metadata
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) -> RemoteTransformerBlockInferenceSession:
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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 stub.rpc_inference(cls._read_inputs_from_queue(inputs_queue, timeout), timeout=timeout)
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return cls(uid, rpc_info, inputs_queue, outputs_stream, **metadata)
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@staticmethod
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async def _read_inputs_from_queue(queue: asyncio.Queue, timeout: Optional[float]) -> AsyncIterator:
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while True:
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next_input_message = await asyncio.wait_for(queue.get(), 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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):
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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 anext(self._outputs_stream)
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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 RemoteSequentialInferenceSession:
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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, timeout: Optional[float] = None, **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: List[RemoteSpanInfo] = []
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self.stack = contextlib.ExitStack()
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self.inference_sessions: List[RemoteTransformerBlockInferenceSession] = []
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self.metadata = metadata
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self.timeout = timeout
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def __enter__(self):
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assert not self.closed and not self.chosen_spans
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self.stack.__enter__()
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# TODO(yozh) replace this code with a fault-tolerant chain that can be reconstructed if some peers fail
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self.chosen_spans.extend(self.sequence_manager.make_sequence())
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for chosen_span in self.chosen_spans:
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stub = TransformerConnectionHandler.get_stub(self.p2p, chosen_span.peer_id)
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span_uids: str = CHAIN_DELIMITER.join(self.sequence_manager.block_uids[chosen_span.start : chosen_span.end])
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inference_session = RemoteExpertWorker.run_coroutine(
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RemoteTransformerBlockInferenceSession._create(
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stub, span_uids, rpc_info=self.sequence_manager.rpc_info, timeout=self.timeout, **self.metadata
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)
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)
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self.inference_sessions.append(inference_session)
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self.stack.enter_context(inference_session)
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return self
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def step(self, inputs: torch.Tensor, prompts: Optional[torch.Tensor] = None, **kwargs):
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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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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] == len(self.sequence_manager)
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for session in self.inference_sessions:
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outputs = session.step(inputs, prompts[self.chosen_spans[0].start : self.chosen_spans[0].end], **kwargs)
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assert outputs.shape == inputs.shape, f"expected {inputs.shape}, got {outputs.shape}"
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inputs = outputs
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return inputs
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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.stack.__exit__(*exc_details or (None, None, None))
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self.inference_sessions.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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