Principle:Bigscience workshop Petals Inference Session Management

Knowledge Sources	Petals Petals: Collaborative Inference and Fine-tuning of Large Models
Domains	Distributed_Computing, NLP, Inference
Last Updated	2026-02-09 14:00 GMT

Overview

A session-based protocol for maintaining persistent connections to distributed transformer block servers, enabling efficient multi-step autoregressive inference with KV cache reuse.

Description

Inference Session Management solves the problem of efficient autoregressive generation in a distributed setting. Without sessions, each generation step would require re-sending the entire context (all previous tokens) through all transformer blocks. With sessions, the KV cache (key-value pairs from attention computation) is stored on the remote servers, and only new tokens need to be processed.

The session establishes a Dijkstra-routed path through the available server network:

• The client's RemoteSequenceManager queries the hivemind DHT to discover which servers host which blocks
• It builds a weighted graph of server spans (throughput, latency) and finds the optimal path
• The session opens persistent bidirectional gRPC streams with each server in the path
• If a server fails mid-session, the client can re-route to alternative servers

Key properties:

• KV cache persistence: Servers allocate memory for key-value cache that persists across generation steps
• Position tracking: The session tracks how many tokens have been processed to avoid redundant computation
• Fault tolerance: Failed servers are replaced mid-session via the _update_sequence mechanism
• Context manager: Sessions are designed to be used with Python's with statement for proper cleanup

Usage

Use this principle when performing multi-step autoregressive text generation with a distributed model. Sessions are essential for efficient generation — without them, inference would be orders of magnitude slower due to redundant computation. Sessions are also used for multi-turn dialogue where KV cache must persist across conversation turns.

Theoretical Basis

KV Cache in Transformers:

In self-attention, for each token position, the model computes Query (Q), Key (K), and Value (V) projections. During autoregressive generation:

$Attention(q_t,K_{1:t},V_{1:t})=softmax(\frac{q_t{K}_{1:t}^T}{\sqrt{d_k}})V_{1:t}$

The KV cache stores $K_{1:t-1}$ and $V_{1:t-1}$ so that at step t, only $k_t$ and $v_t$ need to be computed.

Distributed session protocol:

# Abstract session lifecycle
session = create_session(sequence_manager, max_length)
route = dijkstra_shortest_path(server_graph)
for server in route:
    allocate_kv_cache(server, max_length)
    open_bidirectional_stream(server)

# Each generation step
for step in range(num_tokens):
    hidden = embed(new_token)
    for server in route:
        hidden = server.process_step(hidden)  # Uses cached KVs
    next_token = lm_head(hidden)

Related Pages

Implemented By

• Implementation:Bigscience_workshop_Petals_InferenceSession