petals/tests/test_server_stats.py
Alexander Borzunov 8c546d988a
Test Llama, rebalancing, throughput eval, and all CLI scripts (#452)
This PR extends CI to:

1. Test Llama code using [TinyLlama-v0](https://huggingface.co/Maykeye/TinyLLama-v0).
2. Test rebalancing (sets up a situation where the 1st server needs to change its original position).
3. Check if benchmark scripts run (in case someone breaks its code). Note that the benchmark results are meaningless here (since they're measured on a tiny swarm of CPU servers, with low `--n_steps`).
4. Test `petals.cli.run_dht`.
5. Increase swap space and watch free RAM (a common issue is that actions are cancelled without explanation if there's not enough RAM - so it's a useful reminder + debug tool).
6. Fix flapping tests for bloom-560m by increasing tolerance.

Other minor changes: fix `--help` messages to show defaults, fix docs, tune rebalancing constants.
2023-08-08 19:10:27 +04:00

40 lines
1.8 KiB
Python

import time
import hivemind
import pytest
import torch
from petals import AutoDistributedConfig, RemoteSequential
from petals.server.handler import CACHE_TOKENS_AVAILABLE
from test_utils import *
@pytest.mark.forked
def test_server_info(block_from: int = 2, block_to: int = 5, max_length: int = 100, max_length2: int = 50):
config = AutoDistributedConfig.from_pretrained(MODEL_NAME)
config.allowed_servers = ["QmNV5G3hq2UmAck2htEgsqrmPFBff5goFZAdmKDcZLBZLX"] # PeerID from server2.id
dht = hivemind.DHT(initial_peers=INITIAL_PEERS, client_mode=True, start=True)
blocks1 = RemoteSequential(config, dht=dht, start_block=block_from, end_block=block_to)
blocks2 = RemoteSequential(config, dht=dht, start_block=block_to - 1, end_block=block_to)
info_before = blocks1.sequence_manager.rpc_info
with blocks1.inference_session(max_length=max_length) as sess:
sess.step(torch.randn(1, 1, config.hidden_size))
blocks1.sequence_manager.state.rpc_info = None # invalidate cache
info_inside = blocks1.sequence_manager.rpc_info
with blocks2.inference_session(max_length=max_length2) as sess2:
sess2.step(torch.randn(1, 1, config.hidden_size))
blocks2.sequence_manager.state.rpc_info = None # invalidate cache
info_inside2 = blocks2.sequence_manager.rpc_info
time.sleep(0.1)
blocks1.sequence_manager.state.rpc_info = None # invalidate cache
info_after = blocks1.sequence_manager.rpc_info
assert info_before[CACHE_TOKENS_AVAILABLE] == info_after[CACHE_TOKENS_AVAILABLE]
assert info_before[CACHE_TOKENS_AVAILABLE] - info_inside[CACHE_TOKENS_AVAILABLE] == max_length * len(blocks1)
assert info_inside[CACHE_TOKENS_AVAILABLE] - info_inside2[CACHE_TOKENS_AVAILABLE] == max_length2 * len(blocks2)