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Added primitives for speculative decoding and tests #2686

Added primitives for speculative decoding and tests

Added primitives for speculative decoding and tests #2686

Workflow file for this run

name: Tests
on:
push:
branches: [ main ]
pull_request:
jobs:
run-tests:
strategy:
matrix:
include:
- { model: 'bigscience/bloom-560m', os: 'ubuntu', python-version: '3.8' }
- { model: 'bigscience/bloom-560m', os: 'ubuntu', python-version: '3.11' }
- { model: 'Maykeye/TinyLLama-v0', os: 'ubuntu', python-version: '3.8' }
- { model: 'Maykeye/TinyLLama-v0', os: 'ubuntu', python-version: '3.11' }
- { model: 'Maykeye/TinyLLama-v0', os: 'macos', python-version: '3.10' }
- { model: 'Maykeye/TinyLLama-v0', os: 'macos', python-version: '3.11' }
- { model: 'artek0chumak/TestMixtral', os: 'ubuntu', python-version: '3.8' }
- { model: 'artek0chumak/TestMixtral', os: 'ubuntu', python-version: '3.11' }
fail-fast: false
runs-on: ${{ matrix.os }}-latest
timeout-minutes: 20
steps:
- name: Increase swap space
if: ${{ matrix.os == 'ubuntu' }}
uses: pierotofy/set-swap-space@master
with:
swap-size-gb: 10
- name: Checkout
uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
with:
python-version: ${{ matrix.python-version }}
- name: Cache dependencies
uses: actions/cache@v3
with:
path: ~/.cache/pip
key: Key-v1-${{ matrix.python-version }}-${{ hashFiles('setup.cfg') }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install .[dev]
- name: Test
run: |
set -x # Print executed commands
export MODEL_NAME="${{ matrix.model }}"
export REF_NAME="${{ matrix.model }}"
export ADAPTER_NAME="${{ matrix.model == 'bigscience/bloom-560m' && 'artek0chumak/bloom-560m-safe-peft' || '' }}"
# [Step 1] Set up a tiny test swarm (see https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm)
python -m petals.cli.run_dht \
--identity_path tests/bootstrap.id --host_maddrs /ip4/127.0.0.1/tcp/31337 &> bootstrap.log &
BOOTSTRAP_PID=$!
export INITIAL_PEERS=/ip4/127.0.0.1/tcp/31337/p2p/QmS9KwZptnVdB9FFV7uGgaTq4sEKBwcYeKZDfSpyKDUd1g
# ^-- multiaddr in INITIAL_PEERS is determined by --identity_path and --host_maddrs
until [ -s bootstrap.log ]; do sleep 5; done # wait for DHT init
export RUN_SERVER="python -m petals.cli.run_server $MODEL_NAME \
--device cpu --torch_dtype float32 --initial_peers $INITIAL_PEERS"
export TENSOR_PARALLEL_ARGS="${{ matrix.model == 'bigscience/bloom-560m' && '--tensor_parallel_devices cpu cpu' || '' }}"
$RUN_SERVER --adapters $ADAPTER_NAME --num_blocks 5 --throughput 1 --mean_balance_check_period 10 &> server1.log &
SERVER1_PID=$!
# ^-- rebalacing test: this server chooses blocks 0:5, then sees a gap in the swarm and moves there
sleep 10 # wait for the 1st server to choose blocks
$RUN_SERVER --adapters $ADAPTER_NAME --block_indices 0:5 --throughput 1 --identity_path tests/server2.id &> server2.log &
SERVER2_PID=$!
$RUN_SERVER --adapters $ADAPTER_NAME --num_blocks 14 --throughput auto \
--attn_cache_tokens 2048 --max_chunk_size_bytes 1024 &> server3.log &
SERVER3_PID=$!
# ^-- chunking test
$RUN_SERVER $TENSOR_PARALLEL_ARGS --block_indices 0:2 --throughput auto &> server4.log &
SERVER4_PID=$!
# ^-- tensor parallelism test (not compatible with adapters yet)
sleep 5 # wait for the log files to appear
tail -n 100 -f bootstrap.log server*.log &
LOGGER_PID=$!
sleep 30 # wait for servers to eval throughput, download layers, and rebalance
kill -0 $BOOTSTRAP_PID $SERVER1_PID $SERVER2_PID $SERVER3_PID $SERVER4_PID # ensure all peers survived init
# [Step 2] Run PyTest
# Share disk cache between Petals servers, clients, and HF Transformers
export TRANSFORMERS_CACHE=~/.cache/petals
# Necessary for @pytest.mark.forked to work properly on macOS, see https://github.com/kevlened/pytest-parallel/issues/93
export no_proxy=*
export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
# Limit default ClientConfig.max_retries to see tracebacks instead of retrying indefinitely
export PETALS_MAX_RETRIES=10
pytest tests --durations=0 --durations-min=1.0 -v
# [Step 3] Check if benchmarks work (their results here are meaningless since it's a tiny swarm of CPU servers)
python benchmarks/benchmark_inference.py --model $MODEL_NAME --initial_peers $INITIAL_PEERS --torch_dtype float32 \
--seq_len 3
python benchmarks/benchmark_forward.py --model $MODEL_NAME --initial_peers $INITIAL_PEERS --torch_dtype float32 \
--seq_len 3 --batch_size 3 --n_steps 1
python benchmarks/benchmark_training.py --model $MODEL_NAME --initial_peers $INITIAL_PEERS --torch_dtype float32 \
--seq_len 3 --batch_size 3 --pre_seq_len 1 --n_steps 1 --task cls
python benchmarks/benchmark_training.py --model $MODEL_NAME --initial_peers $INITIAL_PEERS --torch_dtype float32 \
--seq_len 3 --batch_size 3 --pre_seq_len 1 --n_steps 1 --task causal_lm
# [Step 4] Clean up
kill -s SIGINT $BOOTSTRAP_PID $SERVER1_PID $SERVER2_PID $SERVER3_PID $SERVER4_PID $LOGGER_PID