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name: Tests
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on:
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push:
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branches: [ main ]
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pull_request:
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jobs:
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run-tests:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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include:
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- { model: 'bigscience/bloom-560m', python-version: '3.8' }
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- { model: 'bigscience/bloom-560m', python-version: '3.9' }
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- { model: 'bigscience/bloom-560m', python-version: '3.10' }
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- { model: 'bigscience/bloom-560m', python-version: '3.11' }
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- { model: 'Maykeye/TinyLLama-v0', python-version: '3.8' }
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- { model: 'Maykeye/TinyLLama-v0', python-version: '3.11' }
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fail-fast: false
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timeout-minutes: 15
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steps:
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- name: Increase swap space
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uses: pierotofy/set-swap-space@master
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with:
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swap-size-gb: 10
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- name: Checkout
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uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v3
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with:
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python-version: ${{ matrix.python-version }}
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- name: Cache dependencies
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uses: actions/cache@v3
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with:
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path: ~/.cache/pip
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key: Key-v1-${{ matrix.python-version }}-${{ hashFiles('setup.cfg') }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install .[dev]
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- name: Test
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run: |
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export MODEL_NAME="${{ matrix.model }}"
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export REF_NAME="${{ matrix.model }}"
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export ADAPTER_NAME="${{ matrix.model == 'bigscience/bloom-560m' && 'artek0chumak/bloom-560m-safe-peft' || '' }}"
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export TENSOR_PARALLEL_ARGS="${{ matrix.model == 'bigscience/bloom-560m' && '--tensor_parallel_devices cpu cpu' || '' }}"
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# [Step 1] Watch free RAM (lack of RAM is a common issue in CI)
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bash -c 'while true; do free -h && sleep 30s; done' &
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RAM_WATCH_PID=$!
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# [Step 2] Set up a tiny test swarm (see https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm)
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python -m petals.cli.run_dht \
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--identity_path tests/bootstrap.id --host_maddrs /ip4/127.0.0.1/tcp/31337 &> bootstrap.log &
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BOOTSTRAP_PID=$!
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export INITIAL_PEERS=/ip4/127.0.0.1/tcp/31337/p2p/QmS9KwZptnVdB9FFV7uGgaTq4sEKBwcYeKZDfSpyKDUd1g
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# ^-- multiaddr in INITIAL_PEERS is determined by --identity_path and --host_maddrs
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sleep 5 # wait for DHT init
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python -m petals.cli.run_server $MODEL_NAME --adapters $ADAPTER_NAME --torch_dtype float32 --num_blocks 5 \
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--mean_balance_check_period 10 \
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--initial_peers $INITIAL_PEERS --throughput 1 &> server1.log &
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SERVER1_PID=$!
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# ^-- rebalacing test: this server chooses blocks 0:5, then sees a gap in the swarm and moves there
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sleep 10 # wait for the 1st server to choose blocks
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python -m petals.cli.run_server $MODEL_NAME --adapters $ADAPTER_NAME --torch_dtype float32 --block_indices 0:5 \
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--identity_path tests/server2.id \
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--initial_peers $INITIAL_PEERS --throughput 1 &> server2.log &
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SERVER2_PID=$!
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python -m petals.cli.run_server $MODEL_NAME --adapters $ADAPTER_NAME --torch_dtype float32 --num_blocks 14 \
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--attn_cache_tokens 2048 --max_chunk_size_bytes 1024 \
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--initial_peers $INITIAL_PEERS --throughput auto &> server3.log &
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SERVER3_PID=$!
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# ^-- chunking test
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python -m petals.cli.run_server $MODEL_NAME $TENSOR_PARALLEL_ARGS --torch_dtype float32 --block_indices 0:2 \
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--initial_peers $INITIAL_PEERS --throughput auto &> server4.log &
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Add LLaMA support (#323)
This PR:
1. **Abolishes the model conversion procedure.** Now, models are downloaded directly from original repositories like https://huggingface.co/bigscience/bloom. Servers download only shards with blocks to be hosted, and clients download only shards with input/output embeddings and layernorms.
- BLOOM is loaded from `bigscience/bloom`, but we use the DHT prefix `bigscience/bloom-petals` for backward compatibility. Same with smaller BLOOMs and BLOOMZ.
- LLaMA can be loaded from any repo like `username/llama-65b-hf`, but we use the DHT prefix `llama-65b-hf` (without the username) to accomodate blocks from different repos (there're a few of them with minor differences, such as `Llama` vs. `LLaMA` in the class name).
2. **Refactors the client to generalize it for multiple models.** Now, we have `petals.models` packages that contain model-specific code (e.g. `petals.models.bloom`, `petals.models.llama`). General code (e.g. CPU-efficient LM head, p-tuning) is kept in `petals.client`.
3. **Introduces** `WrappedLlamaBlock`, `DistributedLlamaConfig`, `DistributedLlamaForCausalLM`, `DistributedLlamaForSequenceClassification`, and `DistributedLlamaModel` compatible with Petals functionality (p-tuning, adapters, etc.).
4. **Introduces** `AutoDistributedConfig` that automatically chooses the correct config class (`DistributedLlamaConfig` or `DistributedBloomConfig`). The refactored configs contain all model-specific info for both clients and servers.
Upgrade instructions:
- Remove disk caches for blocks in old (converted) format to save disk space. That is, remove `~/.cache/petals/model--bigscience--bloom-petals` and `~/.cache/petals/model--bigscience--bloomz-petals` directories (if present).
11 months ago
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SERVER4_PID=$!
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# ^-- tensor parallelism test (not compatible with adapters yet)
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sleep 5 # wait for the log files to appear
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tail -n 100 -f bootstrap.log server*.log &
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LOGGER_PID=$!
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sleep 30 # wait for servers to eval throughput, download layers, and rebalance
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kill -0 $BOOTSTRAP_PID $SERVER1_PID $SERVER2_PID $SERVER3_PID $SERVER4_PID # ensure all peers survived init
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# [Step 3] Run PyTest
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pytest tests --durations=0 --durations-min=1.0 -v
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# [Step 4] Check if benchmarks work (their results here are meaningless since it's a tiny swarm of CPU servers)
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python benchmarks/benchmark_inference.py --model $MODEL_NAME --initial_peers $INITIAL_PEERS --torch_dtype float32 \
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--seq_len 3
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python benchmarks/benchmark_forward.py --model $MODEL_NAME --initial_peers $INITIAL_PEERS --torch_dtype float32 \
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--seq_len 3 --batch_size 3 --n_steps 1
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python benchmarks/benchmark_training.py --model $MODEL_NAME --initial_peers $INITIAL_PEERS --torch_dtype float32 \
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--seq_len 3 --batch_size 3 --pre_seq_len 1 --n_steps 1 --task cls
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python benchmarks/benchmark_training.py --model $MODEL_NAME --initial_peers $INITIAL_PEERS --torch_dtype float32 \
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--seq_len 3 --batch_size 3 --pre_seq_len 1 --n_steps 1 --task causal_lm
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# [Step 5] Clean up
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kill -0 $BOOTSTRAP_PID $SERVER1_PID $SERVER2_PID $SERVER3_PID $SERVER4_PID # ensure all peers survived tests
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kill -s SIGINT $BOOTSTRAP_PID $SERVER1_PID $SERVER2_PID $SERVER3_PID $SERVER4_PID $LOGGER_PID $RAM_WATCH_PID
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echo "Done!"
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