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- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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experimental support for tensor-parallel inference with torchrun,
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see https://github.com/vllm-project/vllm/issues/11400 for
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the motivation and use case for this example.
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run the script with `torchrun --nproc-per-node=2 torchrun_example.py`,
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the argument 2 should match the `tensor_parallel_size` below.
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see `tests/distributed/test_torchrun_example.py` for the unit test.
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"""
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from vllm import LLM, SamplingParams
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# Create prompts, the same across all ranks
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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# Create sampling parameters, the same across all ranks
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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# Use `distributed_executor_backend="external_launcher"` so that
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# this llm engine/instance only creates one worker.
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llm = LLM(
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model="facebook/opt-125m",
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tensor_parallel_size=2,
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distributed_executor_backend="external_launcher",
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)
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outputs = llm.generate(prompts, sampling_params)
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# all ranks will have the same outputs
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, "
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f"Generated text: {generated_text!r}")
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"""
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Further tips:
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1. to communicate control messages across all ranks, use the cpu group,
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a PyTorch ProcessGroup with GLOO backend.
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```python
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from vllm.distributed.parallel_state import get_world_group
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cpu_group = get_world_group().cpu_group
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torch_rank = dist.get_rank(group=cpu_group)
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if torch_rank == 0:
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# do something for rank 0, e.g. saving the results to disk.
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```
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2. to communicate data across all ranks, use the model's device group,
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a PyTorch ProcessGroup with NCCL backend.
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```python
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from vllm.distributed.parallel_state import get_world_group
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device_group = get_world_group().device_group
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```
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3. to access the model directly in every rank, use the following code:
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```python
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llm.llm_engine.model_executor.driver_worker.worker.model_runner.model
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```
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"""
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