e489ad7a21
- **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>
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
# unit test for `examples/offline_inference/torchrun_example.py`
|
|
|
|
import random
|
|
|
|
import torch.distributed as dist
|
|
|
|
from vllm import LLM, SamplingParams
|
|
from vllm.distributed.parallel_state import get_world_group
|
|
|
|
# Create prompts
|
|
prompts = [
|
|
"Hello, my name is",
|
|
"The president of the United States is",
|
|
"The capital of France is",
|
|
"The future of AI is",
|
|
]
|
|
|
|
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
|
|
|
|
# set different `gpu_memory_utilization` and `swap_space` for different ranks,
|
|
# to test if all ranks agree on the same kv cache configuration.
|
|
llm = LLM(model="facebook/opt-125m",
|
|
tensor_parallel_size=2,
|
|
distributed_executor_backend="external_launcher",
|
|
gpu_memory_utilization=random.uniform(0.7, 0.9),
|
|
swap_space=random.randint(1, 4))
|
|
|
|
outputs = llm.generate(prompts, sampling_params)
|
|
|
|
cpu_group = get_world_group().cpu_group
|
|
|
|
torch_rank = dist.get_rank(group=cpu_group)
|
|
|
|
|
|
def test_consistent_across_ranks(obj):
|
|
if torch_rank == 0:
|
|
dist.broadcast_object_list([obj], src=0, group=cpu_group)
|
|
else:
|
|
container = [None]
|
|
dist.broadcast_object_list(container, src=0, group=cpu_group)
|
|
assert container[0] == obj
|
|
|
|
|
|
test_consistent_across_ranks(
|
|
llm.llm_engine.vllm_config.cache_config.num_cpu_blocks)
|
|
test_consistent_across_ranks(
|
|
llm.llm_engine.vllm_config.cache_config.num_gpu_blocks)
|
|
|
|
# all ranks should have the same outputs
|
|
for output in outputs:
|
|
prompt = output.prompt
|
|
generated_text = output.outputs[0].text
|
|
test_consistent_across_ranks(prompt)
|
|
test_consistent_across_ranks(generated_text)
|
|
print(f"Rank {torch_rank}, Prompt: {prompt!r}, "
|
|
f"Generated text: {generated_text!r}")
|