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[Doc] Add inplace weights loading example #19640
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Signed-off-by: 22quinn <[email protected]>
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Summary of Changes
Hello @22quinn, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces a new example script to the examples/offline_inference
directory. The purpose is to provide a clear, runnable demonstration of how to use the recently added feature that allows initializing the vLLM engine with dummy weights and then loading the real model weights later, inplace, via an RPC call. This helps users understand and utilize this advanced loading capability.
Highlights
- New Example Script: Adds a new Python script (
examples/offline_inference/inplace_weights_loading.py
) specifically designed to showcase the inplace weights loading feature. - Demonstrates Inplace Loading: The example initializes the
LLM
withload_format="dummy"
, then updates the engine's configuration toload_format="auto"
and triggers the actual model loading viallm.collective_rpc("load_model")
. - Verification: Includes a step to run inference after loading the real weights to verify the process was successful.
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Code Review
This pull request adds an example demonstrating the inplace weights loading feature. The suggestions focus on removing unrelated code and enhancing comments for better clarity.
# def aync_main(): | ||
# engine = AsyncLLMEngine.from_engine_args(engine_args) | ||
# example_input = { | ||
# "prompt": "What is LLM?", | ||
# "stream": False, # assume the non-streaming case | ||
# "temperature": 0.0, | ||
# "request_id": 0, | ||
# } | ||
# # start the generation | ||
# results_generator = engine.generate( | ||
# example_input["prompt"], | ||
# SamplingParams(temperature=example_input["temperature"]), | ||
# example_input["request_id"]) | ||
# # get the results | ||
# final_output = None | ||
# async for request_output in results_generator: | ||
# if await request.is_disconnected(): | ||
# # Abort the request if the client disconnects. | ||
# await engine.abort(request_id) | ||
# # Return or raise an error | ||
# ... | ||
# final_output = request_output |
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llm.llm_engine.vllm_config.load_config.load_format = "auto" | ||
llm.collective_rpc("load_model") |
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Consider adding more detail to this comment, explaining the two-step process: updating load_format
and then calling collective_rpc
.
llm.llm_engine.vllm_config.load_config.load_format = "auto" | |
llm.collective_rpc("load_model") | |
# Now load real weights inplace. | |
# 1. Update load_format to 'auto'. | |
# 2. Trigger model loading with collective_rpc. |
Signed-off-by: 22quinn <[email protected]>
Signed-off-by: 22quinn <[email protected]>
Signed-off-by: 22quinn <[email protected]>
Signed-off-by: 22quinn <[email protected]>
Signed-off-by: 22quinn <[email protected]>
llm = LLM( | ||
model="facebook/opt-125m", | ||
load_format="dummy", | ||
enforce_eager=True, |
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to demonstrate it functions ok, i think we'd need tp2 at least
sampling_params = SamplingParams(temperature=0.8, top_p=0.95) | ||
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async def aync_main(): |
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i don't think we need to keep a sync and an async version. as the usage of those collective_rpcs are almost the same.
if you want to be comprehensive, it'll be nicer to follow one of the existing rlhf examples like https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/rlhf_colocate.py
Essential Elements of an Effective PR Description Checklist
supported_models.md
andexamples
for a new model.Purpose
Provide an example of inplace weights loading feature added in #18745 #19884
Test Plan
Test Result
(Optional) Documentation Update