[PERF] Speedup of MRoPE prepare inputs

[vadiklyutiy]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
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Purpose

Speedup of MRoPE prepare inputs.
#16881 got stuck for a while. I combined parts of #16881 and #17617 to minimize changes and brings sufficient speedup.
MRotaryEmbedding.get_next_input_positions_tensor takes a lot of time. Use numpy to speedup it.

Performance Test Result

I used
vllm serve Qwen/Qwen2.5-VL-3B-Instruct --disable-log-requests --no-enable-prefix-caching
as a server and
fib benchmark -rps 50 --input-token-distribution uniform 250 300 --output-token-distribution uniform 150 250 --num-of-imgs-per-req 1 --img-ratios-per-req 512x512 -n 1000 --base-url http://localhost:8000 --endpoint v1/chat/completions --backend openai-chat
to make workload (send 50 requests per sec with one 512x512 image per request).

I decorated GPUModelRunner._prepare_inputs with nvtx to measure the time.
_prepare_inputs average time
Before: 3.869 ms
With this PR: 1.487 ms (speedup 2.6x)
For info, with #17617: 2.511 ms

E2E performance improvement on Qwen2.5-VL-3B-Instruct with high load is around 1.5%.

cc @imkero @ywang96 @simon-mo

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[gemini-code-assist]

Summary of Changes

Hello @vadiklyutiy, 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 focuses on a targeted performance enhancement for the MRoPE input preparation within the model executor. By transitioning a critical position calculation from a PyTorch-based approach to a more efficient NumPy implementation, it aims to reduce latency in the _prepare_inputs stage, thereby contributing to overall system throughput and responsiveness. The changes are designed to be minimally invasive while delivering substantial speedups in a key computational bottleneck.

Highlights

• Performance Optimization: This pull request introduces a significant performance optimization for the Multi-Query Rotary Positional Embedding (MRoPE) input preparation, specifically targeting the get_next_input_positions_tensor function which was identified as a bottleneck.
• NumPy Integration: The core of the optimization involves refactoring the position calculation logic to leverage NumPy for improved efficiency. A new static method, mrope_assign_next_input_positions, has been added to MRotaryEmbedding to perform these calculations directly using NumPy arrays.
• GPU Model Runner Update: The GPUModelRunner has been updated to utilize this new NumPy-based method. This includes creating a NumPy view of the existing CPU-bound position tensor (self.mrope_positions_cpu.numpy()) to allow direct, efficient assignment of calculated positions.
• Measured Speedup: Performance benchmarks show a 2.59x speedup for the _prepare_inputs average time (from 3.869 ms to 1.496 ms) and an overall E2E performance improvement of approximately 1.5% on the Qwen2.5-VL-3B-Instruct model under high load.

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[gemini-code-assist]

Code Review

This pull request aims to speed up the MRoPE input preparation by leveraging numpy for calculations on CPU-pinned memory, which is a great approach. The changes look solid and the performance improvement is significant.

I have one suggestion to further optimize the new numpy-based function by using vectorized operations instead of nested Python loops. This should provide an additional performance boost and make the code more idiomatic.

[WoosukKwon]

Can we just replace get_next_input_positions_tensor with this method?

[vadiklyutiy]

@WoosukKwon yes, sure. One small clarification. Do you mean just remove get_next_input_positions_tensor or use the same name get_next_input_positions_tensor for new function?

[WoosukKwon]

@vadiklyutiy Yes. I think that will cause the minimal change to the codebase?