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| 1 | +# SPDX-License-Identifier: Apache-2.0 |
| 2 | +import pytest |
| 3 | + |
| 4 | +import vllm |
| 5 | +from vllm.lora.request import LoRARequest |
| 6 | + |
| 7 | +# This file contains tests to ensure that LoRA works correctly on the TPU |
| 8 | +# backend. We use a series of custom trained adapters for Qwen2.5-3B-Instruct |
| 9 | +# for this. The adapters are: |
| 10 | +# Username6568/Qwen2.5-3B-Instruct-1_plus_1_equals_x_adapter, where x ranges |
| 11 | +# from 1 to 4. |
| 12 | + |
| 13 | +# These adapters are trained using a standard huggingface peft training script, |
| 14 | +# where all the inputs are "What is 1+1? \n" and all the outputs are "x". We run |
| 15 | +# 100 training iterations with a training batch size of 100. |
| 16 | + |
| 17 | + |
| 18 | +@pytest.fixture(scope="function", autouse=True) |
| 19 | +def use_v1_only(monkeypatch: pytest.MonkeyPatch): |
| 20 | + """ |
| 21 | + Since Multi-LoRA is only supported on the v1 TPU backend, set VLLM_USE_V1=1 |
| 22 | + for all tests in this file |
| 23 | + """ |
| 24 | + with monkeypatch.context() as m: |
| 25 | + m.setenv("VLLM_USE_V1", "1") |
| 26 | + yield |
| 27 | + |
| 28 | + |
| 29 | +def setup_vllm(num_loras: int) -> vllm.LLM: |
| 30 | + return vllm.LLM(model="Qwen/Qwen2.5-3B-Instruct", |
| 31 | + num_scheduler_steps=1, |
| 32 | + max_model_len=256, |
| 33 | + max_seq_len_to_capture=256, |
| 34 | + max_num_seqs=8, |
| 35 | + enable_lora=True, |
| 36 | + max_loras=num_loras, |
| 37 | + max_lora_rank=8) |
| 38 | + |
| 39 | + |
| 40 | +def test_single_lora(): |
| 41 | + """ |
| 42 | + This test ensures we can run a single LoRA adapter on the TPU backend. |
| 43 | + We run "Username6568/Qwen2.5-3B-Instruct-1_plus_1_equals_1_adapter" which |
| 44 | + will force Qwen2.5-3B-Instruct to claim 1+1=1. |
| 45 | + """ |
| 46 | + |
| 47 | + llm = setup_vllm(1) |
| 48 | + |
| 49 | + prompt = "What is 1+1? \n" |
| 50 | + |
| 51 | + lora_request = LoRARequest( |
| 52 | + "lora_adapter_1", 1, |
| 53 | + "Username6568/Qwen2.5-3B-Instruct-1_plus_1_equals_1_adapter") |
| 54 | + output = llm.generate(prompt, |
| 55 | + sampling_params=vllm.SamplingParams(max_tokens=256, |
| 56 | + temperature=0), |
| 57 | + lora_request=lora_request)[0].outputs[0].text |
| 58 | + |
| 59 | + answer = output.strip()[0] |
| 60 | + |
| 61 | + assert answer.isdigit() |
| 62 | + assert int(answer) == 1 |
| 63 | + |
| 64 | + |
| 65 | +def test_lora_hotswapping(): |
| 66 | + """ |
| 67 | + This test ensures we can run multiple LoRA adapters on the TPU backend, even |
| 68 | + if we only have space to store 1. |
| 69 | + |
| 70 | + We run "Username6568/Qwen2.5-3B-Instruct-1_plus_1_equals_x_adapter" which |
| 71 | + will force Qwen2.5-3B-Instruct to claim 1+1=x, for a range of x. |
| 72 | + """ |
| 73 | + |
| 74 | + lora_name_template = \ |
| 75 | + "Username6568/Qwen2.5-3B-Instruct-1_plus_1_equals_{}_adapter" |
| 76 | + lora_requests = [ |
| 77 | + LoRARequest(f"lora_adapter_{i}", i, lora_name_template.format(i)) |
| 78 | + for i in range(1, 5) |
| 79 | + ] |
| 80 | + |
| 81 | + llm = setup_vllm(1) |
| 82 | + |
| 83 | + prompt = "What is 1+1? \n" |
| 84 | + |
| 85 | + for i, req in enumerate(lora_requests): |
| 86 | + output = llm.generate(prompt, |
| 87 | + sampling_params=vllm.SamplingParams( |
| 88 | + max_tokens=256, temperature=0), |
| 89 | + lora_request=req)[0].outputs[0].text |
| 90 | + answer = output.strip()[0] |
| 91 | + |
| 92 | + assert answer.isdigit() |
| 93 | + assert int(answer) == i + 1 |
| 94 | + |
| 95 | + |
| 96 | +def test_multi_lora(): |
| 97 | + """ |
| 98 | + This test ensures we can run multiple LoRA adapters on the TPU backend, when |
| 99 | + we have enough space to store all of them. |
| 100 | + |
| 101 | + We run "Username6568/Qwen2.5-3B-Instruct-1_plus_1_equals_x_adapter" which |
| 102 | + will force Qwen2.5-3B-Instruct to claim 1+1=x, for a range of x. |
| 103 | + """ |
| 104 | + lora_name_template = \ |
| 105 | + "Username6568/Qwen2.5-3B-Instruct-1_plus_1_equals_{}_adapter" |
| 106 | + lora_requests = [ |
| 107 | + LoRARequest(f"lora_adapter_{i}", i, lora_name_template.format(i)) |
| 108 | + for i in range(1, 5) |
| 109 | + ] |
| 110 | + |
| 111 | + llm = setup_vllm(4) |
| 112 | + |
| 113 | + prompt = "What is 1+1? \n" |
| 114 | + |
| 115 | + for i, req in enumerate(lora_requests): |
| 116 | + output = llm.generate(prompt, |
| 117 | + sampling_params=vllm.SamplingParams( |
| 118 | + max_tokens=256, temperature=0), |
| 119 | + lora_request=req)[0].outputs[0].text |
| 120 | + |
| 121 | + answer = output.strip()[0] |
| 122 | + |
| 123 | + assert answer.isdigit() |
| 124 | + assert int(output.strip()[0]) == i + 1 |
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