96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
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from typing import List
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import pytest
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import vllm
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from vllm.assets.image import ImageAsset
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from vllm.lora.request import LoRARequest
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from ..utils import multi_gpu_test
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MODEL_PATH = "openbmb/MiniCPM-Llama3-V-2_5"
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PROMPT_TEMPLATE = (
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"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"
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"(<image>./</image>)\nWhat is in the image?<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n")
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IMAGE_ASSETS = [
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ImageAsset("stop_sign"),
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ImageAsset("cherry_blossom"),
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]
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# After fine-tuning with LoRA, all generated content should start begin `A`.
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EXPECTED_OUTPUT = [
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"A red and white stop sign with a Chinese archway in the background featuring red lanterns and gold accents.", # noqa: E501
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"A pink cherry blossom tree with a blue sky in the background.",
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]
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def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
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sampling_params = vllm.SamplingParams(
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temperature=0,
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max_tokens=5,
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stop_token_ids=[128001, 128009], # eos_id, eot_id
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)
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inputs = [{
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"prompt": PROMPT_TEMPLATE,
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"multi_modal_data": {
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"image": asset.pil_image
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},
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} for asset in IMAGE_ASSETS]
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outputs = llm.generate(
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inputs,
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sampling_params,
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lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
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if lora_id else None,
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)
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# Print the outputs.
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generated_texts: List[str] = []
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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.strip()
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generated_texts.append(generated_text)
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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return generated_texts
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@multi_gpu_test(num_gpus=2)
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@pytest.mark.parametrize("fully_sharded", [True, False])
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def test_minicpmv_tp2(minicpmv_lora_files, fully_sharded):
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llm = vllm.LLM(
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MODEL_PATH,
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enable_lora=True,
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max_num_seqs=2,
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max_loras=4,
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max_lora_rank=64,
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tensor_parallel_size=2,
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trust_remote_code=True,
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fully_sharded_loras=fully_sharded,
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)
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output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
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for i in range(len(EXPECTED_OUTPUT)):
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assert EXPECTED_OUTPUT[i].startswith(output_tp[i])
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@multi_gpu_test(num_gpus=4)
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@pytest.mark.parametrize("fully_sharded", [True, False])
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def test_minicpmv_tp4(minicpmv_lora_files, fully_sharded):
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llm = vllm.LLM(
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MODEL_PATH,
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enable_lora=True,
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max_num_seqs=2,
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max_loras=4,
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max_lora_rank=64,
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tensor_parallel_size=4,
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trust_remote_code=True,
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fully_sharded_loras=fully_sharded,
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)
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output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
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for i in range(len(EXPECTED_OUTPUT)):
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assert EXPECTED_OUTPUT[i].startswith(output_tp[i])
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