[VLM] Clean up Phi-4-MM ViT implementation (#14812)
Signed-off-by: Isotr0py <2037008807@qq.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
This commit is contained in:
Isotr0py
229
tests/models/decoder_only/vision_language/test_phi4mm.py
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tests/models/decoder_only/vision_language/test_phi4mm.py
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# SPDX-License-Identifier: Apache-2.0
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import os
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import re
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from typing import Optional
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import pytest
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from huggingface_hub import snapshot_download
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from transformers import AutoTokenizer
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from vllm.lora.request import LoRARequest
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from vllm.multimodal.image import rescale_image_size
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from vllm.platforms import current_platform
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from vllm.sequence import SampleLogprobs
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from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
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from ....utils import large_gpu_test
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from ...utils import check_logprobs_close
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HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
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"stop_sign":
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"<|user|>\n<|image_1|>\nWhat's the content of the image?<|end|>\n<|assistant|>\n", # noqa: E501
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"cherry_blossom":
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"<|user|>\n<|image_1|>\nPlease infer the season with reason in details.<|end|>\n<|assistant|>\n", # noqa: E501
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})
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HF_MULTIIMAGE_IMAGE_PROMPT = "<|user|>\n<|image_1|>\n<|image_2|>\nDescribe these images.<|end|>\n<|assistant|>\n" # noqa: E501
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model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
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# Since the vision-lora and speech-lora co-exist with the base model,
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# we have to manually specify the path of the lora weights.
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vision_lora_path = os.path.join(model_path, "vision-lora")
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models = [model_path]
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def vllm_to_hf_output(vllm_output: tuple[list[int], str,
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Optional[SampleLogprobs]],
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model: str):
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"""Sanitize vllm output to be comparable with hf output."""
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_, output_str, out_logprobs = vllm_output
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output_str_without_image = re.sub(r"(<\|image_\d+\|>)+", "", output_str)
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assert output_str_without_image[0] == " "
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output_str_without_image = output_str_without_image[1:]
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hf_output_str = output_str_without_image + "<|end|><|endoftext|>"
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tokenizer = AutoTokenizer.from_pretrained(model)
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hf_output_ids = tokenizer.encode(output_str_without_image)
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assert hf_output_ids[0] == 1
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hf_output_ids = hf_output_ids[1:]
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return hf_output_ids, hf_output_str, out_logprobs
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target_dtype = "half"
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# ROCm Triton FA can run into shared memory issues with these models,
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# use other backends in the meantime
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# FIXME (mattwong, gshtrasb, hongxiayan)
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if current_platform.is_rocm():
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os.environ["VLLM_USE_TRITON_FLASH_ATTN"] = "0"
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def run_test(
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hf_runner: type[HfRunner],
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vllm_runner: type[VllmRunner],
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inputs: list[tuple[list[str], PromptImageInput]],
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model: str,
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*,
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max_model_len: int,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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mm_limit: int,
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tensor_parallel_size: int,
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distributed_executor_backend: Optional[str] = None,
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):
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"""Inference result should be the same between hf and vllm.
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All the image fixtures for the test are from IMAGE_ASSETS.
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For huggingface runner, we provide the PIL images as input.
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For vllm runner, we provide MultiModalDataDict objects
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and corresponding MultiModalConfig as input.
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Note, the text input is also adjusted to abide by vllm contract.
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The text output is sanitized to be able to compare with hf.
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"""
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# NOTE: take care of the order. run vLLM first, and then run HF.
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# vLLM needs a fresh new process without cuda initialization.
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# if we run HF first, the cuda initialization will be done and it
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# will hurt multiprocessing backend with fork method (the default method).
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# max_model_len should be greater than image_feature_size
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with vllm_runner(
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model,
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task="generate",
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max_model_len=max_model_len,
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max_num_seqs=2,
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dtype=dtype,
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limit_mm_per_prompt={"image": mm_limit},
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tensor_parallel_size=tensor_parallel_size,
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distributed_executor_backend=distributed_executor_backend,
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enable_lora=True,
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max_lora_rank=320,
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lora_extra_vocab_size=0,
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gpu_memory_utilization=0.8, # set to 0.8 to avoid OOM in CI
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enforce_eager=True,
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) as vllm_model:
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lora_request = LoRARequest("vision", 1, vision_lora_path)
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vllm_model.model.llm_engine.add_lora(lora_request=lora_request)
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vllm_outputs_per_case = [
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vllm_model.generate_greedy_logprobs(prompts,
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max_tokens,
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num_logprobs=num_logprobs,
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images=images)
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for prompts, images in inputs
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]
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# use eager mode for hf runner, since phi3_v didn't work with flash_attn
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hf_model_kwargs = {"_attn_implementation": "eager"}
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with hf_runner(model, dtype=dtype,
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model_kwargs=hf_model_kwargs) as hf_model:
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eos_token_id = hf_model.processor.tokenizer.eos_token_id
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hf_outputs_per_case = [
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hf_model.generate_greedy_logprobs_limit(prompts,
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max_tokens,
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num_logprobs=num_logprobs,
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images=images,
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eos_token_id=eos_token_id,
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num_logits_to_keep=0)
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for prompts, images in inputs
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]
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for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
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vllm_outputs_per_case):
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check_logprobs_close(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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)
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# Since we use _attn_implementation="eager" for hf_runner, there is more
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# significant numerical difference. The basic `logprobs=5` fails to pass.
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@pytest.mark.parametrize("model", models)
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@pytest.mark.parametrize(
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"size_factors",
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[
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# No image
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[],
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# Single-scale
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[1.0],
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# Single-scale, batched
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[1.0, 1.0, 1.0],
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# Multi-scale
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[0.7, 0.75, 1.0],
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],
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)
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@pytest.mark.parametrize("dtype", [target_dtype])
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@pytest.mark.parametrize("max_model_len", [4096])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [10])
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def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
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dtype: str, max_model_len: int, max_tokens: int,
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num_logprobs: int) -> None:
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images = [asset.pil_image for asset in image_assets]
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inputs_per_image = [(
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[prompt for _ in size_factors],
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[rescale_image_size(image, factor) for factor in size_factors],
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) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
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run_test(
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hf_runner,
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vllm_runner,
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inputs_per_image,
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model,
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dtype=dtype,
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max_model_len=max_model_len,
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max_tokens=max_tokens,
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num_logprobs=num_logprobs,
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mm_limit=1,
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tensor_parallel_size=1,
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)
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@large_gpu_test(min_gb=48)
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@pytest.mark.parametrize("model", models)
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@pytest.mark.parametrize(
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"size_factors",
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[
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# No image
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# [],
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# Single-scale
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[1.0],
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# Single-scale, batched
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[1.0, 1.0, 1.0],
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# Multi-scale
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[0.25, 0.5, 1.0],
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],
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)
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@pytest.mark.parametrize("dtype", [target_dtype])
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@pytest.mark.parametrize("max_model_len", [10000])
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@pytest.mark.parametrize("max_tokens", [128])
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@pytest.mark.parametrize("num_logprobs", [10])
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@pytest.mark.xfail(
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reason="Phi-4-MM multi-image inference is divergent with hf model.")
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def test_multi_images_models(hf_runner, vllm_runner, image_assets, model,
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size_factors, dtype: str, max_model_len: int,
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max_tokens: int, num_logprobs: int) -> None:
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images = [asset.pil_image for asset in image_assets]
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inputs_per_case = [
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([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
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[[rescale_image_size(image, factor) for image in images]
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for factor in size_factors])
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]
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run_test(
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hf_runner,
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vllm_runner,
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inputs_per_case,
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model,
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dtype=dtype,
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max_model_len=max_model_len,
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max_tokens=max_tokens,
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num_logprobs=num_logprobs,
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mm_limit=2,
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tensor_parallel_size=1,
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)
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