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[Model] Initialize support for InternVL2 series models (vllm-project#…
…6514) Co-authored-by: Roger Wang <[email protected]>
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import types | ||
from typing import List, Optional, Type | ||
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import pytest | ||
import torch | ||
from huggingface_hub import snapshot_download | ||
from PIL.Image import Image | ||
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from vllm.model_executor.models.internvl import (IMG_CONTEXT, IMG_END, | ||
IMG_START, | ||
image_to_pixel_values) | ||
from vllm.multimodal.utils import rescale_image_size | ||
from vllm.utils import is_cpu | ||
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from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets | ||
from .utils import check_logprobs_close | ||
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pytestmark = pytest.mark.vlm | ||
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HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({ | ||
"stop_sign": | ||
"<|im_start|>User\n<image>\nWhat's the content in the center of the image?<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501 | ||
"cherry_blossom": | ||
"<|im_start|>User\n<image>\nWhat is the season?<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501 | ||
}) | ||
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# we use snapshot_download to prevent conflicts between | ||
# dynamic_module and trust_remote_code for hf_runner | ||
models = [ | ||
snapshot_download("OpenGVLab/InternVL2-1B"), | ||
snapshot_download("OpenGVLab/InternVL2-2B"), | ||
# snapshot_download("OpenGVLab/InternVL2-4B"), # broken | ||
] | ||
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class InternVLProcessor: | ||
"""A simple processor for InternVL2 HF model which misses a processor.""" | ||
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def __init__(self, hf_runner: HfRunner): | ||
self.num_image_token = hf_runner.model.num_image_token | ||
self.tokenizer = hf_runner.tokenizer | ||
self.dtype = hf_runner.model.dtype | ||
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def __call__(self, text: str, images: Image, **kwargs): | ||
pixel_values = image_to_pixel_values(images).to(self.dtype) | ||
num_patches_list = [pixel_values.shape[0]] | ||
for num_patches in num_patches_list: | ||
context_tokens = IMG_CONTEXT * self.num_image_token * num_patches | ||
image_tokens = IMG_START + context_tokens + IMG_END | ||
text = text.replace('<image>', image_tokens, 1) | ||
prompt = self.tokenizer(text, return_tensors="pt") | ||
prompt.update({"pixel_values": pixel_values}) | ||
return prompt | ||
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# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py | ||
def generate( | ||
self, | ||
pixel_values: torch.FloatTensor, | ||
input_ids: torch.FloatTensor, | ||
attention_mask: Optional[torch.LongTensor] = None, | ||
**generate_kwargs, | ||
) -> torch.LongTensor: | ||
"""Generate method for InternVL2 model without fixed use_cache.""" | ||
assert self.img_context_token_id is not None | ||
vit_embeds = self.extract_feature(pixel_values) | ||
input_embeds = self.language_model.get_input_embeddings()(input_ids) | ||
B, N, C = input_embeds.shape | ||
input_embeds = input_embeds.reshape(B * N, C) | ||
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input_ids = input_ids.reshape(B * N) | ||
selected = (input_ids == self.img_context_token_id) | ||
assert selected.sum() != 0 | ||
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device) | ||
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input_embeds = input_embeds.reshape(B, N, C) | ||
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outputs = self.language_model.generate( | ||
inputs_embeds=input_embeds, | ||
attention_mask=attention_mask, | ||
**generate_kwargs, | ||
) | ||
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return outputs | ||
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def run_test( | ||
hf_runner: Type[HfRunner], | ||
vllm_runner: Type[VllmRunner], | ||
image_assets: _ImageAssets, | ||
model: str, | ||
*, | ||
size_factors: List[float], | ||
dtype: str, | ||
max_tokens: int, | ||
num_logprobs: int, | ||
tensor_parallel_size: int, | ||
distributed_executor_backend: Optional[str] = None, | ||
): | ||
"""Inference result should be the same between hf and vllm. | ||
All the image fixtures for the test is under tests/images. | ||
For huggingface runner, we provide the PIL images as input. | ||
For vllm runner, we provide MultiModalDataDict objects | ||
and corresponding vision language config as input. | ||
Note, the text input is also adjusted to abide by vllm contract. | ||
The text output is sanitized to be able to compare with hf. | ||
""" | ||
images = [asset.pil_image for asset in image_assets] | ||
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inputs_per_image = [( | ||
[prompt for _ in size_factors], | ||
[rescale_image_size(image, factor) for factor in size_factors], | ||
) for image, prompt in zip(images, HF_IMAGE_PROMPTS)] | ||
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# NOTE: take care of the order. run vLLM first, and then run HF. | ||
# vLLM needs a fresh new process without cuda initialization. | ||
# if we run HF first, the cuda initialization will be done and it | ||
# will hurt multiprocessing backend with fork method (the default method). | ||
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# max_model_len should be greater than image_feature_size | ||
with vllm_runner(model, | ||
max_model_len=4096, | ||
dtype=dtype, | ||
tensor_parallel_size=tensor_parallel_size, | ||
distributed_executor_backend=distributed_executor_backend, | ||
enforce_eager=True) as vllm_model: | ||
vllm_outputs_per_image = [ | ||
vllm_model.generate_greedy_logprobs(prompts, | ||
max_tokens, | ||
num_logprobs=num_logprobs, | ||
images=images) | ||
for prompts, images in inputs_per_image | ||
] | ||
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with hf_runner(model, dtype=dtype) as hf_model: | ||
img_context_token_id = hf_model.tokenizer.convert_tokens_to_ids( | ||
"<IMG_CONTEXT>") | ||
hf_model.model.img_context_token_id = img_context_token_id | ||
hf_model.processor = InternVLProcessor(hf_model) | ||
hf_model.model.get_output_embeddings = lambda: \ | ||
hf_model.model.language_model.get_output_embeddings() | ||
hf_model.model.generate = types.MethodType(generate, hf_model.model) | ||
eos_token_id = hf_model.tokenizer.eos_token_id | ||
hf_outputs_per_image = [ | ||
hf_model.generate_greedy_logprobs_limit(prompts, | ||
max_tokens, | ||
num_logprobs=num_logprobs, | ||
images=hf_images, | ||
eos_token_id=eos_token_id) | ||
for prompts, hf_images in inputs_per_image | ||
] | ||
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for hf_outputs, vllm_outputs in zip(hf_outputs_per_image, | ||
vllm_outputs_per_image): | ||
# TODO: Check whether using original CLIPVisionModel can improve | ||
# consistency against HF | ||
check_logprobs_close( | ||
outputs_0_lst=hf_outputs, | ||
outputs_1_lst=vllm_outputs, | ||
name_0="hf", | ||
name_1="vllm", | ||
) | ||
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target_dtype = "half" | ||
if is_cpu(): | ||
target_dtype = "bfloat16" | ||
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@pytest.mark.parametrize("model", models) | ||
@pytest.mark.parametrize( | ||
"size_factors", | ||
[ | ||
# No image | ||
[], | ||
# Single-scale | ||
[1.0], | ||
# Single-scale, batched | ||
[1.0, 1.0, 1.0], | ||
# Multi-scale | ||
[0.25, 0.5, 1.0], | ||
], | ||
) | ||
@pytest.mark.parametrize("dtype", [target_dtype]) | ||
@pytest.mark.parametrize("max_tokens", [128]) | ||
@pytest.mark.parametrize("num_logprobs", [5]) | ||
@torch.inference_mode() | ||
def test_models(hf_runner, vllm_runner, image_assets, model, size_factors, | ||
dtype: str, max_tokens: int, num_logprobs: int) -> None: | ||
run_test( | ||
hf_runner, | ||
vllm_runner, | ||
image_assets, | ||
model, | ||
size_factors=size_factors, | ||
dtype=dtype, | ||
max_tokens=max_tokens, | ||
num_logprobs=num_logprobs, | ||
tensor_parallel_size=1, | ||
) |
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