Create gme_inference.py

gme_inference.py

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+from __future__ import annotations
+
+import logging
+import math
+import os
+from typing import Dict, List, Optional
+
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+from tqdm.autonotebook import tqdm
+from transformers import AutoModelForVision2Seq, AutoProcessor
+
+
+class GmeQwen2VL:
+    def __init__(
+        self,
+        model_name: str = "Alibaba-NLP/gme-Qwen2-VL-2B-Instruct",
+        model_path: Optional[str] = None,
+        device: str = "cuda" if torch.cuda.is_available() else "cpu",
+        min_image_tokens=256,
+        max_image_tokens=1280,
+        max_length=1800,
+        **kwargs,
+    ) -> None:
+        model_name = model_path or model_name
+        self.base = AutoModelForVision2Seq.from_pretrained(
+            model_name, torch_dtype=torch.float16, **kwargs
+        )
+        self.base.eval()
+        self.normalize = True
+        self.device = device
+        min_pixels = min_image_tokens * 28 * 28
+        max_pixels = max_image_tokens * 28 * 28
+        self.max_length = max_length
+        self.processor = AutoProcessor.from_pretrained(
+            model_name, min_pixels=min_pixels, max_pixels=max_pixels, **kwargs
+        )
+        self.processor.tokenizer.padding_side = 'right'
+        self.defualt_instruction = 'You are a helpful assistant.'
+        self.sep = ' '
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        # pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        # video_grid_thw: Optional[torch.LongTensor] = None,
+        pooling_mask: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.base.model.embed_tokens(input_ids)
+            if pixel_values is not None:
+                pixel_values = pixel_values.type(self.base.visual.get_dtype())
+                image_embeds = self.base.visual(pixel_values, grid_thw=image_grid_thw).to(inputs_embeds.device)
+                image_mask = input_ids == self.base.config.image_token_id
+                inputs_embeds[image_mask] = image_embeds
+            # if pixel_values_videos is not None:
+            #     pixel_values_videos = pixel_values_videos.type(self.base.visual.get_dtype())
+            #     video_embeds = self.base.visual(pixel_values_videos, grid_thw=video_grid_thw).to(inputs_embeds.device)
+            #     video_mask = input_ids == self.base.config.video_token_id
+            #     inputs_embeds[video_mask] = video_embeds
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(inputs_embeds.device)
+
+        outputs = self.base.model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+        )
+
+        pooling_mask = attention_mask if pooling_mask is None else pooling_mask
+        left_padding = (pooling_mask[:, -1].sum() == pooling_mask.shape[0])  # TODO
+        if left_padding:
+            embeddings = outputs.last_hidden_state[:, -1]
+        else:
+            sequence_lengths = pooling_mask.sum(dim=1) - 1
+            batch_size = outputs.last_hidden_state.shape[0]
+            embeddings = outputs.last_hidden_state[torch.arange(
+                batch_size, device=outputs.last_hidden_state.device
+            ), sequence_lengths]
+        if self.normalize:
+            embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+        return embeddings.contiguous()
+
+    def embed(self, texts: list[str], images: list[Image.Image], is_query=True, instruction=None, **kwargs):
+        self.base.to(self.device)
+        # Inputs must be batched
+        input_texts, input_images = list(), list()
+        for t, i in zip(texts, images):
+            if not is_query or instruction is None:
+                instruction = self.defualt_instruction
+            input_str = ''
+            if i is None:
+                input_images = None  # All examples in the same batch are consistent
+            else:
+                input_str += '<|vision_start|><|image_pad|><|vision_end|>'
+                i = fetch_image(i)
+                input_images.append(i)
+            if t is not None:
+                input_str += t
+            msg = f'<|im_start|>system\n{instruction}<|im_end|>\n<|im_start|>user\n{input_str}<|im_end|>\n<|im_start|>assistant\n<|endoftext|>'
+            input_texts.append(msg)
+
+        inputs = self.processor(
+            text=input_texts,
+            images=input_images,
+            padding=True,
+            truncation=True,
+            max_length=self.max_length,
+            return_tensors='pt'
+        )
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}  # TODO
+        with torch.no_grad():
+            embeddings = self.forward(**inputs)
+        return embeddings
+
+    def encode(self, sentences: list[str], *, prompt_name=None, **kwargs):
+        return self.get_fused_embeddings(texts=sentences, prompt_name=prompt_name, **kwargs)
+
+    def encode_queries(self, queries: List[str], **kwargs):
+        embeddings = self.encode(queries, **kwargs)
+        return embeddings
+
+    def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs):
+        if type(corpus) is dict:
+            sentences = [
+                (corpus["title"][i] + self.sep + corpus["text"][i]).strip()
+                if "title" in corpus
+                else corpus["text"][i].strip()
+                for i in range(len(corpus["text"]))
+            ]
+        else:
+            sentences = [
+                (doc["title"] + self.sep + doc["text"]).strip() if "title" in doc else doc["text"].strip()
+                for doc in corpus
+            ]
+        embeddings = self.encode(sentences, is_query=False, **kwargs)
+        return embeddings
+
+    def get_image_embeddings(self, images: list[Image.Image] | DataLoader, **kwargs):
+        return self.get_fused_embeddings(images=images, **kwargs)
+
+    def get_text_embeddings(self, texts: list[str], **kwargs):
+        return self.get_fused_embeddings(texts=texts, **kwargs)
+
+    def get_fused_embeddings(self, texts: list[str] = None, images: list[Image.Image] | DataLoader = None, **kwargs):
+        if isinstance(images, DataLoader):
+            image_loader = images
+            batch_size = image_loader.batch_size
+            image_loader.dataset.transform = None
+        else:
+            batch_size = kwargs.pop('batch_size', 32)
+            if images is None:
+                image_loader = None
+            else:
+                image_loader = DataLoader(
+                    images,
+                    batch_size=batch_size,
+                    shuffle=False,
+                    collate_fn=custom_collate_fn,
+                    num_workers=min(math.floor(os.cpu_count() / 2), 8),
+                )
+
+        if texts is None:
+            assert image_loader is not None
+            n_batch = len(image_loader)
+        else:
+            n_batch = len(texts) // batch_size + int(len(texts) % batch_size > 0)
+            image_loader = image_loader or [None] * n_batch
+
+        all_embeddings = list()
+        none_batch = [None] * batch_size
+        show_progress_bar = kwargs.pop('show_progress_bar', True)
+        pbar = tqdm(total=n_batch, disable=not show_progress_bar, mininterval=1, miniters=10, desc='encode')
+        for n, img_batch in zip(range(0, n_batch * batch_size, batch_size), image_loader):
+            text_batch = none_batch if texts is None else texts[n: n+batch_size]
+            img_batch = none_batch if img_batch is None else img_batch
+            embeddings = self.embed(texts=text_batch, images=img_batch, **kwargs)
+            pbar.update(1)
+            all_embeddings.append(embeddings.cpu())
+        pbar.close()
+        all_embeddings = torch.cat(all_embeddings, dim=0)
+        return all_embeddings
+
+
+def custom_collate_fn(batch):
+    return batch
+
+
+### Copied from qwen_vl_utils.vision_process.py
+import base64
+from io import BytesIO
+import requests
+
+IMAGE_FACTOR = 28
+MIN_PIXELS = 4 * 28 * 28
+MAX_PIXELS = 16384 * 28 * 28
+MAX_RATIO = 200
+
+
+def round_by_factor(number: int, factor: int) -> int:
+    """Returns the closest integer to 'number' that is divisible by 'factor'."""
+    return round(number / factor) * factor
+
+
+def ceil_by_factor(number: int, factor: int) -> int:
+    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
+    return math.ceil(number / factor) * factor
+
+
+def floor_by_factor(number: int, factor: int) -> int:
+    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
+    return math.floor(number / factor) * factor
+
+
+def smart_resize(
+    height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
+) -> tuple[int, int]:
+    """
+    Rescales the image so that the following conditions are met:
+
+    1. Both dimensions (height and width) are divisible by 'factor'.
+
+    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
+
+    3. The aspect ratio of the image is maintained as closely as possible.
+    """
+    h_bar = max(factor, round_by_factor(height, factor))
+    w_bar = max(factor, round_by_factor(width, factor))
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = floor_by_factor(height / beta, factor)
+        w_bar = floor_by_factor(width / beta, factor)
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = ceil_by_factor(height * beta, factor)
+        w_bar = ceil_by_factor(width * beta, factor)
+
+    if max(h_bar, w_bar) / min(h_bar, w_bar) > MAX_RATIO:
+        logging.warning(
+            f"Absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(h_bar, w_bar) / min(h_bar, w_bar)}"
+        )
+        if h_bar > w_bar:
+            h_bar = w_bar * MAX_RATIO
+        else:
+            w_bar = h_bar * MAX_RATIO
+    return h_bar, w_bar
+
+
+def fetch_image(image: str | Image.Image, size_factor: int = IMAGE_FACTOR) -> Image.Image:
+    image_obj = None
+    if isinstance(image, Image.Image):
+        image_obj = image
+    elif image.startswith("http://") or image.startswith("https://"):
+        image_obj = Image.open(requests.get(image, stream=True).raw)
+    elif image.startswith("file://"):
+        image_obj = Image.open(image[7:])
+    elif image.startswith("data:image"):
+        if "base64," in image:
+            _, base64_data = image.split("base64,", 1)
+            data = base64.b64decode(base64_data)
+            image_obj = Image.open(BytesIO(data))
+    else:
+        image_obj = Image.open(image)
+    if image_obj is None:
+        raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
+    image = image_obj.convert("RGB")
+    ## resize
+    # if "resized_height" in ele and "resized_width" in ele:
+    #     resized_height, resized_width = smart_resize(
+    #         ele["resized_height"],
+    #         ele["resized_width"],
+    #         factor=size_factor,
+    #     )
+    # else:
+    width, height = image.size
+    # min_pixels = ele.get("min_pixels", MIN_PIXELS)
+    # max_pixels = ele.get("max_pixels", MAX_PIXELS)
+    resized_height, resized_width = smart_resize(
+        height,
+        width,
+        factor=size_factor,
+        min_pixels=MIN_PIXELS,
+        max_pixels=MAX_PIXELS,
+    )
+    image = image.resize((resized_width, resized_height))
+
+    return image
+###
+
+
+if __name__ == '__main__':
+    texts = [
+        "What kind of car is this?",
+        "The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023."
+    ]
+    images = [
+        # 'https://en.wikipedia.org/wiki/File:Tesla_Cybertruck_damaged_window.jpg',
+        '/nas-alinlp/linzhang.zx/gme_space/assets/Tesla_Cybertruck_damaged_window.jpg',
+        # 'https://en.wikipedia.org/wiki/File:2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',
+        '/nas-alinlp/linzhang.zx/gme_space/assets/2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',
+    ]
+
+    gme = GmeQwen2VL("/nas-alinlp/linzhang.zx/gme_space/gme-Qwen2-VL-2B-instruct")
+
+    # Single-modal embedding
+    e_text = gme.get_text_embeddings(texts=texts)
+    e_image = gme.get_image_embeddings(images=images)
+    print((e_text * e_image).sum(-1))
+    ## tensor([0.2281, 0.6001], dtype=torch.float16)
+
+    # How to set embedding instruction
+    e_query = gme.get_text_embeddings(texts=texts, instruction='Find an image that matches the given text.')
+    # If is_query=False, we always use the default instruction.
+    e_corpus = gme.get_image_embeddings(images=images, is_query=False)
+    print((e_query * e_corpus).sum(-1))
+    ## tensor([0.2433, 0.7051], dtype=torch.float16)
+
+    # Fused-modal embedding
+    e_fused = gme.get_fused_embeddings(texts=texts, images=images)
+    print((e_fused[0] * e_fused[1]).sum())
+    ## tensor(0.6108, dtype=torch.float16)