update support for the following models:

- o1
- llama 3.2
- llava onevision
- molmo
- nvlm
- phi 3.5
- pixtral
- qwen2vl

src/generate_lib/llama32.py

@@ -0,0 +1,34 @@
+import requests
+import torch
+from PIL import Image
+from tqdm import tqdm
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+
+def generate_response(queries, model_path):
+    model = MllamaForConditionalGeneration.from_pretrained(model_path,
+                                                           torch_dtype=torch.bfloat16,
+                                                           device_map="auto")
+    processor = AutoProcessor.from_pretrained(model_path)
+
+    for k in tqdm(queries):
+        query = queries[k]['question']
+        image = queries[k]["figure_path"]
+        image = Image.open(image).convert('RGB')
+        messages = [
+            {"role": "user", "content": [
+                {"type": "image"},
+                {"type": "text", "text": query}
+            ]}
+        ]
+        input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = processor(
+            image,
+            input_text,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).to(model.device)
+
+        output = model.generate(**inputs, max_new_tokens=1024)
+        response = processor.decode(output[0])
+        response = response.split("<|start_header_id|>assistant<|end_header_id|>")[1].replace("<|eot_id|>", "").strip()
+        queries[k]['response'] = response

src/generate_lib/llavaov.py

@@ -0,0 +1,50 @@
+# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+
+from PIL import Image
+import requests
+import copy
+import torch
+
+import sys
+import warnings
+from tqdm import tqdm
+
+warnings.filterwarnings("ignore")
+def generate_response(queries, model_path):
+    model_name = "llava_qwen"
+    device = "cuda"
+    device_map = "auto"
+    tokenizer, model, image_processor, max_length = load_pretrained_model(model_path, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args
+
+    model.eval()
+
+    for k in tqdm(queries):
+        query = queries[k]['question']
+        image = queries[k]["figure_path"]
+        image = Image.open(image).convert('RGB')
+        image_tensor = process_images([image], image_processor, model.config)
+        image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
+
+        conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
+        question = DEFAULT_IMAGE_TOKEN + "\n{}".format(query)
+        conv = copy.deepcopy(conv_templates[conv_template])
+        conv.append_message(conv.roles[0], question)
+        conv.append_message(conv.roles[1], None)
+        prompt_question = conv.get_prompt()
+
+        input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+        image_sizes = [image.size]
+        cont = model.generate(
+            input_ids,
+            images=image_tensor,
+            image_sizes=image_sizes,
+            do_sample=False,
+            temperature=0,
+            max_new_tokens=4096,
+        )
+        text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0]
+        queries[k]['response'] = text_outputs

src/generate_lib/molmo.py

@@ -0,0 +1,36 @@
+from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
+from PIL import Image
+from tqdm import tqdm
+
+def generate_response(queries, model_path):
+    processor = AutoProcessor.from_pretrained(
+        model_path,
+        trust_remote_code=True,
+        torch_dtype='auto',
+        device_map='auto'
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        trust_remote_code=True,
+        torch_dtype='auto',
+        device_map='auto'
+    )
+
+    for k in tqdm(queries):
+        query = queries[k]['question']
+        image = queries[k]["figure_path"]
+        image = Image.open(image).convert('RGB')
+        inputs = processor.process(
+            images=[image],
+            text=query
+        )
+        inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
+        output = model.generate_from_batch(
+            inputs,
+            GenerationConfig(max_new_tokens=1024, stop_strings="<|endoftext|>"),
+            tokenizer=processor.tokenizer
+        )
+        generated_tokens = output[0,inputs['input_ids'].size(1):]
+        generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+        queries[k]['response'] = generated_text

src/generate_lib/nvlm.py

@@ -0,0 +1,132 @@
+import torch
+from transformers import AutoTokenizer, AutoModel
+import math
+from PIL import Image
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+from tqdm import tqdm
+
+def split_model():
+    device_map = {}
+    world_size = torch.cuda.device_count()
+    num_layers = 80
+    # Since the first GPU will be used for ViT, treat it as half a GPU.
+    num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
+    num_layers_per_gpu = [num_layers_per_gpu] * world_size
+    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
+    layer_cnt = 0
+    for i, num_layer in enumerate(num_layers_per_gpu):
+        for j in range(num_layer):
+            device_map[f'language_model.model.layers.{layer_cnt}'] = i
+            layer_cnt += 1
+    device_map['vision_model'] = 0
+    device_map['mlp1'] = 0
+    device_map['language_model.model.tok_embeddings'] = 0
+    device_map['language_model.model.embed_tokens'] = 0
+    device_map['language_model.output'] = 0
+    device_map['language_model.model.norm'] = 0
+    device_map['language_model.lm_head'] = 0
+    device_map['language_model.model.rotary_emb'] = 0
+    device_map[f'language_model.model.layers.{num_layers - 1}'] = 0
+
+    return device_map
+
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+
+def generate_response(queries, model_path):
+    device_map = split_model()
+    model = AutoModel.from_pretrained(
+        model_path,
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=True,
+        use_flash_attn=False,
+        trust_remote_code=True,
+        device_map=device_map).eval()
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
+    generation_config = dict(max_new_tokens=1024, do_sample=False)
+
+    for k in tqdm(queries):
+        query = queries[k]['question']
+        image = queries[k]["figure_path"]
+        query = f'<image>\n{query}'
+        pixel_values = load_image(image, max_num=12).to(torch.bfloat16)
+        response = model.chat(tokenizer, pixel_values, query, generation_config)
+        queries[k]['response'] = response

src/generate_lib/o1.py

@@ -0,0 +1,68 @@
+import base64
+import requests
+
+def get_client_model(model_path, api_key):
+    assert api_key is not None, "API key is required for using GPT"
+    assert model_path is not None, "Model name is required for using GPT"
+    model = model_path
+    client = None
+    return client, model
+
+def generate_response(image_path, query, model, media_type="image/jpeg", api_key=None, client=None, random_baseline=False):
+    def encode_image(image_path):
+        with open(image_path, "rb") as image_file:
+            return base64.b64encode(image_file.read()).decode('utf-8')
+
+    # Getting the base64 string
+    base64_image = encode_image(image_path)
+
+    headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {api_key}"
+    }
+    if not random_baseline:
+        payload = {
+        "model": model,
+        "messages": [
+            {
+            "role": "user",
+            "content": [
+                {
+                "type": "text",
+                "text": query
+                },
+                {
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/jpeg;base64,{base64_image}"
+                }
+                }
+            ]
+            }
+        ],
+        "temperature": 1.0,
+        "top_p": 1.0,
+        "seed": 42
+        }
+    else:
+        payload = {
+        "model": model,
+        "messages": [
+            {
+            "role": "user",
+            "content": [
+                {
+                "type": "text",
+                "text": query
+                }
+            ]
+            }
+        ],
+        "temperature": 1.0,
+        "top_p": 1.0,
+        "seed": 42
+        }
+
+    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+    response = response.json()
+    return response['choices'][0]['message']['content']

src/generate_lib/phi35.py

@@ -0,0 +1,41 @@
+from PIL import Image 
+import requests 
+from transformers import AutoModelForCausalLM 
+from transformers import AutoProcessor 
+from tqdm import tqdm
+
+def generate_response(queries, model_path):
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                device_map="cuda",
+                                                trust_remote_code=True,
+                                                torch_dtype="auto",
+                                                _attn_implementation='flash_attention_2')
+    processor = AutoProcessor.from_pretrained(model_path,
+                                                trust_remote_code=True,
+                                                num_crops=16)
+    for k in tqdm(queries):
+        query = queries[k]['question']
+        image = queries[k]["figure_path"]
+        image = Image.open(image).convert('RGB')
+        images = [image]
+        query = f"<|image_1|>\n{query}"
+        messages = [
+            {"role": "user", "content": query}
+        ]
+        prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = processor(prompt, images, return_tensors="pt").to("cuda:0")
+        generation_args = {
+            "max_new_tokens": 1000,
+            "temperature": 0.0,
+            "do_sample": False
+        }
+        generate_ids = model.generate(**inputs,
+                                    eos_token_id=processor.tokenizer.eos_token_id,
+                                    **generation_args)
+        generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
+        response = processor.batch_decode(generate_ids,
+                                        skip_special_tokens=True,
+                                        clean_up_tokenization_spaces=False)[0]
+        print(response)
+        queries[k]['response'] = response
+

src/generate_lib/pixtral.py

@@ -0,0 +1,24 @@
+from mistral_inference.transformer import Transformer
+from mistral_inference.generate import generate
+
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage, TextChunk, ImageURLChunk, ImageChunk
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+
+from PIL import Image
+from tqdm import tqdm
+
+def generate_response(queries, model_path):
+    tokenizer = MistralTokenizer.from_file(f"{model_path}/tekken.json")
+    model = Transformer.from_folder(model_path)
+    for k in tqdm(queries):
+        query = queries[k]['question']
+        image = queries[k]["figure_path"]
+        image = Image.open(image).convert('RGB')
+        completion_request = ChatCompletionRequest(messages=[UserMessage(content=[ImageChunk(image=image), TextChunk(text=query)])])
+        encoded = tokenizer.encode_chat_completion(completion_request)
+        images = encoded.images
+        tokens = encoded.tokens
+        out_tokens, _ = generate([tokens], model, images=[images], max_tokens=1024, temperature=0., eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+        response = tokenizer.decode(out_tokens[0])
+        queries[k]['response'] = response