upload generation_utils.py

generation_utils.py

@@ -0,0 +1,288 @@
+from typing import List
+from queue import Queue
+
+import torch
+from PIL import Image
+from copy import deepcopy
+import requests, os
+
+IMAGE_TOKEN_INDEX=-200
+blacklist = ['<image>', '<s>', '</s>']
+max_num_images = 3 # phi has a context length limit of 2048 and each image occupies 576 tokens.
+
+def input_moderation(texts: list[list[str]]):
+    # perform input moderation on each message
+    for text_pair in texts:
+        # in-place operation
+        for b in blacklist:
+            text_pair[0] = text_pair[0].replace(b, '')
+            if text_pair[1] is not None:
+                text_pair[1] = text_pair[1].replace(b, '')
+        
+    return texts
+
+def insert_image_placeholder(t, num_images, placeholder='<image>', sep='\n'):
+    for _ in range(num_images):
+        t = f"{placeholder}{sep}" + t
+    return t
+
+def get_conv(texts):
+    ret = []
+    
+    for conv in texts:
+        ret.append({'from': 'human', 'value': conv[0]})
+        ret.append({'from': 'gpt', 'value': conv[1]}) # this is None for the last one
+
+    return ret
+
+# copied from llava
+def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): 
+    prompt_chunks = [tokenizer(chunk, add_special_tokens=False).input_ids for chunk in prompt.split('<image>')]
+
+    def insert_separator(X, sep):
+        return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
+
+    input_ids = []
+    offset = 0
+    if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
+        offset = 1
+        input_ids.append(prompt_chunks[0][0])
+
+    for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
+        input_ids.extend(x[offset:])
+
+    if return_tensors is not None:
+        if return_tensors == 'pt':
+            return torch.tensor(input_ids, dtype=torch.long)
+        raise ValueError(f'Unsupported tensor type: {return_tensors}')
+    return input_ids
+    
+def preprocess(tokenizer, data: list, return_tensors='pt'):
+    '''
+    [
+        {
+            'from': 'human',
+            'value': xxx,
+        },
+        {
+            'from': 'gpt',
+            'value': xxx
+        }
+    ]
+    '''
+    # needs update
+    if not isinstance(data, list):
+        raise ValueError('must be a list')
+
+    # this is per model (tokenizer)
+    return preprocess_allava(tokenizer, data, return_tensors=return_tensors)
+
+    
+
+def preprocess_vicuna_v1(self, convs: list, return_tensors) -> list: # tokenize and concat the coversations
+    input_ids = None
+    for ind, conv in enumerate(convs):
+        if ind % 2 == 0: # human
+            h = conv['value'].strip()
+            h = f"USER: {h} " 
+            cur_input_ids = self.tokenizer_image_token(prompt=h, return_tensors=return_tensors)
+            
+            if input_ids is None:
+                input_ids = cur_input_ids
+            else:
+                input_ids = torch.cat([input_ids, cur_input_ids])
+
+        else: # gpt
+            g = conv['value']
+            if g is not None:
+                cur_input_ids = self.tokenizer(f"ASSISTANT: {g}</s>", add_special_tokens= False, max_length=self.maxlen, truncation=True, return_tensors='pt').input_ids[0]
+                input_ids = torch.cat([input_ids, cur_input_ids])
+            else:
+                cur_input_ids = self.tokenizer(f"ASSISTANT:", add_special_tokens= False, max_length=self.maxlen, truncation=True, return_tensors='pt').input_ids[0]
+                input_ids = torch.cat([input_ids, cur_input_ids])
+
+
+    return input_ids
+
+def preprocess_allava(tokenizer, convs: list, return_tensors) -> list: # tokenize and concat the coversations
+    input_ids = None
+
+
+    for ind, conv in enumerate(convs):
+        if ind % 2 == 0: # human
+            h = conv['value'].strip()
+            h = f"[INST] {h} [/INST] "
+            cur_input_ids = tokenizer_image_token(prompt=h, tokenizer=tokenizer, return_tensors=return_tensors)
+    
+            if input_ids is None:
+                input_ids = cur_input_ids
+            else:
+                input_ids = torch.cat([input_ids, cur_input_ids])
+
+        else: # gpt
+            g = conv['value']
+            if g is not None:
+                cur_input_ids = tokenizer(f"{g}{tokenizer.eos_token}", add_special_tokens= False, truncation=True, return_tensors='pt').input_ids[0]
+                input_ids = torch.cat([input_ids, cur_input_ids])
+
+    return input_ids
+
+
+# copied from llava
+def get_image_tensors(processor, images, device):
+    list_image_tensors = []
+    crop_size = processor.crop_size
+    for fp in images:
+        if fp is None: # None is used as a placeholder
+            list_image_tensors.append(torch.zeros(3, crop_size['height'], crop_size['width']).to(device))
+            continue
+        elif isinstance(fp, str):
+            image = Image.open(fp).convert('RGB')
+        elif isinstance(fp, Image.Image):
+            image = fp # already an image
+        else:
+            raise TypeError(f'Unsupported type {type(fp)}')
+
+        # this is the way of preprocessing images we used in training, so we impose it here
+        if True:
+            # self.data_args.image_aspect_ratio == 'pad'
+            def expand2square(pil_img, background_color):
+                width, height = pil_img.size
+                if pil_img.mode == 'L':
+                    pil_img = pil_img.convert('RGB')
+
+                if width == height:
+                    return pil_img
+                elif width > height:
+                    result = Image.new(pil_img.mode, (width, width), background_color)
+                    result.paste(pil_img, (0, (width - height) // 2))
+                    return result
+                else:
+                    result = Image.new(pil_img.mode, (height, height), background_color)
+                    result.paste(pil_img, ((height - width) // 2, 0))
+                    return result
+            
+            image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
+            image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+        else:
+            image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] # a tensor
+        list_image_tensors.append(image.to(device))
+        # list_image_tensors.append(image)
+    return list_image_tensors
+
+
+
+
+def build_allava_input(tokenizer, processor, texts, images, history=None, return_history=False, device='cuda'):
+    '''
+    texts: [[]]
+    '''
+
+    ############################
+    # 1. preprocess texts
+    ############################
+    if isinstance(texts, str):
+        texts = [[texts, None]]
+    else:
+        assert isinstance(texts, list) and isinstance(texts[0], list) , 'texts must be a list of list'
+    
+    if history is not None:
+        texts = history + texts # concat them together
+
+    texts = input_moderation(texts)
+
+
+    ############################
+    # 2. preprocess images
+    ############################
+    if isinstance(images, str) or isinstance(images, Image.Image):
+        images = [images]
+
+    valid_images = []
+    if images is None:
+        images = [None]
+    
+    for img in images:
+        try:
+            if os.path.exists(img): # make sure that the path exists
+                img = Image.open(img).convert('RGB') 
+            else: # else it must be a URL
+                img = Image.open(requests.get(img, stream=True).raw)
+
+            valid_images.append(img)
+        except:
+            continue
+        
+    images = valid_images
+
+    if images == []:
+        images = [None]
+        
+
+    assert len(images) < max_num_images, f'Currently at most {max_num_images} images are supported'
+
+    ############################
+    # 3. collate conv
+    ############################
+
+    history = deepcopy(texts) # history is the texts without <image> placeholders
+
+    # insert <image>
+    image_place_holder_inserted = insert_image_placeholder(texts[0][0], len(images) if None not in images else 0) # only insert the placeholders for user input at the 1st round
+    texts[0][0] = image_place_holder_inserted
+
+    # collate strings into conv
+    conv = get_conv(texts)
+
+    # make input ids
+    input_ids = preprocess(tokenizer, conv, return_tensors='pt').unsqueeze(0).to(device)
+
+    list_image_tensors = get_image_tensors(processor, images, device)
+    image_tensors = torch.stack(list_image_tensors)
+
+    try:
+        dtype = torch.bfloat16
+        # if your hardware does not support bf16, the following line raises an error
+        torch.tensor(1, dtype=dtype).cuda()
+    except:
+        # default using fp16
+        dtype = torch.float16
+
+    if return_history:
+        return input_ids, image_tensors, history
+    
+    return input_ids, image_tensors, None
+
+
+
+class TextIterStreamer:
+    def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
+        self.tokenizer = tokenizer
+        self.skip_prompt = skip_prompt
+        self.skip_special_tokens = skip_special_tokens
+        self.tokens = []
+        self.text_queue = Queue()
+        self.next_tokens_are_prompt = True
+
+    def put(self, value):
+        if self.skip_prompt and self.next_tokens_are_prompt:
+            self.next_tokens_are_prompt = False
+        else:
+            if len(value.shape) > 1:
+                value = value[0]
+            self.tokens.extend(value.tolist())
+            self.text_queue.put(
+                self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens))
+
+    def end(self):
+        self.text_queue.put(None)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        value = self.text_queue.get()
+        if value is None:
+            raise StopIteration()
+        else:
+            return value