update caption_cog.py, transformers, and add peft

caption_cog.py

@@ -21,7 +21,7 @@ import time
 import json
 import logging
 import re
-from typing import TYPE_CHECKING, Generator, Optional, List, Tuple, Literal
+from typing import TYPE_CHECKING, Generator, Optional, List, Tuple, Dict, Any
 
 import torch
 from torchvision import transforms
@@ -30,15 +30,19 @@ from PIL import Image
 import PIL.ImageOps as ImageOps
 from pynvml import *
 
-from transformers import AutoModelForCausalLM, LlamaTokenizer, PreTrainedTokenizer
+from transformers import AutoModelForCausalLM, LlamaTokenizer, PreTrainedTokenizer, BitsAndBytesConfig
+from transformers.modeling_outputs import BaseModelOutputWithPast
 from colorama import Fore, Style
 
 from plugins.caption_plugins import load_prompt_alteration_plugin
+from utils.patch_cog import patch_cog
+from data.gen_utils import image_generator, SUPPORTED_EXT
 
-SUPPORTED_EXT = [".jpg", ".png", ".jpeg", ".bmp", ".jfif", ".webp"]
 IMAGE_SIZE: int = 490
 PATCH_SIZE: int = 14
 
+patch_cog() # fixes inv_freq key error with cogvlm, quantization, and newer transformers revisions
+
 def build_conversation_input_ids(
             tokenizer: PreTrainedTokenizer,
             *,
@@ -86,17 +90,6 @@ def build_conversation_input_ids(
             'images': images,
         }
 
-def image_generator(image_dir: str, do_recurse: bool = True) -> Generator[str, None, None]:
-    if do_recurse:
-        for root, dirs, files in os.walk(image_dir):
-            for file in files:
-                if any(file.endswith(ext) for ext in SUPPORTED_EXT):
-                    yield os.path.join(root, file)
-    else:
-        for file in os.listdir(image_dir):
-            if any(file.endswith(ext) for ext in SUPPORTED_EXT):
-                yield os.path.join(image_dir, file)
-
 def get_gpu_memory_map():
     nvmlInit()
     handle = nvmlDeviceGetHandleByIndex(0)
@@ -114,10 +107,25 @@ def save_params(args, gen_kwargs):
     with open(save_path, "w") as f:
         f.write(pretty_print)
 
+def create_bnb_config(args):
+    return BitsAndBytesConfig(
+        bnb_4bit_compute_dtype="float32",
+        bnb_4bit_quant_type= "fp4",
+        bnb_4bit_use_double_quant=False,
+        llm_int8_enable_fp32_cpu_offload=False,
+        llm_int8_has_fp16_weight=False,
+        llm_int8_skip_modules=None,
+        llm_int8_threshold= 6.0,
+        load_in_4bit=True,
+        load_in_8bit=False,
+        quant_method="bitsandbytes"
+    )
 
 def main(args):
     prompt_plugin_fn = load_prompt_alteration_plugin(args.prompt_plugin, args=args)
 
+    bnb_config = create_bnb_config(args)
+
     tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
     model = AutoModelForCausalLM.from_pretrained(
         'THUDM/cogvlm-chat-hf',
@@ -125,7 +133,8 @@ def main(args):
         low_cpu_mem_usage=True,
         trust_remote_code=True, # gee hope they don't get hacked or have a bad internal actor
         #revision=... # no one is actually doing this
-        load_in_4bit=not args.disable_4bit,
+        #load_in_4bit=not args.disable_4bit,
+        quantization_config=bnb_config,
     )
 
     do_sample = args.top_k is not None or args.top_p is not None or args.temp is not None
@@ -214,12 +223,24 @@ def main(args):
             'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
             'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
             'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)] for _ in range(args.num_beams)],
+            'output_hidden_states': True,
+            'return_dict': True
         }
 
+        # print(f"** inputs type: {type(inputs)}") # dict
+        # print(f"** inputs len: {len(inputs)}") # 4
+        # print(f"** inputs keys: {inputs.keys()}") # dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'images'])
+        # print(f"** inputs['images'] shape: {inputs['images'].shape}") # list has no shape
+        # print(f"** image_path: {image_path}")
+
         with torch.no_grad():
             #input_decoded = tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
             #logging.debug(f"inputs decoded: {input_decoded}")
+            #print(f"calling generate with input shapes: {inputs['input_ids'].shape}, {inputs['token_type_ids'].shape}, {inputs['attention_mask'].shape}, {inputs['images'][0][0].shape}")
+            #calling generate with input shapes: torch.Size([1, 1352]), torch.Size([1, 1352]), torch.Size([1, 1352]), torch.Size([3, 490, 490])
             outputs = model.generate(**inputs, **gen_kwargs, force_words_ids=force_words_ids, bad_words_ids=bad_words_ids)
+            print(f"type of outputs: {type(outputs)}, outputs shape: {outputs.shape}") 
+            #print(f"type of hidden_states: {type(hidden_states)}, outputs shape: {hidden_states.shape}")
 
             len_inputs = inputs['input_ids'].shape[1]
             outputs_without_prompt = outputs[:, len_inputs:]

requirements.txt

@@ -1,7 +1,8 @@
 torch==2.1.0 
 torchvision==0.16.0
-transformers==4.35.0
+transformers>=4.38.2
 diffusers[torch]==0.21.4
+peft>=0.9.0
 pynvml==11.4.1
 bitsandbytes==0.41.1
 ftfy==6.1.1