diff --git a/.github/scripts/spellcheck_conf/wordlist.txt b/.github/scripts/spellcheck_conf/wordlist.txt
index ceb26b3ad16c1acb36bb7b2ea73dc6c865f3753d..98fac7ed85428949840f20d5e63059b85fc19a4c 100644
--- a/.github/scripts/spellcheck_conf/wordlist.txt
+++ b/.github/scripts/spellcheck_conf/wordlist.txt
@@ -1522,3 +1522,7 @@ globals
gmail
multiagent
yyy
+jpeg
+toend
+codellama
+DIFFLOG
diff --git a/getting-started/finetuning/datasets/ocrvqa_dataset.py b/getting-started/finetuning/datasets/ocrvqa_dataset.py
index 19ce2262b89effaf3ae561bb5d73b7508ef37194..9597cac62ffcde1ca98d8173b44e871fa44fed02 100644
--- a/getting-started/finetuning/datasets/ocrvqa_dataset.py
+++ b/getting-started/finetuning/datasets/ocrvqa_dataset.py
@@ -3,46 +3,60 @@
import copy
-from datasets import load_dataset
import itertools
+
import torch
+from datasets import load_dataset
+
# check system prompt token seq or user prompt token seq is in the current token list
-def check_header(targets,seq):
- for i in range(len(seq)-3):
- if seq[i:i+3] in targets:
+def check_header(targets, seq):
+ for i in range(len(seq) - 3):
+ if seq[i : i + 3] in targets:
return True
return False
-def replace_target(target,seq):
- for i in range(len(seq)-3):
- if seq[i:i+3] == target:
- seq[i],seq[i+1],seq[i+2] = -100,-100,-100
+
+
+def replace_target(target, seq):
+ for i in range(len(seq) - 3):
+ if seq[i : i + 3] == target:
+ seq[i], seq[i + 1], seq[i + 2] = -100, -100, -100
return seq
+
+
def tokenize_dialogs(dialogs, images, processor):
text_prompt = processor.apply_chat_template(dialogs)
- batch = processor(images=images, text=text_prompt,padding = True, return_tensors="pt")
+ text_prompt = [prompt.replace('<|begin_of_text|>','') for prompt in text_prompt]
+ batch = processor(
+ images=images,
+ text=text_prompt,
+ padding=True,
+ return_tensors="pt",
+ )
label_list = []
for i in range(len(batch["input_ids"])):
dialog_tokens = batch["input_ids"][i].tolist()
labels = copy.copy(dialog_tokens)
- eot_indices = [i for i,n in enumerate(labels) if n == 128009]
+ eot_indices = [i for i, n in enumerate(labels) if n == 128009]
last_idx = 0
# system prompt header "<|start_header_id|>system<|end_header_id|>" has been tokenized to [128006, 9125, 128007]
# user prompt header "<|start_header_id|>user<|end_header_id|>" has been tokenized to [128006, 882, 128007]
- prompt_header_seqs = [[128006, 9125, 128007],[128006, 882, 128007]]
+ prompt_header_seqs = [[128006, 9125, 128007], [128006, 882, 128007]]
for n, idx in enumerate(eot_indices):
- current_seq = labels[last_idx:idx+1]
- if check_header(prompt_header_seqs,current_seq):
+ current_seq = labels[last_idx : idx + 1]
+ if check_header(prompt_header_seqs, current_seq):
# found prompt header, indicating that this seq should be masked
- labels[last_idx:idx+1] = [-100] * (idx-last_idx+1)
+ labels[last_idx : idx + 1] = [-100] * (idx - last_idx + 1)
else:
- last_idx = idx+1
+ last_idx = idx + 1
# Mask all the assistant header prompt <|start_header_id|>assistant<|end_header_id|>, which has been tokenized to [128006, 78191, 128007]
assistant_header_seq = [128006, 78191, 128007]
- labels = replace_target(assistant_header_seq,labels)
- # Mask the padding token and image token 128256
+ labels = replace_target(assistant_header_seq, labels)
+ # Mask the padding token and image token 128256
for i in range(len(labels)):
- if labels[i] == processor.tokenizer.pad_token_id or labels[i] == 128256: # 128256 is image token index
+ if (
+ labels[i] == processor.tokenizer.pad_token_id or labels[i] == 128256
+ ): # 128256 is image token index
labels[i] = -100
label_list.append(labels)
batch["labels"] = torch.tensor(label_list)
@@ -52,39 +66,74 @@ def tokenize_dialogs(dialogs, images, processor):
def get_custom_dataset(dataset_config, processor, split, split_ratio=0.9):
# load_dataset will return DatasetDict that contains all the data in the train set
dataset_dict = load_dataset("HuggingFaceM4/the_cauldron", name="ocrvqa")
- dataset = dataset_dict['train']
+ dataset = dataset_dict["train"]
# Comment out the following line to use the full dataset, for quick testing only use 2000 samples
dataset = dataset.select(range(2000))
- dataset = dataset.train_test_split(test_size=1-split_ratio, shuffle=True, seed=42)[split]
+ dataset = dataset.train_test_split(
+ test_size=1 - split_ratio, shuffle=True, seed=42
+ )[split]
return dataset
+
class OCRVQADataCollator:
def __init__(self, processor):
self.processor = processor
- self.processor.tokenizer.padding_side = "right" # during training, one always uses padding on the right
+ self.processor.tokenizer.padding_side = (
+ "right" # during training, one always uses padding on the right
+ )
+
def __call__(self, samples):
- dialogs,images = [],[]
+ dialogs, images = [], []
for sample in samples:
- image_list,sample_list = sample["images"],sample["texts"]
+ image_list, sample_list = sample["images"], sample["texts"]
if len(image_list) > 1:
raise ValueError("Only support one image per sample")
- image = image_list[0].convert("RGB") # only use the first image
+ image = image_list[0].convert("RGB") # only use the first image
dialog = []
for sample_dict in sample_list:
if not dialog:
# only append image to the first sentence
dialog += [
- {"role":"user","content":[{"type": "image"},{"type": "text", "text": sample_dict["user"].strip()}]},
- {"role":"assistant","content":[{"type": "text", "text": sample_dict["assistant"].strip()}]}
- ]
-
+ {
+ "role": "user",
+ "content": [
+ {"type": "image"},
+ {"type": "text", "text": sample_dict["user"].strip()},
+ ],
+ },
+ {
+ "role": "assistant",
+ "content": [
+ {
+ "type": "text",
+ "text": sample_dict["assistant"].strip(),
+ }
+ ],
+ },
+ ]
+
else:
dialog += [
- {"role":"user","content":[{"type": "text", "text": sample_dict["user"].strip()}]},
- {"role":"assistant","content":[{"type": "text", "text": sample_dict["assistant"].strip()}]}
- ]
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": sample_dict["user"].strip()}
+ ],
+ },
+ {
+ "role": "assistant",
+ "content": [
+ {
+ "type": "text",
+ "text": sample_dict["assistant"].strip(),
+ }
+ ],
+ },
+ ]
dialogs.append(dialog)
images.append([image])
- return tokenize_dialogs(dialogs,images, self.processor)
+ return tokenize_dialogs(dialogs, images, self.processor)
+
+
def get_data_collator(processor):
return OCRVQADataCollator(processor)
diff --git a/getting-started/inference/local_inference/multi_modal_infer.py b/getting-started/inference/local_inference/multi_modal_infer.py
index 071dc868345acb642cb4d1bb19707ae5e08b8683..8bbd0f29da137eb21daddde5feb5c7408d47d3f1 100644
--- a/getting-started/inference/local_inference/multi_modal_infer.py
+++ b/getting-started/inference/local_inference/multi_modal_infer.py
@@ -1,13 +1,15 @@
import argparse
import os
import sys
+
+import gradio as gr
import torch
from accelerate import Accelerator
+from huggingface_hub import HfFolder
+from peft import PeftModel
from PIL import Image as PIL_Image
from transformers import MllamaForConditionalGeneration, MllamaProcessor
-from peft import PeftModel
-import gradio as gr
-from huggingface_hub import HfFolder
+
# Initialize accelerator
accelerator = Accelerator()
device = accelerator.device
@@ -43,24 +45,24 @@ def load_model_and_processor(model_name: str, finetuning_path: str = None):
torch_dtype=torch.bfloat16,
use_safetensors=True,
device_map=device,
- token=hf_token
+ token=hf_token,
+ )
+ processor = MllamaProcessor.from_pretrained(
+ model_name, token=hf_token, use_safetensors=True
)
- processor = MllamaProcessor.from_pretrained(model_name, token=hf_token, use_safetensors=True)
if finetuning_path and os.path.exists(finetuning_path):
print(f"Loading LoRA adapter from '{finetuning_path}'...")
model = PeftModel.from_pretrained(
- model,
- finetuning_path,
- is_adapter=True,
- torch_dtype=torch.bfloat16
+ model, finetuning_path, is_adapter=True, torch_dtype=torch.bfloat16
)
print("LoRA adapter merged successfully")
-
+
model, processor = accelerator.prepare(model, processor)
return model, processor
-def process_image(image_path: str = None, image = None) -> PIL_Image.Image:
+
+def process_image(image_path: str = None, image=None) -> PIL_Image.Image:
"""Process and validate image input"""
if image is not None:
return image.convert("RGB")
@@ -68,29 +70,44 @@ def process_image(image_path: str = None, image = None) -> PIL_Image.Image:
return PIL_Image.open(image_path).convert("RGB")
raise ValueError("No valid image provided")
-def generate_text_from_image(model, processor, image, prompt_text: str, temperature: float, top_p: float):
+
+def generate_text_from_image(
+ model, processor, image, prompt_text: str, temperature: float, top_p: float
+):
"""Generate text from image using model"""
conversation = [
- {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt_text}]}
+ {
+ "role": "user",
+ "content": [{"type": "image"}, {"type": "text", "text": prompt_text}],
+ }
]
- prompt = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
- inputs = processor(image, prompt, return_tensors="pt").to(device)
- output = model.generate(**inputs, temperature=temperature, top_p=top_p, max_new_tokens=MAX_OUTPUT_TOKENS)
- return processor.decode(output[0])[len(prompt):]
+ prompt = processor.apply_chat_template(
+ conversation, add_generation_prompt=True, tokenize=False
+ )
+ inputs = processor(
+ image, prompt, text_kwargs={"add_special_tokens": False}, return_tensors="pt"
+ ).to(device)
+ print("Input Prompt:\n", processor.tokenizer.decode(inputs.input_ids[0]))
+ output = model.generate(
+ **inputs, temperature=temperature, top_p=top_p, max_new_tokens=MAX_OUTPUT_TOKENS
+ )
+ return processor.decode(output[0])[len(prompt) :]
+
def gradio_interface(model_name: str):
"""Create Gradio UI with LoRA support"""
# Initialize model state
current_model = {"model": None, "processor": None}
-
+
def load_or_reload_model(enable_lora: bool, lora_path: str = None):
current_model["model"], current_model["processor"] = load_model_and_processor(
- model_name,
- lora_path if enable_lora else None
+ model_name, lora_path if enable_lora else None
)
return "Model loaded successfully" + (" with LoRA" if enable_lora else "")
- def describe_image(image, user_prompt, temperature, top_k, top_p, max_tokens, history):
+ def describe_image(
+ image, user_prompt, temperature, top_k, top_p, max_tokens, history
+ ):
if image is not None:
try:
processed_image = process_image(image=image)
@@ -100,7 +117,7 @@ def gradio_interface(model_name: str):
processed_image,
user_prompt,
temperature,
- top_p
+ top_p,
)
history.append((user_prompt, result))
except Exception as e:
@@ -112,7 +129,7 @@ def gradio_interface(model_name: str):
with gr.Blocks() as demo:
gr.HTML("<h1 style='text-align: center'>Llama Vision Model Interface</h1>")
-
+
with gr.Row():
with gr.Column(scale=1):
# Model loading controls
@@ -121,58 +138,74 @@ def gradio_interface(model_name: str):
lora_path = gr.Textbox(
label="LoRA Weights Path",
placeholder="Path to LoRA weights folder",
- visible=False
+ visible=False,
)
load_status = gr.Textbox(label="Load Status", interactive=False)
load_button = gr.Button("Load/Reload Model")
# Image and parameter controls
- image_input = gr.Image(label="Image", type="pil", image_mode="RGB", height=512, width=512)
- temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.6, step=0.1)
- top_k = gr.Slider(label="Top-k", minimum=1, maximum=100, value=50, step=1)
- top_p = gr.Slider(label="Top-p", minimum=0.1, maximum=1.0, value=0.9, step=0.1)
- max_tokens = gr.Slider(label="Max Tokens", minimum=50, maximum=MAX_OUTPUT_TOKENS, value=100, step=50)
+ image_input = gr.Image(
+ label="Image", type="pil", image_mode="RGB", height=512, width=512
+ )
+ temperature = gr.Slider(
+ label="Temperature", minimum=0.1, maximum=1.0, value=0.6, step=0.1
+ )
+ top_k = gr.Slider(
+ label="Top-k", minimum=1, maximum=100, value=50, step=1
+ )
+ top_p = gr.Slider(
+ label="Top-p", minimum=0.1, maximum=1.0, value=0.9, step=0.1
+ )
+ max_tokens = gr.Slider(
+ label="Max Tokens",
+ minimum=50,
+ maximum=MAX_OUTPUT_TOKENS,
+ value=100,
+ step=50,
+ )
with gr.Column(scale=2):
chat_history = gr.Chatbot(label="Chat", height=512)
user_prompt = gr.Textbox(
- show_label=False,
- placeholder="Enter your prompt",
- lines=2
+ show_label=False, placeholder="Enter your prompt", lines=2
)
-
+
with gr.Row():
generate_button = gr.Button("Generate")
clear_button = gr.Button("Clear")
# Event handlers
enable_lora.change(
- fn=lambda x: gr.update(visible=x),
- inputs=[enable_lora],
- outputs=[lora_path]
+ fn=lambda x: gr.update(visible=x), inputs=[enable_lora], outputs=[lora_path]
)
-
+
load_button.click(
fn=load_or_reload_model,
inputs=[enable_lora, lora_path],
- outputs=[load_status]
+ outputs=[load_status],
)
generate_button.click(
fn=describe_image,
inputs=[
- image_input, user_prompt, temperature,
- top_k, top_p, max_tokens, chat_history
+ image_input,
+ user_prompt,
+ temperature,
+ top_k,
+ top_p,
+ max_tokens,
+ chat_history,
],
- outputs=[chat_history]
+ outputs=[chat_history],
)
-
+
clear_button.click(fn=clear_chat, outputs=[chat_history])
# Initial model load
load_or_reload_model(False)
return demo
+
def main(args):
"""Main execution flow"""
if args.gradio_ui:
@@ -180,27 +213,30 @@ def main(args):
demo.launch()
else:
model, processor = load_model_and_processor(
- args.model_name,
- args.finetuning_path
+ args.model_name, args.finetuning_path
)
image = process_image(image_path=args.image_path)
result = generate_text_from_image(
- model, processor, image,
- args.prompt_text,
- args.temperature,
- args.top_p
+ model, processor, image, args.prompt_text, args.temperature, args.top_p
)
print("Generated Text:", result)
+
if __name__ == "__main__":
- parser = argparse.ArgumentParser(description="Multi-modal inference with optional Gradio UI and LoRA support")
+ parser = argparse.ArgumentParser(
+ description="Multi-modal inference with optional Gradio UI and LoRA support"
+ )
parser.add_argument("--image_path", type=str, help="Path to the input image")
parser.add_argument("--prompt_text", type=str, help="Prompt text for the image")
- parser.add_argument("--temperature", type=float, default=0.7, help="Sampling temperature")
+ parser.add_argument(
+ "--temperature", type=float, default=0.7, help="Sampling temperature"
+ )
parser.add_argument("--top_p", type=float, default=0.9, help="Top-p sampling")
- parser.add_argument("--model_name", type=str, default=DEFAULT_MODEL, help="Model name")
+ parser.add_argument(
+ "--model_name", type=str, default=DEFAULT_MODEL, help="Model name"
+ )
parser.add_argument("--finetuning_path", type=str, help="Path to LoRA weights")
parser.add_argument("--gradio_ui", action="store_true", help="Launch Gradio UI")
-
+
args = parser.parse_args()
- main(args)
+ main(args)
\ No newline at end of file