diff options
| author | pks <pks@pks.rocks> | 2025-11-21 23:02:16 +0100 |
|---|---|---|
| committer | pks <pks@pks.rocks> | 2025-11-21 23:02:16 +0100 |
| commit | 1cc209768ad0299c396af56231c43fded7dc3515 (patch) | |
| tree | bc7fc3dcfd094078e052807f4e336dc2a5e0a43b | |
| parent | ea7a98c4f6fc0d0df785ef601e9fc30b78e51335 (diff) | |
WIP
| -rw-r--r-- | finetune.py | 131 | ||||
| -rw-r--r-- | inference.py | 46 | ||||
| -rw-r--r-- | pyproject.toml | 3 | ||||
| -rw-r--r-- | uv.lock | 370 |
4 files changed, 436 insertions, 114 deletions
diff --git a/finetune.py b/finetune.py index f747f99..01012c5 100644 --- a/finetune.py +++ b/finetune.py @@ -1,5 +1,11 @@ +import json +import os import torch + from datasets import Dataset, Image +from functools import partial +from glob import glob +from peft import LoraConfig from transformers import ( AutoProcessor, AutoModelForCausalLM, @@ -7,38 +13,7 @@ from transformers import ( BitsAndBytesConfig, ) from trl import SFTTrainer -from peft import LoraConfig -from glob import glob -import json -import os - - -model_name = "google/gemma-3-4b-it" - -bnb_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype=torch.bfloat16, -) - -processor = AutoProcessor.from_pretrained(model_name, use_fast=True) - -model = AutoModelForCausalLM.from_pretrained( - model_name, - quantization_config=bnb_config, - device_map="auto", -) -peft_config = LoraConfig( - lora_alpha=32, - lora_dropout=0.05, - r=16, - task_type="CAUSAL_LM", - bias="none", - target_modules="all-linear", - modules_to_save=["lm_head", "embed_tokens"], -) def make_dataset(base="./baseline"): prompt = "You are a professional English-German translator and also a renowned photography critic.\n\nWrite a detailed caption for this image in a single sentence. Translate the caption into German. The output needs to be JSON, the keys being 'English' and 'German' for the respective captions. Only output the JSON, nothing else." + "<start_of_image>" @@ -59,9 +34,8 @@ def make_dataset(base="./baseline"): return Dataset.from_dict({"image": images, "user": user_prompts, "assistant": assistant_replies}).cast_column("image", Image()) -dataset = make_dataset() -def add_chat_text(example): +def add_chat_text(example, processor): messages = [ {"role": "user", "content": example["user"]}, {"role": "assistant", "content": example["assistant"]}, @@ -71,11 +45,11 @@ def add_chat_text(example): tokenize=False, add_generation_prompt=False, ) + return example -dataset = dataset.map(add_chat_text) -def collate(batch): +def collate(batch, processor): images = [i["image"] for i in batch] texts = [i["text"] for i in batch] out = processor( @@ -91,32 +65,65 @@ def collate(batch): return out -ds_split = dataset.train_test_split(test_size=0.2, seed=42) - -args = TrainingArguments( - output_dir="gemma3-mm-sft-lora", - per_device_train_batch_size=1, - gradient_accumulation_steps=24, - num_train_epochs=1, - learning_rate=1e-5, - fp16=False, - bf16=True, - gradient_checkpointing=True, - gradient_checkpointing_kwargs={"use_reentrant": False}, - optim="paged_adamw_8bit", - remove_unused_columns=False, - logging_steps=10, - save_steps=20, -) +def main(): + model_name = "google/gemma-3-4b-it" + + bnb_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.bfloat16, + ) + + processor = AutoProcessor.from_pretrained(model_name, use_fast=True) + + model = AutoModelForCausalLM.from_pretrained( + model_name, + quantization_config=bnb_config, + device_map="auto", + ) + + peft_config = LoraConfig( + lora_alpha=32, + lora_dropout=0.05, + r=16, + task_type="CAUSAL_LM", + bias="none", + target_modules="all-linear", + modules_to_save=["lm_head", "embed_tokens"], + ) + + dev_ds = make_dataset("./baseline/files_dev").map(partial(add_chat_text, processor=processor)) + train_ds = make_dataset("./baseline/files_train").map(partial(add_chat_text, processor=processor)) + + args = TrainingArguments( + output_dir="gemma3-mm-sft-lora", + per_device_train_batch_size=1, + gradient_accumulation_steps=24, + num_train_epochs=3, + learning_rate=1e-5, + fp16=False, + bf16=True, + gradient_checkpointing=True, + gradient_checkpointing_kwargs={"use_reentrant": False}, + optim="paged_adamw_8bit", + remove_unused_columns=False, + logging_steps=10, + save_steps=100, + ) + + trainer = SFTTrainer( + model=model, + train_dataset=train_ds, + eval_dataset=dev_ds, + data_collator=partial(collate, processor=processor), + args=args, + peft_config=peft_config, + ) + + trainer.train() + trainer.save_model() -trainer = SFTTrainer( - model=model, - train_dataset=ds_split["train"], - eval_dataset=ds_split["test"], - data_collator=collate, - args=args, - peft_config=peft_config, -) -trainer.train() -trainer.save_model() +if __name__ == "__main__": + main() diff --git a/inference.py b/inference.py index fc8c1cc..b08a3f6 100644 --- a/inference.py +++ b/inference.py @@ -26,6 +26,15 @@ def captioning_prompt(image): } ] + +def captioning_prompt_with_source(image, source): + caption = captioning_prompt(image) + prefix = json.dumps({"English": source}).removesuffix("}") + ', "German": "' + caption.append({"role": "assistant", "content": [{"type": "text", "text": prefix}]}) + + return caption + + def translation_prompt(source): return [ { @@ -66,7 +75,7 @@ def main(): from peft import PeftModel model = PeftModel.from_pretrained(model, sys.argv[1]) - if True: + if True: # Generate German translation given English source for filename in glob("*.jsonl"): sys.stderr.write(f"Processing {filename=}\n") with open(filename, "r+") as f: @@ -97,13 +106,13 @@ def main(): f.truncate() sys.stderr.write(f"{decoded=}\n") - else: + elif 2 == 3: # Generate caption & translation from scratch for filename in glob("../Images/*.jpg"): + image = "../Images/" + os.path.basename(filename).removesuffix(".jsonl") + ".jpg" sys.stderr.write(f"Processing {filename=}\n") inputs = make_inputs(processor, captioning_prompt(Image.open(filename)), model.device) - input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): @@ -123,6 +132,37 @@ def main(): sys.stderr.write(f"{decoded=}\n") with open(f"{os.path.basename(filename).removesuffix('.jpg')}.jsonl", "w") as f: f.write(f"{decoded}\n") + elif False: # Generate German translation given English caption and image + for filename in glob("./baseline/files_test/*.jsonl"): + image = "../Images/" + os.path.basename(filename).removesuffix(".jsonl") + ".jpg" + sys.stderr.write(f"Processing {filename=}\n") + with open(filename, "r+") as f: + data = json.loads(f.read()) + prompt = captioning_prompt_with_source(Image.open(image), data["English"]) + inputs = make_inputs(processor, + prompt, + model.device) + + input_len = inputs["input_ids"].shape[-1] # Will not cut off assistant prefix + + with torch.inference_mode(): + generation = model.generate(**inputs, + max_new_tokens=300, + do_sample=True, + top_p=1.0, + top_k=50) + generation = generation[0] # batch size 1 + truncated_generation = generation[input_len:] + + decoded = processor.decode(truncated_generation, skip_special_tokens=True).removeprefix("```json").removesuffix("```").replace("\n", "").strip() + try: + _ = json.loads(decoded) + except: + sys.stderr.write(f"Error loading JSON from string '{decoded}' for {filename=}\n") + + sys.stderr.write(f"{decoded=}\n") + with open(f"{os.path.basename(filename)}", "w") as f: + f.write(f"{decoded}\n") if __name__ == "__main__": diff --git a/pyproject.toml b/pyproject.toml index 1f21c48..bbf1288 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,16 +9,19 @@ dependencies = [ "bitsandbytes>=0.48.2", "datasets>=4.4.1", "diskcache>=5.6.3", + "huggingface-hub>=0.36.0", "ipdb>=0.13.13", "ninja>=1.13.0", "openai>=2.8.0", "packaging>=25.0", "peft>=0.18.0", "pillow>=12.0.0", + "sacrebleu>=2.5.1", "termcolor>=3.2.0", "torchvision>=0.24.1", "transformers>=4.57.1", "trl>=0.25.1", + "unbabel-comet>=2.2.7", ] [tool.uv.sources] @@ -294,6 +294,17 @@ wheels = [ ] [[package]] +name = "entmax" +version = "1.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "torch" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/06/a0/71747f0d98e441d0670b06205afd24d832e88c0ee62129ca47ce88505304/entmax-1.3-py3-none-any.whl", hash = "sha256:41bb1edbd497a1b53b1f0fc80befd543499dc704b4e5436ddf6c5728a2d00546", size = 13359, upload-time = "2024-02-07T10:09:38.865Z" }, +] + +[[package]] name = "executing" version = "2.2.1" source = { registry = "https://pypi.org/simple" } @@ -408,16 +419,19 @@ dependencies = [ { name = "bitsandbytes" }, { name = "datasets" }, { name = "diskcache" }, + { name = "huggingface-hub" }, { name = "ipdb" }, { name = "ninja" }, { name = "openai" }, { name = "packaging" }, { name = "peft" }, { name = "pillow" }, + { name = "sacrebleu" }, { name = "termcolor" }, { name = "torchvision" }, { name = "transformers" }, { name = "trl" }, + { name = "unbabel-comet" }, ] [package.metadata] @@ -427,16 +441,19 @@ requires-dist = [ { name = "bitsandbytes", specifier = ">=0.48.2" }, { name = "datasets", specifier = ">=4.4.1" }, { name = "diskcache", specifier = ">=5.6.3" }, + { name = "huggingface-hub", specifier = ">=0.36.0" }, { name = "ipdb", specifier = ">=0.13.13" }, { name = "ninja", specifier = ">=1.13.0" }, { name = "openai", specifier = ">=2.8.0" }, { name = "packaging", specifier = ">=25.0" }, { name = "peft", specifier = ">=0.18.0" }, { name = "pillow", specifier = ">=12.0.0" }, + { name = "sacrebleu", specifier = ">=2.5.1" }, { name = "termcolor", specifier = ">=3.2.0" }, { name = "torchvision", specifier = ">=0.24.1" }, { name = "transformers", specifier = ">=4.57.1" }, { name = "trl", git = "https://github.com/huggingface/trl.git" }, + { name = "unbabel-comet", specifier = ">=2.2.7" }, ] 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