Merge pull request #570 from mcmonkey4eva/add-train-lora-tab

LoRA Training Tab

css/main.css

@@ -41,7 +41,7 @@ ol li p, ul li p {
     display: inline-block;
 }
 
-#main, #parameters, #chat-settings, #interface-mode, #lora {
+#main, #parameters, #chat-settings, #interface-mode, #lora, #training-tab {
   border: 0;
 }
 

modules/training.py

@@ -0,0 +1,267 @@
+import json
+import sys
+import threading
+import time
+from pathlib import Path
+
+import gradio as gr
+import torch
+import transformers
+from datasets import Dataset, load_dataset
+from peft import (LoraConfig, get_peft_model, get_peft_model_state_dict,
+                  prepare_model_for_int8_training)
+
+from modules import shared, ui
+
+WANT_INTERRUPT = False
+CURRENT_STEPS = 0
+MAX_STEPS = 0
+CURRENT_GRADIENT_ACCUM = 1
+
+def get_dataset(path: str, ext: str):
+    return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path(path).glob(f'*.{ext}'))), key=str.lower)
+
+def create_train_interface():
+    with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
+        lora_name = gr.Textbox(label="Name", info="The name of your new LoRA file")
+        with gr.Row():
+            # TODO: Implement multi-device support.
+            micro_batch_size = gr.Slider(label='Micro Batch Size', value=4, minimum=1, maximum=128, step=1, info='Per-device batch size (NOTE: multiple devices not yet implemented). Increasing this will increase VRAM usage.')
+            batch_size = gr.Slider(label='Batch Size', value=128, minimum=0, maximum=1024, step=4, info='Global batch size. The two batch sizes together determine gradient accumulation (gradientAccum = batch / microBatch). Higher gradient accum values lead to better quality training.')
+
+        with gr.Row():
+            epochs = gr.Number(label='Epochs', value=3, info='Number of times every entry in the dataset should be fed into training. So 1 means feed each item in once, 5 means feed it in five times, etc.')
+            learning_rate = gr.Textbox(label='Learning Rate', value='3e-4', info='Learning rate, in scientific notation. 3e-4 is a good starting base point. 1e-2 is extremely high, 1e-6 is extremely low.')
+
+        # TODO: What is the actual maximum rank? Likely distinct per model. This might be better to somehow be on a log scale.
+        lora_rank = gr.Slider(label='LoRA Rank', value=32, minimum=0, maximum=1024, step=4, info='LoRA Rank, or dimension count. Higher values produce a larger file with better control over the model\'s content. Smaller values produce a smaller file with less overall control. Small values like 4 or 8 are great for stylistic guidance, high values like 128 or 256 are good for teaching content upgrades. Higher ranks also require higher VRAM.')
+        lora_alpha = gr.Slider(label='LoRA Alpha', value=64, minimum=0, maximum=2048, step=4, info='LoRA Alpha. This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.')
+        # TODO: Better explain what this does, in terms of real world effect especially.
+        lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.05, info='Percentage probability for dropout of LoRA layers.')
+        cutoff_len = gr.Slider(label='Cutoff Length', minimum=0, maximum=2048, value=256, step=32, info='Cutoff length for text input. Essentially, how long of a line of text to feed in at a time. Higher values require drastically more VRAM.')
+
+        with gr.Tab(label="Formatted Dataset"):
+            with gr.Row():
+                dataset = gr.Dropdown(choices=get_dataset('training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.')
+                ui.create_refresh_button(dataset, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'json')}, 'refresh-button')
+                eval_dataset = gr.Dropdown(choices=get_dataset('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The dataset file used to evaluate the model after training.')
+                ui.create_refresh_button(eval_dataset, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'json')}, 'refresh-button')
+                format = gr.Dropdown(choices=get_dataset('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.')
+                ui.create_refresh_button(format, lambda : None, lambda : {'choices': get_dataset('training/formats', 'json')}, 'refresh-button')
+        with gr.Tab(label="Raw Text File"):
+            with gr.Row():
+                raw_text_file = gr.Dropdown(choices=get_dataset('training/datasets', 'txt'), value='None', label='Text File', info='The raw text file to use for training.')
+                ui.create_refresh_button(raw_text_file, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'txt')}, 'refresh-button')
+                overlap_len = gr.Slider(label='Overlap Length', minimum=0,maximum=512, value=128, step=16, info='Overlap length - ie how many tokens from the prior chunk of text to include into the next chunk. (The chunks themselves will be of a size determined by Cutoff Length above). Setting overlap to exactly half the cutoff length may be ideal.')
+
+        with gr.Row():
+            start_button = gr.Button("Start LoRA Training")
+            stop_button = gr.Button("Interrupt")
+
+        output = gr.Markdown(value="Ready")
+        start_button.click(do_train, [lora_name, micro_batch_size, batch_size, epochs, learning_rate, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, raw_text_file, overlap_len], [output])
+        stop_button.click(do_interrupt, [], [], cancels=[], queue=False)
+
+def do_interrupt():
+    global WANT_INTERRUPT
+    WANT_INTERRUPT = True
+
+class Callbacks(transformers.TrainerCallback):
+    def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
+        global CURRENT_STEPS, MAX_STEPS
+        CURRENT_STEPS = state.global_step * CURRENT_GRADIENT_ACCUM
+        MAX_STEPS = state.max_steps * CURRENT_GRADIENT_ACCUM
+        if WANT_INTERRUPT:
+            control.should_epoch_stop = True
+            control.should_training_stop = True
+    def on_substep_end(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
+        global CURRENT_STEPS
+        CURRENT_STEPS += 1
+        if WANT_INTERRUPT:
+            control.should_epoch_stop = True
+            control.should_training_stop = True
+
+def clean_path(base_path: str, path: str):
+    """"Strips unusual symbols and forcibly builds a path as relative to the intended directory."""
+    # TODO: Probably could do with a security audit to guarantee there's no ways this can be bypassed to target an unwanted path.
+    # Or swap it to a strict whitelist of [a-zA-Z_0-9]
+    path = path.replace('\\', '/').replace('..', '_')
+    if base_path is None:
+        return path
+    return f'{Path(base_path).absolute()}/{path}'
+
+def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lora_rank: int,
+             lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, raw_text_file: str, overlap_len: int):
+    global WANT_INTERRUPT, CURRENT_STEPS, MAX_STEPS, CURRENT_GRADIENT_ACCUM
+    WANT_INTERRUPT = False
+    CURRENT_STEPS = 0
+    MAX_STEPS = 0
+
+    # == Input validation / processing ==
+    yield "Prepping..."
+    lora_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}"
+    actual_lr = float(learning_rate)
+
+    if cutoff_len <= 0 or micro_batch_size <= 0 or batch_size <= 0 or actual_lr <= 0 or lora_rank <= 0 or lora_alpha <= 0:
+        yield f"Cannot input zeroes."
+        return
+
+    gradient_accumulation_steps = batch_size // micro_batch_size
+    CURRENT_GRADIENT_ACCUM = gradient_accumulation_steps
+    shared.tokenizer.pad_token = 0
+    shared.tokenizer.padding_side = "left"
+
+    def tokenize(prompt):
+        result = shared.tokenizer(prompt, truncation=True, max_length=cutoff_len + 1, padding="max_length")
+        return {
+            "input_ids": result["input_ids"][:-1],
+            "attention_mask": result["attention_mask"][:-1],
+        }
+
+    # == Prep the dataset, format, etc ==
+    if raw_text_file is not None:
+        print("Loading raw text file dataset...")
+        with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r') as file:
+            raw_text = file.read()
+        tokens = shared.tokenizer.encode(raw_text)
+        del raw_text # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM
+        tokens = list(split_chunks(tokens, cutoff_len - overlap_len))
+        for i in range(1, len(tokens)):
+            tokens[i] = tokens[i - 1][-overlap_len:] + tokens[i]
+        text_chunks = [shared.tokenizer.decode(x) for x in tokens]
+        del tokens
+        data = Dataset.from_list([tokenize(x) for x in text_chunks])
+        train_data = data.shuffle()
+        eval_data = None
+        del text_chunks
+
+    else:
+        with open(clean_path('training/formats', f'{format}.json'), 'r') as formatFile:
+            format_data: dict[str, str] = json.load(formatFile)
+
+        if dataset is None:
+            yield "**Missing dataset choice input, cannot continue.**"
+            return
+        if format is None:
+            yield "**Missing format choice input, cannot continue.**"
+            return
+
+        def generate_prompt(data_point: dict[str, str]):
+            for options, data in format_data.items():
+                if set(options.split(',')) == set(x[0] for x in data_point.items() if len(x[1].strip()) > 0):
+                    for key, val in data_point.items():
+                        data = data.replace(f'%{key}%', val)
+                return data
+            raise RuntimeError(f'Data-point "{data_point}" has no keyset match within format "{list(format_data.keys())}"')
+
+        def generate_and_tokenize_prompt(data_point):
+            prompt = generate_prompt(data_point)
+            return tokenize(prompt)
+
+        print("Loading JSON datasets...")
+        data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json'))
+        train_data = data['train'].shuffle().map(generate_and_tokenize_prompt)
+
+        if eval_dataset == 'None':
+            eval_data = None
+        else:
+            eval_data = load_dataset("json", data_files=clean_path('training/datasets', f'{eval_dataset}.json'))
+            eval_data = eval_data['train'].shuffle().map(generate_and_tokenize_prompt)
+    
+    # == Start prepping the model itself ==
+    if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):
+        print("Getting model ready...")
+        prepare_model_for_int8_training(shared.model)
+    
+    print("Prepping for training...")
+    config = LoraConfig(
+        r=lora_rank,
+        lora_alpha=lora_alpha,
+        # TODO: Should target_modules be configurable?
+        target_modules=[ "q_proj", "v_proj" ],
+        lora_dropout=lora_dropout,
+        bias="none",
+        task_type="CAUSAL_LM"
+    )
+    lora_model = get_peft_model(shared.model, config)
+    trainer = transformers.Trainer(
+        model=lora_model,
+        train_dataset=train_data,
+        eval_dataset=eval_data,
+        args=transformers.TrainingArguments(
+            per_device_train_batch_size=micro_batch_size,
+            gradient_accumulation_steps=gradient_accumulation_steps,
+            # TODO: Should more of these be configurable? Probably.
+            warmup_steps=100,
+            num_train_epochs=epochs,
+            learning_rate=actual_lr,
+            fp16=True,
+            logging_steps=20,
+            evaluation_strategy="steps" if eval_data is not None else "no",
+            save_strategy="steps",
+            eval_steps=200 if eval_data is not None else None,
+            save_steps=200,
+            output_dir=lora_name,
+            save_total_limit=3,
+            load_best_model_at_end=True if eval_data is not None else False,
+            # TODO: Enable multi-device support
+            ddp_find_unused_parameters=None
+        ),
+        data_collator=transformers.DataCollatorForLanguageModeling(shared.tokenizer, mlm=False),
+        callbacks=list([Callbacks()])
+    )
+
+    lora_model.config.use_cache = False
+    old_state_dict = lora_model.state_dict
+    lora_model.state_dict = (
+        lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
+    ).__get__(lora_model, type(lora_model))
+
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        lora_model = torch.compile(lora_model)
+
+    # == Main run and monitor loop ==
+    # TODO: save/load checkpoints to resume from?
+    print("Starting training...")
+    yield "Starting..."
+
+    def threadedRun():
+        trainer.train()
+
+    thread = threading.Thread(target=threadedRun)
+    thread.start()
+    lastStep = 0
+    startTime = time.perf_counter()
+
+    while thread.is_alive():
+        time.sleep(0.5)
+        if WANT_INTERRUPT:
+            yield "Interrupting, please wait... *(Run will stop after the current training step completes.)*"
+        elif CURRENT_STEPS != lastStep:
+            lastStep = CURRENT_STEPS
+            timeElapsed = time.perf_counter() - startTime
+            if timeElapsed <= 0:
+                timerInfo = ""
+                totalTimeEstimate = 999
+            else:
+                its = CURRENT_STEPS / timeElapsed
+                if its > 1:
+                    timerInfo = f"`{its:.2f}` it/s"
+                else:
+                    timerInfo = f"`{1.0/its:.2f}` s/it"
+                totalTimeEstimate = (1.0/its) * (MAX_STEPS)
+            yield f"Running... **{CURRENT_STEPS}** / **{MAX_STEPS}** ... {timerInfo}, `{timeElapsed:.0f}`/`{totalTimeEstimate:.0f}` seconds"
+
+    print("Training complete, saving...")
+    lora_model.save_pretrained(lora_name)
+
+    if WANT_INTERRUPT:
+        print("Training interrupted.")
+        yield f"Interrupted. Incomplete LoRA saved to `{lora_name}`"
+    else:
+        print("Training complete!")
+        yield f"Done! LoRA saved to `{lora_name}`"
+
+def split_chunks(arr, step):
+    for i in range(0, len(arr), step):
+        yield arr[i:i + step]

requirements.txt

@@ -10,4 +10,5 @@ rwkv==0.7.1
 safetensors==0.3.0
 sentencepiece
 tqdm
+datasets
 git+https://github.com/huggingface/transformers

server.py

@@ -9,10 +9,8 @@ from pathlib import Path
 
 import gradio as gr
 
-import modules.chat as chat
 import modules.extensions as extensions_module
-import modules.shared as shared
-import modules.ui as ui
+from modules import chat, shared, training, ui
 from modules.html_generator import generate_chat_html
 from modules.LoRA import add_lora_to_model
 from modules.models import load_model, load_soft_prompt
@@ -57,7 +55,7 @@ def get_available_softprompts():
     return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('softprompts').glob('*.zip'))), key=str.lower)
 
 def get_available_loras():
-    return ['None'] + sorted([item.name for item in list(Path('shared.args.lora_dir').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
+    return ['None'] + sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
 
 def unload_model():
     shared.model = shared.tokenizer = None
@@ -475,6 +473,9 @@ def create_interface():
             shared.gradio['Stop'].click(stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None)
             shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")
 
+        with gr.Tab("Training", elem_id="training-tab"):
+            training.create_train_interface()
+
         with gr.Tab("Interface mode", elem_id="interface-mode"):
             modes = ["default", "notebook", "chat", "cai_chat"]
             current_mode = "default"

training/formats/alpaca-chatbot-format.json

@@ -0,0 +1,4 @@
+{
+    "instruction,output": "User: %instruction%\nAssistant: %output%",
+    "instruction,input,output": "User: %instruction%: %input%\nAssistant: %output%"
+}

training/formats/alpaca-format.json

@@ -0,0 +1,4 @@
+{
+    "instruction,output": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%",
+    "instruction,input,output": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%"
+}