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https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
Fix camelCase to snake_case to match repo format standard
This commit is contained in:
Alex "mcmonkey" Goodwin
parent
2f0571bfa4
commit
6368dad7db
@ -25,35 +25,40 @@ def get_json_dataset(path: str):
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def create_train_interface():
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with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
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loraName = gr.Textbox(label="Name", info="The name of your new LoRA file")
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lora_name = gr.Textbox(label="Name", info="The name of your new LoRA file")
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with gr.Row():
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# TODO: Implement multi-device support.
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microBatchSize = 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.')
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batchSize = gr.Slider(label='Batch Size', value=128, minimum=1, 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.')
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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.')
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batch_size = gr.Slider(label='Batch Size', value=128, minimum=1, 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.')
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with gr.Row():
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epochs = gr.Number(label='Epochs', value=1, 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.')
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learningRate = 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.')
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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.')
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# TODO: What is the actual maximum rank? Likely distinct per model. This might be better to somehow be on a log scale.
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loraRank = gr.Slider(label='LoRA Rank', value=8, minimum=1, 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.')
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loraAlpha = gr.Slider(label='LoRA Alpha', value=16, minimum=1, 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.')
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lora_rank = gr.Slider(label='LoRA Rank', value=8, minimum=1, 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.')
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lora_alpha = gr.Slider(label='LoRA Alpha', value=16, minimum=1, 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.')
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# TODO: Better explain what this does, in terms of real world effect especially.
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loraDropout = 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.')
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cutoffLen = gr.Slider(label='Cutoff Length', minimum=1,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.')
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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.')
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cutoff_len = gr.Slider(label='Cutoff Length', minimum=1,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.')
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with gr.Row():
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datasetFunction = get_json_dataset('training/datasets')
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dataset = gr.Dropdown(choices=datasetFunction(), value='None', label='Dataset', info='The dataset file to use for training.')
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ui.create_refresh_button(dataset, lambda : None, lambda : {'choices': datasetFunction()}, 'refresh-button')
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evalDataset = gr.Dropdown(choices=datasetFunction(), value='None', label='Evaluation Dataset', info='The dataset file used to evaluate the model after training.')
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ui.create_refresh_button(evalDataset, lambda : None, lambda : {'choices': datasetFunction()}, 'refresh-button')
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formatsFunction = get_json_dataset('training/formats')
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format = gr.Dropdown(choices=formatsFunction(), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.')
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ui.create_refresh_button(format, lambda : None, lambda : {'choices': formatsFunction()}, 'refresh-button')
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dataset_function = get_json_dataset('training/datasets')
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dataset = gr.Dropdown(choices=dataset_function(), value='None', label='Dataset', info='The dataset file to use for training.')
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ui.create_refresh_button(dataset, lambda : None, lambda : {'choices': dataset_function()}, 'refresh-button')
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eval_dataset = gr.Dropdown(choices=dataset_function(), value='None', label='Evaluation Dataset', info='The dataset file used to evaluate the model after training.')
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ui.create_refresh_button(eval_dataset, lambda : None, lambda : {'choices': dataset_function()}, 'refresh-button')
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formats_function = get_json_dataset('training/formats')
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format = gr.Dropdown(choices=formats_function(), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.')
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ui.create_refresh_button(format, lambda : None, lambda : {'choices': formats_function()}, 'refresh-button')
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with gr.Row():
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startButton = gr.Button("Start LoRA Training")
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stopButton = gr.Button("Interrupt")
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start_button = gr.Button("Start LoRA Training")
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stop_button = gr.Button("Interrupt")
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output = gr.Markdown(value="Ready")
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startEvent = startButton.click(do_train, [loraName, microBatchSize, batchSize, epochs, learningRate, loraRank, loraAlpha, loraDropout, cutoffLen, dataset, evalDataset, format], [output])
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stopButton.click(doInterrupt, [], [], cancels=[], queue=False)
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startEvent = 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], [output])
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stop_button.click(doInterrupt, [], [], cancels=[], queue=False)
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def doInterrupt():
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global WANT_INTERRUPT
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@ -74,108 +79,119 @@ class Callbacks(transformers.TrainerCallback):
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control.should_epoch_stop = True
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control.should_training_stop = True
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def cleanPath(basePath: str, path: str):
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def cleanPath(base_path: str, path: str):
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""""Strips unusual symbols and forcibly builds a path as relative to the intended directory."""
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# TODO: Probably could do with a security audit to guarantee there's no ways this can be bypassed to target an unwanted path.
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# Or swap it to a strict whitelist of [a-zA-Z_0-9]
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path = path.replace('\\', '/').replace('..', '_')
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if basePath is None:
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if base_path is None:
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return path
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return f'{Path(basePath).absolute()}/{path}'
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return f'{Path(base_path).absolute()}/{path}'
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def do_train(loraName: str, microBatchSize: int, batchSize: int, epochs: int, learningRate: float, loraRank: int, loraAlpha: int, loraDropout: float, cutoffLen: int, dataset: str, evalDataset: str, format: str):
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def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: float, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str):
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global WANT_INTERRUPT, CURRENT_STEPS, MAX_STEPS, CURRENT_GRADIENT_ACCUM
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WANT_INTERRUPT = False
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CURRENT_STEPS = 0
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MAX_STEPS = 0
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yield "Prepping..."
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# == Input validation / processing ==
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yield "Prepping..."
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# TODO: --lora-dir PR once pulled will need to be applied here
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loraName = f"loras/{cleanPath(None, loraName)}"
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lora_name = f"loras/{cleanPath(None, lora_name)}"
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if dataset is None:
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return "**Missing dataset choice input, cannot continue.**"
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if format is None:
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return "**Missing format choice input, cannot continue.**"
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gradientAccumulationSteps = batchSize // microBatchSize
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CURRENT_GRADIENT_ACCUM = gradientAccumulationSteps
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actualLR = float(learningRate)
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gradient_accumulation_steps = batch_size // micro_batch_size
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CURRENT_GRADIENT_ACCUM = gradient_accumulation_steps
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actual_lr = float(learning_rate)
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shared.tokenizer.pad_token = 0
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shared.tokenizer.padding_side = "left"
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# == Prep the dataset, format, etc ==
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with open(cleanPath('training/formats', f'{format}.json'), 'r') as formatFile:
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formatData: dict[str, str] = json.load(formatFile)
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format_data: dict[str, str] = json.load(formatFile)
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def tokenize(prompt):
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result = shared.tokenizer(prompt, truncation=True, max_length=cutoffLen + 1, padding="max_length")
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result = shared.tokenizer(prompt, truncation=True, max_length=cutoff_len + 1, padding="max_length")
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return {
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"input_ids": result["input_ids"][:-1],
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"attention_mask": result["attention_mask"][:-1],
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}
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def generate_prompt(data_point: dict[str, str]):
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for options, data in formatData.items():
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for options, data in format_data.items():
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if set(options.split(',')) == set(x[0] for x in data_point.items() if len(x[1].strip()) > 0):
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for key, val in data_point.items():
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data = data.replace(f'%{key}%', val)
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return data
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raise RuntimeError(f'Data-point "{data_point}" has no keyset match within format "{list(formatData.keys())}"')
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raise RuntimeError(f'Data-point "{data_point}" has no keyset match within format "{list(format_data.keys())}"')
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def generate_and_tokenize_prompt(data_point):
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prompt = generate_prompt(data_point)
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return tokenize(prompt)
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print("Loading datasets...")
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data = load_dataset("json", data_files=cleanPath('training/datasets', f'{dataset}.json'))
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train_data = data['train'].shuffle().map(generate_and_tokenize_prompt)
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if evalDataset == 'None':
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evalData = None
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if eval_dataset == 'None':
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eval_data = None
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else:
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evalData = load_dataset("json", data_files=cleanPath('training/datasets', f'{evalDataset}.json'))
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evalData = evalData['train'].shuffle().map(generate_and_tokenize_prompt)
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eval_data = load_dataset("json", data_files=cleanPath('training/datasets', f'{eval_dataset}.json'))
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eval_data = eval_data['train'].shuffle().map(generate_and_tokenize_prompt)
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# == Start prepping the model itself ==
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if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):
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print("Getting model ready...")
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prepare_model_for_int8_training(shared.model)
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print("Prepping for training...")
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config = LoraConfig(
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r=loraRank,
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lora_alpha=loraAlpha,
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r=lora_rank,
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lora_alpha=lora_alpha,
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# TODO: Should target_modules be configurable?
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target_modules=[ "q_proj", "v_proj" ],
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lora_dropout=loraDropout,
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lora_dropout=lora_dropout,
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bias="none",
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task_type="CAUSAL_LM"
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)
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loraModel = get_peft_model(shared.model, config)
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lora_model = get_peft_model(shared.model, config)
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trainer = transformers.Trainer(
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model=loraModel,
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model=lora_model,
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train_dataset=train_data,
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eval_dataset=evalData,
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eval_dataset=eval_data,
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args=transformers.TrainingArguments(
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per_device_train_batch_size=microBatchSize,
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gradient_accumulation_steps=gradientAccumulationSteps,
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per_device_train_batch_size=micro_batch_size,
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gradient_accumulation_steps=gradient_accumulation_steps,
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# TODO: Should more of these be configurable? Probably.
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warmup_steps=100,
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num_train_epochs=epochs,
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learning_rate=actualLR,
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learning_rate=actual_lr,
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fp16=True,
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logging_steps=20,
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evaluation_strategy="steps" if evalData is not None else "no",
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evaluation_strategy="steps" if eval_data is not None else "no",
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save_strategy="steps",
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eval_steps=200 if evalData is not None else None,
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eval_steps=200 if eval_data is not None else None,
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save_steps=200,
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output_dir=loraName,
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output_dir=lora_name,
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save_total_limit=3,
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load_best_model_at_end=True if evalData is not None else False,
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load_best_model_at_end=True if eval_data is not None else False,
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# TODO: Enable multi-device support
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ddp_find_unused_parameters=None
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),
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data_collator=transformers.DataCollatorForLanguageModeling(shared.tokenizer, mlm=False),
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callbacks=list([Callbacks()])
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)
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loraModel.config.use_cache = False
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old_state_dict = loraModel.state_dict
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loraModel.state_dict = (
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lora_model.config.use_cache = False
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old_state_dict = lora_model.state_dict
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lora_model.state_dict = (
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lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
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).__get__(loraModel, type(loraModel))
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).__get__(lora_model, type(lora_model))
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if torch.__version__ >= "2" and sys.platform != "win32":
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loraModel = torch.compile(loraModel)
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lora_model = torch.compile(lora_model)
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# == Main run and monitor loop ==
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# TODO: save/load checkpoints to resume from?
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@ -210,11 +226,11 @@ def do_train(loraName: str, microBatchSize: int, batchSize: int, epochs: int, le
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yield f"Running... **{CURRENT_STEPS}** / **{MAX_STEPS}** ... {timerInfo}, `{timeElapsed:.0f}`/`{totalTimeEstimate:.0f}` seconds"
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print("Training complete, saving...")
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loraModel.save_pretrained(loraName)
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lora_model.save_pretrained(lora_name)
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if WANT_INTERRUPT:
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print("Training interrupted.")
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yield f"Interrupted. Incomplete LoRA saved to `{loraName}`"
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yield f"Interrupted. Incomplete LoRA saved to `{lora_name}`"
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else:
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print("Training complete!")
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yield f"Done! LoRA saved to `{loraName}`"
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yield f"Done! LoRA saved to `{lora_name}`"
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