From 8da237223ed008c418386a805524929ddebb59ba Mon Sep 17 00:00:00 2001 From: "Alex \"mcmonkey\" Goodwin" Date: Sat, 25 Mar 2023 12:48:35 -0700 Subject: [PATCH] document options better --- modules/training.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/modules/training.py b/modules/training.py index 0e210c52..250093a0 100644 --- a/modules/training.py +++ b/modules/training.py @@ -14,27 +14,27 @@ def get_json_dataset(path: str): def create_train_interface(): with gr.Tab('Train LoRA', elem_id='lora-train-tab'): loraName = gr.Textbox(label="Name", info="The name of your new LoRA file") - # TODO: Add explanations of batch sizes and recommendations. Note that batch/microBatch determines gradient accumulation and explain what that means. Note the effects on VRAM usage from changing these values. - microBatchSize = gr.Slider(label='Micro Batch Size', value=4, minimum=1, maximum=128, step=1, info='(TODO)') - batchSize = gr.Slider(label='Batch Size', value=128, minimum=1, maximum=1024, step=4, info='(TODO)') + # TODO: Implement multi-device support. + 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.') + 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.') epochs = gr.Slider(label='Epochs', value=1, minimum=1, maximum=1000, 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.') 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.') # TODO: What is the actual maximum rank? Likely distinct per model. This might be better to somehow be on a log scale. 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.') 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.') - # TODO: Better explain what this does. + # TODO: Better explain what this does, in terms of real world effect especially. 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.') 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.') with gr.Row(): datasetFunction = get_json_dataset('training/datasets') - dataset = gr.Dropdown(choices=datasetFunction(), value='None', label='Dataset') + dataset = gr.Dropdown(choices=datasetFunction(), value='None', label='Dataset', info='The dataset file to use for training.') ui.create_refresh_button(dataset, lambda : None, lambda : {'choices': datasetFunction()}, 'refresh-button') with gr.Row(): - evalDataset = gr.Dropdown(choices=datasetFunction(), value='None', label='Evaluation Dataset') + evalDataset = gr.Dropdown(choices=datasetFunction(), value='None', label='Evaluation Dataset', info='The dataset file used to evaluate the model after training.') ui.create_refresh_button(evalDataset, lambda : None, lambda : {'choices': datasetFunction()}, 'refresh-button') with gr.Row(): formatsFunction = get_json_dataset('training/formats') - format = gr.Dropdown(choices=formatsFunction(), value='None', label='Data Format') + format = gr.Dropdown(choices=formatsFunction(), 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': formatsFunction()}, 'refresh-button') startButton = gr.Button("Start LoRA Training") output = gr.Markdown(value="(...)")