app.py

import gradio as gr
import matplotlib.pyplot as plt
import yaml
import json
from pathlib import Path
import io
from utils import calculate_memory_components, plot_memory_breakdown


def load_config_from_content(content):
    try:
        # Try parsing as JSON first
        try:
            config = json.loads(content)
            # Check if this is a multimodal model with text_config
            if 'text_config' in config:
                # Use text_config for model parameters
                text_config = config['text_config']
                return {
                    'hidden_size': text_config['hidden_size'],
                    'num_layers': text_config['num_hidden_layers'],
                    'vocab_size': config.get('vocab_size', 256000),  # Default for multimodal models
                    'intermediate_size': text_config['intermediate_size'],
                    'seq_len': 2048,  # Default value since not in config
                    'mbs': 1,        # Default value
                    'batch_accum': 1, # Default value
                    'tp': 1,         # Default value
                    'pp': 1,         # Default value
                    'dp': 1,         # Default value
                    'zero_stage': 0,  # Default value
                    'tie_word_embeddings': config.get('tie_word_embeddings', True),
                    'num_attention_heads': text_config['num_attention_heads'],
                    'num_key_value_heads': text_config.get('num_key_value_heads', text_config['num_attention_heads']),
                    'full_checkpointing': False  # Default value
                }
            else:
                # Original code for non-multimodal models
                return {
                    'hidden_size': config['hidden_size'],
                    'num_layers': config['num_hidden_layers'],
                    'vocab_size': config['vocab_size'],
                    'intermediate_size': config['intermediate_size'],
                    'seq_len': 2048,  # Default value since not in config
                    'mbs': 1,        # Default value
                    'batch_accum': 1, # Default value
                    'tp': 1,         # Default value
                    'pp': 1,         # Default value
                    'dp': 1,         # Default value
                    'zero_stage': 0,  # Default value
                    'tie_word_embeddings': config.get('tie_word_embeddings', True),
                    'num_attention_heads': config['num_attention_heads'],
                    'num_key_value_heads': config.get('num_key_value_heads', config['num_attention_heads']),
                    'full_checkpointing': False  # Default value
                }
        except json.JSONDecodeError:
            # If not JSON, try YAML
            config = yaml.safe_load(content)
            
            # Extract relevant parameters from YAML config
            model_config = config['model']['model_config']
            parallelism = config['parallelism']
            tokens = config['tokens']
            optimizer = config['optimizer']
            
            return {
                'hidden_size': model_config['hidden_size'],
                'num_layers': model_config['num_hidden_layers'],
                'vocab_size': model_config['vocab_size'],
                'intermediate_size': model_config['intermediate_size'],
                'seq_len': tokens['sequence_length'],
                'mbs': tokens['micro_batch_size'],
                'batch_accum': tokens['batch_accumulation_per_replica'],
                'tp': parallelism['tp'],
                'pp': parallelism['pp'],
                'dp': parallelism['dp'],
                'zero_stage': optimizer['zero_stage'],
                'tie_word_embeddings': model_config['tie_word_embeddings'],
                'num_attention_heads': model_config['num_attention_heads'],
                'num_key_value_heads': model_config.get('num_key_value_heads', model_config['num_attention_heads']),
                'full_checkpointing': optimizer.get('full_checkpointing', False)  # Renamed from fsdp_checkpointing
            }
    except Exception as e:
        raise gr.Error(f"Error parsing configuration: {str(e)}")

def load_config_from_yaml_file(yaml_path):
    if not yaml_path:
        return None
    with open(yaml_path.name, 'r') as f:
        return load_config_from_content(f.read())

def format_config_display(config):
    if not config:
        return "No configuration loaded"

    # Calculate number of parameters
    vocab_embeddings = config['vocab_size'] * config['hidden_size'] * (1 if config['tie_word_embeddings'] else 2)
    
    layer_params = (
        (config['hidden_size'] * config['hidden_size'] * (1 + 2*config['num_key_value_heads']/config['num_attention_heads']))  # qkv_proj
        + (config['hidden_size'] * config['hidden_size'])     # out_proj
        + (config['hidden_size'] * 2 * config['intermediate_size'])  # gate_up_proj
        + (config['intermediate_size'] * config['hidden_size'])      # down_proj
    )
    total_params = (vocab_embeddings + config['num_layers'] * layer_params)
    params_billions = total_params / 1_000_000_000

    sections = {
        "Model Architecture": [
            "hidden_size", "num_layers", "vocab_size", 
            "intermediate_size", "tie_word_embeddings", "num_attention_heads", "num_key_value_heads",
            ("num_params", f"{params_billions:.2f}B")  # Show params in billions
        ],
        "Training Configuration": [
            "seq_len", "mbs", "batch_accum"
        ],
        "Parallelism": [
            "tp", "pp", "dp", "zero_stage", "full_checkpointing"
        ]
    }

    output = "<div style='display: flex;'>"
    for section_name, params in sections.items():
        output += f"<div style='flex: 1; padding-right: 20px;'><h3>{section_name}</h3>"
        for param in params:
            if isinstance(param, tuple):
                # Handle custom parameter display
                param_name, value = param
                output += f"<b>{param_name}</b>: {value}<br>"
            else:
                value = config.get(param, 'N/A')
                output += f"<b>{param}</b>: {value}<br>"
        output += "</div>"
    output += "</div>"
    return output


def process_yaml_and_plot(config):
    if not config:
        return None, None, "No configuration loaded", None
    fig1, fig2, memory_usage_peak_tbi = plot_memory_breakdown(**config)
    oom_prediction = "OOM" if memory_usage_peak_tbi > 75000 else "No OOM"
    return fig1, fig2, format_config_display(config), oom_prediction
with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Accordion("Configuration Input", open=True):
                config_text = gr.Textbox(
                    label="Paste YAML or JSON configuration",
                    placeholder="Paste your YAML or JSON configuration here...",
                    lines=10
                )
                config_submit = gr.Button("Calculate Memory from Config")
            
            with gr.Accordion("Manual Configuration", open=True):
                with gr.Accordion("Model Architecture", open=True):
                    with gr.Row():
                        hidden_size = gr.Number(4096, label="Hidden Size")
                        num_layers = gr.Number(32, label="Number of Layers")
                    with gr.Row():
                        vocab_size = gr.Number(50432, label="Vocabulary Size")
                        intermediate_size = gr.Number(11008, label="Intermediate Size")
                    with gr.Row():
                        num_attention_heads = gr.Number(32, label="Number of Attention Heads")
                        num_key_value_heads = gr.Number(32, label="Number of Key Value Heads")
                    tie_word_embeddings = gr.Checkbox(True, label="Tie Word Embeddings")
                
                with gr.Accordion("Training Configuration", open=True):
                    with gr.Row():
                        seq_len = gr.Number(2048, label="Sequence Length")
                        mbs = gr.Number(1, label="Micro Batch Size")
                        batch_accum = gr.Number(1, label="Gradient Accumulation Steps")
                
                with gr.Accordion("Parallelism", open=True):
                    with gr.Row():
                        tp = gr.Number(1, label="Tensor Parallelism")
                        pp = gr.Number(1, label="Pipeline Parallelism")
                        dp = gr.Number(1, label="Data Parallelism")
                    zero_stage = gr.Radio([0, 1, 2, 3], value=0, label="ZeRO Stage")
                    full_checkpointing = gr.Checkbox(False, label="Full Activation Checkpointing")

                manual_submit = gr.Button("Calculate Memory (Manual Input)")
        with gr.Column(scale=2):
            config_display = gr.Markdown(label="Configuration Values")
            oom_display = gr.Text(label="OOM Prediction")
            plot1 = gr.Plot(label="Memory Component Breakdown")
            plot2 = gr.Plot(label="Aggregate Memory Metrics")

    # Handle config text input
    config_submit.click(
        lambda x: process_yaml_and_update_ui(load_config_from_content(x) if x else None),
        inputs=[config_text],
        outputs=[
            plot1, plot2, config_display, oom_display,
            hidden_size, num_attention_heads, num_key_value_heads, num_layers, 
            vocab_size, intermediate_size, seq_len, mbs, batch_accum,
            tp, pp, dp, zero_stage, tie_word_embeddings, full_checkpointing
        ]
    )

    def process_yaml_and_update_ui(config):
        if not config:
            return [None, None, "No configuration loaded", None] + [gr.update() for _ in range(14)]
        
        fig1, fig2, memory_usage_peak_tbi = plot_memory_breakdown(**config)
        oom_prediction = "OOM" if memory_usage_peak_tbi > 75000 else "No OOM"
        
        # Return values for all outputs including UI updates
        return [
            fig1, fig2, 
            format_config_display(config), 
            oom_prediction,
            # UI component updates
            config['hidden_size'],
            config['num_attention_heads'],
            config['num_key_value_heads'],
            config['num_layers'],
            config['vocab_size'],
            config['intermediate_size'],
            config['seq_len'],
            config['mbs'],
            config['batch_accum'],
            config['tp'],
            config['pp'],
            config['dp'],
            config['zero_stage'],
            config['tie_word_embeddings'],
            config['full_checkpointing']
        ]

    # Handle manual input
    def manual_input_to_config(*args):
        config = {
            'hidden_size': args[0],
            'num_layers': args[3],
            'vocab_size': args[4],
            'intermediate_size': args[5],
            'seq_len': args[6],
            'mbs': args[7],
            'batch_accum': args[8],
            'tp': args[9],
            'pp': args[10],
            'dp': args[11],
            'zero_stage': args[12],
            'tie_word_embeddings': args[13],
            'num_attention_heads': args[1],
            'num_key_value_heads': args[2],
            'full_checkpointing': args[14]  # Renamed from fsdp_checkpointing
        }
        return process_yaml_and_update_ui(config)

    manual_submit.click(
        manual_input_to_config,
        inputs=[
            hidden_size, num_attention_heads, num_key_value_heads, num_layers, vocab_size, intermediate_size,
            seq_len, mbs, batch_accum, tp, pp, dp, zero_stage,
            tie_word_embeddings, full_checkpointing  # Renamed from fsdp_checkpointing
        ],
        outputs=[plot1, plot2, config_display, oom_display]
    )

if __name__ == "__main__":
    demo.launch()