app.py

import gradio as gr
import torch
import os
import logging
import time
import tempfile
import shutil
import subprocess
from datetime import datetime
from huggingface_hub import HfApi
from transformers import AutoConfig, AutoModel, AutoTokenizer
from optimum.onnxruntime import ORTQuantizer
from optimum.onnxruntime.configuration import AutoQuantizationConfig
import torch.nn.utils.prune as prune

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    logging.warning("HF_TOKEN environment variable not set. Packaging and uploading will fail.")

api = HfApi()
OUTPUT_DIR = "optimized_models"
os.makedirs(OUTPUT_DIR, exist_ok=True)

def stage_1_analyze_model(model_id: str):
    log_stream = "[STAGE 1] Analyzing model...\n"
    try:
        config = AutoConfig.from_pretrained(model_id, trust_remote_code=True, token=HF_TOKEN)
        model_type = config.model_type
        
        analysis_report = f"""
        ### Model Analysis Report
        - **Model ID:** `{model_id}`
        - **Architecture:** `{model_type}`
        """
        
        recommendation = ""
        if 'llama' in model_type or 'gpt' in model_type or 'mistral' in model_type or 'gemma' in model_type:
            recommendation = "**Recommendation:** This is a Large Language Model (LLM). For the best CPU performance and community support, the **GGUF Pipeline** is highly recommended. The ONNX pipeline is a viable alternative."
        else:
            recommendation = "**Recommendation:** This is likely an encoder model. The **ONNX Pipeline** is recommended. Pruning may offer size reduction, but its impact on performance can vary."

        log_stream += f"Analysis complete. Architecture: {model_type}.\n"
        return log_stream, analysis_report + "\n" + recommendation, gr.Accordion(open=True)
    except Exception as e:
        error_msg = f"Failed to analyze model '{model_id}'. Error: {e}"
        logging.error(error_msg)
        return log_stream + error_msg, "Could not analyze model. Please check the model ID and try again.", gr.Accordion(open=False)

def stage_2_prune_model(model, prune_percentage: float):
    if prune_percentage == 0:
        return model, "Skipped pruning as percentage was 0."
    log_stream = "[STAGE 2] Pruning model...\n"
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            prune.l1_unstructured(module, name='weight', amount=prune_percentage / 100.0)
            prune.remove(module, 'weight')
    log_stream += f"Pruning complete with {prune_percentage}% target.\n"
    return model, log_stream

def stage_3_4_onnx_quantize(model_path: str, calibration_data_path: str):
    log_stream = "[STAGE 3 & 4] Converting to ONNX and Quantizing...\n"
    run_id = datetime.now().strftime("%Y%m%d-%H%M%S")
    model_name = os.path.basename(model_path)
    onnx_path = os.path.join(OUTPUT_DIR, f"{model_name}-{run_id}-onnx")
    
    try:
        log_stream += "Executing `optimum-cli export onnx` via subprocess...\n"
        export_command = [
            "optimum-cli", "export", "onnx",
            "--model", model_path,
            "--trust-remote-code",
            onnx_path
        ]
        process = subprocess.run(export_command, check=True, capture_output=True, text=True)
        log_stream += process.stdout
        if process.stderr: log_stream += f"[STDERR]\n{process.stderr}\n"
        log_stream += f"Successfully exported base model to ONNX at: {onnx_path}\n"
    except subprocess.CalledProcessError as e:
        error_msg = f"Failed during `optimum-cli export onnx`. Error:\n{e.stderr}"
        logging.error(error_msg)
        raise RuntimeError(error_msg)

    try:
        quantizer = ORTQuantizer.from_pretrained(onnx_path)
        
        if calibration_data_path:
            log_stream += "Performing STATIC quantization with user-provided calibration data.\n"
            dqconfig = AutoQuantizationConfig.avx512_vnni(is_static=True, per_channel=False)
            from datasets import load_dataset
            calibration_dataset = quantizer.get_calibration_dataset(
                "text",
                dataset_args={"path": calibration_data_path, "split": "train"},
                num_samples=100,
                dataset_num_proc=1,
            )
            quantized_path = os.path.join(onnx_path, "quantized-static")
            quantizer.quantize(save_dir=quantized_path, quantization_config=dqconfig, calibration_dataset=calibration_dataset)
        else:
            log_stream += "Performing DYNAMIC quantization.\n"
            dqconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False)
            quantized_path = os.path.join(onnx_path, "quantized-dynamic")
            quantizer.quantize(save_dir=quantized_path, quantization_config=dqconfig)
            
        log_stream += f"Successfully quantized model to: {quantized_path}\n"
        return quantized_path, log_stream
    except Exception as e:
        error_msg = f"Failed during ONNX quantization step. Error: {e}"
        logging.error(error_msg, exc_info=True)
        raise RuntimeError(error_msg)

def stage_3_4_gguf_quantize(model_id: str, quantization_strategy: str):
    log_stream = f"[STAGE 3 & 4] Converting to GGUF with '{quantization_strategy}' quantization...\n"
    run_id = datetime.now().strftime("%Y%m%d-%H%M%S")
    model_name = model_id.replace('/', '_')
    gguf_path = os.path.join(OUTPUT_DIR, f"{model_name}-{run_id}-gguf")
    os.makedirs(gguf_path, exist_ok=True)
    output_file = os.path.join(gguf_path, "model.gguf")

    try:
        log_stream += "Executing `optimum-cli export gguf` via subprocess...\n"
        export_command = [
            "optimum-cli", "export", "gguf",
            "--model", model_id,
            "--quantization_strategy", quantization_strategy,
            "--trust-remote-code",
            output_file
        ]
        process = subprocess.run(export_command, check=True, capture_output=True, text=True)
        log_stream += process.stdout
        if process.stderr: log_stream += f"[STDERR]\n{process.stderr}\n"
        log_stream += f"Successfully exported and quantized model to GGUF at: {gguf_path}\n"
        return gguf_path, log_stream
    except subprocess.CalledProcessError as e:
        error_msg = f"Failed during `optimum-cli export gguf`. Error:\n{e.stderr}"
        logging.error(error_msg)
        raise RuntimeError(error_msg)

def stage_5_package_and_upload(model_id: str, optimized_model_path: str, pipeline_log: str, options: dict):
    log_stream = "[STAGE 5] Packaging and Uploading...\n"
    if not HF_TOKEN:
        return "Skipping upload: HF_TOKEN not found.", log_stream + "Skipping upload: HF_TOKEN not found."

    try:
        repo_name = f"{model_id.split('/')[-1]}-amop-cpu-{options['pipeline_type'].lower()}"
        repo_url = api.create_repo(repo_id=repo_name, exist_ok=True, token=HF_TOKEN)
        
        if options['pipeline_type'] == "GGUF":
            template_file = "model_card_template_gguf.md"
        else:
            template_file = "model_card_template.md"
        
        with open(template_file, "r", encoding="utf-8") as f:
            template_content = f.read()
        
        model_card_content = template_content.format(
            repo_name=repo_name, model_id=model_id, optimization_date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            pruning_status="Enabled" if options.get('prune', False) else "Disabled",
            pruning_percent=options.get('prune_percent', 0),
            quant_type=options.get('quant_type', 'N/A'),
            repo_id=repo_url.repo_id, pipeline_log=pipeline_log
        )
        readme_path = os.path.join(optimized_model_path, "README.md")
        with open(readme_path, "w", encoding="utf-8") as f:
            f.write(model_card_content)
        
        if options['pipeline_type'] == "ONNX":
            tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
            tokenizer.save_pretrained(optimized_model_path)
        
        api.upload_folder(folder_path=optimized_model_path, repo_id=repo_url.repo_id, repo_type="model", token=HF_TOKEN)
        
        final_message = f"Success! Your optimized model is available at: huggingface.co/{repo_url.repo_id}"
        log_stream += "Upload complete.\n"
        return final_message, log_stream
    except Exception as e:
        error_msg = f"Failed to upload to the Hub. Error: {e}"
        logging.error(error_msg, exc_info=True)
        return f"Error: {error_msg}", log_stream + error_msg

def run_amop_pipeline(model_id: str, pipeline_type: str, do_prune: bool, prune_percent: float, onnx_quant_type: str, calibration_file, gguf_quant_type: str):
    if not model_id:
        yield {log_output: "Please enter a Model ID.", final_output: gr.Label(value="Idle", label="Status")}
        return

    initial_log = f"[START] AMOP {pipeline_type} Pipeline Initiated.\n"
    yield {
        run_button: gr.Button(interactive=False, value="🚀 Running..."),
        analyze_button: gr.Button(interactive=False),
        final_output: gr.Label(value={"label": f"RUNNING ({pipeline_type})"}, show_label=True),
        log_output: initial_log
    }

    full_log = initial_log
    temp_model_dir = None
    try:
        repo_name_suffix = f"-amop-cpu-{pipeline_type.lower()}"
        repo_id_for_link = f"{api.whoami()['name']}/{model_id.split('/')[-1]}{repo_name_suffix}"
        
        if pipeline_type == "ONNX":
            full_log += "Loading base model for pruning...\n"
            yield {final_output: gr.Label(value="Loading model (1/5)"), log_output: full_log}
            model = AutoModel.from_pretrained(model_id, trust_remote_code=True)
            tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
            full_log += f"Successfully loaded base model '{model_id}'.\n"

            yield {final_output: gr.Label(value="Pruning model (2/5)"), log_output: full_log}
            if do_prune:
                model, log = stage_2_prune_model(model, prune_percent)
                full_log += log
            else:
                full_log += "[STAGE 2] Pruning skipped by user.\n"

            temp_model_dir = tempfile.mkdtemp()
            model.save_pretrained(temp_model_dir)
            tokenizer.save_pretrained(temp_model_dir)
            full_log += f"Saved intermediate model to temporary directory: {temp_model_dir}\n"
            
            yield {final_output: gr.Label(value="Converting to ONNX (3/5)"), log_output: full_log}
            calib_path = calibration_file.name if onnx_quant_type == "Static" and calibration_file else None
            optimized_path, log = stage_3_4_onnx_quantize(temp_model_dir, calib_path)
            full_log += log
            options = {'pipeline_type': 'ONNX', 'prune': do_prune, 'prune_percent': prune_percent, 'quant_type': onnx_quant_type}

        elif pipeline_type == "GGUF":
            full_log += "[STAGE 1 & 2] Loading and Pruning are skipped for GGUF pipeline.\n"
            yield {final_output: gr.Label(value="Converting to GGUF (3/5)"), log_output: full_log}
            optimized_path, log = stage_3_4_gguf_quantize(model_id, gguf_quant_type)
            full_log += log
            options = {'pipeline_type': 'GGUF', 'quant_type': gguf_quant_type}
        
        else:
            raise ValueError("Invalid pipeline type selected.")

        yield {final_output: gr.Label(value="Packaging & Uploading (4/5)"), log_output: full_log}
        final_message, log = stage_5_package_and_upload(model_id, optimized_path, full_log, options)
        full_log += log

        yield {
            final_output: gr.Label(value="SUCCESS", label="Status"),
            log_output: full_log,
            success_box: gr.Markdown(f"✅ **Success!** Your optimized model is available here: [{repo_id_for_link}](https://huggingface.co/{repo_id_for_link})", visible=True),
            run_button: gr.Button(interactive=True, value="Run Optimization Pipeline", variant="primary"),
            analyze_button: gr.Button(interactive=True, value="Analyze Model")
        }

    except Exception as e:
        logging.error(f"AMOP Pipeline failed. Error: {e}", exc_info=True)
        full_log += f"\n[ERROR] Pipeline failed: {e}"
        yield {
            final_output: gr.Label(value="ERROR", label="Status"),
            log_output: full_log,
            success_box: gr.Markdown(f"❌ **An error occurred.** Check the logs for details.", visible=True),
            run_button: gr.Button(interactive=True, value="Run Optimization Pipeline", variant="primary"),
            analyze_button: gr.Button(interactive=True, value="Analyze Model")
        }
    finally:
        if temp_model_dir and os.path.exists(temp_model_dir):
            shutil.rmtree(temp_model_dir)
            logging.info(f"Cleaned up temporary directory: {temp_model_dir}")


with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🚀 AMOP: Adaptive Model Optimization Pipeline")
    gr.Markdown("Turn any Hugging Face Hub model into a CPU-optimized version using ONNX or GGUF.")
    
    if not HF_TOKEN:
        gr.Warning("You have not set your HF_TOKEN in the Space secrets! The final 'upload' step will be skipped. Please add a secret with the key `HF_TOKEN` and your Hugging Face write token as the value.")

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 1. Select a Model")
            model_id_input = gr.Textbox(
                label="Hugging Face Model ID", 
                placeholder="e.g., gpt2, meta-llama/Llama-2-7b-chat-hf",
            )
            analyze_button = gr.Button("🔍 Analyze Model", variant="secondary")
            
            with gr.Accordion("⚙️ 2. Configure Optimization", open=False) as optimization_accordion:
                analysis_report_output = gr.Markdown()
                
                pipeline_type_radio = gr.Radio(
                    ["ONNX", "GGUF"], label="Select Optimization Pipeline", info="GGUF is recommended for LLMs, ONNX for others."
                )
                
                with gr.Group(visible=False) as onnx_options:
                    gr.Markdown("#### ONNX Pipeline Options")
                    prune_checkbox = gr.Checkbox(label="Enable Pruning", value=False, info="Removes redundant weights. Applied before ONNX conversion.")
                    prune_slider = gr.Slider(minimum=0, maximum=90, value=20, step=5, label="Pruning Percentage (%)")
                    onnx_quant_radio = gr.Radio(["Dynamic", "Static"], label="ONNX Quantization Type", value="Dynamic", info="Static may offer better performance but requires calibration data.")
                    calibration_file_upload = gr.File(label="Upload Calibration Data (.txt)", visible=False, file_types=['.txt'])

                with gr.Group(visible=False) as gguf_options:
                    gr.Markdown("#### GGUF Pipeline Options")
                    gguf_quant_dropdown = gr.Dropdown(
                        ["q4_k_m", "q5_k_m", "q8_0", "f16"],
                        label="GGUF Quantization Strategy",
                        value="q4_k_m",
                        info="q4_k_m is a good balance of size and quality."
                    )
                
                run_button = gr.Button("🚀 Run Optimization Pipeline", variant="primary")
        
        with gr.Column(scale=2):
            gr.Markdown("### Pipeline Status & Logs")
            final_output = gr.Label(value="Idle", label="Status", show_label=True)
            success_box = gr.Markdown(visible=False)
            log_output = gr.Textbox(label="Live Logs", lines=20, interactive=False, max_lines=20)

    def update_ui_for_pipeline(pipeline_type):
        return {
            onnx_options: gr.Group(visible=pipeline_type == "ONNX"),
            gguf_options: gr.Group(visible=pipeline_type == "GGUF")
        }
    
    def update_ui_for_quant_type(quant_type):
        return gr.File(visible=quant_type == "Static")

    pipeline_type_radio.change(fn=update_ui_for_pipeline, inputs=pipeline_type_radio, outputs=[onnx_options, gguf_options])
    onnx_quant_radio.change(fn=update_ui_for_quant_type, inputs=onnx_quant_radio, outputs=[calibration_file_upload])

    analyze_button.click(
        fn=stage_1_analyze_model,
        inputs=[model_id_input],
        outputs=[log_output, analysis_report_output, optimization_accordion]
    )
    
    run_button.click(
        fn=run_amop_pipeline,
        inputs=[model_id_input, pipeline_type_radio, prune_checkbox, prune_slider, onnx_quant_radio, calibration_file_upload, gguf_quant_dropdown],
        outputs=[run_button, analyze_button, final_output, log_output, success_box]
    )

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