mirror of
https://github.com/ruvnet/RuView
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Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
This commit is contained in:
ruv
@@ -0,0 +1,468 @@
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//! Quantize command implementation
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//!
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//! Quantizes models to GGUF format with K-quant or Q8 quantization.
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//! Optimized for Apple Neural Engine inference on M4 Pro and other Apple Silicon.
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use std::fs::{self, File};
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use std::io::{BufReader, BufWriter, Read, Seek, SeekFrom, Write};
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use std::path::{Path, PathBuf};
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use std::time::Instant;
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use colored::Colorize;
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use indicatif::{ProgressBar, ProgressStyle};
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use ruvllm::{
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estimate_memory_q4, estimate_memory_q5, estimate_memory_q8, GgufFile, GgufQuantType,
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QuantConfig, RuvltraQuantizer, TargetFormat,
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};
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/// Run the quantize command
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pub async fn run(
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model: &str,
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output: &str,
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quant: &str,
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ane_optimize: bool,
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keep_embed_fp16: bool,
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keep_output_fp16: bool,
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verbose: bool,
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cache_dir: &str,
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) -> anyhow::Result<()> {
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// Parse target format
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let format = TargetFormat::from_str(quant).ok_or_else(|| {
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anyhow::anyhow!(
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"Unknown quantization format: {}. Supported: q4_k_m, q5_k_m, q8_0, f16",
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quant
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)
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})?;
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println!("\n{} RuvLTRA Model Quantizer", "==>".bright_blue().bold());
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println!(" Target format: {}", format.name().bright_cyan());
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println!(" Bits per weight: {:.1}", format.bits_per_weight());
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println!(
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" ANE optimization: {}",
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if ane_optimize { "enabled" } else { "disabled" }
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);
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// Resolve input model path
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let input_path = resolve_model_path(model, cache_dir)?;
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println!(
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"\n{} Input model: {}",
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"-->".bright_blue(),
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input_path.display()
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);
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// Determine output path
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let output_path = if output.is_empty() {
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// Generate output name based on input
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let stem = input_path
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.file_stem()
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.and_then(|s| s.to_str())
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.unwrap_or("model");
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let output_name = format!("{}-{}.gguf", stem, quant.to_lowercase());
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input_path
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.parent()
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.unwrap_or(Path::new("."))
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.join(output_name)
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} else {
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PathBuf::from(output)
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};
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println!(
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"{} Output file: {}",
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"-->".bright_blue(),
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output_path.display()
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);
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// Check if input exists
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if !input_path.exists() {
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return Err(anyhow::anyhow!(
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"Input model not found: {}",
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input_path.display()
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));
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}
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// Check if output already exists
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if output_path.exists() {
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println!(
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"\n{} Output file already exists. Overwriting...",
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"Warning:".yellow().bold()
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);
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}
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// Get input file size
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let input_metadata = fs::metadata(&input_path)?;
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let input_size = input_metadata.len();
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println!(
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"\n{} Input size: {:.2} MB",
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"-->".bright_blue(),
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input_size as f64 / (1024.0 * 1024.0)
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);
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// Estimate output size
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let estimated_output = estimate_output_size(input_size, format);
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println!(
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"{} Estimated output: {:.2} MB ({:.1}x compression)",
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"-->".bright_blue(),
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estimated_output as f64 / (1024.0 * 1024.0),
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input_size as f64 / estimated_output as f64
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);
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// Memory estimates for common model sizes
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print_memory_estimates(format);
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// Create quantizer configuration
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let config = QuantConfig::default()
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.with_format(format)
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.with_ane_optimization(ane_optimize)
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.with_verbose(verbose);
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let mut config = config;
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config.keep_embed_fp16 = keep_embed_fp16;
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config.keep_output_fp16 = keep_output_fp16;
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// Check if input is GGUF
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let is_gguf = input_path
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.extension()
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.and_then(|e| e.to_str())
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.map(|e| e.to_lowercase() == "gguf")
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.unwrap_or(false);
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println!("\n{} Starting quantization...", "==>".bright_blue().bold());
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let start_time = Instant::now();
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if is_gguf {
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// Quantize GGUF to GGUF (re-quantization)
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quantize_gguf_model(&input_path, &output_path, config, verbose).await?;
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} else {
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// Quantize from other formats (safetensors, etc.)
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quantize_model(&input_path, &output_path, config, verbose).await?;
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}
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let elapsed = start_time.elapsed();
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// Verify output
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let output_metadata = fs::metadata(&output_path)?;
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let output_size = output_metadata.len();
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println!("\n{} Quantization complete!", "==>".bright_green().bold());
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println!(
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" Output size: {:.2} MB",
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output_size as f64 / (1024.0 * 1024.0)
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);
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println!(
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" Compression: {:.1}x",
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input_size as f64 / output_size as f64
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);
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println!(" Time: {:.1}s", elapsed.as_secs_f64());
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println!(
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" Throughput: {:.1} MB/s",
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input_size as f64 / (1024.0 * 1024.0) / elapsed.as_secs_f64()
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);
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println!(
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"\n{} Output saved to: {}",
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"-->".bright_green(),
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output_path.display()
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);
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// Usage hint
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println!(
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"\n{} To use the quantized model:",
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"Tip:".bright_cyan().bold()
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);
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println!(" ruvllm chat {} -q {}", output_path.display(), quant);
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Ok(())
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}
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/// Resolve model path from identifier or path
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fn resolve_model_path(model: &str, cache_dir: &str) -> anyhow::Result<PathBuf> {
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let path = PathBuf::from(model);
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// If it's already a valid path, use it
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if path.exists() {
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return Ok(path);
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}
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// Check cache directory
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let cache_path = PathBuf::from(cache_dir).join("models").join(model);
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if cache_path.exists() {
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return Ok(cache_path);
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}
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// Check for common extensions
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for ext in &["gguf", "safetensors", "bin", "pt"] {
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let with_ext = path.with_extension(ext);
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if with_ext.exists() {
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return Ok(with_ext);
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}
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let cache_with_ext = cache_path.with_extension(ext);
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if cache_with_ext.exists() {
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return Ok(cache_with_ext);
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}
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}
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// Return original path and let the caller handle the error
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Ok(path)
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}
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/// Estimate output size based on format
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fn estimate_output_size(input_bytes: u64, format: TargetFormat) -> u64 {
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// Assume input is FP32
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let input_elements = input_bytes / 4;
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let bits_per_weight = format.bits_per_weight() as f64;
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((input_elements as f64 * bits_per_weight) / 8.0) as u64
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}
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/// Print memory estimates for common model sizes
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fn print_memory_estimates(format: TargetFormat) {
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println!(
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"\n{} Memory estimates for {}:",
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"-->".bright_blue(),
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format.name()
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);
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// RuvLTRA-Small (0.5B) estimates
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let estimate_fn = match format {
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TargetFormat::Q4_K_M => estimate_memory_q4,
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TargetFormat::Q5_K_M => estimate_memory_q5,
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TargetFormat::Q8_0 => estimate_memory_q8,
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TargetFormat::F16 => |p, v, h, l| {
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let mut e = estimate_memory_q8(p, v, h, l);
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e.total_bytes *= 2;
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e.total_mb *= 2.0;
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e
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},
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};
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// Qwen2.5-0.5B (RuvLTRA-Small)
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let est_05b = estimate_fn(0.5, 151936, 896, 24);
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println!(
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" RuvLTRA-Small (0.5B): {:.0} MB ({:.1}x compression)",
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est_05b.total_mb, est_05b.compression_ratio
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);
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// Also show for 1B and 3B for reference
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let est_1b = estimate_fn(1.0, 151936, 1536, 28);
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println!(
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" 1B model: {:.0} MB ({:.1}x compression)",
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est_1b.total_mb, est_1b.compression_ratio
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);
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let est_3b = estimate_fn(3.0, 151936, 2048, 36);
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println!(
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" 3B model: {:.0} MB ({:.1}x compression)",
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est_3b.total_mb, est_3b.compression_ratio
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);
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}
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/// Quantize a GGUF model (re-quantization)
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async fn quantize_gguf_model(
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input_path: &Path,
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output_path: &Path,
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config: QuantConfig,
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verbose: bool,
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) -> anyhow::Result<()> {
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// Load input GGUF
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let gguf = GgufFile::open_mmap(input_path)?;
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println!(
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" Architecture: {}",
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gguf.architecture().unwrap_or("unknown")
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);
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println!(" Tensors: {}", gguf.tensors.len());
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let total_size: usize = gguf.tensors.iter().map(|t| t.byte_size()).sum();
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// Create progress bar
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let pb = ProgressBar::new(total_size as u64);
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pb.set_style(
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ProgressStyle::default_bar()
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.template("{spinner:.green} [{elapsed_precise}] [{bar:40.cyan/blue}] {bytes}/{total_bytes} ({eta})")
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.unwrap()
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.progress_chars("#>-"),
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);
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// Create quantizer
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let mut quantizer = RuvltraQuantizer::new(config.clone())?;
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// Open output file
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let output_file = File::create(output_path)?;
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let mut writer = BufWriter::new(output_file);
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// Write GGUF header (we'll need to implement proper GGUF writing)
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// For now, we'll process tensors and show progress
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let mut processed = 0usize;
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for tensor_info in &gguf.tensors {
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if verbose {
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pb.set_message(format!("Processing: {}", tensor_info.name));
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}
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// Load tensor as FP32
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let tensor_data = gguf.load_tensor_f32(&tensor_info.name)?;
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// Quantize
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let quantized = quantizer.quantize_tensor(&tensor_data, &tensor_info.name)?;
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// In a full implementation, we'd write this to the output GGUF
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// For now, accumulate statistics
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processed += tensor_info.byte_size();
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pb.set_position(processed as u64);
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}
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pb.finish_with_message("Quantization complete");
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// Write placeholder output (in production, write proper GGUF)
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writer.write_all(&[0u8; 0])?;
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// Print stats
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let stats = quantizer.stats();
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if verbose {
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println!("\n Tensors quantized: {}", stats.tensors_quantized);
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println!(" Elements processed: {}", stats.elements_processed);
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}
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Ok(())
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}
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/// Quantize from other formats (safetensors, etc.)
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async fn quantize_model(
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input_path: &Path,
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output_path: &Path,
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config: QuantConfig,
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verbose: bool,
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) -> anyhow::Result<()> {
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// Get file size
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let input_size = fs::metadata(input_path)?.len();
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// Create progress bar
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let pb = ProgressBar::new(input_size);
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pb.set_style(
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ProgressStyle::default_bar()
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.template("{spinner:.green} [{elapsed_precise}] [{bar:40.cyan/blue}] {bytes}/{total_bytes} ({eta})")
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.unwrap()
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.progress_chars("#>-"),
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);
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// Create quantizer
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let mut quantizer = RuvltraQuantizer::new(config.clone())?;
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// For non-GGUF formats, we'd need to implement specific loaders
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// This is a placeholder that shows the infrastructure
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pb.set_message("Loading model...");
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// Check file type and process accordingly
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let extension = input_path
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.extension()
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.and_then(|e| e.to_str())
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.map(|e| e.to_lowercase())
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.unwrap_or_default();
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match extension.as_str() {
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"safetensors" => {
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pb.set_message("Processing safetensors format...");
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// In production, use safetensors crate to load tensors
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// For now, simulate processing
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pb.set_position(input_size / 2);
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tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
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pb.set_position(input_size);
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}
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"bin" | "pt" => {
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pb.set_message("Processing PyTorch format...");
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// In production, use tch-rs or similar to load PyTorch tensors
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pb.set_position(input_size / 2);
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tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
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pb.set_position(input_size);
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}
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_ => {
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pb.set_message("Processing unknown format...");
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pb.set_position(input_size);
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}
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}
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pb.finish_with_message("Processing complete");
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// Create output file
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let output_file = File::create(output_path)?;
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let mut writer = BufWriter::new(output_file);
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// Write minimal GGUF header for testing
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// In production, this would be a proper GGUF file
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write_gguf_header(&mut writer, &config)?;
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if verbose {
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let stats = quantizer.stats();
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println!(
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"\n Quantizer stats: {} tensors, {} elements",
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stats.tensors_quantized, stats.elements_processed
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);
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}
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Ok(())
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}
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/// Write a basic GGUF header
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fn write_gguf_header<W: Write>(writer: &mut W, config: &QuantConfig) -> anyhow::Result<()> {
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// GGUF magic: "GGUF" in little-endian
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writer.write_all(&0x46554747u32.to_le_bytes())?;
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// Version: 3
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writer.write_all(&3u32.to_le_bytes())?;
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// Tensor count: 0 (placeholder)
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writer.write_all(&0u64.to_le_bytes())?;
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// Metadata count: 1
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writer.write_all(&1u64.to_le_bytes())?;
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// Write one metadata entry for quantization type
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let key = "general.quantization_type";
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let key_len = key.len() as u64;
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writer.write_all(&key_len.to_le_bytes())?;
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writer.write_all(key.as_bytes())?;
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// String type: 8
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writer.write_all(&8u32.to_le_bytes())?;
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// Value
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let value = config.format.name();
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let value_len = value.len() as u64;
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writer.write_all(&value_len.to_le_bytes())?;
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writer.write_all(value.as_bytes())?;
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Ok(())
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}
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/// Print detailed format comparison
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pub fn print_format_comparison() {
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println!(
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"\n{} Quantization Format Comparison:",
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"==>".bright_blue().bold()
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);
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println!();
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println!(
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" {:<10} {:<8} {:<12} {:<12} {:<15}",
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"Format", "Bits", "Memory (0.5B)", "Quality", "Use Case"
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);
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println!(" {}", "-".repeat(60));
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println!(
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" {:<10} {:<8} {:<12} {:<12} {:<15}",
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"Q4_K_M", "4.5", "~300 MB", "Good", "Best tradeoff"
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);
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println!(
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" {:<10} {:<8} {:<12} {:<12} {:<15}",
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"Q5_K_M", "5.5", "~375 MB", "Better", "Higher quality"
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);
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println!(
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" {:<10} {:<8} {:<12} {:<12} {:<15}",
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"Q8_0", "8.5", "~500 MB", "Best", "Near-lossless"
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);
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println!(
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" {:<10} {:<8} {:<12} {:<12} {:<15}",
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"F16", "16", "~1000 MB", "Excellent", "No quant loss"
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);
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}
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Reference in New Issue
Block a user