use std::net::SocketAddr;
use std::path::PathBuf;
use anyhow::{Context, Result};
use clap::Parser;
fn main() -> Result<()> {
let cli = modelc::cli::Cli::parse();
match &cli.command {
modelc::cli::Commands::Compile {
input,
output,
format,
arch,
port,
bind,
listen,
target,
debug,
} => {
let listen_sa = modelc::cli::compile_listen(bind.as_str(), *port, listen.as_deref())?;
let embedded_release = !debug;
eprintln!("modelc {}", modelc::CLI_VERSION);
modelc::compiler::compile(
input,
output.as_deref(),
format.as_ref(),
arch.as_ref(),
listen_sa,
target.as_deref(),
embedded_release,
)?;
}
modelc::cli::Commands::Inspect { input, format, readme } => {
if *readme {
let mut model = modelc::compiler::inspect_model(input, format.as_ref())?;
model.dequantize_in_place();
println!("# {} Model Card\n", model.name);
println!("## Overview\n");
println!("- **Architecture**: {}", model.architecture);
println!("- **Tensors**: {}", model.tensors.len());
println!("- **Parameters**: {}", model.total_params());
println!("- **Total bytes**: {}", model.total_bytes());
println!("\n## Metadata\n");
if model.metadata.is_empty() {
println!("_No metadata._");
} else {
for (k, v) in &model.metadata {
println!("- **{}**: {}", k, v);
}
}
println!("\n## Tensors\n");
let mut names: Vec<&str> = model.tensors.keys().map(|s| s.as_str()).collect();
names.sort_unstable();
for name in names {
let td = &model.tensors[name];
println!(
"- `{}`: shape={:?}, dtype={:?}, bytes={}",
name, td.shape, td.dtype, td.byte_len()
);
}
} else {
modelc::compiler::inspect(input, format.as_ref())?;
}
}
modelc::cli::Commands::Pack {
input,
output,
format,
arch,
compress,
quantize,
prune,
} => {
eprintln!("modelc {}", modelc::CLI_VERSION);
let output_path = output.clone().unwrap_or_else(|| {
let mut p = input.to_path_buf();
p.set_extension("modelc");
p
});
modelc::compiler::pack(
input,
Some(&output_path),
format.as_ref(),
arch.as_ref(),
*compress,
quantize.as_ref(),
*prune,
)?;
}
modelc::cli::Commands::Run { input, port, bind, profile } => {
let path = modelc::store::resolve_model_path(input)?;
eprintln!("modelc run: loading {:?}...", path);
let mut model = modelc::pack::unpack(&path)?;
model.dequantize_in_place();
eprintln!(
" model: {} | architecture: {} | tensors: {} | params: {}",
model.name,
model.architecture,
model.tensors.len(),
model.total_params(),
);
if *profile {
eprintln!(" profiling enabled");
}
let addr: SocketAddr = format!("{}:{}", bind.trim(), port)
.parse()
.map_err(|e| anyhow::anyhow!("invalid bind address: {}", e))?;
let rt = tokio::runtime::Runtime::new()?;
rt.block_on(modelc::serve::run_server(model, addr, *profile))?;
}
modelc::cli::Commands::List => {
let models = modelc::store::list_models()?;
print_model_list(&models);
}
modelc::cli::Commands::Search { query } => {
let models = modelc::store::search_models(query)?;
if models.is_empty() {
println!("No models matching '{}'.", query);
} else {
println!("Models matching '{}':", query);
print_model_list(&models);
}
}
modelc::cli::Commands::Pull { source, name, version } => {
let is_url = source.starts_with("http://") || source.starts_with("https://");
let model_name = name.clone().unwrap_or_else(|| {
if is_url {
source
.trim_end_matches(".modelc")
.rsplit('/')
.next()
.unwrap_or("model")
.to_string()
} else {
PathBuf::from(source)
.file_stem()
.and_then(|s| s.to_str())
.unwrap_or("model")
.to_string()
}
});
let dest = if is_url {
modelc::store::download(source, &model_name)?
} else {
let source_path = PathBuf::from(source);
if !source_path.is_file() {
anyhow::bail!("source not found: {:?}", source);
}
modelc::store::install(&source_path, &model_name)?
};
if let Some(ver) = version {
let versioned = modelc::store::store_dir()?
.join(format!("{}.v{}.modelc", model_name, ver));
std::fs::copy(&dest, &versioned)
.with_context(|| format!("failed to create versioned copy {:?}", versioned))?;
println!("Installed '{}' v{} -> {:?}", model_name, ver, versioned);
}
println!("Installed '{}' -> {:?}", model_name, dest);
}
modelc::cli::Commands::Versions { name } => {
let versions = modelc::store::list_versions(name)?;
if versions.is_empty() {
println!("No versions found for '{}'.", name);
} else {
println!("Versions for '{}':", name);
for (ver, path) in versions {
let size_mb = std::fs::metadata(&path).map(|m| m.len()).unwrap_or(0) as f64
/ (1024.0 * 1024.0);
println!(" v{} {:>8.2} MB {:?}", ver, size_mb, path);
}
}
}
modelc::cli::Commands::Switch { name, version } => {
let dest = modelc::store::switch_version(name, *version)?;
println!("Switched '{}' to v{} -> {:?}", name, version, dest);
}
modelc::cli::Commands::Bench {
input,
warmup,
iterations,
} => {
let path = modelc::store::resolve_model_path(input)?;
let mut model = modelc::pack::unpack(&path)?;
model.dequantize_in_place();
eprintln!(
"Benchmarking {} (arch: {}, tensors: {}, params: {})",
model.name,
model.architecture,
model.tensors.len(),
model.total_params(),
);
run_benchmark(&model, *warmup, *iterations)?;
}
modelc::cli::Commands::Verify { input } => {
let path = modelc::store::resolve_model_path(input)?;
modelc::pack::verify(&path)?;
}
modelc::cli::Commands::Export { input, output } => {
let path = modelc::store::resolve_model_path(input)?;
let output_path = output.clone().unwrap_or_else(|| {
let mut p = path.clone();
p.set_extension("safetensors");
p
});
modelc::pack::export_to_safetensors(&path, &output_path)?;
}
modelc::cli::Commands::Completions { shell } => {
modelc::cli::generate_completions(shell)?;
}
modelc::cli::Commands::Containerize { input, output, base_image } => {
let path = modelc::store::resolve_model_path(input)?;
let out_dir = output.clone().unwrap_or_else(|| {
let mut p = std::env::current_dir().unwrap_or_default();
p.push("modelc-docker");
p
});
modelc::containerize::containerize(&path, &out_dir, base_image)?;
}
modelc::cli::Commands::Lora { model, adapter, alpha, output } => {
let path = modelc::store::resolve_model_path(model)?;
eprintln!("Loading model from {:?}...", path);
let mut m = modelc::pack::unpack(&path)?;
m.dequantize_in_place();
eprintln!("Applying LoRA adapter {:?} (alpha={})...", adapter, alpha);
modelc::lora::apply_lora(&mut m, adapter, *alpha)?;
let out_path = output.clone().unwrap_or_else(|| {
let mut p = path.clone();
p.set_file_name(format!("{}.lora.modelc", p.file_stem().unwrap().to_string_lossy()));
p
});
modelc::pack::pack(&m, &out_path, false)?;
eprintln!("Saved adapted model -> {:?}", out_path);
}
}
Ok(())
}
fn run_benchmark(model: &modelc::model::Model, warmup: usize, iterations: usize) -> Result<()> {
use modelc::runtime::serve::Runtime;
use std::time::Instant;
let runtime = Runtime::from_raw(&model.tensors);
let input_size = if model.architecture == "mlp" {
if let Some(w) = runtime.get("weight") {
w.shape.get(1).copied().unwrap_or(4)
} else {
let mut sizes: Vec<usize> = model.tensors
.keys()
.filter(|k| k.starts_with("layer") && k.ends_with(".weight"))
.filter_map(|k| runtime.get(k).map(|t| t.shape.get(1).copied().unwrap_or(4)))
.collect();
sizes.sort();
sizes.first().copied().unwrap_or(4)
}
} else {
4
};
let input: Vec<f32> = (0..input_size).map(|i| i as f32 * 0.1).collect();
for _ in 0..warmup {
let _ = benchmark_inference(model, &runtime, &input);
}
let start = Instant::now();
for _ in 0..iterations {
let _ = benchmark_inference(model, &runtime, &input);
}
let elapsed = start.elapsed();
let total_ms = elapsed.as_secs_f64() * 1000.0;
let avg_ms = total_ms / iterations as f64;
let throughput = iterations as f64 / elapsed.as_secs_f64();
println!("Benchmark results ({} iterations):", iterations);
println!(" total time: {:.3} ms", total_ms);
println!(" avg latency: {:.3} ms", avg_ms);
println!(" throughput: {:.1} inferences/sec", throughput);
Ok(())
}
fn benchmark_inference(
model: &modelc::model::Model,
runtime: &modelc::runtime::serve::Runtime,
input: &[f32],
) -> Vec<f32> {
use modelc::runtime::tensor::Tensor;
use modelc::runtime::ops;
if model.architecture == "mlp" {
if let (Some(w), Some(b)) = (runtime.get("weight"), runtime.get("bias")) {
let x = Tensor::from_vec(input.to_vec(), vec![1, input.len()]);
let mut out = ops::matmul(&x, w);
out = ops::add(&out, b);
return out.data;
}
let mut ids: Vec<u32> = model
.tensors
.keys()
.filter_map(|key| {
let tail = key.strip_prefix("layer")?;
let (idx, suf) = tail.split_once('.')?;
if suf == "weight" {
idx.parse::<u32>().ok()
} else {
None
}
})
.collect();
ids.sort_unstable();
ids.dedup();
if !ids.is_empty() {
let mut cur = input.to_vec();
let last = ids.len() - 1;
for (i, id) in ids.iter().enumerate() {
let w_name = format!("layer{id}.weight");
let b_name = format!("layer{id}.bias");
if let (Some(w), Some(b)) = (runtime.get(&w_name), runtime.get(&b_name)) {
let x = Tensor::from_vec(cur, vec![1, w.shape[1]]);
let mut out = ops::matmul(&x, w);
out = ops::add(&out, b);
cur = out.data;
if i != last {
cur = ops::relu(&Tensor::from_vec(cur.clone(), vec![cur.len()])).data;
}
}
}
return cur;
}
}
if let Some(name) = runtime.tensor_names().first() {
if let Some(t) = runtime.get(name) {
if t.shape.len() == 2 {
let x = Tensor::from_vec(input.to_vec(), vec![1, input.len()]);
let out = ops::matmul(&x, t);
return out.data;
}
}
}
input.to_vec()
}
fn print_model_list(models: &[modelc::store::InstalledModel]) {
if models.is_empty() {
println!("No models installed. Use `modelc pull <source>` to add one.");
return;
}
println!("Installed models:");
for m in models {
let size_mb = m.size_bytes as f64 / (1024.0 * 1024.0);
let arch = m.architecture.as_deref().unwrap_or("unknown");
let params = m.params.map(|p| format!("{} params", p)).unwrap_or_default();
let comp = if m.compressed { " [zstd]" } else { "" };
println!(
" {:20} {:>8.2} MB {:12} {:>14}{}",
m.name, size_mb, arch, params, comp
);
}
}