Small tweaks to tensor-tools. (#517)

candle-core/examples/tensor-tools.rs

@@ -6,7 +6,7 @@ enum Format {
     Safetensors,
     Npz,
     Ggml,
-    PyTorch,
+    Pth,
     Pickle,
 }
 
@@ -16,9 +16,10 @@ impl Format {
             .extension()
             .and_then(|e| e.to_str())
             .and_then(|e| match e {
+                // We don't infer any format for .bin as it can be used for ggml or pytorch.
                 "safetensors" | "safetensor" => Some(Self::Safetensors),
                 "npz" => Some(Self::Npz),
-                "pth" | "pt" => Some(Self::PyTorch),
+                "pth" | "pt" => Some(Self::Pth),
                 "ggml" => Some(Self::Ggml),
                 _ => None,
             })
@@ -29,18 +30,19 @@ impl Format {
 enum Command {
     Ls {
         files: Vec<std::path::PathBuf>,
+
         /// The file format to use, if unspecified infer from the file extension.
         #[arg(long, value_enum)]
         format: Option<Format>,
+
+        /// Enable verbose mode.
+        #[arg(short, long)]
+        verbose: bool,
     },
 }
 
 #[derive(Parser, Debug, Clone)]
 struct Args {
-    /// Enable verbose mode.
-    #[arg(short, long)]
-    verbose: bool,
-
     #[command(subcommand)]
     command: Command,
 }
@@ -86,7 +88,7 @@ fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> R
                 println!("{name}: [{shape:?}; {dtype}]")
             }
         }
-        Format::PyTorch => {
+        Format::Pth => {
             let mut tensors = candle_core::pickle::read_pth_tensor_info(file)?;
             tensors.sort_by(|a, b| a.name.cmp(&b.name));
             for tensor_info in tensors.iter() {
@@ -126,13 +128,17 @@ fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> R
 fn main() -> anyhow::Result<()> {
     let args = Args::parse();
     match args.command {
-        Command::Ls { files, format } => {
+        Command::Ls {
+            files,
+            format,
+            verbose,
+        } => {
             let multiple_files = files.len() > 1;
             for file in files.iter() {
                 if multiple_files {
                     println!("--- {file:?} ---");
                 }
-                run_ls(file, format.clone(), args.verbose)?
+                run_ls(file, format.clone(), verbose)?
             }
         }
     }

candle-core/src/pickle.rs

@@ -490,13 +490,14 @@ impl From<Object> for E {
 
 // https://github.com/pytorch/pytorch/blob/4eac43d046ded0f0a5a5fa8db03eb40f45bf656e/torch/_utils.py#L198
 // Arguments: storage, storage_offset, size, stride, requires_grad, backward_hooks
-fn rebuild_args(args: Object) -> Result<(Layout, DType, String)> {
+fn rebuild_args(args: Object) -> Result<(Layout, DType, String, usize)> {
     let mut args = args.tuple()?;
     let stride = Vec::<usize>::try_from(args.remove(3))?;
     let size = Vec::<usize>::try_from(args.remove(2))?;
     let offset = args.remove(1).int()? as usize;
     let storage = args.remove(0).persistent_load()?;
     let mut storage = storage.tuple()?;
+    let storage_size = storage.remove(4).int()? as usize;
     let path = storage.remove(2).unicode()?;
     let (_module_name, class_name) = storage.remove(1).class()?;
     let dtype = match class_name.as_str() {
@@ -510,7 +511,7 @@ fn rebuild_args(args: Object) -> Result<(Layout, DType, String)> {
         }
     };
     let layout = Layout::new(crate::Shape::from(size), stride, offset);
-    Ok((layout, dtype, path))
+    Ok((layout, dtype, path, storage_size))
 }
 
 #[derive(Debug, Clone)]
@@ -519,6 +520,7 @@ pub struct TensorInfo {
     pub dtype: DType,
     pub layout: Layout,
     pub path: String,
+    pub storage_size: usize,
 }
 
 pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(file: P) -> Result<Vec<TensorInfo>> {
@@ -576,7 +578,7 @@ pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(file: P) -> Result<Vec<Te
                     _ => continue,
                 };
                 match rebuild_args(args) {
-                    Ok((layout, dtype, file_path)) => {
+                    Ok((layout, dtype, file_path, storage_size)) => {
                         let mut path = dir_name.clone();
                         path.push(file_path);
                         tensor_infos.push(TensorInfo {
@@ -584,6 +586,7 @@ pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(file: P) -> Result<Vec<Te
                             dtype,
                             layout,
                             path: path.to_string_lossy().into_owned(),
+                            storage_size,
                         })
                     }
                     Err(err) => {