Resurrect the llama npy support. (#140)

candle-core/src/error.rs

@@ -139,6 +139,9 @@ pub enum Error {
         rhs_stride: Vec<usize>,
         mnk: (usize, usize, usize),
     },
+
+    #[error("cannot find tensor {path}")]
+    CannotFindTensor { path: String },
 }
 
 pub type Result<T> = std::result::Result<T, Error>;

candle-core/src/npy.rs

@@ -1,10 +1,10 @@
-//! Numpy support for literals.
+//! Numpy support for tensors.
 //!
 //! The spec for the npy format can be found in
 //! [npy-format](https://docs.scipy.org/doc/numpy-1.14.2/neps/npy-format.html).
-//! The functions from this module can be used to read literals from npy/npz files
-//! or write literals to these files. A npy file contains a single literal (unnamed)
-//! whereas a npz file can contain multiple named literals. npz files are also compressed.
+//! The functions from this module can be used to read tensors from npy/npz files
+//! or write tensors to these files. A npy file contains a single tensor (unnamed)
+//! whereas a npz file can contain multiple named tensors. npz files are also compressed.
 //!
 //! These two formats are easy to use in Python using the numpy library.
 //!
@@ -232,7 +232,7 @@ impl Tensor {
         }
     }
 
-    /// Reads a npy file and return the stored multi-dimensional array as a literal.
+    /// Reads a npy file and return the stored multi-dimensional array as a tensor.
     pub fn read_npy<T: AsRef<Path>>(path: T) -> Result<Self> {
         let mut reader = File::open(path.as_ref())?;
         let header = read_header(&mut reader)?;

candle-core/src/utils.rs

@@ -10,3 +10,10 @@ pub fn get_num_threads() -> usize {
         Some(_) | None => num_cpus::get(),
     }
 }
+
+pub fn has_mkl() -> bool {
+    #[cfg(feature = "mkl")]
+    return true;
+    #[cfg(not(feature = "mkl"))]
+    return false;
+}