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-rw-r--r--candle-core/src/npy.rs18
1 files changed, 9 insertions, 9 deletions
diff --git a/candle-core/src/npy.rs b/candle-core/src/npy.rs
index 6302cf71..e17ba02a 100644
--- a/candle-core/src/npy.rs
+++ b/candle-core/src/npy.rs
@@ -307,39 +307,39 @@ impl Tensor {
header.push('\n');
f.write_all(&[(header.len() % 256) as u8, (header.len() / 256) as u8])?;
f.write_all(header.as_bytes())?;
- let elem_count = self.elem_count();
+ let vs = self.flatten_all()?;
match self.dtype() {
DType::BF16 => {
- let vs = self.reshape(elem_count)?.to_vec1::<bf16>()?;
+ let vs = vs.to_vec1::<bf16>()?;
for &v in vs.reinterpret_cast() {
f.write_u16::<LittleEndian>(v)?
}
}
DType::F16 => {
- let vs = self.reshape(elem_count)?.to_vec1::<f16>()?;
+ let vs = vs.to_vec1::<f16>()?;
for &v in vs.reinterpret_cast() {
f.write_u16::<LittleEndian>(v)?
}
}
DType::F32 => {
// TODO: Avoid using a buffer when data is already on the CPU.
- for v in self.reshape(elem_count)?.to_vec1::<f32>()? {
+ for v in vs.to_vec1::<f32>()? {
f.write_f32::<LittleEndian>(v)?
}
}
DType::F64 => {
- for v in self.reshape(elem_count)?.to_vec1::<f64>()? {
+ for v in vs.to_vec1::<f64>()? {
f.write_f64::<LittleEndian>(v)?
}
}
DType::U32 => {
- for v in self.reshape(elem_count)?.to_vec1::<u32>()? {
+ for v in vs.to_vec1::<u32>()? {
f.write_u32::<LittleEndian>(v)?
}
}
DType::U8 => {
- let data = self.reshape(elem_count)?.to_vec1::<u8>()?;
- f.write_all(&data)?;
+ let vs = vs.to_vec1::<u8>()?;
+ f.write_all(&vs)?;
}
}
Ok(())
@@ -373,7 +373,7 @@ pub struct NpzTensors {
index_per_name: HashMap<String, usize>,
path: std::path::PathBuf,
// We do not store a zip reader as it needs mutable access to extract data. Instead we
- // re-create a zip reader each time.
+ // re-create a zip reader for each tensor.
}
impl NpzTensors {