Get the ggml based llama to generate some text. (#464)

* Add more stats to the ggml example.

* Build a quantized model from the file content.

* Move the tensor retrieval in the main crate.

* Start adding the forward pass.

* Add more to the forward pass of the quantized llama.

* Apply the attention layers.

* Add the sampling loop.

* Get the sampling loop to work.

* Minor tweak.

* Add a quantize/dequantize test.

* Bugfix.

* Add a comment + swap the order.

* Bugfixes.

1 files changed, 14 insertions, 4 deletions

diff --git a/candle-core/src/quantized/ggml_file.rs b/candle-core/src/quantized/ggml_file.rs
index ee23cdde..7afb8670 100644
--- a/candle-core/src/quantized/ggml_file.rs
+++ b/candle-core/src/quantized/ggml_file.rs
@@ -3,6 +3,7 @@
 use super::{k_quants, GgmlDType};
 use crate::Result;
 use byteorder::{LittleEndian, ReadBytesExt};
+use std::collections::HashMap;
 
 // https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.h#L37
 #[derive(Debug, Clone, Copy, PartialEq, Eq)]
@@ -163,6 +164,9 @@ fn read_one_tensor<R: std::io::Seek + std::io::Read>(
     let ggml_dtype = GgmlDType::from_u32(ggml_dtype)?;
     let mut dims = vec![0u32; n_dims as usize];
     reader.read_u32_into::<LittleEndian>(&mut dims)?;
+    // The dimensions are stored in reverse order, see for example:
+    // https://github.com/ggerganov/llama.cpp/blob/b5ffb2849d23afe73647f68eec7b68187af09be6/convert.py#L969
+    dims.reverse();
     let mut name = vec![0u8; name_len as usize];
     reader.read_exact(&mut name)?;
     let name = String::from_utf8_lossy(&name).into_owned();
@@ -174,7 +178,6 @@ fn read_one_tensor<R: std::io::Seek + std::io::Read>(
     let dims = dims.iter().map(|&u| u as usize).collect::<Vec<_>>();
     let tensor_elems = dims.iter().product::<usize>();
     let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.blck_size();
-    println!("{name} {ggml_dtype:?} {dims:?}");
     // TODO: Mmap version to avoid copying the data around?
     let mut raw_data = vec![0u8; size_in_bytes];
     reader.read_exact(&mut raw_data)?;
@@ -188,7 +191,7 @@ pub struct Content {
     pub magic: VersionedMagic,
     pub hparams: HParams,
     pub vocab: Vocab,
-    pub tensors: Vec<(String, super::QTensor)>,
+    pub tensors: HashMap<String, super::QTensor>,
 }
 
 impl Content {
@@ -199,11 +202,11 @@ impl Content {
         let magic = VersionedMagic::read(reader)?;
         let hparams = HParams::read(reader)?;
         let vocab = Vocab::read(reader, hparams.n_vocab as usize)?;
-        let mut tensors = vec![];
+        let mut tensors = HashMap::new();
 
         while reader.stream_position()? != last_position {
             let (name, tensor) = read_one_tensor(reader, magic)?;
-            tensors.push((name, tensor))
+            tensors.insert(name, tensor);
         }
         Ok(Self {
             magic,
@@ -212,4 +215,11 @@ impl Content {
             tensors,
         })
     }
+
+    pub fn remove(&mut self, name: &str) -> Result<super::QTensor> {
+        match self.tensors.remove(name) {
+            None => crate::bail!("cannot find tensor with name '{name}'"),
+            Some(tensor) => Ok(tensor),
+        }
+    }
 }