Split out the quantized file. (#456)

candle-core/src/quantized/ggml_file.rs

@@ -0,0 +1,294 @@
+//! Support for the GGML file format.
+
+use super::{k_quants, GgmlDType};
+use crate::{DType, Device, Result, Tensor};
+use byteorder::{LittleEndian, ReadBytesExt};
+
+// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.h#L37
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+enum Magic {
+    Ggjt,
+    Ggla,
+    Ggmf,
+    Ggml,
+    Ggsn,
+}
+
+impl TryFrom<u32> for Magic {
+    type Error = crate::Error;
+    fn try_from(value: u32) -> Result<Self> {
+        let magic = match value {
+            0x67676a74 => Self::Ggjt,
+            0x67676c61 => Self::Ggla,
+            0x67676d66 => Self::Ggmf,
+            0x67676d6c => Self::Ggml,
+            0x6767736e => Self::Ggsn,
+            _ => crate::bail!("unknown magic {value:08x}"),
+        };
+        Ok(magic)
+    }
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum VersionedMagic {
+    GgmlUnversioned,
+    GgmfV1,
+    GgjtV1,
+    GgjtV2,
+    GgjtV3,
+}
+
+impl VersionedMagic {
+    fn read<R: std::io::Read>(reader: &mut R) -> Result<Self> {
+        let magic = reader.read_u32::<LittleEndian>()?;
+        let magic = Magic::try_from(magic)?;
+        if magic == Magic::Ggml {
+            return Ok(Self::GgmlUnversioned);
+        }
+        let version = reader.read_u32::<LittleEndian>()?;
+        let versioned_magic = match (magic, version) {
+            (Magic::Ggmf, 1) => Self::GgmfV1,
+            (Magic::Ggjt, 1) => Self::GgjtV1,
+            (Magic::Ggjt, 2) => Self::GgjtV2,
+            (Magic::Ggjt, 3) => Self::GgjtV3,
+            _ => crate::bail!("ggml: unsupported magic/version {magic:?}/{version}"),
+        };
+        Ok(versioned_magic)
+    }
+
+    fn align32(&self) -> bool {
+        match self {
+            Self::GgmlUnversioned | Self::GgmfV1 => false,
+            Self::GgjtV1 | Self::GgjtV2 | Self::GgjtV3 => true,
+        }
+    }
+}
+
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub struct HParams {
+    pub n_vocab: u32,
+    pub n_embd: u32,
+    pub n_mult: u32,
+    pub n_head: u32,
+    pub n_layer: u32,
+    pub n_rot: u32,
+    pub ftype: u32,
+}
+
+impl HParams {
+    fn read<R: std::io::Read>(reader: &mut R) -> Result<Self> {
+        let n_vocab = reader.read_u32::<LittleEndian>()?;
+        let n_embd = reader.read_u32::<LittleEndian>()?;
+        let n_mult = reader.read_u32::<LittleEndian>()?;
+        let n_head = reader.read_u32::<LittleEndian>()?;
+        let n_layer = reader.read_u32::<LittleEndian>()?;
+        let n_rot = reader.read_u32::<LittleEndian>()?;
+        let ftype = reader.read_u32::<LittleEndian>()?;
+        Ok(Self {
+            n_vocab,
+            n_embd,
+            n_mult,
+            n_head,
+            n_layer,
+            n_rot,
+            ftype,
+        })
+    }
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub struct Vocab {
+    pub token_score_pairs: Vec<(Vec<u8>, f32)>,
+}
+
+impl Vocab {
+    fn read<R: std::io::Read>(reader: &mut R, n_vocab: usize) -> Result<Self> {
+        // https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.cpp#L556
+        let mut token_score_pairs = Vec::with_capacity(n_vocab);
+        for _index in 0..n_vocab {
+            let len = reader.read_u32::<LittleEndian>()? as usize;
+            let mut word = vec![0u8; len];
+            reader.read_exact(&mut word)?;
+            let score = reader.read_f32::<LittleEndian>()?;
+            token_score_pairs.push((word, score))
+        }
+        Ok(Self { token_score_pairs })
+    }
+}
+
+fn dequantize_and_create_tensor<T: super::GgmlType>(
+    raw_data: &[u8],
+    tensor_elems: usize,
+    size_in_bytes: usize,
+    dims: Vec<usize>,
+    device: &Device,
+) -> Result<Tensor> {
+    let mut f32_data = vec![0f32; tensor_elems];
+    let raw_data_ptr = raw_data.as_ptr();
+    let n_blocks = size_in_bytes / std::mem::size_of::<T>();
+    let raw_data = unsafe { std::slice::from_raw_parts(raw_data_ptr as *const T, n_blocks) };
+    T::to_float(raw_data, &mut f32_data)?;
+    Tensor::from_vec(f32_data, dims, device)
+}
+
+/// Creates a [Tensor] from a raw GGML tensor.
+pub fn tensor_from_ggml(
+    ggml_dtype: GgmlDType,
+    raw_data: &[u8],
+    dims: Vec<usize>,
+    dtype: DType,
+    device: &Device,
+) -> Result<Tensor> {
+    let tensor_elems = dims.iter().product::<usize>();
+    let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.blck_size();
+
+    let tensor = match ggml_dtype {
+        GgmlDType::F32 => Tensor::from_raw_buffer(raw_data, DType::F32, &dims, device),
+        GgmlDType::F16 => Tensor::from_raw_buffer(raw_data, DType::F16, &dims, device),
+        GgmlDType::Q4_0 => dequantize_and_create_tensor::<k_quants::BlockQ4_0>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q4_1 => dequantize_and_create_tensor::<k_quants::BlockQ4_1>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q5_0 => dequantize_and_create_tensor::<k_quants::BlockQ5_0>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q5_1 => dequantize_and_create_tensor::<k_quants::BlockQ5_1>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q8_0 => dequantize_and_create_tensor::<k_quants::BlockQ8_0>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q2K => dequantize_and_create_tensor::<k_quants::BlockQ2K>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q3K => dequantize_and_create_tensor::<k_quants::BlockQ3K>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q4K => dequantize_and_create_tensor::<k_quants::BlockQ4K>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q5K => dequantize_and_create_tensor::<k_quants::BlockQ5K>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        GgmlDType::Q6K => dequantize_and_create_tensor::<k_quants::BlockQ6K>(
+            raw_data,
+            tensor_elems,
+            size_in_bytes,
+            dims,
+            device,
+        ),
+        _ => crate::bail!("quantized type {dtype:?} is not supported yet"),
+    }?;
+    //We only have ggml-quant to f32 conversions, meaning we have to convert to the desired type
+    if tensor.dtype() != dtype {
+        tensor.to_dtype(dtype)
+    } else {
+        Ok(tensor)
+    }
+}
+
+fn read_one_tensor<R: std::io::Seek + std::io::Read>(
+    reader: &mut R,
+    magic: VersionedMagic,
+    dtype: DType,
+    device: &Device,
+) -> Result<(String, Tensor)> {
+    let n_dims = reader.read_u32::<LittleEndian>()?;
+    let name_len = reader.read_u32::<LittleEndian>()?;
+    let ggml_dtype = reader.read_u32::<LittleEndian>()?;
+    let ggml_dtype = GgmlDType::from_u32(ggml_dtype)?;
+    let mut dims = vec![0u32; n_dims as usize];
+    reader.read_u32_into::<LittleEndian>(&mut dims)?;
+    let mut name = vec![0u8; name_len as usize];
+    reader.read_exact(&mut name)?;
+    let name = String::from_utf8_lossy(&name).into_owned();
+
+    if magic.align32() {
+        let pos = reader.stream_position()?;
+        reader.seek(std::io::SeekFrom::Current(((32 - pos % 32) % 32) as i64))?;
+    }
+    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)?;
+    match tensor_from_ggml(ggml_dtype, &raw_data, dims, dtype, device) {
+        Ok(tensor) => Ok((name, tensor)),
+        Err(e) => crate::bail!("Error creating tensor {name}: {e}"),
+    }
+}
+
+#[derive(Debug)]
+pub struct Content {
+    pub magic: VersionedMagic,
+    pub hparams: HParams,
+    pub vocab: Vocab,
+    pub tensors: Vec<(String, Tensor)>,
+}
+
+impl Content {
+    pub fn read<R: std::io::Seek + std::io::Read>(
+        reader: &mut R,
+        dtype: DType,
+        device: &Device,
+    ) -> Result<Content> {
+        // https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.cpp#L505
+        let last_position = reader.seek(std::io::SeekFrom::End(0))?;
+        reader.seek(std::io::SeekFrom::Start(0))?;
+        let magic = VersionedMagic::read(reader)?;
+        let hparams = HParams::read(reader)?;
+        let vocab = Vocab::read(reader, hparams.n_vocab as usize)?;
+        let mut tensors = vec![];
+
+        while reader.stream_position()? != last_position {
+            let (name, tensor) = read_one_tensor(reader, magic, dtype, device)?;
+            tensors.push((name, tensor))
+        }
+        Ok(Self {
+            magic,
+            hparams,
+            vocab,
+            tensors,
+        })
+    }
+}