candle-core 0.9.1

use super::{GgmlDType, QStorage};
use crate::backend::BackendStorage;
use crate::{DType, MetalDevice, MetalStorage, Result, Shape};
use metal::Buffer;
use std::sync::Arc;

pub struct QMetalStorage {
    dtype: GgmlDType,
    device: MetalDevice,
    buffer: Arc<Buffer>,
}

impl QMetalStorage {
    pub fn zeros(device: &MetalDevice, elem_count: usize, dtype: GgmlDType) -> Result<Self> {
        let size = elem_count * dtype.type_size() / dtype.block_size();
        let buffer = device.allocate_zeros(size)?;
        Ok(Self {
            buffer,
            device: device.clone(),
            dtype,
        })
    }

    pub fn dtype(&self) -> GgmlDType {
        self.dtype
    }

    pub fn device(&self) -> &MetalDevice {
        &self.device
    }

    pub fn buffer(&self) -> &Buffer {
        &self.buffer
    }

    pub fn dequantize(&self, elem_count: usize) -> Result<MetalStorage> {
        use crate::quantized::k_quants::GgmlType;

        let buffer = self.device.new_buffer_managed(self.buffer.length())?;
        let command_buffer = self.device.command_buffer()?;
        command_buffer.set_label("to_cpu");
        let blit = command_buffer.new_blit_command_encoder();
        blit.set_label("blit_to_cpu");
        blit.copy_from_buffer(&self.buffer, 0, &buffer, 0, self.buffer.length());
        blit.end_encoding();
        self.device.wait_until_completed()?;
        let mut out = vec![0.0; elem_count];
        let block_len = elem_count / self.dtype.block_size();
        match self.dtype {
            GgmlDType::F32 => {
                let vec: Vec<f32> = read_to_vec(&buffer, block_len);
                f32::to_float(&vec, &mut out)?;
            }
            GgmlDType::F16 => {
                let vec: Vec<half::f16> = read_to_vec(&buffer, block_len);
                half::f16::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q4_0 => {
                let vec: Vec<crate::quantized::BlockQ4_0> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ4_0::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q4_1 => {
                let vec: Vec<crate::quantized::BlockQ4_1> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ4_1::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q5_0 => {
                let vec: Vec<crate::quantized::BlockQ5_0> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ5_0::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q5_1 => {
                let vec: Vec<crate::quantized::BlockQ5_1> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ5_1::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q8_0 => {
                let vec: Vec<crate::quantized::BlockQ8_0> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ8_0::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q8_1 => {
                let vec: Vec<crate::quantized::BlockQ8_1> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ8_1::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q2K => {
                let vec: Vec<crate::quantized::BlockQ2K> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ2K::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q3K => {
                let vec: Vec<crate::quantized::BlockQ3K> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ3K::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q4K => {
                let vec: Vec<crate::quantized::BlockQ4K> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ4K::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q5K => {
                let vec: Vec<crate::quantized::BlockQ5K> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ5K::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q6K => {
                let vec: Vec<crate::quantized::BlockQ6K> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ6K::to_float(&vec, &mut out)?;
            }
            GgmlDType::Q8K => {
                let vec: Vec<crate::quantized::BlockQ8K> = read_to_vec(&buffer, block_len);
                crate::quantized::BlockQ8K::to_float(&vec, &mut out)?;
            }
        }

        let buffer = self.device.new_buffer_with_data(&out)?;
        Ok(MetalStorage::new(
            buffer,
            self.device.clone(),
            elem_count,
            DType::F32,
        ))
    }

    pub fn quantize(&mut self, src: &MetalStorage) -> Result<()> {
        // Quantization only happens on CPU for now.
        let src = src.to_cpu::<f32>()?;
        let elem_count = src.len();
        let src = crate::Storage::Cpu(crate::CpuStorage::F32(src));
        let mut qcpu_storage = crate::Device::Cpu.qzeros(elem_count, self.dtype)?;
        qcpu_storage.quantize(&src)?;
        let buffer = self.device.new_buffer_with_data(&qcpu_storage.data()?)?;
        self.buffer = buffer;
        Ok(())
    }

    pub fn storage_size_in_bytes(&self) -> usize {
        self.buffer.length() as usize
    }

    pub fn fwd(
        &self,
        self_shape: &Shape,
        storage: &MetalStorage,
        layout: &crate::Layout,
    ) -> Result<(MetalStorage, Shape)> {
        use crate::MetalError;

        if !layout.is_contiguous() {
            crate::bail!("input tensor is not contiguous {layout:?}")
        }
        let src_shape = layout.shape();
        // self is transposed so n is first then k.
        if src_shape.rank() < 2 {
            crate::bail!("input tensor has only one dimension {layout:?}")
        }
        let (n, k) = self_shape.dims2()?;
        let mut dst_shape = src_shape.dims().to_vec();

        // We always use a single batch dimension and stack all the tensors in the batch on the
        // second dimension as the implementation in candle-metal-kernels doesn't handle batch
        // properly.
        let m = match dst_shape.len() {
            3 => dst_shape[0] * dst_shape[1],
            2 => dst_shape[0],
            n => crate::bail!("Invalid rank {n} for quantized matmul metal"),
        };
        let last_k = dst_shape.pop().unwrap();
        if last_k != k {
            crate::bail!("input tensor {layout:?} incompatible with {:?}", self_shape)
        }
        dst_shape.push(n);
        let dst_shape = Shape::from(dst_shape);
        let device = storage.device().clone();
        let dst = device.new_buffer(dst_shape.elem_count(), DType::F32, "qmatmul")?;
        let command_buffer = device.command_buffer()?;
        // In some cases it would be better to use the mm variant, though it has its drawbacks
        // around memory alignemnt.
        for batch_id in 0..m {
            candle_metal_kernels::call_quantized_matmul_mv_t(
                device.device(),
                &command_buffer,
                device.kernels(),
                self.dtype.into(),
                (1, 1, n, k),
                storage.buffer(),
                (layout.start_offset() + batch_id * k) * storage.dtype().size_in_bytes(),
                &self.buffer,
                batch_id * n * DType::F32.size_in_bytes(),
                &dst,
            )
            .map_err(MetalError::from)?;
        }
        let dst_storage = crate::MetalStorage::new(dst, device, dst_shape.elem_count(), DType::F32);
        Ok((dst_storage, dst_shape))
    }
}

pub fn load_quantized<T: super::GgmlType + Send + Sync + 'static>(
    device: &MetalDevice,
    data: &[T],
) -> Result<QStorage> {
    let buffer = device.new_buffer_with_data(data)?;
    let device = device.clone();
    Ok(QStorage::Metal(QMetalStorage {
        dtype: T::DTYPE,
        device,
        buffer,
    }))
}

fn read_to_vec<T: Clone>(buffer: &Buffer, n: usize) -> Vec<T> {
    let ptr = buffer.contents() as *const T;
    assert!(!ptr.is_null());
    let slice = unsafe { std::slice::from_raw_parts(ptr, n) };
    slice.to_vec()
}

impl From<GgmlDType> for candle_metal_kernels::GgmlDType {
    fn from(value: GgmlDType) -> Self {
        match value {
            GgmlDType::Q4_0 => candle_metal_kernels::GgmlDType::Q4_0,
            GgmlDType::Q4_1 => candle_metal_kernels::GgmlDType::Q4_1,
            GgmlDType::Q5_0 => candle_metal_kernels::GgmlDType::Q5_0,
            GgmlDType::Q5_1 => candle_metal_kernels::GgmlDType::Q5_1,
            GgmlDType::Q8_0 => candle_metal_kernels::GgmlDType::Q8_0,
            GgmlDType::Q8_1 => candle_metal_kernels::GgmlDType::Q8_1,
            GgmlDType::Q2K => candle_metal_kernels::GgmlDType::Q2K,
            GgmlDType::Q3K => candle_metal_kernels::GgmlDType::Q3K,
            GgmlDType::Q4K => candle_metal_kernels::GgmlDType::Q4K,
            GgmlDType::Q5K => candle_metal_kernels::GgmlDType::Q5K,
            GgmlDType::Q6K => candle_metal_kernels::GgmlDType::Q6K,
            GgmlDType::Q8K => candle_metal_kernels::GgmlDType::Q8K,
            GgmlDType::F16 => candle_metal_kernels::GgmlDType::F16,
            GgmlDType::F32 => candle_metal_kernels::GgmlDType::F32,
        }
    }
}