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use crate::storage::{BinaryOp, UnaryOp};
use crate::{DType, Error, Result, Shape, StridedIndex};
use ggblas::batched_sgemm;
// TODO: Think about whether we would be better off with a dtype and
// a buffer as an owned slice of bytes.
#[derive(Debug, Clone)]
pub enum CpuStorage {
F32(Vec<f32>),
F64(Vec<f64>),
}
impl CpuStorage {
pub fn dtype(&self) -> DType {
match self {
Self::F32(_) => DType::F32,
Self::F64(_) => DType::F64,
}
}
pub fn as_slice<D: crate::WithDType>(&self) -> Result<&[D]> {
D::cpu_storage_as_slice(self)
}
pub fn as_mut_slice<D: crate::WithDType>(&mut self) -> Result<&mut [D]> {
D::cpu_storage_as_mut_slice(self)
}
pub(crate) fn affine_impl(
&self,
shape: &Shape,
stride: &[usize],
mul: f64,
add: f64,
) -> Result<Self> {
match self {
Self::F32(storage) => {
let index = StridedIndex::new(shape.dims(), stride);
let mul = mul as f32;
let add = add as f32;
let data = index.map(|i| storage[i] * mul + add).collect();
Ok(Self::F32(data))
}
Self::F64(storage) => {
let index = StridedIndex::new(shape.dims(), stride);
let data = index.map(|i| storage[i] * mul + add).collect();
Ok(Self::F64(data))
}
}
}
pub(crate) fn unary_impl<B: UnaryOp>(&self, shape: &Shape, stride: &[usize]) -> Result<Self> {
// TODO: Different code path for the contiguous case?
match self {
Self::F32(storage) => {
let index = StridedIndex::new(shape.dims(), stride);
let data = index.map(|i| B::f32(storage[i])).collect();
Ok(Self::F32(data))
}
Self::F64(storage) => {
let index = StridedIndex::new(shape.dims(), stride);
let data = index.map(|i| B::f64(storage[i])).collect();
Ok(Self::F64(data))
}
}
}
pub(crate) fn binary_impl<B: BinaryOp>(
&self,
rhs: &Self,
shape: &Shape,
lhs_stride: &[usize],
rhs_stride: &[usize],
) -> Result<Self> {
// The ggml implementation has different paths based on whether the rhs is contiguous
// or not, for now we only consider the general case but we should benchmark and do the
// same if it helps.
// https://github.com/ggerganov/llama.cpp/blob/aacdbd40562684665b6f7b8ba6695b7a2088bbb0/ggml.c#L7895
match (self, rhs) {
(Self::F32(lhs), Self::F32(rhs)) => {
let lhs_index = StridedIndex::new(shape.dims(), lhs_stride);
let rhs_index = StridedIndex::new(shape.dims(), rhs_stride);
let data = lhs_index
.zip(rhs_index)
.map(|(lhs_i, rhs_i)| B::f32(lhs[lhs_i], rhs[rhs_i]))
.collect();
Ok(Self::F32(data))
}
(Self::F64(lhs), Self::F64(rhs)) => {
let lhs_index = StridedIndex::new(shape.dims(), lhs_stride);
let rhs_index = StridedIndex::new(shape.dims(), rhs_stride);
let data = lhs_index
.zip(rhs_index)
.map(|(lhs_i, rhs_i)| B::f64(lhs[lhs_i], rhs[rhs_i]))
.collect();
Ok(Self::F64(data))
}
_ => {
// This should be covered by the dtype check above.
Err(Error::DTypeMismatchBinaryOp {
lhs: self.dtype(),
rhs: rhs.dtype(),
op: B::NAME,
})
}
}
}
pub(crate) fn matmul_impl(
&self,
rhs: &Self,
(b, m, n, k): (usize, usize, usize, usize),
lhs_stride: &[usize],
rhs_stride: &[usize],
) -> Result<Self> {
println!("rhs {rhs:?}");
println!("lhs_stride {lhs_stride:?}");
println!("rhs_stride {rhs_stride:?}");
// todo!("matmul");
let a_skip: usize = m * k;
let b_skip: usize = n * k;
let c_skip: usize = m * n;
let mut c = Self::F32(vec![0.0; b * m * n]);
batched_sgemm(
self.as_slice()?,
a_skip,
rhs.as_slice()?,
b_skip,
c.as_mut_slice()?,
c_skip,
m,
n,
k,
b,
);
Ok(c)
}
pub(crate) fn ones_impl(shape: &Shape, dtype: DType) -> Self {
let elem_count = shape.elem_count();
match dtype {
DType::F32 => {
let data = vec![1f32; elem_count];
Self::F32(data)
}
DType::F64 => {
let data = vec![1f64; elem_count];
Self::F64(data)
}
}
}
pub(crate) fn zeros_impl(shape: &Shape, dtype: DType) -> Self {
let elem_count = shape.elem_count();
match dtype {
DType::F32 => {
let data = vec![0f32; elem_count];
Self::F32(data)
}
DType::F64 => {
let data = vec![0f64; elem_count];
Self::F64(data)
}
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::{Device, Tensor};
#[test]
fn simple_matmul() -> Result<()> {
let data = vec![1.0f32, 2.0, 3.0, 4.0];
let a = Tensor::from_slice(&data, (2, 2), Device::Cpu)?;
let data = vec![1.0f32, 2.0, 3.0, 4.0];
let b = Tensor::from_slice(&data, (2, 2), Device::Cpu)?;
let c = a.matmul(&b)?;
assert_eq!(c.to_vec2::<f32>()?, &[&[7.0f32, 10.0], &[15.0, 22.0]]);
let data = vec![1.0f32, 2.0];
let a = Tensor::from_slice(&data, (2, 1), Device::Cpu)?;
let data = vec![3.0f32, 4.0];
let b = Tensor::from_slice(&data, (1, 2), Device::Cpu)?;
let c = a.matmul(&b)?;
assert_eq!(c.to_vec2::<f32>()?, &[&[3.0, 4.0], &[6.0, 8.0]]);
let data: Vec<_> = (0..6).map(|i| i as f32).collect();
let a = Tensor::from_slice(&data, (2, 3), Device::Cpu)?;
let data: Vec<_> = (0..6).map(|i| (i + 2) as f32).collect();
let b = Tensor::from_slice(&data, (3, 2), Device::Cpu)?;
let c = a.matmul(&b)?;
assert_eq!(c.to_vec2::<f32>()?, &[&[16., 19.], &[52., 64.]]);
let data: Vec<_> = (0..12).map(|i| i as f32).collect();
let a = Tensor::from_slice(&data, (2, 2, 3), Device::Cpu)?;
let data: Vec<_> = (0..12).map(|i| (i + 2) as f32).collect();
let b = Tensor::from_slice(&data, (2, 3, 2), Device::Cpu)?;
let c = a.matmul(&b)?;
assert_eq!(
c.to_vec3::<f32>()?,
&[&[&[16., 19.], &[52., 64.]], &[&[214., 235.], &[304., 334.]]]
);
Ok(())
}
}
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