use crate::axes::Axis;
use crate::internal::*;
use ndarray::*;
use tract_linalg::block_quant::{BlockQuantValue, PackedBlockQuantFact, PackedBlockQuantFormat};
use tract_linalg::mmm::MMMInputValue;
use tract_linalg::pack::PackedFormat;
use super::ModePicker;
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct OptMatMulPack {
pub(crate) packers: Vec<PackedFormat>,
pub(crate) mode_picker: ModePicker,
pub(crate) k_axis: usize,
pub(crate) mn_axis: usize,
}
impl Op for OptMatMulPack {
fn name(&self) -> StaticName {
"OptMatMulPack".into()
}
fn info(&self) -> TractResult<Vec<String>> {
Ok(vec![format!("{:?}. k axis: {}, mn axis: {}", self.packers, self.k_axis, self.mn_axis)])
}
op_as_typed_op!();
impl_op_same_as!();
}
impl EvalOp for OptMatMulPack {
fn is_stateless(&self) -> bool {
true
}
fn eval_with_session(
&self,
_node_id: usize,
session: &SessionState,
mut inputs: TVec<TValue>,
) -> TractResult<TVec<TValue>> {
self.do_eval(session, inputs.remove(0))
}
}
impl TypedOp for OptMatMulPack {
fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
match self.mode_picker {
ModePicker::Single => ensure!(self.packers.len() == 1),
ModePicker::VecVsMat => ensure!(self.packers.len() == 2),
}
let k = inputs[0].shape[self.k_axis].clone();
let mn = inputs[0].shape[self.mn_axis].clone();
let opaque_fact = DynPackedOpaqueFact { k, mn, packers: self.packers.clone() };
Ok(tvec!(Opaque::datum_type()
.fact(self.output_shape(&inputs[0].shape))
.with_opaque_fact(opaque_fact)))
}
fn axes_mapping(
&self,
inputs: &[&TypedFact],
outputs: &[&TypedFact],
) -> TractResult<AxesMapping> {
let mut axes: Vec<Axis> = (0..inputs[0].rank())
.filter(|&ix| ix != self.k_axis && ix != self.mn_axis)
.enumerate()
.zip('a'..)
.map(|((o, i), repr)| Axis::new(repr, 1, 1).input(0, i).output(0, o))
.collect();
axes.push(Axis::new('K', 1, 1).input(0, self.k_axis));
axes.push(Axis::new('M', 1, 1).input(0, self.mn_axis));
axes.push(Axis::new('P', 1, 1).output(0, outputs[0].rank()));
AxesMapping::new(1, 1, axes)
}
as_op!();
}
impl OptMatMulPack {
fn do_eval(&self, _session: &SessionState, input: TValue) -> TractResult<TVec<TValue>> {
unsafe {
let mode = self.mode_picker.pick(input.shape()[self.mn_axis])?;
let packer = &self.packers[mode];
let output_shape: TVec<usize> = self.output_shape(input.shape());
let stores = if output_shape.iter().all(|d| *d == 1) {
tensor0::<Opaque>(
packer.pack_tensor_view(&input.view(), self.k_axis, self.mn_axis)?.into(),
)
.into_shape(&output_shape)?
} else {
let mut stores = Tensor::uninitialized_dt(Opaque::datum_type(), &output_shape)?;
let mut stores_view = stores.to_array_view_mut::<Opaque>()?;
let mut bc_shape: TVec<usize> = input.shape().into();
bc_shape[self.k_axis] = 1;
bc_shape[self.mn_axis] = 1;
for coord in indices(&*bc_shape) {
let offset = coord
.as_array_view()
.iter()
.zip(input.strides())
.map(|(x, s)| *x as isize * s)
.sum::<isize>()
* input.datum_type().size_of() as isize;
let mut pack_coords: TVec<usize> = coord.slice().into();
pack_coords.remove(self.k_axis.max(self.mn_axis));
pack_coords.remove(self.k_axis.min(self.mn_axis));
stores_view[&*pack_coords] = packer
.pack_tensor_view(
&TensorView::from_bytes(&input, offset, input.shape(), input.strides()),
self.k_axis,
self.mn_axis,
)?
.into();
}
stores
};
Ok(tvec!(stores.into_tvalue()))
}
}
pub fn output_shape<D: DimLike>(&self, input: &[D]) -> TVec<D> {
let mut packed_shape: TVec<D> = input.into();
packed_shape.remove(self.mn_axis.max(self.k_axis));
packed_shape.remove(self.mn_axis.min(self.k_axis));
packed_shape
}
}
#[derive(Hash, Clone, Debug, PartialEq, Eq)]
pub struct DynPackedOpaqueFact {
pub k: TDim,
pub mn: TDim,
pub packers: Vec<PackedFormat>,
}
impl OpaqueFact for DynPackedOpaqueFact {
fn mem_size(&self) -> TDim {
self.k.clone() * &self.mn * self.packers[0].dt.size_of()
}
fn same_as(&self, other: &dyn OpaqueFact) -> bool {
other.downcast_ref::<Self>().is_some_and(|o| o == self)
}
}
#[derive(Debug, Clone, Hash, Eq, PartialEq)]
pub struct OptSimpleMatMulPack {
pub(crate) packed_format: PackedBlockQuantFormat,
pub(crate) k: usize,
pub(crate) m: usize,
}
impl Op for OptSimpleMatMulPack {
fn name(&self) -> StaticName {
"OptSimpleMatMulPack".into()
}
op_as_typed_op!();
impl_op_same_as!();
}
impl EvalOp for OptSimpleMatMulPack {
fn is_stateless(&self) -> bool {
true
}
fn state(
&self,
_session: &mut SessionState,
_node_id: usize,
) -> TractResult<Option<Box<dyn OpState>>> {
Ok(None)
}
fn eval(&self, inputs: TVec<TValue>) -> TractResult<TVec<TValue>> {
let input = args_1!(inputs);
let mut output = tensor1(
&input
.as_slice::<Opaque>()?
.iter()
.map(|i| {
let i = i.downcast_ref::<BlockQuantValue>().unwrap();
let iv: Box<dyn MMMInputValue> =
Box::new(self.packed_format.pack(&i.value, i.fact.k())?);
Ok(Opaque(Arc::new(iv)))
})
.collect::<TractResult<Vec<_>>>()?,
);
output.set_shape(input.shape())?;
Ok(tvec!(output.into_tvalue()))
}
}
impl TypedOp for OptSimpleMatMulPack {
fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
let fact = Opaque::fact(inputs[0].shape.clone()).with_opaque_fact(PackedBlockQuantFact {
format: self.packed_format.clone(),
shape: tvec!(self.m, self.k),
});
Ok(tvec!(fact))
}
as_op!();
}