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refactor/tensor: convert to the new memory management API
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Refactor code CUDA and Native backend to match #autumnai/collenchyma/62 that
provides enchanced memory management and syncronization. Since memory
management is now automatic, `*_plain` variants of functions are removed.

BREAKING CHANGE: *_plain versions of API functions are removed, arguments of
their counterpart functions may have changed in mutablity.

REFERENCE: autumnai/collenchyma#37, autumnai/collenchyma#62

refactor/native: convert to the new memory management API

Convert Native backend. Code now compiles.
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@alexandermorozov
alexandermorozov committed Apr 23, 2016
1 parent 411c7a7 commit cde71d0
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Showing 6 changed files with 296 additions and 790 deletions.
425 changes: 145 additions & 280 deletions src/frameworks/cuda/helper.rs
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5 changes: 3 additions & 2 deletions src/frameworks/cuda/mod.rs
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Original file line number Diff line number Diff line change
@@ -1,11 +1,12 @@
//! Provides BLAS for a CUDA backend.
#![allow(missing_docs)]
use ::plugin::*;
use collenchyma::backend::Backend;
use collenchyma::tensor::ITensorDesc;
use collenchyma::tensor::{SharedTensor, ITensorDesc};
use collenchyma::plugin::Error as PluginError;
use collenchyma::frameworks::cuda::Cuda;
use cublas;
use ::plugin::*;
use ::transpose::Transpose;

#[macro_use]
pub mod helper;
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228 changes: 122 additions & 106 deletions src/frameworks/native.rs
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Original file line number Diff line number Diff line change
@@ -1,176 +1,191 @@
//! Provides BLAS for a Native backend.
use ::operation::*;
use ::plugin::*;
use ::transpose::*;
use collenchyma::backend::Backend;
use collenchyma::memory::MemoryType;
use collenchyma::frameworks::native::Native;
use collenchyma::plugin::Error;
use collenchyma::tensor::{SharedTensor, ITensorDesc};
use rblas::math::mat::Mat;
use rblas::matrix::Matrix;
use rblas;

macro_rules! impl_asum_for {
($t:ident, $b:ty) => (
impl IOperationAsum<$t> for $b {
fn compute(&self, x: &MemoryType, result: &mut MemoryType) -> Result<(), Error> {
let x_slice = try!(x.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `x`."))).as_slice::<$t>();
let mut r_slice = try!(result.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `result`."))).as_mut_slice::<$t>();
r_slice[0] = rblas::Asum::asum(x_slice);
Ok(())
}
macro_rules! read {
($x:ident, $t:ident, $slf:ident) => (
try!($x.read($slf.device())).as_native()
.expect("Broken invariant: not a CUDA memory")
.as_slice::<$t>();
)
}

macro_rules! read_write {
($x:ident, $t: ident, $slf:ident) => (
try!($x.read_write($slf.device())).as_mut_native()
.expect("Broken invariant: not a CUDA memory")
.as_mut_slice::<$t>();
)
}

macro_rules! write_only {
($x:ident, $t: ident, $slf:ident) => (
try!($x.write_only($slf.device())).as_mut_native()
.expect("Broken invariant: not a CUDA memory")
.as_mut_slice::<$t>();
)
}


macro_rules! iblas_asum_for_native {
($t:ident) => (
fn asum(&self, x: &SharedTensor<$t>, result: &mut SharedTensor<$t>)
-> Result<(), ::collenchyma::error::Error> {
let r_slice = write_only!(result, $t, self);
r_slice[0] = rblas::Asum::asum(read!(x, $t, self));
Ok(())
}
);
}

macro_rules! impl_axpy_for {
($t:ident, $b:ty) => (
impl IOperationAxpy<$t> for $b {
fn compute(&self, a: &MemoryType, x: &MemoryType, y: &mut MemoryType) -> Result<(), Error> {
let a_slice = try!(a.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `a`."))).as_slice::<$t>();
let x_slice = try!(x.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `x`."))).as_slice::<$t>();
let y_slice = try!(y.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `y`."))).as_mut_slice::<$t>();
rblas::Axpy::axpy(&a_slice[0], x_slice, y_slice);
Ok(())
}
macro_rules! iblas_axpy_for_native {
($t:ident) => (
fn axpy(&self, a: &SharedTensor<$t>, x: &SharedTensor<$t>,
y: &mut SharedTensor<$t>)
-> Result<(), ::collenchyma::error::Error> {
rblas::Axpy::axpy(
&read!(a, $t, self)[0],
read!(x, $t, self),
read_write!(y, $t, self));
Ok(())
}
);
}

macro_rules! impl_copy_for {
($t:ident, $b:ty) => (
impl IOperationCopy<$t> for $b {
fn compute(&self, x: &MemoryType, y: &mut MemoryType) -> Result<(), Error> {
let x_slice = try!(x.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `x`."))).as_slice::<$t>();
let y_slice = try!(y.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `y`."))).as_mut_slice::<$t>();
rblas::Copy::copy(x_slice, y_slice);
Ok(())
}
macro_rules! iblas_copy_for_native {
($t:ident) => (
fn copy(&self, x: &SharedTensor<$t>, y: &mut SharedTensor<$t>)
-> Result<(), ::collenchyma::error::Error> {
rblas::Copy::copy(
read!(x, $t, self),
write_only!(y, $t, self));
Ok(())
}
);
}

macro_rules! impl_dot_for {
($t:ident, $b:ty) => (
impl IOperationDot<$t> for $b {
fn compute(&self, x: &MemoryType, y: &MemoryType, result: &mut MemoryType) -> Result<(), Error> {
let x_slice = try!(x.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `x`."))).as_slice::<$t>();
let y_slice = try!(y.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `y`."))).as_slice::<$t>();
let mut r_slice = try!(result.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `result`."))).as_mut_slice::<$t>();
r_slice[0] = rblas::Dot::dot(x_slice, y_slice);
Ok(())
}
macro_rules! iblas_dot_for_native {
($t:ident) => (
fn dot(&self, x: &SharedTensor<$t>, y: &SharedTensor<$t>,
result: &mut SharedTensor<$t>
) -> Result<(), ::collenchyma::error::Error> {
let r_slice = write_only!(result, $t, self);
r_slice[0] = rblas::Dot::dot(read!(x, $t, self), read!(y, $t, self));
Ok(())
}
);
}

macro_rules! impl_nrm2_for {
($t:ident, $b:ty) => (
impl IOperationNrm2<$t> for $b {
fn compute(&self, x: &MemoryType, result: &mut MemoryType) -> Result<(), Error> {
let x_slice = try!(x.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `x`."))).as_slice::<$t>();
let mut r_slice = try!(result.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `result`."))).as_mut_slice::<$t>();
r_slice[0] = rblas::Nrm2::nrm2(x_slice);
Ok(())
}
macro_rules! iblas_nrm2_for_native {
($t:ident) => (
fn nrm2(&self, x: &SharedTensor<$t>, result: &mut SharedTensor<$t>)
-> Result<(), ::collenchyma::error::Error> {
let r_slice = write_only!(result, $t, self);
r_slice[0] = rblas::Nrm2::nrm2(read!(x, $t, self));
Ok(())
}
);
}

macro_rules! impl_scale_for {
($t:ident, $b:ty) => (
impl IOperationScale<$t> for $b {
fn compute(&self, a: &MemoryType, x: &mut MemoryType) -> Result<(), Error> {
let a_slice = try!(a.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `a`."))).as_slice::<$t>();
let mut x_slice = try!(x.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `x`."))).as_mut_slice::<$t>();
rblas::Scal::scal(&a_slice[0], x_slice);
Ok(())
}
macro_rules! iblas_scal_for_native {
($t:ident) => (
fn scal(&self, a: &SharedTensor<$t>, x: &mut SharedTensor<$t>)
-> Result<(), ::collenchyma::error::Error> {
rblas::Scal::scal(
&read!(a, $t, self)[0],
read_write!(x, $t, self));
Ok(())
}
);
}

macro_rules! impl_swap_for {
($t:ident, $b:ty) => (
impl IOperationSwap<$t> for $b {
fn compute(&self, x: &mut MemoryType, y: &mut MemoryType) -> Result<(), Error> {
let mut x_slice = try!(x.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `x`."))).as_mut_slice::<$t>();
let mut y_slice = try!(y.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `y`."))).as_mut_slice::<$t>();
rblas::Swap::swap(x_slice, y_slice);
Ok(())
}
macro_rules! iblas_swap_for_native {
($t:ident) => (
fn swap(&self, x: &mut SharedTensor<$t>, y: &mut SharedTensor<$t>)
-> Result<(), ::collenchyma::error::Error> {
rblas::Swap::swap(read_write!(x, $t, self), read_write!(y, $t, self));
Ok(())
}
);
}

macro_rules! impl_gemm_for {
($t:ident, $b:ty) => (
impl IOperationGemm<$t> for $b {
fn compute(&self, alpha: &MemoryType, at: Transpose, a_dims: &[usize], a: &MemoryType, bt: Transpose, b_dims: &[usize], b: &MemoryType, beta: &MemoryType, c_dims: &[usize], c: &mut MemoryType) -> Result<(), ::collenchyma::error::Error> {
let alpha_slice = try!(alpha.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `alpha`."))).as_slice::<$t>();
let a_slice = try!(a.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `a`."))).as_slice::<$t>();
let beta_slice = try!(beta.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `beta`."))).as_slice::<$t>();
let b_slice = try!(b.as_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `b`."))).as_slice::<$t>();
let mut c_slice = try!(c.as_mut_native().ok_or(Error::MissingMemoryForDevice("Unable to receive native memory for `c`."))).as_mut_slice::<$t>();

let a_matrix = as_matrix(a_slice, a_dims);
let b_matrix = as_matrix(b_slice, b_dims);
let mut c_matrix = as_matrix(c_slice, c_dims);
rblas::Gemm::gemm(&alpha_slice[0], at.to_rblas(), &a_matrix, bt.to_rblas(), &b_matrix, &beta_slice[0], &mut c_matrix);
read_from_matrix(&c_matrix, c_slice);
Ok(())
}
macro_rules! iblas_gemm_for_native {
($t:ident) => (
fn gemm(&self,
alpha: &SharedTensor<$t>,
at: Transpose,
a: &SharedTensor<$t>,
bt: Transpose,
b: &SharedTensor<$t>,
beta: &SharedTensor<$t>,
c: &mut SharedTensor<$t>
) -> Result<(), ::collenchyma::error::Error> {
let c_dims = c.desc().clone(); // FIXME: clone() can be removed

let a_slice = read!(a, $t, self);
let b_slice = read!(b, $t, self);
let c_slice = write_only!(c, $t, self);

let a_matrix = as_matrix(a_slice, a.desc().dims());
let b_matrix = as_matrix(b_slice, b.desc().dims());
let mut c_matrix = as_matrix(c_slice, &c_dims);
rblas::Gemm::gemm(
&read!(alpha, $t, self)[0],
at.to_rblas(),
&a_matrix,
bt.to_rblas(),
&b_matrix,
&read!(beta, $t, self)[0],
&mut c_matrix);
read_from_matrix(&c_matrix, c_slice);
Ok(())
}
);
}

macro_rules! impl_iblas_for {
($t:ident, $b:ty) => (
impl_asum_for!($t, $b);
impl_axpy_for!($t, $b);
impl_copy_for!($t, $b);
impl_dot_for!($t, $b);
impl_nrm2_for!($t, $b);
impl_scale_for!($t, $b);
impl_swap_for!($t, $b);

impl_gemm_for!($t, $b);

impl IBlas<$t> for $b { }

// Level 1

impl Asum<$t> for $b {
iblas_asum_for!($t, $b);
iblas_asum_for_native!($t);
}

impl Axpy<$t> for $b {
iblas_axpy_for!($t, $b);
iblas_axpy_for_native!($t);
}

impl Copy<$t> for $b {
iblas_copy_for!($t, $b);
iblas_copy_for_native!($t);
}

impl Dot<$t> for $b {
iblas_dot_for!($t, $b);
iblas_dot_for_native!($t);
}

impl Nrm2<$t> for $b {
iblas_nrm2_for!($t, $b);
iblas_nrm2_for_native!($t);
}

impl Scal<$t> for $b {
iblas_scale_for!($t, $b);
iblas_scal_for_native!($t);
}

impl Swap<$t> for $b {
iblas_swap_for!($t, $b);
iblas_swap_for_native!($t);
}

impl Gemm<$t> for $b {
iblas_gemm_for!($t, $b);
iblas_gemm_for_native!($t);
}
);
}
Expand Down Expand Up @@ -239,14 +254,15 @@ mod test {
#[test]
fn it_converts_correctly_to_and_from_matrix() {
let backend = get_native_backend();
let mut a = SharedTensor::<f32>::new(backend.device(), &vec![3, 2]).unwrap();
write_to_memory(a.get_mut(backend.device()).unwrap(),
let mut a = SharedTensor::<f32>::new(&vec![3, 2]).unwrap();
write_to_memory(a.write_only(backend.device()).unwrap(),
&[2f32, 5f32,
2f32, 5f32,
2f32, 5f32]);

{
let a_slice_in = a.get(backend.device()).unwrap().as_native().unwrap().as_slice::<f32>();
let a_slice_in = a.read(backend.device()).unwrap()
.as_native().unwrap().as_slice::<f32>();
let a_mat = as_matrix(a_slice_in, &[3, 2]);
// right
assert_eq!(a_mat[0][0], 2f32);
Expand Down
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