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Start refactoring the stride.

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This commit is contained in:
laurent
2023-06-28 12:57:30 +01:00
parent d461d9d751
commit c1bbbf94f6
5 changed files with 124 additions and 108 deletions
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46
candle-core/src/cpu_backend.rs
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@ -1,5 +1,5 @@
use crate::op::{BinaryOp, UnaryOp}; use crate::op::{BinaryOp, UnaryOp};
use crate::{DType, Error, Result, Shape, StridedIndex}; use crate::{DType, Error, Layout, Result, Shape, StridedIndex};
use gemm::{gemm, Parallelism}; use gemm::{gemm, Parallelism};
use half::{bf16, f16}; use half::{bf16, f16};
@ -18,12 +18,11 @@ pub enum CpuStorage {
fn wcond<T: Copy>( fn wcond<T: Copy>(
pred: &[u32], pred: &[u32],
shape: &Shape, layout: &Layout,
stride: &[usize],
t: &[T], t: &[T],
stride_t: &[usize], layout_t: &Layout,
f: &[T], f: &[T],
stride_f: &[usize], layout_f: &Layout,
) -> Vec<T> { ) -> Vec<T> {
if shape.is_contiguous(stride) && shape.is_contiguous(stride_t) && shape.is_contiguous(stride_f) if shape.is_contiguous(stride) && shape.is_contiguous(stride_t) && shape.is_contiguous(stride_f)
{ {
@ -73,12 +72,7 @@ fn sum_impl1<T: Copy + num_traits::NumAssign>(
Ok(dst) Ok(dst)
} }
fn unary_map<T: Copy, U: Copy, F: FnMut(T) -> U>( fn unary_map<T: Copy, U: Copy, F: FnMut(T) -> U>(vs: &[T], layout: &Layout, mut f: F) -> Vec<U> {
vs: &[T],
shape: &Shape,
stride: &[usize],
mut f: F,
) -> Vec<U> {
if shape.is_contiguous(stride) { if shape.is_contiguous(stride) {
vs[..shape.elem_count()].iter().map(|&v| f(v)).collect() vs[..shape.elem_count()].iter().map(|&v| f(v)).collect()
} else { } else {
@ -461,65 +455,59 @@ impl CpuStorage {
Ok(()) Ok(())
} }
pub(crate) fn affine_impl( pub(crate) fn affine(&self, layout: &Layout, mul: f64, add: f64) -> Result<Self> {
&self,
shape: &Shape,
stride: &[usize],
mul: f64,
add: f64,
) -> Result<Self> {
match self { match self {
Self::U32(storage) => { Self::U32(storage) => {
let mul = mul as u32; let mul = mul as u32;
let add = add as u32; let add = add as u32;
let data = unary_map(storage, shape, stride, |v| v * mul + add); let data = unary_map(storage, layout, |v| v * mul + add);
Ok(Self::U32(data)) Ok(Self::U32(data))
} }
Self::BF16(storage) => { Self::BF16(storage) => {
let mul = bf16::from_f64(mul); let mul = bf16::from_f64(mul);
let add = bf16::from_f64(add); let add = bf16::from_f64(add);
let data = unary_map(storage, shape, stride, |v| v * mul + add); let data = unary_map(storage, layout, |v| v * mul + add);
Ok(Self::BF16(data)) Ok(Self::BF16(data))
} }
Self::F16(storage) => { Self::F16(storage) => {
let mul = f16::from_f64(mul); let mul = f16::from_f64(mul);
let add = f16::from_f64(add); let add = f16::from_f64(add);
let data = unary_map(storage, shape, stride, |v| v * mul + add); let data = unary_map(storage, layout, |v| v * mul + add);
Ok(Self::F16(data)) Ok(Self::F16(data))
} }
Self::F32(storage) => { Self::F32(storage) => {
let mul = mul as f32; let mul = mul as f32;
let add = add as f32; let add = add as f32;
let data = unary_map(storage, shape, stride, |v| v * mul + add); let data = unary_map(storage, layout, |v| v * mul + add);
Ok(Self::F32(data)) Ok(Self::F32(data))
} }
Self::F64(storage) => { Self::F64(storage) => {
let data = unary_map(storage, shape, stride, |v| v * mul + add); let data = unary_map(storage, layout, |v| v * mul + add);
Ok(Self::F64(data)) Ok(Self::F64(data))
} }
} }
} }
pub(crate) fn unary_impl<B: UnaryOp>(&self, shape: &Shape, stride: &[usize]) -> Result<Self> { pub(crate) fn unary_impl<B: UnaryOp>(&self, layout: &Layout) -> Result<Self> {
match self { match self {
Self::BF16(storage) => { Self::BF16(storage) => {
let data = unary_map(storage, shape, stride, B::bf16); let data = unary_map(storage, layout, B::bf16);
Ok(Self::BF16(data)) Ok(Self::BF16(data))
} }
Self::F16(storage) => { Self::F16(storage) => {
let data = unary_map(storage, shape, stride, B::f16); let data = unary_map(storage, layout, B::f16);
Ok(Self::F16(data)) Ok(Self::F16(data))
} }
Self::F32(storage) => { Self::F32(storage) => {
let data = unary_map(storage, shape, stride, B::f32); let data = unary_map(storage, layout, B::f32);
Ok(Self::F32(data)) Ok(Self::F32(data))
} }
Self::F64(storage) => { Self::F64(storage) => {
let data = unary_map(storage, shape, stride, B::f64); let data = unary_map(storage, layout, B::f64);
Ok(Self::F64(data)) Ok(Self::F64(data))
} }
Self::U32(storage) => { Self::U32(storage) => {
let data = unary_map(storage, shape, stride, B::u32); let data = unary_map(storage, layout, B::u32);
Ok(Self::U32(data)) Ok(Self::U32(data))
} }
} }

47
candle-core/src/layout.rs Normal file
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@ -0,0 +1,47 @@
use crate::Shape;
#[derive(Debug, PartialEq, Eq, Clone)]
pub struct Layout {
shape: Shape,
// The strides are given in number of elements and not in bytes.
stride: Vec<usize>,
start_offset: usize,
}
impl Layout {
pub fn contiguous<S: Into<Shape>>(shape: S) -> Self {
let shape = shape.into();
let stride = shape.stride_contiguous();
Self {
shape,
stride,
start_offset: 0,
}
}
pub fn dims(&self) -> &[usize] {
self.shape.dims()
}
pub fn shape(&self) -> &Shape {
&self.shape
}
pub fn stride(&self) -> &[usize] {
&self.stride
}
pub fn start_offset(&self) -> usize {
self.start_offset
}
/// Returns true if the data is stored in a C contiguous (aka row major) way.
pub fn is_contiguous(&self) -> bool {
self.shape.is_contiguous(&self.stride)
}
/// Returns true if the data is stored in a Fortran contiguous (aka column major) way.
pub fn is_fortran_contiguous(&self) -> bool {
self.shape.is_fortran_contiguous(&self.stride)
}
}

2
candle-core/src/lib.rs
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@ -7,6 +7,7 @@ pub mod display;
mod dtype; mod dtype;
mod dummy_cuda_backend; mod dummy_cuda_backend;
mod error; mod error;
mod layout;
mod npy; mod npy;
mod op; mod op;
mod shape; mod shape;
@ -19,6 +20,7 @@ pub use cpu_backend::CpuStorage;
pub use device::{Device, DeviceLocation}; pub use device::{Device, DeviceLocation};
pub use dtype::{DType, WithDType}; pub use dtype::{DType, WithDType};
pub use error::{Error, Result}; pub use error::{Error, Result};
pub use layout::Layout;
pub use shape::Shape; pub use shape::Shape;
pub use storage::Storage; pub use storage::Storage;
use strided_index::StridedIndex; use strided_index::StridedIndex;

63
candle-core/src/storage.rs
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@ -1,4 +1,4 @@
use crate::{op, CpuStorage, CudaStorage, DType, Device, Error, Result, Shape}; use crate::{op, CpuStorage, CudaStorage, DType, Device, Error, Layout, Result, Shape};
// We do not want to implement Clone on Storage as cloning may fail because of // We do not want to implement Clone on Storage as cloning may fail because of
// out of memory. Instead try_clone should be used. // out of memory. Instead try_clone should be used.
@ -53,33 +53,27 @@ impl Storage {
} }
} }
pub(crate) fn affine_impl( pub(crate) fn affine(&self, layout: &Layout, mul: f64, add: f64) -> Result<Self> {
&self,
shape: &Shape,
stride: &[usize],
mul: f64,
add: f64,
) -> Result<Self> {
match self { match self {
Storage::Cpu(storage) => { Storage::Cpu(storage) => {
let storage = storage.affine_impl(shape, stride, mul, add)?; let storage = storage.affine(layout, mul, add)?;
Ok(Self::Cpu(storage)) Ok(Self::Cpu(storage))
} }
Self::Cuda(storage) => { Self::Cuda(storage) => {
let storage = storage.affine_impl(shape, stride, mul, add)?; let storage = storage.affine(layout, mul, add)?;
Ok(Self::Cuda(storage)) Ok(Self::Cuda(storage))
} }
} }
} }
pub(crate) fn sum(&self, shape: &Shape, stride: &[usize], s: &[usize]) -> Result<Self> { pub(crate) fn sum(&self, layout: &Layout, s: &[usize]) -> Result<Self> {
match self { match self {
Storage::Cpu(storage) => { Storage::Cpu(storage) => {
let storage = storage.sum(shape, stride, s)?; let storage = storage.sum(layout, s)?;
Ok(Self::Cpu(storage)) Ok(Self::Cpu(storage))
} }
Self::Cuda(storage) => { Self::Cuda(storage) => {
let storage = storage.sum(shape, stride, s)?; let storage = storage.sum(layout, s)?;
Ok(Self::Cuda(storage)) Ok(Self::Cuda(storage))
} }
} }
@ -93,32 +87,28 @@ impl Storage {
Ok(()) Ok(())
} }
pub(crate) fn to_dtype(&self, shape: &Shape, stride: &[usize], dtype: DType) -> Result<Self> { pub(crate) fn to_dtype(&self, layout: &Layout, dtype: DType) -> Result<Self> {
match self { match self {
Storage::Cpu(storage) => { Storage::Cpu(storage) => {
let storage = storage.to_dtype(shape, stride, dtype)?; let storage = storage.to_dtype(layout, dtype)?;
Ok(Self::Cpu(storage)) Ok(Self::Cpu(storage))
} }
Self::Cuda(storage) => { Self::Cuda(storage) => {
let storage = storage.to_dtype(shape, stride, dtype)?; let storage = storage.to_dtype(layout, dtype)?;
Ok(Self::Cuda(storage)) Ok(Self::Cuda(storage))
} }
} }
} }
pub(crate) fn unary_impl<B: op::UnaryOp>( pub(crate) fn unary_impl<B: op::UnaryOp>(&self, layout: &Layout) -> Result<Self> {
&self,
shape: &Shape,
stride: &[usize],
) -> Result<Self> {
// TODO: Different code path for the contiguous case? // TODO: Different code path for the contiguous case?
match self { match self {
Storage::Cpu(storage) => { Storage::Cpu(storage) => {
let storage = storage.unary_impl::<B>(shape, stride)?; let storage = storage.unary_impl::<B>(layout)?;
Ok(Self::Cpu(storage)) Ok(Self::Cpu(storage))
} }
Self::Cuda(storage) => { Self::Cuda(storage) => {
let storage = storage.unary_impl::<B>(shape, stride)?; let storage = storage.unary_impl::<B>(layout)?;
Ok(Self::Cuda(storage)) Ok(Self::Cuda(storage))
} }
} }
@ -127,19 +117,18 @@ impl Storage {
pub(crate) fn binary_impl<B: op::BinaryOp>( pub(crate) fn binary_impl<B: op::BinaryOp>(
&self, &self,
rhs: &Self, rhs: &Self,
shape: &Shape, lhs_layout: &Layout,
lhs_stride: &[usize], rhs_layout: &Layout,
rhs_stride: &[usize],
) -> Result<Self> { ) -> Result<Self> {
self.same_device(rhs, B::NAME)?; self.same_device(rhs, B::NAME)?;
self.same_dtype(rhs, B::NAME)?; self.same_dtype(rhs, B::NAME)?;
match (self, rhs) { match (self, rhs) {
(Storage::Cpu(lhs), Storage::Cpu(rhs)) => { (Storage::Cpu(lhs), Storage::Cpu(rhs)) => {
let storage = lhs.binary_impl::<B>(rhs, shape, lhs_stride, rhs_stride)?; let storage = lhs.binary_impl::<B>(rhs, lhs_layout, rhs_layout)?;
Ok(Self::Cpu(storage)) Ok(Self::Cpu(storage))
} }
(Self::Cuda(lhs), Self::Cuda(rhs)) => { (Self::Cuda(lhs), Self::Cuda(rhs)) => {
let storage = lhs.binary_impl::<B>(rhs, shape, lhs_stride, rhs_stride)?; let storage = lhs.binary_impl::<B>(rhs, lhs_layout, rhs_layout)?;
Ok(Self::Cuda(storage)) Ok(Self::Cuda(storage))
} }
(lhs, rhs) => { (lhs, rhs) => {
@ -156,23 +145,22 @@ impl Storage {
pub(crate) fn where_cond( pub(crate) fn where_cond(
&self, &self,
shape: &Shape, layout: &Shape,
stride: &[usize],
t: &Self, t: &Self,
stride_t: &[usize], layout_t: &Layout,
f: &Self, f: &Self,
stride_f: &[usize], layout_f: &Layout,
) -> Result<Self> { ) -> Result<Self> {
self.same_device(t, "where")?; self.same_device(t, "where")?;
self.same_device(f, "where")?; self.same_device(f, "where")?;
t.same_dtype(f, "where")?; t.same_dtype(f, "where")?;
match (self, t, f) { match (self, t, f) {
(Storage::Cpu(cond), Storage::Cpu(t), Storage::Cpu(f)) => { (Storage::Cpu(cond), Storage::Cpu(t), Storage::Cpu(f)) => {
let storage = cond.where_cond(shape, stride, t, stride_t, f, stride_f)?; let storage = cond.where_cond(layout, t, layout_t, f, layout_f)?;
Ok(Self::Cpu(storage)) Ok(Self::Cpu(storage))
} }
(Self::Cuda(cond), Self::Cuda(t), Self::Cuda(f)) => { (Self::Cuda(cond), Self::Cuda(t), Self::Cuda(f)) => {
let storage = cond.where_cond(shape, stride, t, stride_t, f, stride_f)?; let storage = cond.where_cond(layout, t, layout_t, f, layout_f)?;
Ok(Self::Cuda(storage)) Ok(Self::Cuda(storage))
} }
(_, lhs, rhs) => Err(Error::DeviceMismatchBinaryOp { (_, lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
@ -185,8 +173,7 @@ impl Storage {
pub(crate) fn embedding_impl( pub(crate) fn embedding_impl(
&self, &self,
shape: &Shape, layout: &Layout,
stride: &[usize],
rhs: &Self, rhs: &Self,
hidden_size: usize, hidden_size: usize,
vocab_size: usize, vocab_size: usize,
@ -194,11 +181,11 @@ impl Storage {
self.same_device(rhs, "embedding")?; self.same_device(rhs, "embedding")?;
match (self, rhs) { match (self, rhs) {
(Storage::Cpu(lhs), Storage::Cpu(rhs)) => { (Storage::Cpu(lhs), Storage::Cpu(rhs)) => {
let storage = lhs.embedding_impl(shape, stride, rhs, hidden_size, vocab_size)?; let storage = lhs.embedding_impl(layout, rhs, hidden_size, vocab_size)?;
Ok(Self::Cpu(storage)) Ok(Self::Cpu(storage))
} }
(Self::Cuda(lhs), Self::Cuda(rhs)) => { (Self::Cuda(lhs), Self::Cuda(rhs)) => {
let storage = lhs.embedding_impl(shape, stride, rhs, hidden_size, vocab_size)?; let storage = lhs.embedding_impl(layout, rhs, hidden_size, vocab_size)?;
Ok(Self::Cuda(storage)) Ok(Self::Cuda(storage))
} }
(lhs, rhs) => Err(Error::DeviceMismatchBinaryOp { (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {

74
candle-core/src/tensor.rs
View File

@ -1,4 +1,4 @@
use crate::{op::Op, storage::Storage, DType, Device, Error, Result, Shape}; use crate::{op::Op, storage::Storage, DType, Device, Error, Layout, Result, Shape};
use std::sync::Arc; use std::sync::Arc;
/// Unique identifier for tensors. /// Unique identifier for tensors.
@ -17,9 +17,7 @@ impl TensorId {
pub struct Tensor_ { pub struct Tensor_ {
id: TensorId, id: TensorId,
storage: Arc<Storage>, storage: Arc<Storage>,
shape: Shape, layout: Layout,
// The strides are given in number of elements and not in bytes.
stride: Vec<usize>,
op: Option<Op>, op: Option<Op>,
is_variable: bool, is_variable: bool,
} }
@ -50,7 +48,7 @@ macro_rules! unary_op {
let shape = self.shape(); let shape = self.shape();
let storage = self let storage = self
.storage .storage
.unary_impl::<crate::op::$op_name>(self.shape(), self.stride())?; .unary_impl::<crate::op::$op_name>(self.layout())?;
let op = if self.track_op() { let op = if self.track_op() {
Some(Op::$op_name(self.clone())) Some(Op::$op_name(self.clone()))
} else { } else {
@ -67,9 +65,8 @@ macro_rules! binary_op {
let shape = self.same_shape_binary_op(rhs, stringify!($fn_name))?; let shape = self.same_shape_binary_op(rhs, stringify!($fn_name))?;
let storage = self.storage.binary_impl::<crate::op::$op_name>( let storage = self.storage.binary_impl::<crate::op::$op_name>(
&rhs.storage, &rhs.storage,
shape, self.layout(),
self.stride(), rhs.layout(),
rhs.stride(),
)?; )?;
let op = if self.track_op() || rhs.track_op() { let op = if self.track_op() || rhs.track_op() {
Some(Op::$op_name(self.clone(), rhs.clone())) Some(Op::$op_name(self.clone(), rhs.clone()))
@ -107,13 +104,10 @@ fn from_storage<S: Into<Shape>>(
op: Option<Op>, op: Option<Op>,
is_variable: bool, is_variable: bool,
) -> Tensor { ) -> Tensor {
let shape = shape.into();
let stride = shape.stride_contiguous();
let tensor_ = Tensor_ { let tensor_ = Tensor_ {
id: TensorId::new(), id: TensorId::new(),
storage: Arc::new(storage), storage: Arc::new(storage),
shape, layout: Layout::contiguous(shape),
stride,
op, op,
is_variable, is_variable,
}; };
@ -342,8 +336,7 @@ impl Tensor {
} }
pub fn affine(&self, mul: f64, add: f64) -> Result<Self> { pub fn affine(&self, mul: f64, add: f64) -> Result<Self> {
let shape = self.shape(); let storage = self.storage.affine(self.layout(), mul, add)?;
let storage = self.storage.affine_impl(shape, self.stride(), mul, add)?;
let op = if self.track_op() { let op = if self.track_op() {
Some(Op::Affine { Some(Op::Affine {
arg: self.clone(), arg: self.clone(),
@ -353,7 +346,7 @@ impl Tensor {
} else { } else {
None None
}; };
Ok(from_storage(storage, shape.clone(), op, false)) Ok(from_storage(storage, self.shape(), op, false))
} }
/// Returns a new tensor that is a narrowed version of the input, the dimension `dim` /// Returns a new tensor that is a narrowed version of the input, the dimension `dim`
@ -401,9 +394,7 @@ impl Tensor {
exp.broadcast_div(&sum_exp) exp.broadcast_div(&sum_exp)
} else { } else {
let shape = self.shape(); let shape = self.shape();
let mut storage = self let mut storage = self.storage.unary_impl::<crate::op::Exp>(self.layout())?;
.storage
.unary_impl::<crate::op::Exp>(shape, self.stride())?;
// The resulting storage is contiguous. // The resulting storage is contiguous.
storage.divide_by_sum_over_dim(shape, dim)?; storage.divide_by_sum_over_dim(shape, dim)?;
let op = if self.track_op() { let op = if self.track_op() {
@ -416,7 +407,7 @@ impl Tensor {
} }
pub fn sum(&self, sum_dims: &[usize]) -> Result<Self> { pub fn sum(&self, sum_dims: &[usize]) -> Result<Self> {
let storage = self.storage.sum(self.shape(), &self.stride, sum_dims)?; let storage = self.storage.sum(self.layout(), sum_dims)?;
let op = if self.track_op() { let op = if self.track_op() {
Some(Op::Sum(self.clone(), sum_dims.to_vec())) Some(Op::Sum(self.clone(), sum_dims.to_vec()))
} else { } else {
@ -461,8 +452,8 @@ impl Tensor {
let storage = self.storage.matmul_impl( let storage = self.storage.matmul_impl(
&rhs.storage, &rhs.storage,
(batching, m, n, k), (batching, m, n, k),
self.stride(), self.layout(),
rhs.stride(), rhs.layout(),
)?; )?;
let op = if self.track_op() || rhs.track_op() { let op = if self.track_op() || rhs.track_op() {
Some(Op::Matmul(self.clone(), rhs.clone())) Some(Op::Matmul(self.clone(), rhs.clone()))
@ -476,12 +467,11 @@ impl Tensor {
let _shap = self.same_shape_binary_op(on_true, "where_cond")?; let _shap = self.same_shape_binary_op(on_true, "where_cond")?;
let shape = self.same_shape_binary_op(on_false, "where_cond")?; let shape = self.same_shape_binary_op(on_false, "where_cond")?;
let storage = self.storage.where_cond( let storage = self.storage.where_cond(
shape, self.layout(),
self.stride(),
&on_true.storage, &on_true.storage,
on_true.stride(), on_true.layout(),
&on_false.storage, &on_false.storage,
on_false.stride(), on_false.layout(),
)?; )?;
let op = if self.track_op() || on_true.track_op() || on_false.track_op() { let op = if self.track_op() || on_true.track_op() || on_false.track_op() {
Some(Op::WhereCond( Some(Op::WhereCond(
@ -498,10 +488,10 @@ impl Tensor {
pub fn embedding(ids: &Self, rhs: &Self) -> Result<Self> { pub fn embedding(ids: &Self, rhs: &Self) -> Result<Self> {
if !rhs.is_contiguous() { if !rhs.is_contiguous() {
return Err(Error::RequiresContiguous { op: "embedding" }); return Err(Error::RequiresContiguous { op: "embedding" });
} else if rhs.shape().rank() != 2 || ids.shape().rank() != 1 { } else if rhs.rank() != 2 || ids.rank() != 1 {
return Err(Error::ShapeMismatchBinaryOp { return Err(Error::ShapeMismatchBinaryOp {
lhs: ids.shape.clone(), lhs: ids.shape().clone(),
rhs: rhs.shape.clone(), rhs: rhs.shape().clone(),
op: "embedding", op: "embedding",
}); });
} }
@ -509,7 +499,7 @@ impl Tensor {
let seq_len = ids_shape.r1()?; let seq_len = ids_shape.r1()?;
let (vocab_size, hidden_size) = rhs.shape().r2()?; let (vocab_size, hidden_size) = rhs.shape().r2()?;
let storage = ids.storage.embedding_impl( let storage = ids.storage.embedding_impl(
ids_shape, ids.layout(),
&ids.stride, &ids.stride,
&rhs.storage, &rhs.storage,
hidden_size, hidden_size,
@ -625,8 +615,13 @@ impl Tensor {
self.shape().dims() self.shape().dims()
} }
pub fn stride(&self) -> &[usize] { pub fn layout(&self) -> &Layout {
&self.stride &self.layout
}
// TODO: Rename to `stride` once the PR that introduced the layout has been merged.
pub fn stride_tmp(&self) -> &[usize] {
&self.layout.stride()
} }
pub fn rank(&self) -> usize { pub fn rank(&self) -> usize {
@ -734,12 +729,12 @@ impl Tensor {
/// Returns true if the data is stored in a C contiguous (aka row major) way. /// Returns true if the data is stored in a C contiguous (aka row major) way.
pub fn is_contiguous(&self) -> bool { pub fn is_contiguous(&self) -> bool {
self.shape.is_contiguous(&self.stride) self.layout.is_contiguous()
} }
/// Returns true if the data is stored in a Fortran contiguous (aka column major) way. /// Returns true if the data is stored in a Fortran contiguous (aka column major) way.
pub fn is_fortran_contiguous(&self) -> bool { pub fn is_fortran_contiguous(&self) -> bool {
self.shape.is_fortran_contiguous(&self.stride) self.layout.is_fortran_contiguous()
} }
/// Compared to clone, this copies the actual storage but may fail because of running out of /// Compared to clone, this copies the actual storage but may fail because of running out of
@ -748,8 +743,7 @@ impl Tensor {
let tensor_ = Tensor_ { let tensor_ = Tensor_ {
id: TensorId::new(), id: TensorId::new(),
storage: Arc::new(self.storage.try_clone()?), storage: Arc::new(self.storage.try_clone()?),
shape: self.shape.clone(), layout: self.layout.clone(),
stride: self.stride.clone(),
op: None, // TODO op: None, // TODO
is_variable: false, is_variable: false,
}; };
@ -762,8 +756,7 @@ impl Tensor {
let tensor_ = Tensor_ { let tensor_ = Tensor_ {
id: TensorId::new(), id: TensorId::new(),
storage: self.storage.clone(), storage: self.storage.clone(),
shape: self.shape.clone(), layout: self.layout.clone(),
stride: self.stride.clone(),
op: None, op: None,
is_variable: false, is_variable: false,
}; };
@ -796,8 +789,7 @@ impl Tensor {
let tensor_ = Tensor_ { let tensor_ = Tensor_ {
id: TensorId::new(), id: TensorId::new(),
storage: Arc::new(storage), storage: Arc::new(storage),
shape: self.shape.clone(), layout: self.layout.clone(),
stride: self.stride.clone(),
op, op,
is_variable: false, is_variable: false,
}; };
@ -810,7 +802,7 @@ impl Tensor {
pub fn broadcast_left<S: Into<Shape>>(&self, left_shape: S) -> Result<Self> { pub fn broadcast_left<S: Into<Shape>>(&self, left_shape: S) -> Result<Self> {
let left_shape = left_shape.into(); let left_shape = left_shape.into();
let mut dims = left_shape.into_dims(); let mut dims = left_shape.into_dims();
dims.extend(self.shape.dims()); dims.extend(self.dims());
self.broadcast_as(dims) self.broadcast_as(dims)
} }
@ -866,7 +858,7 @@ impl Tensor {
Ok(self.clone()) Ok(self.clone())
} else { } else {
let shape = self.shape(); let shape = self.shape();
let storage = self.storage.to_dtype(shape, self.stride(), dtype)?; let storage = self.storage.to_dtype(self.layout(), dtype)?;
let op = if self.track_op() { let op = if self.track_op() {
Some(Op::ToDType(self.clone())) Some(Op::ToDType(self.clone()))
} else { } else {