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Rename the .r functions to .dims so as to be a bit more explicit. (#220)

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This commit is contained in:
Laurent Mazare
2023-07-22 11:39:27 +02:00
committed by GitHub
parent 52c5d8c087
commit 43c7223292
18 changed files with 56 additions and 50 deletions
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2
candle-core/src/cpu_backend.rs
View File

@ -1688,7 +1688,7 @@ impl BackendStorage for CpuStorage {
fn embedding(&self, ids_l: &Layout, rhs: &Self, rhs_l: &Layout) -> Result<Self> {
let ids = self.as_slice::<u32>()?;
let (vocab_size, hidden_size) = rhs_l.shape().r2()?;
let (vocab_size, hidden_size) = rhs_l.shape().dims2()?;
Embedding {
vocab_size,
hidden_size,

2
candle-core/src/cuda_backend.rs
View File

@ -620,7 +620,7 @@ impl<'a> Map1 for Embedding<'a> {
let shape = ids_l.shape();
let (v_size, h_size) = rhs_l
.shape()
.r2()
.dims2()
.map_err(|e| CudaError::WrappedError(Box::new(e)))
.w()?;
let dims = shape.dims();

18
candle-core/src/shape.rs
View File

@ -87,6 +87,12 @@ macro_rules! extract_dims {
}
}
}
impl crate::Tensor {
pub fn $fn_name(&self) -> Result<$out_type> {
self.shape().$fn_name()
}
}
impl std::convert::TryInto<$out_type> for Shape {
type Error = crate::Error;
fn try_into(self) -> std::result::Result<$out_type, Self::Error> {
@ -328,23 +334,23 @@ impl<D1: Dim, D2: Dim, D3: Dim> Dims for (D1, D2, D3) {
}
}
extract_dims!(r0, 0, |_: &Vec<usize>| (), ());
extract_dims!(r1, 1, |d: &[usize]| d[0], usize);
extract_dims!(r2, 2, |d: &[usize]| (d[0], d[1]), (usize, usize));
extract_dims!(dims0, 0, |_: &Vec<usize>| (), ());
extract_dims!(dims1, 1, |d: &[usize]| d[0], usize);
extract_dims!(dims2, 2, |d: &[usize]| (d[0], d[1]), (usize, usize));
extract_dims!(
r3,
dims3,
3,
|d: &[usize]| (d[0], d[1], d[2]),
(usize, usize, usize)
);
extract_dims!(
r4,
dims4,
4,
|d: &[usize]| (d[0], d[1], d[2], d[3]),
(usize, usize, usize, usize)
);
extract_dims!(
r5,
dims5,
5,
|d: &[usize]| (d[0], d[1], d[2], d[3], d[4]),
(usize, usize, usize, usize, usize)

12
candle-core/src/tensor.rs
View File

@ -772,7 +772,7 @@ impl Tensor {
/// Applies a 1D convolution over the input tensor.
pub fn conv1d(&self, kernel: &Self, padding: usize, stride: usize) -> Result<Self> {
let (c_out, c_in_k, k_size) = kernel.shape().r3()?;
let (c_out, c_in_k, k_size) = kernel.dims3()?;
let (b_size, c_in, l_in) = match *self.dims() {
[b_size, c_in, l_in] => (Some(b_size), c_in, l_in),
[c_in, l_in] => (None, c_in, l_in),
@ -931,8 +931,8 @@ impl Tensor {
.bt())?
}
let ids_shape = ids.shape();
let seq_len = ids_shape.r1()?;
let (_, hidden_size) = rhs.shape().r2()?;
let seq_len = ids_shape.dims1()?;
let (_, hidden_size) = rhs.dims2()?;
let storage = ids
.storage()
.embedding(ids.layout(), &rhs.storage(), rhs.layout())?;
@ -1013,7 +1013,7 @@ impl Tensor {
// The number of element in indexes must match the dimension on which the add is
// performed on the source tensor (and the index values from `indexes` are taken from
// the target tensor self)
mismatch || source_dims[dim] != indexes.shape().r1()?
mismatch || source_dims[dim] != indexes.dims1()?
};
if mismatch {
Err(Error::ShapeMismatchBinaryOp {
@ -1144,7 +1144,7 @@ impl Tensor {
/// Returns the data contained in a 2D tensor as a vector of vector of scalar values.
pub fn to_vec2<S: crate::WithDType>(&self) -> Result<Vec<Vec<S>>> {
let (dim1, dim2) = self.shape().r2()?;
let (dim1, dim2) = self.dims2()?;
let from_cpu_storage = |cpu_storage: &crate::CpuStorage| {
let data = S::cpu_storage_as_slice(cpu_storage)?;
let mut rows = vec![];
@ -1164,7 +1164,7 @@ impl Tensor {
/// Returns the data contained in a 3D tensor.
pub fn to_vec3<S: crate::WithDType>(&self) -> Result<Vec<Vec<Vec<S>>>> {
let (dim1, dim2, dim3) = self.shape().r3()?;
let (dim1, dim2, dim3) = self.dims3()?;
let from_cpu_storage = |cpu_storage: &crate::CpuStorage| {
let data = S::cpu_storage_as_slice(cpu_storage)?;
let mut top_rows = vec![];

10
candle-core/tests/tensor_tests.rs
View File

@ -4,7 +4,7 @@ use test_utils::to_vec3_round;
fn zeros(device: &Device) -> Result<()> {
let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
let (dim1, dim2) = tensor.shape().r2()?;
let (dim1, dim2) = tensor.dims2()?;
assert_eq!(dim1, 5);
assert_eq!(dim2, 2);
Ok(())
@ -12,7 +12,7 @@ fn zeros(device: &Device) -> Result<()> {
fn add_mul(device: &Device) -> Result<()> {
let tensor = Tensor::new(&[3f32, 1., 4.], device)?;
let dim1 = tensor.shape().r1()?;
let dim1 = tensor.dims1()?;
assert_eq!(dim1, 3);
let content: Vec<f32> = tensor.to_vec1()?;
assert_eq!(content, [3., 1., 4.]);
@ -28,7 +28,7 @@ fn add_mul(device: &Device) -> Result<()> {
fn tensor_2d(device: &Device) -> Result<()> {
let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
let tensor = Tensor::new(data, device)?;
let dims = tensor.shape().r2()?;
let dims = tensor.dims2()?;
assert_eq!(dims, (2, 5));
let content: Vec<Vec<f32>> = tensor.to_vec2()?;
assert_eq!(content, data);
@ -41,7 +41,7 @@ fn binary_op(device: &Device) -> Result<()> {
let data2 = &[[5f32, 5., 5., 5., 5.], [2., 1., 7., 8., 2.]];
let tensor2 = Tensor::new(data2, device)?;
let tensor = (&tensor + (&tensor * &tensor)? / (&tensor + &tensor2))?;
let dims = tensor.shape().r2()?;
let dims = tensor.dims2()?;
assert_eq!(dims, (2, 5));
let content: Vec<Vec<f32>> = tensor.to_vec2()?;
assert_eq!(content[0], [4.125, 1.1666666, 5.7777777, 1.1666666, 7.5]);
@ -56,7 +56,7 @@ fn binary_op(device: &Device) -> Result<()> {
fn transpose(device: &Device) -> Result<()> {
let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
let tensor = Tensor::new(data, device)?.t()?;
let dims = tensor.shape().r2()?;
let dims = tensor.dims2()?;
assert_eq!(dims, (5, 2));
assert_eq!(
tensor.to_vec2::<f32>()?,