mirror of
https://github.com/huggingface/candle.git
synced 2025-06-16 18:48:51 +00:00
e27b4700ad
* Indexing with max-value results in zero/no-op. * Add some testing. * Also adapt the metal kernels. * Another test. * Fix.
1883 lines
59 KiB
Rust
1883 lines
59 KiB
Rust
use candle_core::{test_device, test_utils, DType, Device, IndexOp, Result, Tensor, D};
|
|
|
|
fn zeros(device: &Device) -> Result<()> {
|
|
let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
|
|
let (dim1, dim2) = tensor.dims2()?;
|
|
assert_eq!(dim1, 5);
|
|
assert_eq!(dim2, 2);
|
|
Ok(())
|
|
}
|
|
|
|
fn ones(device: &Device) -> Result<()> {
|
|
assert_eq!(
|
|
Tensor::ones((2, 3), DType::U8, device)?.to_vec2::<u8>()?,
|
|
[[1, 1, 1], [1, 1, 1]],
|
|
);
|
|
assert_eq!(
|
|
Tensor::ones((2, 3), DType::U32, device)?.to_vec2::<u32>()?,
|
|
[[1, 1, 1], [1, 1, 1]],
|
|
);
|
|
assert_eq!(
|
|
Tensor::ones((2, 3), DType::I64, device)?.to_vec2::<i64>()?,
|
|
[[1, 1, 1], [1, 1, 1]],
|
|
);
|
|
assert_eq!(
|
|
Tensor::ones((2, 3), DType::F32, device)?.to_vec2::<f32>()?,
|
|
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
|
|
);
|
|
if !device.is_metal() {
|
|
assert_eq!(
|
|
Tensor::ones((2, 3), DType::F64, device)?.to_vec2::<f64>()?,
|
|
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
|
|
);
|
|
}
|
|
assert_eq!(
|
|
Tensor::ones((2, 3), DType::F16, device)?.to_vec2::<half::f16>()?,
|
|
[
|
|
[
|
|
half::f16::from_f32(1.0),
|
|
half::f16::from_f32(1.0),
|
|
half::f16::from_f32(1.0)
|
|
],
|
|
[
|
|
half::f16::from_f32(1.0),
|
|
half::f16::from_f32(1.0),
|
|
half::f16::from_f32(1.0)
|
|
]
|
|
],
|
|
);
|
|
assert_eq!(
|
|
Tensor::ones((2, 3), DType::BF16, device)?.to_vec2::<half::bf16>()?,
|
|
[
|
|
[
|
|
half::bf16::from_f32(1.0),
|
|
half::bf16::from_f32(1.0),
|
|
half::bf16::from_f32(1.0)
|
|
],
|
|
[
|
|
half::bf16::from_f32(1.0),
|
|
half::bf16::from_f32(1.0),
|
|
half::bf16::from_f32(1.0)
|
|
]
|
|
],
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn full(device: &Device) -> Result<()> {
|
|
let tensor = Tensor::zeros((3, 4), DType::U32, device)?;
|
|
tensor.const_set(42u32.into())?;
|
|
assert_eq!(
|
|
tensor.to_vec2::<u32>()?,
|
|
[[42, 42, 42, 42], [42, 42, 42, 42], [42, 42, 42, 42]]
|
|
);
|
|
tensor.i((.., 2))?.const_set(1337u32.into())?;
|
|
assert_eq!(
|
|
tensor.to_vec2::<u32>()?,
|
|
[[42, 42, 1337, 42], [42, 42, 1337, 42], [42, 42, 1337, 42]]
|
|
);
|
|
tensor.i((2, ..))?.const_set(1u32.into())?;
|
|
assert_eq!(
|
|
tensor.to_vec2::<u32>()?,
|
|
[[42, 42, 1337, 42], [42, 42, 1337, 42], [1, 1, 1, 1]]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn const_set(device: &Device) -> Result<()> {
|
|
assert_eq!(
|
|
Tensor::full(42u32, (2, 3), device)?.to_vec2::<u32>()?,
|
|
[[42, 42, 42], [42, 42, 42]],
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn arange(device: &Device) -> Result<()> {
|
|
assert_eq!(
|
|
Tensor::arange(0u8, 5u8, device)?.to_vec1::<u8>()?,
|
|
[0, 1, 2, 3, 4],
|
|
);
|
|
assert_eq!(
|
|
Tensor::arange_step(0u8, 5u8, 2, device)?.to_vec1::<u8>()?,
|
|
[0, 2, 4],
|
|
);
|
|
assert_eq!(
|
|
Tensor::arange_step(0u8, 5u8, 3, device)?.to_vec1::<u8>()?,
|
|
[0, 3],
|
|
);
|
|
assert_eq!(
|
|
Tensor::arange_step(5i64, 0i64, -1, device)?.to_vec1::<i64>()?,
|
|
[5, 4, 3, 2, 1],
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn add_mul(device: &Device) -> Result<()> {
|
|
let tensor = Tensor::new(&[3f32, 1., 4.], device)?;
|
|
let dim1 = tensor.dims1()?;
|
|
assert_eq!(dim1, 3);
|
|
let content: Vec<f32> = tensor.to_vec1()?;
|
|
assert_eq!(content, [3., 1., 4.]);
|
|
let tensor = Tensor::add(&tensor, &tensor)?;
|
|
let content: Vec<f32> = tensor.to_vec1()?;
|
|
assert_eq!(content, [6., 2., 8.]);
|
|
let tensor = Tensor::mul(&tensor, &tensor)?;
|
|
let content: Vec<f32> = tensor.to_vec1()?;
|
|
assert_eq!(content, [36., 4., 64.]);
|
|
Ok(())
|
|
}
|
|
|
|
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.dims2()?;
|
|
assert_eq!(dims, (2, 5));
|
|
let content: Vec<Vec<f32>> = tensor.to_vec2()?;
|
|
assert_eq!(content, data);
|
|
Ok(())
|
|
}
|
|
|
|
fn clamp(device: &Device) -> Result<()> {
|
|
let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
let tensor = tensor.clamp(1.5, 6.2)?;
|
|
assert_eq!(
|
|
tensor.to_vec2::<f32>()?,
|
|
[[3.0, 1.5, 4.0, 1.5, 5.0], [2.0, 1.5, 6.2, 6.2, 2.0]],
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn asort(device: &Device) -> Result<()> {
|
|
let data = &[[3f32, 1., 4., 1.1, 5.], [2.1, 1., 7., 8., 2.]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
let indexes = tensor.arg_sort_last_dim(true)?;
|
|
assert_eq!(
|
|
indexes.to_vec2::<u32>()?,
|
|
[[1, 3, 0, 2, 4], [1, 4, 0, 2, 3]],
|
|
);
|
|
let indexes = tensor.arg_sort_last_dim(false)?;
|
|
assert_eq!(
|
|
indexes.to_vec2::<u32>()?,
|
|
[[4, 2, 0, 3, 1], [3, 2, 0, 4, 1]],
|
|
);
|
|
let (sorted, indexes) = tensor.sort_last_dim(true)?;
|
|
assert_eq!(
|
|
indexes.to_vec2::<u32>()?,
|
|
[[1, 3, 0, 2, 4], [1, 4, 0, 2, 3]],
|
|
);
|
|
assert_eq!(
|
|
sorted.to_vec2::<f32>()?,
|
|
[[1.0, 1.1, 3.0, 4.0, 5.0], [1.0, 2.0, 2.1, 7.0, 8.0]]
|
|
);
|
|
let (sorted, indexes) = tensor.sort_last_dim(false)?;
|
|
assert_eq!(
|
|
indexes.to_vec2::<u32>()?,
|
|
[[4, 2, 0, 3, 1], [3, 2, 0, 4, 1]],
|
|
);
|
|
assert_eq!(
|
|
sorted.to_vec2::<f32>()?,
|
|
[[5.0, 4.0, 3.0, 1.1, 1.0], [8.0, 7.0, 2.1, 2.0, 1.0]]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn unary_op(device: &Device) -> Result<()> {
|
|
let data = &[[-3f32, 1., 4., -0.1, 0.5], [2.7, -1.8, -0.28, 1.8, 2.8]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&tensor.gelu()?, 4)?,
|
|
[
|
|
[-0.0036, 0.8412, 3.9999, -0.046, 0.3457],
|
|
[2.6911, -0.0647, -0.1091, 1.7353, 2.7933]
|
|
]
|
|
);
|
|
let t_f16 = tensor.to_dtype(DType::F16)?.gelu()?.to_dtype(DType::F32)?;
|
|
let max_diff = (tensor.gelu()? - t_f16)?.flatten_all()?.max(0)?;
|
|
assert!(max_diff.to_vec0::<f32>()? < 5e-3);
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&tensor.gelu_erf()?, 4)?,
|
|
[
|
|
[-0.004, 0.8413, 3.9999, -0.046, 0.3457],
|
|
[2.6906, -0.0647, -0.1091, 1.7353, 2.7928]
|
|
]
|
|
);
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&tensor.erf()?, 4)?,
|
|
[
|
|
[-1.0, 0.8427, 1.0, -0.1125, 0.5205],
|
|
[0.9999, -0.9891, -0.3079, 0.9891, 0.9999]
|
|
]
|
|
);
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&tensor.silu()?, 4)?,
|
|
[
|
|
[-0.1423, 0.7311, 3.9281, -0.0475, 0.3112],
|
|
[2.53, -0.2553, -0.1205, 1.5447, 2.6395]
|
|
]
|
|
);
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&tensor.ceil()?, 4)?,
|
|
[[-3.0, 1.0, 4.0, -0.0, 1.0], [3.0, -1.0, -0.0, 2.0, 3.0]]
|
|
);
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&tensor.floor()?, 4)?,
|
|
[[-3.0, 1.0, 4.0, -1.0, 0.0], [2.0, -2.0, -1.0, 1.0, 2.0]]
|
|
);
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&tensor.round()?, 4)?,
|
|
[[-3.0, 1.0, 4.0, -0.0, 1.0], [3.0, -2.0, -0.0, 2.0, 3.0]]
|
|
);
|
|
let tensor = Tensor::new(&[2997.9246, 314.15926f32], device)?;
|
|
assert_eq!(
|
|
test_utils::to_vec1_round(&tensor.round_to(2)?, 4)?,
|
|
[2997.92, 314.16]
|
|
);
|
|
assert_eq!(
|
|
test_utils::to_vec1_round(&tensor.round_to(-2)?, 4)?,
|
|
[3000.0, 300.]
|
|
);
|
|
let tensor = Tensor::new(
|
|
&[-1.01f32, -0.9, -0.1, 0.0, -0.0, 0.1, 0.9, 1.0, 1.1],
|
|
device,
|
|
)?;
|
|
assert_eq!(
|
|
tensor.sign()?.to_vec1::<f32>()?,
|
|
[-1., -1., -1., 0., 0., 1., 1., 1., 1.]
|
|
);
|
|
let tensor = Tensor::new(&[-1.0f32, 0., -2., 3.], device)?;
|
|
let y = tensor.elu(2.)?;
|
|
assert_eq!(
|
|
test_utils::to_vec1_round(&y, 4)?,
|
|
[-1.2642, 0.0000, -1.7293, 3.0000]
|
|
);
|
|
// This test failed on metal prior to the following PR:
|
|
// https://github.com/huggingface/candle/pull/2490
|
|
let y = tensor.reshape((2, 2))?.t()?.elu(2.)?.flatten_all()?;
|
|
assert_eq!(
|
|
test_utils::to_vec1_round(&y, 4)?,
|
|
[-1.2642, -1.7293, 0.0000, 3.0000]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn binary_op(device: &Device) -> Result<()> {
|
|
let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
|
|
let tensor1 = Tensor::new(data, device)?;
|
|
let data2 = &[[5f32, 5., 5., 5., 5.], [2., 1., 7., 8., 2.]];
|
|
let tensor2 = Tensor::new(data2, device)?;
|
|
let tensor = (&tensor1 + (&tensor1 * &tensor1)? / (&tensor1 + &tensor2))?;
|
|
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]);
|
|
assert_eq!(content[1], [3.0, 1.5, 10.5, 12.0, 3.0]);
|
|
#[allow(clippy::eq_op)]
|
|
let tensor = (&tensor - &tensor)?;
|
|
let content: Vec<Vec<f32>> = tensor.to_vec2()?;
|
|
assert_eq!(content[0], [0., 0., 0., 0., 0.]);
|
|
|
|
let min = tensor1.minimum(&(&tensor2 * 0.5)?)?;
|
|
let max = tensor1.maximum(&(&tensor2 * 0.5)?)?;
|
|
assert_eq!(
|
|
min.to_vec2::<f32>()?,
|
|
[[2.5, 1.0, 2.5, 1.0, 2.5], [1.0, 0.5, 3.5, 4.0, 1.0]],
|
|
);
|
|
assert_eq!(
|
|
max.to_vec2::<f32>()?,
|
|
[[3.0, 2.5, 4.0, 2.5, 5.0], [2.0, 1.0, 7.0, 8.0, 2.0]]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
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.dims2()?;
|
|
assert_eq!(dims, (5, 2));
|
|
assert_eq!(
|
|
tensor.to_vec2::<f32>()?,
|
|
&[[3f32, 2.], [1., 1.], [4., 7.], [1., 8.], [5., 2.]]
|
|
);
|
|
assert_eq!(tensor.t()?.to_vec2::<f32>()?, data);
|
|
assert_eq!(tensor.contiguous()?.t()?.to_vec2::<f32>()?, data);
|
|
assert_eq!(((tensor + 1.)?.t()? - 1.)?.to_vec2::<f32>()?, data);
|
|
Ok(())
|
|
}
|
|
|
|
fn var(device: &Device) -> Result<()> {
|
|
// Values taken from https://pytorch.org/docs/stable/generated/torch.var.html
|
|
let data = &[
|
|
[0.2035f32, 1.2959, 1.8101, -0.4644],
|
|
[1.5027, -0.3270, 0.5905, 0.6538],
|
|
[-1.5745, 1.3330, -0.5596, -0.6548],
|
|
[0.1264, -0.5080, 1.6420, 0.1992],
|
|
];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&tensor.var_keepdim(1)?, 4)?,
|
|
&[[1.0631], [0.559], [1.4893], [0.8258]]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn sum(device: &Device) -> Result<()> {
|
|
let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
tensor.sum_keepdim(2)?.to_vec3::<u32>()?,
|
|
&[[[8], [15]], [[10], [18]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.sum_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[[5, 2, 11], [9, 7, 17]]],
|
|
);
|
|
assert_eq!(tensor.sum_keepdim((0, 2, 1))?.to_vec3::<u32>()?, &[[[51]]],);
|
|
assert_eq!(
|
|
tensor.t()?.sum_keepdim(1)?.t()?.to_vec3::<u32>()?,
|
|
&[[[8], [15]], [[10], [18]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.sum_keepdim((2, 1))?.to_vec3::<u32>()?,
|
|
&[[[8 + 15]], [[10 + 18]]]
|
|
);
|
|
let data: Vec<u32> = (0..4000u32).collect();
|
|
let tensor = Tensor::new(data.as_slice(), device)?;
|
|
assert_eq!(tensor.sum_keepdim(0)?.to_vec1::<u32>()?, &[7998000]);
|
|
let tensor = tensor.reshape((2000, 2))?;
|
|
assert_eq!(tensor.sum_keepdim((0, 1))?.to_vec2::<u32>()?, &[[7998000]]);
|
|
assert_eq!(
|
|
tensor.sum_keepdim(0)?.sum_keepdim(1)?.to_vec2::<u32>()?,
|
|
&[[7998000]]
|
|
);
|
|
assert_eq!(
|
|
tensor.sum_keepdim(1)?.sum_keepdim(0)?.to_vec2::<u32>()?,
|
|
&[[7998000]]
|
|
);
|
|
assert_eq!(
|
|
tensor.sum_keepdim(0)?.to_vec2::<u32>()?,
|
|
&[[3998000, 4000000]]
|
|
);
|
|
|
|
// Make the tensor non contiguous.
|
|
let tensor = tensor.t()?.contiguous()?.t()?;
|
|
assert_eq!(tensor.sum_keepdim((0, 1))?.to_vec2::<u32>()?, &[[7998000]]);
|
|
assert_eq!(
|
|
tensor.sum_keepdim(0)?.sum_keepdim(1)?.to_vec2::<u32>()?,
|
|
&[[7998000]]
|
|
);
|
|
assert_eq!(
|
|
tensor.sum_keepdim(1)?.sum_keepdim(0)?.to_vec2::<u32>()?,
|
|
&[[7998000]]
|
|
);
|
|
assert_eq!(
|
|
tensor.sum_keepdim(0)?.to_vec2::<u32>()?,
|
|
&[[3998000, 4000000]]
|
|
);
|
|
|
|
let t1 = tensor.reshape((200, 5, 4))?;
|
|
let t2 = t1.transpose(0, 2)?.contiguous()?.transpose(0, 2)?;
|
|
for tensor in [t1, t2] {
|
|
assert_eq!(
|
|
tensor.sum_keepdim((0, 1, 2))?.to_vec3::<u32>()?,
|
|
&[[[7998000]]]
|
|
);
|
|
assert_eq!(
|
|
tensor
|
|
.sum_keepdim(0)?
|
|
.sum_keepdim(2)?
|
|
.sum_keepdim(1)?
|
|
.to_vec3::<u32>()?,
|
|
&[[[7998000]]]
|
|
);
|
|
assert_eq!(
|
|
tensor
|
|
.sum_keepdim(0)?
|
|
.sum_keepdim((1, 2))?
|
|
.to_vec3::<u32>()?,
|
|
&[[[7998000]]]
|
|
);
|
|
assert_eq!(
|
|
tensor
|
|
.sum_keepdim(1)?
|
|
.sum_keepdim((0, 2))?
|
|
.to_vec3::<u32>()?,
|
|
&[[[7998000]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.sum_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[
|
|
[398000, 398200, 398400, 398600],
|
|
[398800, 399000, 399200, 399400],
|
|
[399600, 399800, 400000, 400200],
|
|
[400400, 400600, 400800, 401000],
|
|
[401200, 401400, 401600, 401800]
|
|
]]
|
|
);
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
fn min(device: &Device) -> Result<()> {
|
|
let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
tensor.min_keepdim(2)?.to_vec3::<u32>()?,
|
|
&[[[1], [1]], [[1], [2]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.min_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[[2, 1, 4], [1, 2, 8]]],
|
|
);
|
|
let data: Vec<u32> = (200..4000u32).collect();
|
|
let tensor = Tensor::new(data.as_slice(), device)?;
|
|
assert_eq!(tensor.min_keepdim(0)?.to_vec1::<u32>()?, &[200]);
|
|
let tensor = tensor.reshape((1900, 2))?;
|
|
assert_eq!(
|
|
tensor.min_keepdim(0)?.min_keepdim(1)?.to_vec2::<u32>()?,
|
|
&[[200]]
|
|
);
|
|
assert_eq!(
|
|
tensor.min_keepdim(1)?.min_keepdim(0)?.to_vec2::<u32>()?,
|
|
&[[200]]
|
|
);
|
|
assert_eq!(tensor.min_keepdim(0)?.to_vec2::<u32>()?, &[[200, 201]]);
|
|
|
|
// Make the tensor non contiguous.
|
|
let tensor = tensor.t()?.contiguous()?.t()?;
|
|
assert_eq!(
|
|
tensor.min_keepdim(0)?.min_keepdim(1)?.to_vec2::<u32>()?,
|
|
&[[200]]
|
|
);
|
|
assert_eq!(
|
|
tensor.min_keepdim(1)?.min_keepdim(0)?.to_vec2::<u32>()?,
|
|
&[[200]]
|
|
);
|
|
assert_eq!(tensor.min_keepdim(0)?.to_vec2::<u32>()?, &[[200, 201]]);
|
|
|
|
let t1 = tensor.reshape((190, 5, 4))?;
|
|
let t2 = t1.transpose(0, 2)?.contiguous()?.transpose(0, 2)?;
|
|
for tensor in [t1, t2] {
|
|
assert_eq!(
|
|
tensor
|
|
.min_keepdim(0)?
|
|
.min_keepdim(2)?
|
|
.min_keepdim(1)?
|
|
.to_vec3::<u32>()?,
|
|
&[[[200]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.min_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[
|
|
[200, 201, 202, 203],
|
|
[204, 205, 206, 207],
|
|
[208, 209, 210, 211],
|
|
[212, 213, 214, 215],
|
|
[216, 217, 218, 219]
|
|
]]
|
|
);
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
fn max(device: &Device) -> Result<()> {
|
|
let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
tensor.max_keepdim(2)?.to_vec3::<u32>()?,
|
|
&[[[4], [9]], [[7], [8]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.max_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[[3, 1, 7], [8, 5, 9]]],
|
|
);
|
|
let data: Vec<u32> = (200..4000u32).collect();
|
|
let tensor = Tensor::new(data.as_slice(), device)?;
|
|
assert_eq!(tensor.max_keepdim(0)?.to_vec1::<u32>()?, &[3999]);
|
|
let tensor = tensor.reshape((1900, 2))?;
|
|
assert_eq!(
|
|
tensor.max_keepdim(0)?.max_keepdim(1)?.to_vec2::<u32>()?,
|
|
&[[3999]]
|
|
);
|
|
assert_eq!(
|
|
tensor.max_keepdim(1)?.max_keepdim(0)?.to_vec2::<u32>()?,
|
|
&[[3999]]
|
|
);
|
|
assert_eq!(tensor.max_keepdim(0)?.to_vec2::<u32>()?, &[[3998, 3999]]);
|
|
|
|
// Make the tensor non contiguous.
|
|
let tensor = tensor.t()?.contiguous()?.t()?;
|
|
assert_eq!(
|
|
tensor.max_keepdim(0)?.max_keepdim(1)?.to_vec2::<u32>()?,
|
|
&[[3999]]
|
|
);
|
|
assert_eq!(
|
|
tensor.max_keepdim(1)?.max_keepdim(0)?.to_vec2::<u32>()?,
|
|
&[[3999]]
|
|
);
|
|
assert_eq!(tensor.max_keepdim(0)?.to_vec2::<u32>()?, &[[3998, 3999]]);
|
|
|
|
let t1 = tensor.reshape((190, 5, 4))?;
|
|
let t2 = t1.transpose(0, 2)?.contiguous()?.transpose(0, 2)?;
|
|
for tensor in [t1, t2] {
|
|
assert_eq!(
|
|
tensor
|
|
.max_keepdim(0)?
|
|
.max_keepdim(2)?
|
|
.max_keepdim(1)?
|
|
.to_vec3::<u32>()?,
|
|
&[[[3999]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.max_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[
|
|
[3980, 3981, 3982, 3983],
|
|
[3984, 3985, 3986, 3987],
|
|
[3988, 3989, 3990, 3991],
|
|
[3992, 3993, 3994, 3995],
|
|
[3996, 3997, 3998, 3999]
|
|
]]
|
|
);
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
fn argmin(device: &Device) -> Result<()> {
|
|
let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
tensor.argmin_keepdim(2)?.to_vec3::<u32>()?,
|
|
&[[[1], [0]], [[1], [1]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.argmin_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[[1, 0, 0], [0, 1, 1]]],
|
|
);
|
|
let data: Vec<u32> = (200..4000u32).collect();
|
|
let tensor = Tensor::new(data.as_slice(), device)?;
|
|
assert_eq!(tensor.argmin_keepdim(0)?.to_vec1::<u32>()?, &[0]);
|
|
let tensor = tensor.reshape((1900, 2))?;
|
|
assert_eq!(
|
|
tensor
|
|
.argmin_keepdim(0)?
|
|
.argmin_keepdim(1)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
|
|
);
|
|
assert_eq!(
|
|
tensor
|
|
.argmin_keepdim(1)?
|
|
.argmin_keepdim(0)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
|
|
);
|
|
assert_eq!(tensor.argmin_keepdim(0)?.to_vec2::<u32>()?, &[[0, 0]]);
|
|
|
|
// Make the tensor non contiguous.
|
|
let tensor = tensor.t()?.contiguous()?.t()?;
|
|
assert_eq!(
|
|
tensor
|
|
.argmin_keepdim(0)?
|
|
.argmin_keepdim(1)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
|
|
);
|
|
assert_eq!(
|
|
tensor
|
|
.argmin_keepdim(1)?
|
|
.argmin_keepdim(0)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
|
|
);
|
|
assert_eq!(tensor.argmin_keepdim(0)?.to_vec2::<u32>()?, &[[0, 0]]);
|
|
|
|
let t1 = tensor.reshape((190, 5, 4))?;
|
|
let t2 = t1.transpose(0, 2)?.contiguous()?.transpose(0, 2)?;
|
|
for tensor in [t1, t2] {
|
|
assert_eq!(
|
|
tensor
|
|
.argmin_keepdim(0)?
|
|
.argmin_keepdim(2)?
|
|
.argmin_keepdim(1)?
|
|
.to_vec3::<u32>()?,
|
|
&[[[0]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.argmin_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
]]
|
|
);
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
fn argmax(device: &Device) -> Result<()> {
|
|
let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
tensor.argmax_keepdim(2)?.to_vec3::<u32>()?,
|
|
&[[[2], [2]], [[2], [0]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.argmax_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[[0, 0, 1], [1, 0, 0]]],
|
|
);
|
|
let data: Vec<u32> = (200..4000u32).collect();
|
|
let tensor = Tensor::new(data.as_slice(), device)?;
|
|
assert_eq!(tensor.argmax_keepdim(0)?.to_vec1::<u32>()?, &[3799]);
|
|
let tensor = tensor.reshape((1900, 2))?;
|
|
assert_eq!(
|
|
tensor
|
|
.argmax_keepdim(0)?
|
|
.argmax_keepdim(1)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
|
|
);
|
|
assert_eq!(
|
|
tensor
|
|
.argmax_keepdim(1)?
|
|
.argmax_keepdim(0)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
|
|
);
|
|
assert_eq!(tensor.argmax_keepdim(0)?.to_vec2::<u32>()?, &[[1899, 1899]]);
|
|
|
|
// Make the tensor non contiguous.
|
|
let tensor = tensor.t()?.contiguous()?.t()?;
|
|
assert_eq!(
|
|
tensor
|
|
.argmax_keepdim(0)?
|
|
.argmax_keepdim(1)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
|
|
);
|
|
assert_eq!(
|
|
tensor
|
|
.argmax_keepdim(1)?
|
|
.argmax_keepdim(0)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
|
|
);
|
|
assert_eq!(tensor.argmax_keepdim(0)?.to_vec2::<u32>()?, &[[1899, 1899]]);
|
|
|
|
let t1 = tensor.reshape((190, 5, 4))?;
|
|
let t2 = t1.transpose(0, 2)?.contiguous()?.transpose(0, 2)?;
|
|
for tensor in [t1, t2] {
|
|
assert_eq!(
|
|
tensor
|
|
.argmax_keepdim(0)?
|
|
.argmax_keepdim(2)?
|
|
.argmax_keepdim(1)?
|
|
.to_vec3::<u32>()?,
|
|
&[[[0]]]
|
|
);
|
|
assert_eq!(
|
|
tensor.argmax_keepdim(0)?.to_vec3::<u32>()?,
|
|
&[[
|
|
[189, 189, 189, 189],
|
|
[189, 189, 189, 189],
|
|
[189, 189, 189, 189],
|
|
[189, 189, 189, 189],
|
|
[189, 189, 189, 189],
|
|
]]
|
|
);
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
fn narrow(device: &Device) -> Result<()> {
|
|
let data = &[[[3f32, 1., 4.], [1., 5., 9.]], [[2., 1., 7.], [8., 2., 8.]]];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
tensor.narrow(2, 1, 2)?.to_vec3::<f32>()?,
|
|
&[[[1.0, 4.0], [5.0, 9.0]], [[1.0, 7.0], [2.0, 8.0]]],
|
|
);
|
|
assert_eq!(
|
|
tensor.narrow(1, 1, 1)?.to_vec3::<f32>()?,
|
|
&[[[1.0, 5.0, 9.0]], [[8.0, 2.0, 8.0]]],
|
|
);
|
|
assert_eq!(
|
|
tensor.narrow(0, 0, 1)?.to_vec3::<f32>()?,
|
|
&[[[3.0, 1.0, 4.0], [1.0, 5.0, 9.0]]],
|
|
);
|
|
assert_eq!(
|
|
tensor.narrow(0, 1, 1)?.to_vec3::<f32>()?,
|
|
&[[[2.0, 1.0, 7.0], [8.0, 2.0, 8.0]]],
|
|
);
|
|
// The following has been checked against PyTorch via:
|
|
// import torch
|
|
// t = torch.tensor([[[3., 1., 4.], [1., 5., 9.]], [[2., 1., 7.], [8., 2., 8.]]])
|
|
// t.transpose(-1, -2).narrow(1, 1, 2)
|
|
assert_eq!(
|
|
tensor.t()?.narrow(1, 1, 2)?.to_vec3::<f32>()?,
|
|
&[[[1.0, 5.0], [4.0, 9.0]], [[1.0, 2.0], [7.0, 8.0]]],
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn broadcast(device: &Device) -> Result<()> {
|
|
let data = &[3f32, 1., 4.];
|
|
let tensor = Tensor::new(data, device)?;
|
|
assert_eq!(
|
|
tensor.broadcast_left((3, 1))?.to_vec3::<f32>()?,
|
|
&[[[3.0, 1.0, 4.0]], [[3.0, 1.0, 4.0]], [[3.0, 1.0, 4.0]]]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn slice_set(device: &Device) -> Result<()> {
|
|
let (b, h, max_t, d) = (2, 4, 7, 3);
|
|
let cache = Tensor::zeros((b, h, max_t, d), DType::F32, device)?;
|
|
let tensor = Tensor::randn(0f32, 1f32, (b, h, 4, d), device)?;
|
|
cache.slice_set(&tensor, 2, 0)?;
|
|
let cache_t = cache.narrow(2, 0, 4)?;
|
|
let diff = (cache_t - &tensor)?.abs()?.sum_all()?.to_vec0::<f32>()?;
|
|
assert_eq!(diff, 0.);
|
|
cache.slice_set(&tensor, 2, 1)?;
|
|
let cache_t = cache.narrow(2, 1, 4)?;
|
|
let diff = (cache_t - &tensor)?.abs()?.sum_all()?.to_vec0::<f32>()?;
|
|
assert_eq!(diff, 0.);
|
|
let ones = Tensor::ones((b, h, 1, d), DType::F32, device)?;
|
|
cache.slice_set(&ones, 2, 6)?;
|
|
let diff = cache.narrow(2, 5, 1)?.abs()?.sum_all()?.to_vec0::<f32>()?;
|
|
assert_eq!(diff, 0.);
|
|
let diff = (cache.narrow(2, 6, 1)? - 1.)?
|
|
.abs()?
|
|
.sum_all()?
|
|
.to_vec0::<f32>()?;
|
|
assert_eq!(diff, 0.);
|
|
// This used to create a deadlock rather than returning an actual error.
|
|
assert!(cache.slice_set(&cache, 0, 0).is_err());
|
|
Ok(())
|
|
}
|
|
|
|
fn cat(device: &Device) -> Result<()> {
|
|
// 1D
|
|
let t1 = Tensor::new(&[3f32, 1., 4.], device)?;
|
|
let t2 = Tensor::new(&[1f32, 5., 9., 2.], device)?;
|
|
let t3 = Tensor::new(&[6f32, 5., 3., 5., 8., 9.], device)?;
|
|
assert_eq!(Tensor::cat(&[&t1], 0)?.to_vec1::<f32>()?, [3f32, 1., 4.],);
|
|
assert_eq!(
|
|
Tensor::cat(&[&t1, &t2], 0)?.to_vec1::<f32>()?,
|
|
[3f32, 1., 4., 1., 5., 9., 2.],
|
|
);
|
|
assert_eq!(
|
|
Tensor::cat(&[&t1, &t2, &t3], 0)?.to_vec1::<f32>()?,
|
|
[3f32, 1., 4., 1., 5., 9., 2., 6., 5., 3., 5., 8., 9.],
|
|
);
|
|
|
|
// 2D
|
|
let data = &[[3f32, 1., 4., 1., 5.], [2., 7., 1., 8., 2.]];
|
|
let t1 = Tensor::new(data, device)?;
|
|
let data2 = &[[5f32, 5., 5., 5., 5.], [2., 7., 1., 8., 2.]];
|
|
let t2 = Tensor::new(data2, device)?;
|
|
assert_eq!(
|
|
Tensor::cat(&[&t1, &t2], 0)?.to_vec2::<f32>()?,
|
|
[
|
|
[3.0, 1.0, 4.0, 1.0, 5.0],
|
|
[2.0, 7.0, 1.0, 8.0, 2.0],
|
|
[5.0, 5.0, 5.0, 5.0, 5.0],
|
|
[2.0, 7.0, 1.0, 8.0, 2.0]
|
|
]
|
|
);
|
|
// PyTorch equivalent:
|
|
// import torch
|
|
// t1 = torch.tensor([[3, 1, 4, 1, 5], [2, 7, 1, 8, 2]])
|
|
// t2 = torch.tensor([[5]*5, [2, 7, 1, 8, 2]])
|
|
// torch.cat([t1.t(), t2.t()], dim=1).t()
|
|
assert_eq!(
|
|
Tensor::cat(&[&t1.t()?, &t2.t()?], 1)?
|
|
.t()?
|
|
.to_vec2::<f32>()?,
|
|
[
|
|
[3.0, 1.0, 4.0, 1.0, 5.0],
|
|
[2.0, 7.0, 1.0, 8.0, 2.0],
|
|
[5.0, 5.0, 5.0, 5.0, 5.0],
|
|
[2.0, 7.0, 1.0, 8.0, 2.0]
|
|
]
|
|
);
|
|
assert_eq!(
|
|
Tensor::cat(&[&t1, &t2], 1)?.to_vec2::<f32>()?,
|
|
[
|
|
[3.0, 1.0, 4.0, 1.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0],
|
|
[2.0, 7.0, 1.0, 8.0, 2.0, 2.0, 7.0, 1.0, 8.0, 2.0]
|
|
]
|
|
);
|
|
|
|
// 3D
|
|
let t1 = Tensor::arange(0, 48i64, device)?.reshape((2, 6, 4))?;
|
|
let t2 = Tensor::arange(100, 124i64, device)?.reshape((2, 3, 4))?;
|
|
let t3 = Tensor::arange(10000, 10032i64, device)?.reshape((2, 4, 4))?;
|
|
|
|
let t_cat = Tensor::cat(&[&t1, &t2, &t3], 1)?;
|
|
|
|
let t1 = t1.t()?.contiguous()?.t()?;
|
|
let t2 = t2.t()?.contiguous()?.t()?;
|
|
let t3 = t3.t()?.contiguous()?.t()?;
|
|
let t_cat2 = Tensor::cat(&[&t1, &t2, &t3], 1)?;
|
|
|
|
let diff = t_cat.eq(&t_cat2)?.to_dtype(DType::F32)?.sum_all()?;
|
|
assert_eq!(diff.to_vec0::<f32>()?, 104.0);
|
|
assert_eq!(t_cat.i((0, 0, 0))?.to_vec0::<i64>()?, 0);
|
|
assert_eq!(t_cat.i((0, 4, 0))?.to_vec0::<i64>()?, 16);
|
|
assert_eq!(t_cat.i((0, 5, 0))?.to_vec0::<i64>()?, 20);
|
|
assert_eq!(t_cat.i((1, 5, 0))?.to_vec0::<i64>()?, 44);
|
|
assert_eq!(t_cat.i((0, 6, 0))?.to_vec0::<i64>()?, 100);
|
|
assert_eq!(t_cat.i((1, 6, 0))?.to_vec0::<i64>()?, 112);
|
|
assert_eq!(t_cat.i((0, 6, 1))?.to_vec0::<i64>()?, 101);
|
|
assert_eq!(t_cat.i((0, 7, 1))?.to_vec0::<i64>()?, 105);
|
|
assert_eq!(t_cat.i((0, 12, 1))?.to_vec0::<i64>()?, 10013);
|
|
assert_eq!(t_cat.i((1, 12, 3))?.to_vec0::<i64>()?, 10031);
|
|
Ok(())
|
|
}
|
|
|
|
fn embeddings(device: &Device) -> Result<()> {
|
|
let ids = Tensor::new(&[0u32, 2u32, 1u32], device)?;
|
|
let t = Tensor::new(&[[0f32, 1f32], [2f32, 3f32], [4f32, 5f32]], device)?;
|
|
let hs = t.embedding(&ids)?;
|
|
assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 1.0], [4.0, 5.0], [2.0, 3.0]]);
|
|
let hs = t.index_select(&ids, 0)?;
|
|
assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 1.0], [4.0, 5.0], [2.0, 3.0]]);
|
|
let hs = t.index_select(&ids.to_dtype(DType::I64)?, 0)?;
|
|
assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 1.0], [4.0, 5.0], [2.0, 3.0]]);
|
|
let ids = Tensor::new(&[u32::MAX, 2u32, u32::MAX], device)?;
|
|
let hs = t.index_select(&ids, 0)?;
|
|
assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 0.0], [4.0, 5.0], [0.0, 0.0]]);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn index_select_fail() -> Result<()> {
|
|
// Check that an error is properly reported on out of bounds.
|
|
let ids = Tensor::new(&[4u32, 2u32, 1u32], &Device::Cpu)?;
|
|
let t = Tensor::new(&[[0f32, 1f32], [2f32, 3f32], [4f32, 5f32]], &Device::Cpu)?;
|
|
let hs = t.index_select(&ids, 0);
|
|
assert!(hs.is_err());
|
|
Ok(())
|
|
}
|
|
|
|
// The test below triggers an unwinding panic as there is a panic within the
|
|
// #[cfg(feature = "cuda")]
|
|
// #[test]
|
|
// #[should_panic]
|
|
// fn index_select_fail_gpu() {
|
|
// // Check that a panic happens for out of bounds in cuda
|
|
// if let Ok(device) = Device::new_cuda(0) {
|
|
// if let Ok(ids) = Tensor::new(&[4u32, 2u32, 1u32], &device) {
|
|
// if let Ok(t) = Tensor::new(&[[0f32, 1f32], [2f32, 3f32], [4f32, 5f32]], &device) {
|
|
// let _ = t.index_select(&ids, 0);
|
|
// }
|
|
// }
|
|
// }
|
|
// }
|
|
|
|
fn cmp(device: &Device) -> Result<()> {
|
|
let t1 = Tensor::new(&[[0f32, 1f32], [2f32, 3f32], [4f32, 5f32]], device)?;
|
|
let t2 = Tensor::new(&[[1f32, 0f32], [3f32, 3f32], [4f32, 7f32]], device)?;
|
|
assert_eq!(t1.eq(&t2)?.to_vec2::<u8>()?, &[[0, 0], [0, 1], [1, 0]]);
|
|
assert_eq!(t1.ne(&t2)?.to_vec2::<u8>()?, &[[1, 1], [1, 0], [0, 1]]);
|
|
assert_eq!(t1.le(&t2)?.to_vec2::<u8>()?, &[[1, 0], [1, 1], [1, 1]]);
|
|
assert_eq!(t1.lt(&t2)?.to_vec2::<u8>()?, &[[1, 0], [1, 0], [0, 1]]);
|
|
assert_eq!(t1.gt(&t2)?.to_vec2::<u8>()?, &[[0, 1], [0, 0], [0, 0]]);
|
|
assert_eq!(t1.ge(&t2)?.to_vec2::<u8>()?, &[[0, 1], [0, 1], [1, 0]]);
|
|
Ok(())
|
|
}
|
|
|
|
fn index_select(device: &Device) -> Result<()> {
|
|
let ids = Tensor::new(&[0u32, 2u32, 1u32], device)?;
|
|
let t = Tensor::arange(0f32, 12f32, device)?.reshape((4, 3))?;
|
|
assert_eq!(
|
|
t.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0],
|
|
[3.0, 4.0, 5.0],
|
|
[6.0, 7.0, 8.0],
|
|
[9.0, 10.0, 11.0]
|
|
]
|
|
);
|
|
for dtype in [DType::U8, DType::U32, DType::I64] {
|
|
let ids = ids.to_dtype(dtype)?;
|
|
let hs = t.index_select(&ids, 1)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 2.0, 1.0],
|
|
[3.0, 5.0, 4.0],
|
|
[6.0, 8.0, 7.0],
|
|
[9.0, 11.0, 10.0]
|
|
]
|
|
);
|
|
let hs = t.index_select(&ids, 0)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[[0.0, 1.0, 2.0], [6.0, 7.0, 8.0], [3.0, 4.0, 5.0]]
|
|
);
|
|
// Prior to https://github.com/huggingface/candle/pull/1022
|
|
// There would be a bug where the last values in the result tensor would be set to 0.
|
|
let ids = Tensor::new(&[0u32, 2u32, 1u32, 0u32, 2u32, 1u32], device)?;
|
|
let hs = t.index_select(&ids, 0)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0],
|
|
[6.0, 7.0, 8.0],
|
|
[3.0, 4.0, 5.0],
|
|
[0.0, 1.0, 2.0],
|
|
[6.0, 7.0, 8.0],
|
|
[3.0, 4.0, 5.0],
|
|
]
|
|
);
|
|
|
|
// Test when selecting dim > 0 with ids size different from elem count of
|
|
// target dim in source/input.
|
|
let ids = Tensor::new(&[1u32, 0u32, 1u32], device)?;
|
|
let t = Tensor::arange(1f32, 5f32, device)?.reshape((2, 2))?;
|
|
assert_eq!(t.to_vec2::<f32>()?, &[[1.0, 2.0], [3.0, 4.0]]);
|
|
let hs = t.index_select(&ids, 1)?;
|
|
assert_eq!(hs.to_vec2::<f32>()?, &[[2.0, 1.0, 2.0], [4.0, 3.0, 4.0]]);
|
|
}
|
|
|
|
Ok(())
|
|
}
|
|
|
|
fn index_add(device: &Device) -> Result<()> {
|
|
let ids = Tensor::new(&[0u32, 1u32, 1u32], device)?;
|
|
let t = Tensor::arange(0f32, 12f32, device)?.reshape((4, 3))?;
|
|
assert_eq!(
|
|
t.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0],
|
|
[3.0, 4.0, 5.0],
|
|
[6.0, 7.0, 8.0],
|
|
[9.0, 10.0, 11.0]
|
|
]
|
|
);
|
|
let init = Tensor::ones((4, 2), DType::F32, device)?;
|
|
let hs = init.index_add(&ids, &t, 1)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[[1.0, 4.0], [4.0, 10.0], [7.0, 16.0], [10.0, 22.0]],
|
|
);
|
|
let init = Tensor::zeros((4, 2), DType::F32, device)?;
|
|
let ids = Tensor::new(&[1u32, 0u32, 0u32], device)?;
|
|
let hs = init.index_add(&ids, &t, 1)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[[3.0, 0.0], [9.0, 3.0], [15.0, 6.0], [21.0, 9.0]],
|
|
);
|
|
|
|
let init = Tensor::zeros((6, 3), DType::F32, device)?;
|
|
let ids = Tensor::new(&[5u32, 0u32, 1u32, 0u32], device)?;
|
|
let hs = init.index_add(&ids, &t, 0)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[
|
|
[12.0, 14.0, 16.0],
|
|
[6.0, 7.0, 8.0],
|
|
[0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0],
|
|
[0.0, 1.0, 2.0]
|
|
]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn slice_scatter(device: &Device) -> Result<()> {
|
|
let t = Tensor::arange(0f32, 12f32, device)?.reshape((4, 3))?;
|
|
assert_eq!(
|
|
t.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0],
|
|
[3.0, 4.0, 5.0],
|
|
[6.0, 7.0, 8.0],
|
|
[9.0, 10.0, 11.0]
|
|
]
|
|
);
|
|
let src = Tensor::arange(100f32, 106f32, device)?.reshape((2, 3))?;
|
|
assert_eq!(
|
|
t.slice_scatter0(&src, 0)?.to_vec2::<f32>()?,
|
|
&[
|
|
[100.0, 101.0, 102.0],
|
|
[103.0, 104.0, 105.0],
|
|
[6.0, 7.0, 8.0],
|
|
[9.0, 10.0, 11.0]
|
|
]
|
|
);
|
|
assert_eq!(
|
|
t.slice_scatter0(&src, 1)?.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0],
|
|
[100.0, 101.0, 102.0],
|
|
[103.0, 104.0, 105.0],
|
|
[9.0, 10.0, 11.0]
|
|
]
|
|
);
|
|
assert_eq!(
|
|
t.slice_scatter0(&src, 2)?.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0],
|
|
[3.0, 4.0, 5.0],
|
|
[100.0, 101.0, 102.0],
|
|
[103.0, 104.0, 105.0],
|
|
]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn scatter(device: &Device) -> Result<()> {
|
|
let t = Tensor::arange(0f32, 12f32, device)?.reshape((4, 3))?;
|
|
assert_eq!(
|
|
t.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0],
|
|
[3.0, 4.0, 5.0],
|
|
[6.0, 7.0, 8.0],
|
|
[9.0, 10.0, 11.0]
|
|
]
|
|
);
|
|
let ids = Tensor::new(&[[0u32, 1, 2], [3, 4, 0], [3, 3, 1], [2, 0, 4]], device)?;
|
|
let init = Tensor::ones((4, 5), DType::F32, device)?;
|
|
let hs = init.scatter_add(&ids, &t, 1)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[
|
|
[1.0, 2.0, 3.0, 1.0, 1.0],
|
|
[6.0, 1.0, 1.0, 4.0, 5.0],
|
|
[1.0, 9.0, 1.0, 14.0, 1.0],
|
|
[11.0, 1.0, 10.0, 1.0, 12.0]
|
|
]
|
|
);
|
|
|
|
let hs = init.scatter(&ids, &t, 1)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0, 1.0, 1.0],
|
|
[5.0, 1.0, 1.0, 3.0, 4.0],
|
|
[1.0, 8.0, 1.0, 7.0, 1.0],
|
|
[10.0, 1.0, 9.0, 1.0, 11.0]
|
|
]
|
|
);
|
|
|
|
let init = Tensor::ones((6, 3), DType::F32, device)?;
|
|
let hs = init.scatter_add(&ids, &t, 0)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[
|
|
[1.0, 11.0, 6.0],
|
|
[1.0, 2.0, 9.0],
|
|
[10.0, 1.0, 3.0],
|
|
[10.0, 8.0, 1.0],
|
|
[1.0, 5.0, 12.0],
|
|
[1.0, 1.0, 1.0]
|
|
]
|
|
);
|
|
let hs = init.scatter(&ids, &t, 0)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 10.0, 5.0],
|
|
[1.0, 1.0, 8.0],
|
|
[9.0, 1.0, 2.0],
|
|
[6.0, 7.0, 1.0],
|
|
[1.0, 4.0, 11.0],
|
|
[1.0, 1.0, 1.0]
|
|
]
|
|
);
|
|
|
|
let hs = {
|
|
let ids = Tensor::new(
|
|
&[
|
|
[0u32, u32::MAX, 2],
|
|
[3, 4, u32::MAX],
|
|
[3, 3, 1],
|
|
[u32::MAX, u32::MAX, 4],
|
|
],
|
|
device,
|
|
)?;
|
|
init.scatter(&ids, &t, 0)?
|
|
};
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 1.0],
|
|
[1.0, 1.0, 8.0],
|
|
[1.0, 1.0, 2.0],
|
|
[6.0, 7.0, 1.0],
|
|
[1.0, 4.0, 11.0],
|
|
[1.0, 1.0, 1.0]
|
|
]
|
|
);
|
|
|
|
init.scatter_set(&ids, &t, 0)?;
|
|
assert_eq!(
|
|
init.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 10.0, 5.0],
|
|
[1.0, 1.0, 8.0],
|
|
[9.0, 1.0, 2.0],
|
|
[6.0, 7.0, 1.0],
|
|
[1.0, 4.0, 11.0],
|
|
[1.0, 1.0, 1.0]
|
|
]
|
|
);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
fn gather(device: &Device) -> Result<()> {
|
|
let ids = Tensor::new(&[[0u32], [2u32], [1u32], [0u32]], device)?;
|
|
let t = Tensor::arange(0f32, 12f32, device)?.reshape((4, 3))?;
|
|
assert_eq!(
|
|
t.to_vec2::<f32>()?,
|
|
&[
|
|
[0.0, 1.0, 2.0],
|
|
[3.0, 4.0, 5.0],
|
|
[6.0, 7.0, 8.0],
|
|
[9.0, 10.0, 11.0]
|
|
]
|
|
);
|
|
let hs = t.gather(&ids, 1)?;
|
|
assert_eq!(hs.to_vec2::<f32>()?, &[[0.0], [5.0], [7.0], [9.0]]);
|
|
let ids = Tensor::new(
|
|
&[[0u32, 0u32], [2u32, 0u32], [1u32, 1u32], [0u32, 2u32]],
|
|
device,
|
|
)?;
|
|
let hs = t.gather(&ids, 1)?;
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[[0.0, 0.0], [5.0, 3.0], [7.0, 7.0], [9.0, 11.0]]
|
|
);
|
|
let ids = Tensor::new(&[[0u32, 2u32, 0u32]], device)?;
|
|
let hs = t.gather(&ids, 0)?;
|
|
assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 7.0, 2.0]]);
|
|
let ids = Tensor::new(&[[0u32, 2u32, 0u32], [0u32, 1u32, 1u32]], device)?;
|
|
let hs = t.gather(&ids, 0)?;
|
|
assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 7.0, 2.0], [0.0, 4.0, 5.0]]);
|
|
|
|
let hs = {
|
|
let ids = Tensor::new(
|
|
&[
|
|
[0u32, 0u32],
|
|
[2u32, u32::MAX],
|
|
[u32::MAX, 1u32],
|
|
[0u32, 2u32],
|
|
],
|
|
device,
|
|
)?;
|
|
t.gather(&ids, 1)?
|
|
};
|
|
assert_eq!(
|
|
hs.to_vec2::<f32>()?,
|
|
&[[0.0, 0.0], [5.0, 0.0], [0.0, 7.0], [9.0, 11.0]]
|
|
);
|
|
|
|
// Random data
|
|
|
|
// Dim: 0
|
|
let t = Tensor::new(
|
|
&[
|
|
[
|
|
[108_f32, -47., 16., -56., -83., -130., 210.],
|
|
[253., 95., 151., 228., -210., -123., -127.],
|
|
[-9., -217., 2., -78., 163., 245., -204.],
|
|
[-246., 79., -238., 88., -226., -184., 171.],
|
|
[8., -48., -153., 234., -34., 166., -153.],
|
|
[124., 0., -10., -61., -242., -15., -238.],
|
|
],
|
|
[
|
|
[12., -64., -199., 244., -240., 156., -128.],
|
|
[173., -57., 4., -198., 233., -110., 238.],
|
|
[95., 82., 0., 240., 53., -211., 209.],
|
|
[-122., 167., -212., 227., -144., 61., 118.],
|
|
[-63., -146., 200., 244., 168., -167., 116.],
|
|
[-125., -147., 110., -253., -178., -250., -18.],
|
|
],
|
|
[
|
|
[57., 86., -50., 56., 92., 205., -78.],
|
|
[-137., -156., -18., 248., -61., -239., 14.],
|
|
[-248., -30., -50., -70., -251., 250., -83.],
|
|
[-221., 67., 72., 59., -24., -154., 232.],
|
|
[-144., -23., -74., 5., 93., 171., 205.],
|
|
[46., -77., -38., -226., 246., 161., -17.],
|
|
],
|
|
[
|
|
[-153., -231., -236., 161., 126., 2., -22.],
|
|
[-229., -41., 209., 164., 234., 160., 57.],
|
|
[223., 254., -186., -162., -46., -160., -102.],
|
|
[65., 30., 213., -253., 59., 224., -154.],
|
|
[-82., -203., -177., 17., 31., -256., -246.],
|
|
[176., -135., -65., 54., -56., 210., 76.],
|
|
],
|
|
[
|
|
[-10., -245., 168., 124., -14., -33., -178.],
|
|
[25., -43., -39., 132., -89., 169., 179.],
|
|
[187., -215., 32., -133., 87., -7., -168.],
|
|
[-224., -215., -5., -230., -58., -162., 128.],
|
|
[158., -137., -122., -100., -202., -83., 136.],
|
|
[30., -185., -144., 250., 209., -40., 127.],
|
|
],
|
|
[
|
|
[-196., 108., -245., 122., 146., -228., 62.],
|
|
[-1., -66., 160., 137., 13., -172., -21.],
|
|
[244., 199., -164., 28., 119., -175., 198.],
|
|
[-62., 253., -162., 195., -95., -230., -211.],
|
|
[123., -72., -26., -107., -139., 64., 245.],
|
|
[11., -126., -182., 108., -12., 184., -127.],
|
|
],
|
|
[
|
|
[-159., 126., 176., 161., 73., -111., -138.],
|
|
[-187., 214., -217., -33., -223., -201., -212.],
|
|
[-61., -120., -166., -172., -95., 53., 196.],
|
|
[-33., 86., 134., -152., 154., -53., 74.],
|
|
[186., -28., -154., -174., 141., -109., 217.],
|
|
[82., 35., 252., 145., 181., 74., -87.],
|
|
],
|
|
],
|
|
device,
|
|
)?;
|
|
|
|
let ids = Tensor::new(
|
|
&[
|
|
[
|
|
[6_u32, 6, 4, 3, 4, 4, 6],
|
|
[3, 3, 2, 4, 4, 4, 6],
|
|
[3, 3, 0, 2, 4, 6, 4],
|
|
[2, 5, 1, 2, 6, 6, 1],
|
|
[2, 1, 6, 5, 3, 2, 3],
|
|
[6, 1, 0, 1, 0, 2, 6],
|
|
],
|
|
[
|
|
[4, 6, 4, 3, 3, 3, 2],
|
|
[4, 3, 2, 4, 4, 4, 6],
|
|
[2, 3, 0, 2, 4, 6, 4],
|
|
[6, 5, 1, 2, 6, 6, 1],
|
|
[4, 1, 6, 5, 3, 2, 3],
|
|
[1, 1, 0, 1, 0, 2, 6],
|
|
],
|
|
[
|
|
[3, 6, 4, 3, 3, 3, 2],
|
|
[2, 3, 2, 4, 4, 4, 6],
|
|
[4, 3, 0, 2, 4, 6, 4],
|
|
[0, 5, 1, 2, 6, 6, 1],
|
|
[6, 1, 6, 5, 3, 2, 3],
|
|
[4, 1, 0, 1, 0, 2, 6],
|
|
],
|
|
[
|
|
[0, 6, 4, 3, 3, 3, 2],
|
|
[5, 3, 2, 4, 4, 4, 6],
|
|
[0, 3, 0, 2, 4, 6, 4],
|
|
[3, 5, 1, 2, 6, 6, 1],
|
|
[0, 1, 6, 5, 3, 2, 3],
|
|
[3, 1, 0, 1, 0, 2, 6],
|
|
],
|
|
],
|
|
device,
|
|
)?;
|
|
|
|
let hs = t.gather(&ids, 0)?;
|
|
assert_eq!(
|
|
hs.to_vec3::<f32>()?,
|
|
&[
|
|
[
|
|
[-159_f32, 126., 168., 161., -14., -33., -138.],
|
|
[-229., -41., -18., 132., -89., 169., -212.],
|
|
[223., 254., 2., -70., 87., 53., -168.],
|
|
[-221., 253., -212., 59., 154., -53., 118.],
|
|
[-144., -146., -154., -107., 31., 171., -246.],
|
|
[82., -147., -10., -253., -242., 161., -87.]
|
|
],
|
|
[
|
|
[-10., 126., 168., 161., 126., 2., -78.],
|
|
[25., -41., -18., 132., -89., 169., -212.],
|
|
[-248., 254., 2., -70., 87., 53., -168.],
|
|
[-33., 253., -212., 59., 154., -53., 118.],
|
|
[158., -146., -154., -107., 31., 171., -246.],
|
|
[-125., -147., -10., -253., -242., 161., -87.]
|
|
],
|
|
[
|
|
[-153., 126., 168., 161., 126., 2., -78.],
|
|
[-137., -41., -18., 132., -89., 169., -212.],
|
|
[187., 254., 2., -70., 87., 53., -168.],
|
|
[-246., 253., -212., 59., 154., -53., 118.],
|
|
[186., -146., -154., -107., 31., 171., -246.],
|
|
[30., -147., -10., -253., -242., 161., -87.]
|
|
],
|
|
[
|
|
[108., 126., 168., 161., 126., 2., -78.],
|
|
[-1., -41., -18., 132., -89., 169., -212.],
|
|
[-9., 254., 2., -70., 87., 53., -168.],
|
|
[65., 253., -212., 59., 154., -53., 118.],
|
|
[8., -146., -154., -107., 31., 171., -246.],
|
|
[176., -147., -10., -253., -242., 161., -87.]
|
|
]
|
|
]
|
|
);
|
|
|
|
// Dim: 1
|
|
let t = Tensor::new(
|
|
&[
|
|
[
|
|
[-117_f32, -175., 69., -163.],
|
|
[200., 242., -21., -67.],
|
|
[179., 150., -126., -75.],
|
|
[-118., 38., -138., -13.],
|
|
[-221., 136., -185., 180.],
|
|
[58., 182., -204., -149.],
|
|
],
|
|
[
|
|
[3., -148., -58., -154.],
|
|
[-43., 45., -108., 4.],
|
|
[-69., -249., -71., -21.],
|
|
[80., 110., -152., -235.],
|
|
[-88., 7., 92., -250.],
|
|
[-186., 207., -242., 98.],
|
|
],
|
|
[
|
|
[238., 19., 64., -242.],
|
|
[-150., -97., 218., 58.],
|
|
[111., -233., 204., -212.],
|
|
[-242., -232., 83., 42.],
|
|
[153., 62., -251., 219.],
|
|
[-117., 36., -119., 10.],
|
|
],
|
|
[
|
|
[215., 159., -169., -27.],
|
|
[-83., 101., -88., 169.],
|
|
[-205., 93., 225., -64.],
|
|
[-162., 240., 214., 23.],
|
|
[-112., 6., 21., 245.],
|
|
[-38., 113., 93., 215.],
|
|
],
|
|
[
|
|
[91., -188., -148., 101.],
|
|
[74., 203., -35., 55.],
|
|
[-116., -130., -153., -96.],
|
|
[58., 22., -45., -194.],
|
|
[-221., -134., 73., 159.],
|
|
[-203., -254., 31., 235.],
|
|
],
|
|
[
|
|
[105., -53., 61., 186.],
|
|
[-195., 234., 75., -1.],
|
|
[51., 139., 160., -108.],
|
|
[-173., -167., 161., 19.],
|
|
[83., -246., 156., -222.],
|
|
[109., 39., -149., 137.],
|
|
],
|
|
],
|
|
device,
|
|
)?;
|
|
|
|
let ids = Tensor::new(
|
|
&[
|
|
[[4_u32, 4, 4, 2]],
|
|
[[0, 4, 4, 3]],
|
|
[[1, 5, 3, 4]],
|
|
[[0, 3, 3, 2]],
|
|
[[1, 1, 5, 2]],
|
|
[[1, 4, 5, 4]],
|
|
],
|
|
device,
|
|
)?;
|
|
|
|
let hs = t.gather(&ids, 1)?;
|
|
assert_eq!(
|
|
hs.to_vec3::<f32>()?,
|
|
&[
|
|
[[-221., 136., -185., -75.]],
|
|
[[3., 7., 92., -235.]],
|
|
[[-150., 36., 83., 219.]],
|
|
[[215., 240., 214., -64.]],
|
|
[[74., 203., 31., -96.]],
|
|
[[-195., -246., -149., -222.]]
|
|
]
|
|
);
|
|
|
|
// Dim: 2
|
|
let t = Tensor::new(
|
|
&[
|
|
[[-162_f32, 202.], [-126., -39.], [35., -65.], [1., 80.]],
|
|
[[37., 248.], [-191., 89.], [117., -40.], [-217., 220.]],
|
|
],
|
|
device,
|
|
)?;
|
|
|
|
let ids = Tensor::new(&[[[1_u32], [0], [1], [1]], [[0], [1], [0], [1]]], device)?;
|
|
|
|
let hs = t.gather(&ids, 2)?;
|
|
assert_eq!(
|
|
hs.to_vec3::<f32>()?,
|
|
&[
|
|
[[202.], [-126.], [-65.], [80.]],
|
|
[[37.], [89.], [117.], [220.]]
|
|
]
|
|
);
|
|
|
|
let t = Tensor::new(
|
|
&[
|
|
[[-21_f32, -197.], [194., 122.]],
|
|
[[255., -106.], [-191., 250.]],
|
|
[[33., -117.], [43., 10.]],
|
|
[[-130., 238.], [-217., -92.]],
|
|
],
|
|
device,
|
|
)?;
|
|
|
|
let ids = Tensor::new(
|
|
&[
|
|
[[0_u32, 1], [1, 0]],
|
|
[[1, 0], [0, 1]],
|
|
[[0, 1], [0, 1]],
|
|
[[1, 0], [1, 0]],
|
|
],
|
|
device,
|
|
)?;
|
|
|
|
let hs = t.gather(&ids, 2)?;
|
|
assert_eq!(
|
|
hs.to_vec3::<f32>()?,
|
|
&[
|
|
[[-21., -197.], [122., 194.]],
|
|
[[-106., 255.], [-191., 250.]],
|
|
[[33., -117.], [43., 10.]],
|
|
[[238., -130.], [-92., -217.]]
|
|
]
|
|
);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
fn broadcasting(device: &Device) -> Result<()> {
|
|
let t1 = Tensor::arange(0f32, 24f32, device)?.reshape((4, 2, 3))?;
|
|
let t2 = Tensor::new(&[100f32, 200f32], device)?;
|
|
let s = t1.broadcast_add(&t2.reshape((2, 1))?)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[100.0, 101.0, 102.0], [203.0, 204.0, 205.0]],
|
|
[[106.0, 107.0, 108.0], [209.0, 210.0, 211.0]],
|
|
[[112.0, 113.0, 114.0], [215.0, 216.0, 217.0]],
|
|
[[118.0, 119.0, 120.0], [221.0, 222.0, 223.0]]
|
|
]
|
|
);
|
|
let s = t1.t()?.broadcast_add(&t2)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[100.0, 203.0], [101.0, 204.0], [102.0, 205.0]],
|
|
[[106.0, 209.0], [107.0, 210.0], [108.0, 211.0]],
|
|
[[112.0, 215.0], [113.0, 216.0], [114.0, 217.0]],
|
|
[[118.0, 221.0], [119.0, 222.0], [120.0, 223.0]]
|
|
]
|
|
);
|
|
let s = t1.broadcast_sub(&t2.reshape((2, 1))?)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[-100.0, -99.0, -98.0], [-197.0, -196.0, -195.0]],
|
|
[[-94.0, -93.0, -92.0], [-191.0, -190.0, -189.0]],
|
|
[[-88.0, -87.0, -86.0], [-185.0, -184.0, -183.0]],
|
|
[[-82.0, -81.0, -80.0], [-179.0, -178.0, -177.0]]
|
|
]
|
|
);
|
|
let s = t1.t()?.broadcast_sub(&t2)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[-100.0, -197.0], [-99.0, -196.0], [-98.0, -195.0]],
|
|
[[-94.0, -191.0], [-93.0, -190.0], [-92.0, -189.0]],
|
|
[[-88.0, -185.0], [-87.0, -184.0], [-86.0, -183.0]],
|
|
[[-82.0, -179.0], [-81.0, -178.0], [-80.0, -177.0]]
|
|
]
|
|
);
|
|
// Test a narrowed version as this uses a layout start_offset.
|
|
let t1 = t1.i(2..)?;
|
|
let s = t1.broadcast_add(&t2.reshape((2, 1))?)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[112.0, 113.0, 114.0], [215.0, 216.0, 217.0]],
|
|
[[118.0, 119.0, 120.0], [221.0, 222.0, 223.0]]
|
|
]
|
|
);
|
|
let s = t1.t()?.broadcast_add(&t2)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[112.0, 215.0], [113.0, 216.0], [114.0, 217.0]],
|
|
[[118.0, 221.0], [119.0, 222.0], [120.0, 223.0]]
|
|
]
|
|
);
|
|
let s = t1.broadcast_sub(&t2.reshape((2, 1))?)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[-88.0, -87.0, -86.0], [-185.0, -184.0, -183.0]],
|
|
[[-82.0, -81.0, -80.0], [-179.0, -178.0, -177.0]]
|
|
]
|
|
);
|
|
let s = t1.t()?.broadcast_sub(&t2)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[-88.0, -185.0], [-87.0, -184.0], [-86.0, -183.0]],
|
|
[[-82.0, -179.0], [-81.0, -178.0], [-80.0, -177.0]]
|
|
]
|
|
);
|
|
let t3 = Tensor::new(1f32, device)?.broadcast_div(&t2)?;
|
|
let s = t1.broadcast_mul(&t2.reshape((2, 1))?)?;
|
|
let s_div = t1.broadcast_div(&t3.reshape((2, 1))?)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[1200.0, 1300.0, 1400.0], [3000.0, 3200.0, 3400.0]],
|
|
[[1800.0, 1900.0, 2000.0], [4200.0, 4400.0, 4600.0]]
|
|
]
|
|
);
|
|
assert_eq!(s.to_vec3::<f32>()?, s_div.to_vec3::<f32>()?,);
|
|
let s = t1.t()?.broadcast_mul(&t2)?;
|
|
let s_div = t1.t()?.broadcast_div(&t3)?;
|
|
assert_eq!(
|
|
s.to_vec3::<f32>()?,
|
|
&[
|
|
[[1200.0, 3000.0], [1300.0, 3200.0], [1400.0, 3400.0]],
|
|
[[1800.0, 4200.0], [1900.0, 4400.0], [2000.0, 4600.0]]
|
|
]
|
|
);
|
|
assert_eq!(s.to_vec3::<f32>()?, s_div.to_vec3::<f32>()?,);
|
|
Ok(())
|
|
}
|
|
|
|
fn randn(device: &Device) -> Result<()> {
|
|
let tensor = Tensor::randn(0f32, 1f32, (5, 3), device)?;
|
|
assert_eq!(tensor.dims(), [5, 3]);
|
|
// Check that the seed gets updated by checking that
|
|
// a new series of numbers is generated each time
|
|
let tensor2 = Tensor::randn(0f32, 1f32, (5, 3), device)?;
|
|
assert_ne!(tensor.to_vec2::<f32>()?, tensor2.to_vec2::<f32>()?);
|
|
let tensor = Tensor::rand(0f32, 1f32, (5, 3), device)?;
|
|
assert_eq!(tensor.dims(), [5, 3]);
|
|
// Check that the seed gets updated by checking that
|
|
// a new series of numbers is generated each time
|
|
let tensor2 = Tensor::rand(0f32, 1f32, (5, 3), device)?;
|
|
assert_ne!(tensor.to_vec2::<f32>()?, tensor2.to_vec2::<f32>()?);
|
|
// We do not expect deterministic elements at any index.
|
|
// There once was a bug that had a deterministic zero element in evenly sized tensors.
|
|
const N: usize = 2;
|
|
let v = (0..100)
|
|
.map(|_| Tensor::randn(0f32, 1f32, N, device).and_then(|t| t.to_vec1::<f32>()))
|
|
.collect::<Result<Vec<_>>>()?;
|
|
assert!(
|
|
(0..N).all(|i| v.windows(2).any(|pair| pair[0][i] != pair[1][i])),
|
|
"There are deterministic values in the randn tensors"
|
|
);
|
|
let v = (0..100)
|
|
.map(|_| Tensor::rand(0f32, 1f32, N, device).and_then(|t| t.to_vec1::<f32>()))
|
|
.collect::<Result<Vec<_>>>()?;
|
|
assert!(
|
|
(0..N).all(|i| v.windows(2).any(|pair| pair[0][i] != pair[1][i])),
|
|
"There are deterministic values in the rand tensors"
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
fn zero_dim(device: &Device) -> Result<()> {
|
|
let t = Tensor::zeros((4, 0, 1), DType::F32, device)?;
|
|
assert_eq!(t.dims3()?, (4, 0, 1));
|
|
let t2 = Tensor::zeros((4, 3, 1), DType::F32, device)?;
|
|
let t_cat = Tensor::cat(&[&t, &t2], 1)?;
|
|
assert_eq!(t_cat.dims3()?, (4, 3, 1));
|
|
let t_cat = Tensor::cat(&[&t, &t], 1)?;
|
|
assert_eq!(t_cat.dims3()?, (4, 0, 1));
|
|
let t_unary = t.sqrt()?;
|
|
assert_eq!(t_unary.dims3()?, (4, 0, 1));
|
|
let t_plus = (&t + 1.)?;
|
|
assert_eq!(t_plus.dims3()?, (4, 0, 1));
|
|
let t_mm = t2.matmul(&t.t()?)?;
|
|
assert_eq!(t_mm.dims3()?, (4, 3, 0));
|
|
let t_mm = t.matmul(&t2.t()?)?;
|
|
assert_eq!(t_mm.dims3()?, (4, 0, 3));
|
|
let t_mm = t.t()?.matmul(&t)?;
|
|
assert_eq!(t_mm.dims3()?, (4, 1, 1));
|
|
Ok(())
|
|
}
|
|
|
|
test_device!(zeros, zeros_cpu, zeros_gpu, zeros_metal);
|
|
test_device!(ones, ones_cpu, ones_gpu, ones_metal);
|
|
test_device!(full, full_cpu, full_gpu, full_metal);
|
|
test_device!(const_set, cs_cpu, cs_gpu, cs_metal);
|
|
test_device!(arange, arange_cpu, arange_gpu, arange_metal);
|
|
test_device!(add_mul, add_mul_cpu, add_mul_gpu, add_mul_metal);
|
|
test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu, tensor_2d_metal);
|
|
test_device!(narrow, narrow_cpu, narrow_gpu, narrow_metal);
|
|
test_device!(broadcast, broadcast_cpu, broadcast_gpu, broadcast_metal);
|
|
test_device!(slice_set, ss_cpu, ss_gpu, ss_metal);
|
|
test_device!(cat, cat_cpu, cat_gpu, cat_metal);
|
|
test_device!(sum, sum_cpu, sum_gpu, sum_metal);
|
|
test_device!(min, min_cpu, min_gpu, min_metal);
|
|
test_device!(max, max_cpu, max_gpu, max_metal);
|
|
test_device!(argmax, argmax_cpu, argmax_gpu, argmax_metal);
|
|
test_device!(argmin, argmin_cpu, argmin_gpu, argmin_metal);
|
|
test_device!(transpose, transpose_cpu, transpose_gpu, transpose_metal);
|
|
test_device!(unary_op, unary_op_cpu, unary_op_gpu, unary_op_metal);
|
|
test_device!(binary_op, binary_op_cpu, binary_op_gpu, binary_op_metal);
|
|
test_device!(embeddings, embeddings_cpu, embeddings_gpu, embeddings_metal);
|
|
test_device!(cmp, cmp_cpu, cmp_gpu, cmp_metal);
|
|
test_device!(
|
|
broadcasting,
|
|
broadcasting_cpu,
|
|
broadcasting_gpu,
|
|
broadcasting_metal
|
|
);
|
|
test_device!(
|
|
index_select,
|
|
index_select_cpu,
|
|
index_select_gpu,
|
|
index_select_metal
|
|
);
|
|
test_device!(index_add, index_add_cpu, index_add_gpu, index_add_metal);
|
|
test_device!(gather, gather_cpu, gather_gpu, gather_metal);
|
|
test_device!(scatter, scatter_cpu, scatter_gpu, scatter_metal);
|
|
test_device!(
|
|
slice_scatter,
|
|
slice_scatter_cpu,
|
|
slice_scatter_gpu,
|
|
slice_scatter_metal
|
|
);
|
|
test_device!(randn, randn_cpu, randn_gpu, randn_metal);
|
|
test_device!(clamp, clamp_cpu, clamp_gpu, clamp_metal);
|
|
test_device!(asort, asort_cpu, asort_gpu, asort_metal);
|
|
test_device!(var, var_cpu, var_gpu, var_metal);
|
|
test_device!(zero_dim, zero_dim_cpu, zero_dim_gpu, zero_dim_metal);
|
|
|
|
// There was originally a bug on the CPU implementation for randn
|
|
// https://github.com/huggingface/candle/issues/381
|
|
#[test]
|
|
fn randn_hasneg() -> Result<()> {
|
|
let t = Tensor::randn(0f32, 1f32, 200, &Device::Cpu)?.to_vec1::<f32>()?;
|
|
if t.iter().all(|&v| v >= 0.) {
|
|
candle_core::bail!("all values in tensors are non-negative")
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn pad_with_same() -> Result<()> {
|
|
let t = Tensor::arange(1f32, 5f32, &Device::Cpu)?.reshape((2, 2))?;
|
|
let t0 = t.pad_with_same(0, 1, 2)?;
|
|
assert_eq!(
|
|
t0.to_vec2::<f32>()?,
|
|
[[1.0, 2.0], [1.0, 2.0], [3.0, 4.0], [3.0, 4.0], [3.0, 4.0]]
|
|
);
|
|
let t1 = t.pad_with_same(1, 1, 2)?;
|
|
assert_eq!(
|
|
t1.to_vec2::<f32>()?,
|
|
[[1.0, 1.0, 2.0, 2.0, 2.0], [3.0, 3.0, 4.0, 4.0, 4.0]]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn i64_abs() -> Result<()> {
|
|
let t = Tensor::new(&[-42i64, 1337], &Device::Cpu)?;
|
|
let t = t.abs()?;
|
|
assert_eq!(t.to_vec1::<i64>()?, [42, 1337]);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn tril_triu_eye() -> Result<()> {
|
|
let t = Tensor::tril2(4, DType::F32, &Device::Cpu)?;
|
|
assert_eq!(
|
|
t.to_vec2::<f32>()?,
|
|
[
|
|
[1.0, 0.0, 0.0, 0.0],
|
|
[1.0, 1.0, 0.0, 0.0],
|
|
[1.0, 1.0, 1.0, 0.0],
|
|
[1.0, 1.0, 1.0, 1.0]
|
|
],
|
|
);
|
|
let t = Tensor::triu2(4, DType::F32, &Device::Cpu)?;
|
|
assert_eq!(
|
|
t.to_vec2::<f32>()?,
|
|
[
|
|
[1.0, 1.0, 1.0, 1.0],
|
|
[0.0, 1.0, 1.0, 1.0],
|
|
[0.0, 0.0, 1.0, 1.0],
|
|
[0.0, 0.0, 0.0, 1.0]
|
|
]
|
|
);
|
|
let t = Tensor::eye(4, DType::F32, &Device::Cpu)?;
|
|
assert_eq!(
|
|
t.to_vec2::<f32>()?,
|
|
[
|
|
[1.0, 0.0, 0.0, 0.0],
|
|
[0.0, 1.0, 0.0, 0.0],
|
|
[0.0, 0.0, 1.0, 0.0],
|
|
[0.0, 0.0, 0.0, 1.0]
|
|
]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn cumsum() -> Result<()> {
|
|
let t = &[3f32, 1., 4., 1., 5.];
|
|
let t = Tensor::new(t, &Device::Cpu)?;
|
|
assert_eq!(t.cumsum(0)?.to_vec1::<f32>()?, [3., 4., 8., 9., 14.]);
|
|
let t = t.unsqueeze(1)?;
|
|
assert_eq!(
|
|
t.cumsum(0)?.to_vec2::<f32>()?,
|
|
[[3.0], [4.0], [8.0], [9.0], [14.0]]
|
|
);
|
|
assert_eq!(
|
|
t.cumsum(1)?.to_vec2::<f32>()?,
|
|
[[3.0], [1.0], [4.0], [1.0], [5.0]]
|
|
);
|
|
let t = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
|
|
let t = Tensor::new(t, &Device::Cpu)?;
|
|
assert_eq!(
|
|
t.cumsum(1)?.to_vec2::<f32>()?,
|
|
[[3.0, 4.0, 8.0, 9.0, 14.0], [2.0, 3.0, 10.0, 18.0, 20.0]],
|
|
);
|
|
assert_eq!(
|
|
t.cumsum(0)?.to_vec2::<f32>()?,
|
|
[[3.0, 1.0, 4.0, 1.0, 5.0], [5.0, 2.0, 11.0, 9.0, 7.0]]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
/// A helper function for floating point comparison. Both a and b must be 1D Tensor and contains the same amount of data.
|
|
/// Assertion passes if the difference of all pairs of a and b is smaller than epsilon.
|
|
fn assert_close(a: &Tensor, b: &Tensor, epsilon: f64) -> Result<()> {
|
|
let a_vec: Vec<f64> = a.to_vec1()?;
|
|
let b_vec: Vec<f64> = b.to_vec1()?;
|
|
|
|
assert_eq!(a_vec.len(), b_vec.len());
|
|
for (a, b) in a_vec.iter().zip(b_vec.iter()) {
|
|
assert!((a - b).abs() < epsilon);
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn log_sum_exp() -> Result<()> {
|
|
let input = Tensor::new(
|
|
&[
|
|
[[1f64, 2., 3.], [4., 5., 6.]],
|
|
[[-1000.0, -999.0, -1001.0], [1000.0, 999.0, 1001.0]],
|
|
],
|
|
&Device::Cpu,
|
|
)?;
|
|
|
|
let output = input.log_sum_exp(D::Minus1)?;
|
|
// The expectations obtained from pytorch.
|
|
let expected = Tensor::new(&[[3.4076, 6.4076], [-998.5924, 1001.4076]], &Device::Cpu)?;
|
|
assert_eq!(output.dims(), expected.dims());
|
|
assert_close(&output.flatten_all()?, &expected.flatten_all()?, 0.00001)?;
|
|
|
|
assert_eq!(
|
|
input.log_sum_exp((0, 1))?.to_vec1::<f64>()?,
|
|
[1000.0, 999.0, 1001.0]
|
|
);
|
|
assert_eq!(
|
|
input.log_sum_exp(())?.to_vec3::<f64>()?,
|
|
input.to_vec3::<f64>()?
|
|
);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn pow() -> Result<()> {
|
|
let lhs = Tensor::new(&[[1f32, 2., 3.], [4., 5., 6.]], &Device::Cpu)?;
|
|
let rhs = (&lhs - 2.)?;
|
|
let res = lhs.pow(&rhs)?;
|
|
assert_eq!(
|
|
test_utils::to_vec2_round(&res, 3)?,
|
|
[[1.0, 1.0, 3.0], [16.0, 125.0, 1296.0]]
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_flip_1d() -> Result<()> {
|
|
// 1D: [0, 1, 2, 3, 4]
|
|
let t = Tensor::arange(0.0, 5.0, &Device::Cpu)?.reshape((5,))?;
|
|
let flipped = t.flip(&[0])?;
|
|
// Expected: [4, 3, 2, 1, 0]
|
|
let expected = Tensor::from_vec(vec![4.0, 3.0, 2.0, 1.0, 0.0], (5,), &Device::Cpu)?;
|
|
candle_core::test_utils::assert_tensor_eq(&flipped, &expected)?;
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_flip_2d() -> Result<()> {
|
|
// 2D:
|
|
// [[0, 1, 2],
|
|
// [3, 4, 5]]
|
|
let t = Tensor::arange(0.0, 6.0, &Device::Cpu)?.reshape((2, 3))?;
|
|
let flipped = t.flip(&[0, 1])?;
|
|
// Expected:
|
|
// [[5, 4, 3],
|
|
// [2, 1, 0]]
|
|
let expected = Tensor::from_vec(vec![5.0, 4.0, 3.0, 2.0, 1.0, 0.0], (2, 3), &Device::Cpu)?;
|
|
candle_core::test_utils::assert_tensor_eq(&flipped, &expected)?;
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_flip_3d_channels() -> Result<()> {
|
|
// 3D:
|
|
// [[[0,1,2],
|
|
// [3,4,5]],
|
|
//
|
|
// [[6,7,8],
|
|
// [9,10,11]]]
|
|
let t = Tensor::arange(0.0, 12.0, &Device::Cpu)?.reshape((2, 2, 3))?;
|
|
let flipped = t.flip(&[2])?;
|
|
// Expected:
|
|
// [[[2,1,0],
|
|
// [5,4,3]],
|
|
//
|
|
// [[8,7,6],
|
|
// [11,10,9]]]
|
|
let expected = Tensor::from_vec(
|
|
vec![2.0, 1.0, 0.0, 5.0, 4.0, 3.0, 8.0, 7.0, 6.0, 11.0, 10.0, 9.0],
|
|
(2, 2, 3),
|
|
&Device::Cpu,
|
|
)?;
|
|
candle_core::test_utils::assert_tensor_eq(&flipped, &expected)?;
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn tensor_new() -> Result<()> {
|
|
let t1 = Tensor::new(vec![1f32, 2.0, 3.0], &Device::Cpu)?;
|
|
assert_eq!(t1.to_vec1::<f32>()?, [1.0, 2.0, 3.0]);
|
|
let t2 = Tensor::new(vec![vec![1f32, 2., 3.], vec![4., 5., 6.]], &Device::Cpu)?;
|
|
assert_eq!(t2.to_vec2::<f32>()?, [[1., 2., 3.], [4., 5., 6.]]);
|
|
let t3 = Tensor::new(
|
|
vec![
|
|
vec![vec![1f32, 2., 3.], vec![4., 5., 6.]],
|
|
vec![vec![3f32, 1., 4.], vec![1., 5., 9.]],
|
|
],
|
|
&Device::Cpu,
|
|
)?;
|
|
assert_eq!(
|
|
t3.to_vec3::<f32>()?,
|
|
[
|
|
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
|
|
[[3.0, 1.0, 4.0], [1.0, 5.0, 9.0]]
|
|
]
|
|
);
|
|
Ok(())
|
|
}
|