mirror of
https://github.com/huggingface/candle.git
synced 2025-06-15 18:28:24 +00:00
8f7973958c
* fix: fix index_select cuda kernel for src target dim different than ids dim when selecting dim > 0 * cargo fmt
1067 lines
34 KiB
Rust
1067 lines
34 KiB
Rust
use candle_core::{test_device, test_utils, DType, Device, IndexOp, Result, Tensor};
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fn zeros(device: &Device) -> Result<()> {
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let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
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let (dim1, dim2) = tensor.dims2()?;
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assert_eq!(dim1, 5);
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assert_eq!(dim2, 2);
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Ok(())
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}
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fn ones(device: &Device) -> Result<()> {
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assert_eq!(
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Tensor::ones((2, 3), DType::U8, device)?.to_vec2::<u8>()?,
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[[1, 1, 1], [1, 1, 1]],
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);
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assert_eq!(
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Tensor::ones((2, 3), DType::U32, device)?.to_vec2::<u32>()?,
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[[1, 1, 1], [1, 1, 1]],
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);
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assert_eq!(
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Tensor::ones((2, 3), DType::I64, device)?.to_vec2::<i64>()?,
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[[1, 1, 1], [1, 1, 1]],
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);
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assert_eq!(
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Tensor::ones((2, 3), DType::F32, device)?.to_vec2::<f32>()?,
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[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
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);
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assert_eq!(
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Tensor::ones((2, 3), DType::F64, device)?.to_vec2::<f64>()?,
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[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
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);
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Ok(())
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}
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fn add_mul(device: &Device) -> Result<()> {
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let tensor = Tensor::new(&[3f32, 1., 4.], device)?;
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let dim1 = tensor.dims1()?;
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assert_eq!(dim1, 3);
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let content: Vec<f32> = tensor.to_vec1()?;
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assert_eq!(content, [3., 1., 4.]);
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let tensor = Tensor::add(&tensor, &tensor)?;
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let content: Vec<f32> = tensor.to_vec1()?;
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assert_eq!(content, [6., 2., 8.]);
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let tensor = Tensor::mul(&tensor, &tensor)?;
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let content: Vec<f32> = tensor.to_vec1()?;
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assert_eq!(content, [36., 4., 64.]);
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Ok(())
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}
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fn tensor_2d(device: &Device) -> Result<()> {
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let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
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let tensor = Tensor::new(data, device)?;
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let dims = tensor.dims2()?;
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assert_eq!(dims, (2, 5));
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let content: Vec<Vec<f32>> = tensor.to_vec2()?;
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assert_eq!(content, data);
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Ok(())
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}
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fn clamp(device: &Device) -> Result<()> {
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let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
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let tensor = Tensor::new(data, device)?;
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let tensor = tensor.clamp(1.5, 6.2)?;
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assert_eq!(
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tensor.to_vec2::<f32>()?,
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[[3.0, 1.5, 4.0, 1.5, 5.0], [2.0, 1.5, 6.2, 6.2, 2.0]],
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);
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Ok(())
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}
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fn unary_op(device: &Device) -> Result<()> {
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let data = &[[-3f32, 1., 4., -0.1, 0.5], [2.7, -1.8, -0.28, 1.8, 2.8]];
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let tensor = Tensor::new(data, device)?;
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assert_eq!(
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test_utils::to_vec2_round(&tensor.gelu()?, 4)?,
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[
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[-0.0036, 0.8412, 3.9999, -0.046, 0.3457],
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[2.6911, -0.0647, -0.1091, 1.7353, 2.7933]
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]
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);
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assert_eq!(
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test_utils::to_vec2_round(&tensor.gelu_erf()?, 4)?,
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[
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[-0.004, 0.8413, 3.9999, -0.046, 0.3457],
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[2.6906, -0.0647, -0.1091, 1.7353, 2.7928]
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]
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);
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assert_eq!(
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test_utils::to_vec2_round(&tensor.erf()?, 4)?,
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[
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[-1.0, 0.8427, 1.0, -0.1125, 0.5205],
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[0.9999, -0.9891, -0.3079, 0.9891, 0.9999]
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]
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);
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assert_eq!(
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test_utils::to_vec2_round(&tensor.ceil()?, 4)?,
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[[-3.0, 1.0, 4.0, -0.0, 1.0], [3.0, -1.0, -0.0, 2.0, 3.0]]
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);
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assert_eq!(
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test_utils::to_vec2_round(&tensor.floor()?, 4)?,
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[[-3.0, 1.0, 4.0, -1.0, 0.0], [2.0, -2.0, -1.0, 1.0, 2.0]]
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);
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assert_eq!(
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test_utils::to_vec2_round(&tensor.round()?, 4)?,
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[[-3.0, 1.0, 4.0, -0.0, 1.0], [3.0, -2.0, -0.0, 2.0, 3.0]]
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);
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Ok(())
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}
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fn binary_op(device: &Device) -> Result<()> {
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let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
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let tensor1 = Tensor::new(data, device)?;
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let data2 = &[[5f32, 5., 5., 5., 5.], [2., 1., 7., 8., 2.]];
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let tensor2 = Tensor::new(data2, device)?;
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let tensor = (&tensor1 + (&tensor1 * &tensor1)? / (&tensor1 + &tensor2))?;
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let dims = tensor.dims2()?;
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assert_eq!(dims, (2, 5));
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let content: Vec<Vec<f32>> = tensor.to_vec2()?;
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assert_eq!(content[0], [4.125, 1.1666666, 5.7777777, 1.1666666, 7.5]);
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assert_eq!(content[1], [3.0, 1.5, 10.5, 12.0, 3.0]);
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#[allow(clippy::eq_op)]
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let tensor = (&tensor - &tensor)?;
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let content: Vec<Vec<f32>> = tensor.to_vec2()?;
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assert_eq!(content[0], [0., 0., 0., 0., 0.]);
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let min = tensor1.minimum(&(&tensor2 * 0.5)?)?;
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let max = tensor1.maximum(&(&tensor2 * 0.5)?)?;
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assert_eq!(
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min.to_vec2::<f32>()?,
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[[2.5, 1.0, 2.5, 1.0, 2.5], [1.0, 0.5, 3.5, 4.0, 1.0]],
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);
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assert_eq!(
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max.to_vec2::<f32>()?,
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[[3.0, 2.5, 4.0, 2.5, 5.0], [2.0, 1.0, 7.0, 8.0, 2.0]]
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);
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Ok(())
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}
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fn transpose(device: &Device) -> Result<()> {
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let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
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let tensor = Tensor::new(data, device)?.t()?;
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let dims = tensor.dims2()?;
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assert_eq!(dims, (5, 2));
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assert_eq!(
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tensor.to_vec2::<f32>()?,
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&[[3f32, 2.], [1., 1.], [4., 7.], [1., 8.], [5., 2.]]
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);
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assert_eq!(tensor.t()?.to_vec2::<f32>()?, data);
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assert_eq!(tensor.contiguous()?.t()?.to_vec2::<f32>()?, data);
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assert_eq!(((tensor + 1.)?.t()? - 1.)?.to_vec2::<f32>()?, data);
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Ok(())
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}
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fn sum(device: &Device) -> Result<()> {
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let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
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let tensor = Tensor::new(data, device)?;
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assert_eq!(
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tensor.sum_keepdim(2)?.to_vec3::<u32>()?,
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&[[[8], [15]], [[10], [18]]]
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);
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assert_eq!(
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tensor.sum_keepdim(0)?.to_vec3::<u32>()?,
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&[[[5, 2, 11], [9, 7, 17]]],
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);
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assert_eq!(tensor.sum_keepdim((0, 2, 1))?.to_vec3::<u32>()?, &[[[51]]],);
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assert_eq!(
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tensor.t()?.sum_keepdim(1)?.t()?.to_vec3::<u32>()?,
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&[[[8], [15]], [[10], [18]]]
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);
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assert_eq!(
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tensor.sum_keepdim((2, 1))?.to_vec3::<u32>()?,
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&[[[8 + 15]], [[10 + 18]]]
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);
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let data: Vec<u32> = (0..4000u32).collect();
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let tensor = Tensor::new(data.as_slice(), device)?;
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assert_eq!(tensor.sum_keepdim(0)?.to_vec1::<u32>()?, &[7998000]);
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let tensor = tensor.reshape((2000, 2))?;
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assert_eq!(tensor.sum_keepdim((0, 1))?.to_vec2::<u32>()?, &[[7998000]]);
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assert_eq!(
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tensor.sum_keepdim(0)?.sum_keepdim(1)?.to_vec2::<u32>()?,
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&[[7998000]]
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);
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assert_eq!(
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tensor.sum_keepdim(1)?.sum_keepdim(0)?.to_vec2::<u32>()?,
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&[[7998000]]
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);
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assert_eq!(
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tensor.sum_keepdim(0)?.to_vec2::<u32>()?,
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&[[3998000, 4000000]]
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);
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// Make the tensor non contiguous.
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let tensor = tensor.t()?.contiguous()?.t()?;
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assert_eq!(tensor.sum_keepdim((0, 1))?.to_vec2::<u32>()?, &[[7998000]]);
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assert_eq!(
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tensor.sum_keepdim(0)?.sum_keepdim(1)?.to_vec2::<u32>()?,
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&[[7998000]]
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);
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assert_eq!(
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tensor.sum_keepdim(1)?.sum_keepdim(0)?.to_vec2::<u32>()?,
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&[[7998000]]
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);
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assert_eq!(
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tensor.sum_keepdim(0)?.to_vec2::<u32>()?,
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&[[3998000, 4000000]]
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);
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let t1 = tensor.reshape((200, 5, 4))?;
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let t2 = t1.transpose(0, 2)?.contiguous()?.transpose(0, 2)?;
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for tensor in [t1, t2] {
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assert_eq!(
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tensor.sum_keepdim((0, 1, 2))?.to_vec3::<u32>()?,
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&[[[7998000]]]
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);
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assert_eq!(
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tensor
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.sum_keepdim(0)?
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.sum_keepdim(2)?
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.sum_keepdim(1)?
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.to_vec3::<u32>()?,
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&[[[7998000]]]
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);
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assert_eq!(
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tensor
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.sum_keepdim(0)?
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.sum_keepdim((1, 2))?
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.to_vec3::<u32>()?,
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&[[[7998000]]]
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);
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assert_eq!(
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tensor
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.sum_keepdim(1)?
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.sum_keepdim((0, 2))?
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.to_vec3::<u32>()?,
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&[[[7998000]]]
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);
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assert_eq!(
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tensor.sum_keepdim(0)?.to_vec3::<u32>()?,
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&[[
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[398000, 398200, 398400, 398600],
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[398800, 399000, 399200, 399400],
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[399600, 399800, 400000, 400200],
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[400400, 400600, 400800, 401000],
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[401200, 401400, 401600, 401800]
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]]
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);
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}
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Ok(())
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}
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fn min(device: &Device) -> Result<()> {
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let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
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let tensor = Tensor::new(data, device)?;
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assert_eq!(
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tensor.min_keepdim(2)?.to_vec3::<u32>()?,
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&[[[1], [1]], [[1], [2]]]
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);
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assert_eq!(
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tensor.min_keepdim(0)?.to_vec3::<u32>()?,
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&[[[2, 1, 4], [1, 2, 8]]],
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);
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let data: Vec<u32> = (200..4000u32).collect();
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let tensor = Tensor::new(data.as_slice(), device)?;
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assert_eq!(tensor.min_keepdim(0)?.to_vec1::<u32>()?, &[200]);
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let tensor = tensor.reshape((1900, 2))?;
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assert_eq!(
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tensor.min_keepdim(0)?.min_keepdim(1)?.to_vec2::<u32>()?,
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&[[200]]
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);
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assert_eq!(
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tensor.min_keepdim(1)?.min_keepdim(0)?.to_vec2::<u32>()?,
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&[[200]]
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);
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assert_eq!(tensor.min_keepdim(0)?.to_vec2::<u32>()?, &[[200, 201]]);
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// Make the tensor non contiguous.
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let tensor = tensor.t()?.contiguous()?.t()?;
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assert_eq!(
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tensor.min_keepdim(0)?.min_keepdim(1)?.to_vec2::<u32>()?,
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&[[200]]
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);
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assert_eq!(
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tensor.min_keepdim(1)?.min_keepdim(0)?.to_vec2::<u32>()?,
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&[[200]]
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);
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assert_eq!(tensor.min_keepdim(0)?.to_vec2::<u32>()?, &[[200, 201]]);
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let t1 = tensor.reshape((190, 5, 4))?;
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let t2 = t1.transpose(0, 2)?.contiguous()?.transpose(0, 2)?;
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for tensor in [t1, t2] {
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assert_eq!(
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tensor
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.min_keepdim(0)?
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.min_keepdim(2)?
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.min_keepdim(1)?
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.to_vec3::<u32>()?,
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&[[[200]]]
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);
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assert_eq!(
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tensor.min_keepdim(0)?.to_vec3::<u32>()?,
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&[[
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[200, 201, 202, 203],
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[204, 205, 206, 207],
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[208, 209, 210, 211],
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[212, 213, 214, 215],
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[216, 217, 218, 219]
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]]
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);
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}
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Ok(())
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}
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fn max(device: &Device) -> Result<()> {
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let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
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let tensor = Tensor::new(data, device)?;
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assert_eq!(
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tensor.max_keepdim(2)?.to_vec3::<u32>()?,
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&[[[4], [9]], [[7], [8]]]
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);
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assert_eq!(
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tensor.max_keepdim(0)?.to_vec3::<u32>()?,
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&[[[3, 1, 7], [8, 5, 9]]],
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);
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let data: Vec<u32> = (200..4000u32).collect();
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let tensor = Tensor::new(data.as_slice(), device)?;
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assert_eq!(tensor.max_keepdim(0)?.to_vec1::<u32>()?, &[3999]);
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let tensor = tensor.reshape((1900, 2))?;
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assert_eq!(
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tensor.max_keepdim(0)?.max_keepdim(1)?.to_vec2::<u32>()?,
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&[[3999]]
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);
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assert_eq!(
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tensor.max_keepdim(1)?.max_keepdim(0)?.to_vec2::<u32>()?,
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&[[3999]]
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);
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assert_eq!(tensor.max_keepdim(0)?.to_vec2::<u32>()?, &[[3998, 3999]]);
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|
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// Make the tensor non contiguous.
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let tensor = tensor.t()?.contiguous()?.t()?;
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assert_eq!(
|
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tensor.max_keepdim(0)?.max_keepdim(1)?.to_vec2::<u32>()?,
|
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&[[3999]]
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);
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assert_eq!(
|
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tensor.max_keepdim(1)?.max_keepdim(0)?.to_vec2::<u32>()?,
|
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&[[3999]]
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);
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assert_eq!(tensor.max_keepdim(0)?.to_vec2::<u32>()?, &[[3998, 3999]]);
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|
|
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let t1 = tensor.reshape((190, 5, 4))?;
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let t2 = t1.transpose(0, 2)?.contiguous()?.transpose(0, 2)?;
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for tensor in [t1, t2] {
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assert_eq!(
|
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tensor
|
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.max_keepdim(0)?
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.max_keepdim(2)?
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.max_keepdim(1)?
|
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.to_vec3::<u32>()?,
|
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&[[[3999]]]
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);
|
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assert_eq!(
|
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tensor.max_keepdim(0)?.to_vec3::<u32>()?,
|
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&[[
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[3980, 3981, 3982, 3983],
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|
[3984, 3985, 3986, 3987],
|
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[3988, 3989, 3990, 3991],
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[3992, 3993, 3994, 3995],
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[3996, 3997, 3998, 3999]
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]]
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);
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}
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Ok(())
|
|
}
|
|
|
|
fn argmin(device: &Device) -> Result<()> {
|
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let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
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let tensor = Tensor::new(data, device)?;
|
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assert_eq!(
|
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tensor.argmin_keepdim(2)?.to_vec3::<u32>()?,
|
|
&[[[1], [0]], [[1], [1]]]
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);
|
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assert_eq!(
|
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tensor.argmin_keepdim(0)?.to_vec3::<u32>()?,
|
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&[[[1, 0, 0], [0, 1, 1]]],
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);
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|
let data: Vec<u32> = (200..4000u32).collect();
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|
let tensor = Tensor::new(data.as_slice(), device)?;
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|
assert_eq!(tensor.argmin_keepdim(0)?.to_vec1::<u32>()?, &[0]);
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let tensor = tensor.reshape((1900, 2))?;
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|
assert_eq!(
|
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tensor
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|
.argmin_keepdim(0)?
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|
.argmin_keepdim(1)?
|
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.to_vec2::<u32>()?,
|
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&[[0]]
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);
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assert_eq!(
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tensor
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.argmin_keepdim(1)?
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|
.argmin_keepdim(0)?
|
|
.to_vec2::<u32>()?,
|
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&[[0]]
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);
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assert_eq!(tensor.argmin_keepdim(0)?.to_vec2::<u32>()?, &[[0, 0]]);
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|
|
|
// Make the tensor non contiguous.
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|
let tensor = tensor.t()?.contiguous()?.t()?;
|
|
assert_eq!(
|
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tensor
|
|
.argmin_keepdim(0)?
|
|
.argmin_keepdim(1)?
|
|
.to_vec2::<u32>()?,
|
|
&[[0]]
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);
|
|
assert_eq!(
|
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tensor
|
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.argmin_keepdim(1)?
|
|
.argmin_keepdim(0)?
|
|
.to_vec2::<u32>()?,
|
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&[[0]]
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);
|
|
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 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]
|
|
]
|
|
);
|
|
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]]);
|
|
Ok(())
|
|
}
|
|
|
|
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]
|
|
]
|
|
);
|
|
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_add(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 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]
|
|
]
|
|
);
|
|
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]]);
|
|
Ok(())
|
|
}
|
|
|
|
fn matmul(device: &Device) -> Result<()> {
|
|
let data = vec![1.0f32, 2.0, 3.0, 4.0];
|
|
let a = Tensor::from_slice(&data, (2, 2), device)?;
|
|
let data = vec![1.0f32, 2.0, 3.0, 4.0];
|
|
let b = Tensor::from_slice(&data, (2, 2), device)?;
|
|
|
|
let c = a.matmul(&b)?;
|
|
assert_eq!(c.to_vec2::<f32>()?, &[[7.0f32, 10.0], [15.0, 22.0]]);
|
|
|
|
let data = vec![1.0f32, 2.0];
|
|
let a = Tensor::from_slice(&data, (2, 1), device)?;
|
|
let data = vec![3.0f32, 4.0];
|
|
let b = Tensor::from_slice(&data, (1, 2), device)?;
|
|
let c = a.matmul(&b)?;
|
|
assert_eq!(c.to_vec2::<f32>()?, &[&[3.0, 4.0], &[6.0, 8.0]]);
|
|
|
|
let data: Vec<_> = (0..6).map(|i| i as f32).collect();
|
|
let a = Tensor::from_slice(&data, (2, 3), device)?;
|
|
let data: Vec<_> = (0..6).map(|i| (i + 2) as f32).collect();
|
|
let b = Tensor::from_slice(&data, (3, 2), device)?;
|
|
let c = a.matmul(&b)?;
|
|
assert_eq!(c.to_vec2::<f32>()?, &[&[16., 19.], &[52., 64.]]);
|
|
|
|
let data: Vec<_> = (0..12).map(|i| i as f32).collect();
|
|
let a = Tensor::from_slice(&data, (2, 2, 3), device)?;
|
|
let data: Vec<_> = (0..12).map(|i| (i + 2) as f32).collect();
|
|
let b = Tensor::from_slice(&data, (2, 3, 2), device)?;
|
|
let expected = [[[16., 19.], [52., 64.]], [[214., 235.], [304., 334.]]];
|
|
|
|
let c = a.matmul(&b)?;
|
|
assert_eq!(c.to_vec3::<f32>()?, &expected);
|
|
|
|
// Also perform the matmul on contiguous transposed versions.
|
|
let a_tt = a.t()?.contiguous()?.t()?;
|
|
assert!(!a_tt.is_contiguous());
|
|
assert_eq!(a.dims(), a_tt.dims());
|
|
assert_eq!(a_tt.stride(), &[6, 1, 2]);
|
|
|
|
let b_tt = b.t()?.contiguous()?.t()?;
|
|
assert!(!b_tt.is_contiguous());
|
|
assert_eq!(b.dims(), b_tt.dims());
|
|
assert_eq!(b_tt.stride(), &[6, 1, 3]);
|
|
|
|
assert_eq!(a_tt.matmul(&b)?.to_vec3::<f32>()?, &expected);
|
|
assert_eq!(a.matmul(&b_tt)?.to_vec3::<f32>()?, &expected);
|
|
assert_eq!(a_tt.matmul(&b_tt)?.to_vec3::<f32>()?, &expected);
|
|
Ok(())
|
|
}
|
|
|
|
fn broadcast_matmul(device: &Device) -> Result<()> {
|
|
let lhs = Tensor::randn(0f32, 1f32, (3, 1, 4, 5), device)?;
|
|
let rhs = Tensor::randn(0f32, 1f32, (6, 5, 2), device)?;
|
|
let out = lhs.broadcast_matmul(&rhs)?;
|
|
assert_eq!(out.dims(), &[3, 6, 4, 2]);
|
|
for idx1 in 0..3 {
|
|
for idx2 in 0..6 {
|
|
let out = out.i((idx1, idx2))?;
|
|
let lhs = lhs.i((idx1, 0))?;
|
|
let rhs = rhs.i(idx2)?;
|
|
let out2 = lhs.matmul(&rhs);
|
|
let sum_diff2 = (out - out2)?.sqr()?.sum_all()?;
|
|
// With cuda, we see errors of up to ~1e-12.
|
|
assert!(sum_diff2.to_vec0::<f32>()? < 1e-6)
|
|
}
|
|
}
|
|
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]);
|
|
let tensor = Tensor::rand(0f32, 1f32, (5, 3), device)?;
|
|
assert_eq!(tensor.dims(), [5, 3]);
|
|
Ok(())
|
|
}
|
|
|
|
test_device!(zeros, zeros_cpu, zeros_gpu);
|
|
test_device!(ones, ones_cpu, ones_gpu);
|
|
test_device!(add_mul, add_mul_cpu, add_mul_gpu);
|
|
test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu);
|
|
test_device!(narrow, narrow_cpu, narrow_gpu);
|
|
test_device!(broadcast, broadcast_cpu, broadcast_gpu);
|
|
test_device!(cat, cat_cpu, cat_gpu);
|
|
test_device!(sum, sum_cpu, sum_gpu);
|
|
test_device!(min, min_cpu, min_gpu);
|
|
test_device!(max, max_cpu, max_gpu);
|
|
test_device!(argmax, argmax_cpu, argmax_gpu);
|
|
test_device!(argmin, argmin_cpu, argmin_gpu);
|
|
test_device!(transpose, transpose_cpu, transpose_gpu);
|
|
test_device!(unary_op, unary_op_cpu, unary_op_gpu);
|
|
test_device!(binary_op, binary_op_cpu, binary_op_gpu);
|
|
test_device!(embeddings, embeddings_cpu, embeddings_gpu);
|
|
test_device!(cmp, cmp_cpu, cmp_gpu);
|
|
test_device!(matmul, matmul_cpu, matmul_gpu);
|
|
test_device!(broadcast_matmul, broadcast_matmul_cpu, broadcast_matmul_gpu);
|
|
test_device!(broadcasting, broadcasting_cpu, broadcasting_gpu);
|
|
test_device!(index_select, index_select_cpu, index_select_gpu);
|
|
test_device!(index_add, index_add_cpu, index_add_gpu);
|
|
test_device!(gather, gather_cpu, gather_gpu);
|
|
test_device!(scatter_add, scatter_add_cpu, scatter_add_gpu);
|
|
test_device!(slice_scatter, slice_scatter_cpu, slice_scatter_gpu);
|
|
test_device!(randn, randn_cpu, randn_gpu);
|
|
test_device!(clamp, clamp_cpu, clamp_gpu);
|
|
|
|
// 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(())
|
|
}
|