mirror of
https://github.com/huggingface/candle.git
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226 lines
8.0 KiB
Rust
226 lines
8.0 KiB
Rust
use anyhow::Result;
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use candle_core::{test_device, test_utils, Device, Tensor};
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/* This test is based on the following script.
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import torch
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torch.manual_seed(4242)
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t = torch.randn((1, 4, 5))
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w = torch.randn((2, 4, 3))
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print(t.flatten())
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print(w.flatten())
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res = torch.nn.functional.conv1d(t, w)
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print(res.flatten())
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res = torch.nn.functional.conv1d(t, w, padding=1)
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print(res.flatten())
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*/
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fn conv1d(dev: &Device) -> Result<()> {
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let t = Tensor::new(
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&[
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0.4056f32, -0.8689, -0.0773, -1.5630, 1.2279, -0.9287, -1.7030, 0.1370, 0.1866, 0.4145,
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1.8025, -0.1536, 2.2013, -0.6836, 0.2477, 1.3127, -0.6957, 0.3278, -1.0124, 0.5599,
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],
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dev,
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)?
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.reshape((1, 4, 5))?;
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let w = Tensor::new(
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&[
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-0.8404f32, -0.3490, 0.0130, 1.3123, 0.1763, -1.9249, 1.4270, 0.9421, 0.8670, -0.7181,
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-1.1111, 0.8869, -1.2429, 1.8357, 1.6052, -1.3844, 0.3951, -1.2036, 0.6686, 1.6261,
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-0.6451, -0.0840, -1.4247, 0.5512,
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],
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dev,
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)?
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.reshape((2, 4, 3))?;
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let res = t.conv1d(&w, 0, 1, 1)?;
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assert_eq!(res.dims(), [1, 2, 3]);
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[2.6357, -1.3336, 4.1393, -1.1784, 3.5675, 0.5069]
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);
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let res = t.conv1d(&w, /*padding*/ 1, 1, 1)?;
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assert_eq!(res.dims(), [1, 2, 5]);
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// Same as pytorch default padding: use zeros.
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[2.4509, 2.6357, -1.3336, 4.1393, 0.5657, 1.8091, -1.1784, 3.5675, 0.5069, 3.3352]
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);
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Ok(())
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}
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fn conv1d_small(dev: &Device) -> Result<()> {
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let t = Tensor::new(&[0.4056f32, -0.8689, -0.0773, -1.5630], dev)?.reshape((1, 1, 4))?;
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let w = Tensor::new(&[1f32, 0., 0.], dev)?.reshape((1, 1, 3))?;
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let res = t.conv1d(&w, 0, 1, 1)?;
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assert_eq!(res.dims(), [1, 1, 2]);
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[0.4056, -0.8689]
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);
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let res = t.conv1d(&w, /*padding*/ 1, 1, 1)?;
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assert_eq!(res.dims(), [1, 1, 4]);
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[0.0, 0.4056, -0.8689, -0.0773],
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);
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Ok(())
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}
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/* This test is based on the following script.
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import torch
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torch.manual_seed(4242)
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t = torch.randn((1, 4, 5, 5))
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w = torch.randn((2, 4, 3, 3))
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print(t.flatten())
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print(w.flatten())
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res = torch.nn.functional.conv2d(t, w)
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print(res.flatten())
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*/
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fn conv2d(dev: &Device) -> Result<()> {
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let t = Tensor::new(
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&[
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0.4056f32, -0.8689, -0.0773, -1.5630, -2.8012, -1.5059, 0.3972, 1.0852, 0.4997, 3.0616,
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1.6541, 0.0964, -0.8338, -1.6523, -0.8323, -0.1699, 0.0823, 0.3526, 0.6843, 0.2395,
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1.2279, -0.9287, -1.7030, 0.1370, 0.6047, 0.3770, -0.6266, 0.3529, 2.2013, -0.6836,
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0.2477, 1.3127, -0.2260, 0.2622, -1.2974, -0.8140, -0.8404, -0.3490, 0.0130, 1.3123,
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1.7569, -0.3956, -1.8255, 0.1727, -0.3538, 2.6941, 1.0529, 0.4219, -0.2071, 1.1586,
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0.4717, 0.3865, -0.5690, -0.5010, -0.1310, 0.7796, 0.6630, -0.2021, 2.6090, 0.2049,
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0.6466, -0.5042, -0.0603, -1.6538, -1.2429, 1.8357, 1.6052, -1.3844, 0.3323, -1.3712,
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0.9634, -0.4799, -0.6451, -0.0840, -1.4247, 0.5512, -0.1747, -0.5509, -0.3742, 0.3790,
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-0.4431, -0.4720, -0.7890, 0.2620, 0.7875, 0.5377, -0.6779, -0.8088, 1.9098, 1.2006,
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-0.8000, -0.4983, 1.5480, 0.8265, -0.1025, 0.5138, 0.5748, 0.3821, -0.4607, 0.0085,
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],
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dev,
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)?;
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let w = Tensor::new(
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&[
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-0.9325f32, 0.6451, -0.8537, 0.2378, 0.8764, -0.1832, 0.2987, -0.6488, -0.2273,
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-2.4184, -0.1192, -0.4821, -0.5079, -0.5766, -2.4729, 1.6734, 0.4558, 0.2851, 1.1514,
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-0.9013, 1.0662, -0.1817, -0.0259, 0.1709, 0.5367, 0.7513, 0.8086, -2.2586, -0.5027,
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0.9141, -1.3086, -1.3343, -1.5669, -0.1657, 0.7958, 0.1432, 0.3896, -0.4501, 0.1667,
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0.0714, -0.0952, 1.2970, -0.1674, -0.3178, 1.0677, 0.3060, 0.7080, 0.1914, 1.1679,
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-0.3602, 1.9265, -1.8626, -0.5112, -0.0982, 0.2621, 0.6565, 0.5908, 1.0089, -0.1646,
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1.8032, -0.6286, 0.2016, -0.3370, 1.2555, 0.8009, -0.6488, -0.4652, -1.5685, 1.5860,
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0.5583, 0.4623, 0.6026,
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],
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dev,
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)?;
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let t = t.reshape((1, 4, 5, 5))?;
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let w = w.reshape((2, 4, 3, 3))?;
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let res = t.conv2d(&w, 0, 1, 1)?;
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assert_eq!(res.dims(), [1, 2, 3, 3]);
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[
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-4.2812, 2.0923, 5.2187, 7.5184, 0.752, -14.9426, 10.0087, 4.391, 0.2918, 1.6715,
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10.389, 3.6023, -4.2808, 0.2672, 5.3646, -5.2023, -2.1955, -9.4075
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]
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);
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Ok(())
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}
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/* This test is based on the following script.
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import torch
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torch.manual_seed(4242)
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t = torch.randn((1, 2, 3, 3))
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w = torch.randn((1, 2, 1, 1))
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print(t.flatten())
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print(w.flatten())
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res = torch.nn.functional.conv2d(t, w)
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print(res.flatten())
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*/
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fn conv2d_small(dev: &Device) -> Result<()> {
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let t = Tensor::new(
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&[
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0.4056f32, -0.8689, 0.6843, 0.2395, 1.2279, -0.9287, -1.7030, 0.1370, 0.1866, 0.4145,
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-0.6266, 0.3529, 2.2013, -0.6836, 0.2477, 1.3127, -0.6957, 0.3278,
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],
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dev,
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)?;
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let w = Tensor::new(&[-0.9259f32, 1.3017], dev)?;
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let t = t.reshape((1, 2, 3, 3))?;
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let w = w.reshape((1, 2, 1, 1))?;
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let res = t.conv2d(&w, 0, 1, 1)?;
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assert_eq!(res.dims(), [1, 1, 3, 3]);
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[0.164, -0.0111, -0.1742, 2.6437, -2.0268, 1.1823, 3.2855, -1.0324, 0.2539]
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);
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let res = t.conv2d(&w, 2, 1, 1)?;
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assert_eq!(res.dims(), [1, 1, 7, 7]);
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[
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0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1640, -0.0111, -0.1742, 0.0000, 0.0000,
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0.0000, 0.0000, 2.6437, -2.0268, 1.1823, 0.0000, 0.0000, 0.0000, 0.0000, 3.2855,
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-1.0324, 0.2539, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000
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]
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);
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Ok(())
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}
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fn conv2d_smaller(dev: &Device) -> Result<()> {
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let t = Tensor::new(
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&[
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0.4056f32, -0.8689, 0.6843, 0.2395, 1.2279, -0.9287, -1.7030, 0.1370, 0.1866,
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],
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dev,
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)?;
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let w = Tensor::new(&[1f32, 1., 1., 1., 1., 1., 1., 1., 1.], dev)?;
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let t = t.reshape((1, 1, 3, 3))?;
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let w = w.reshape((1, 1, 3, 3))?;
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let res = t.conv2d(&w, 0, 1, 1)?;
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assert_eq!(res.dims(), [1, 1, 1, 1]);
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[-0.6197]
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);
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Ok(())
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}
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/* This test is based on the following script.
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import torch
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torch.manual_seed(4242)
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t = torch.randn((1, 2, 4, 2))
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w = torch.randn((1, 2, 1, 1))
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print(t.flatten())
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print(w.flatten())
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res = torch.nn.functional.conv2d(t, w)
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print(res.flatten())
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*/
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fn conv2d_non_square(dev: &Device) -> Result<()> {
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let t = Tensor::new(
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&[
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0.4056f32, -0.8689, -0.0773, -1.5630, -2.8012, -1.5059, 0.3972, 1.0852, 0.4997, 3.0616,
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1.6541, 0.0964, -0.8338, -1.6523, -0.8323, -0.1699,
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],
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dev,
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)?;
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let w = Tensor::new(&[-1.1351f32, 1.3841], dev)?;
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let t = t.reshape((1, 2, 4, 2))?;
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let w = w.reshape((1, 2, 1, 1))?;
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let res = t.conv2d(&w, 0, 1, 1)?;
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assert_eq!(res.dims(), [1, 1, 4, 2]);
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assert_eq!(
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test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
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[0.2312, 5.2238, 2.3772, 1.9076, 2.0256, -0.5776, -1.6028, -1.467]
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);
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Ok(())
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}
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test_device!(conv1d, conv1d_cpu, conv1d_gpu);
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test_device!(conv1d_small, conv1d_small_cpu, conv1d_small_gpu);
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test_device!(conv2d, conv2d_cpu, conv2d_gpu);
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test_device!(
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conv2d_non_square,
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conv2d_non_square_cpu,
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conv2d_non_square_gpu
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);
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test_device!(conv2d_small, conv2d_small_cpu, conv2d_small_gpu);
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test_device!(conv2d_smaller, conv2d_smaller_cpu, conv2d_smaller_gpu);
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