T1-1-19 Add cosh operator

Author: GordonYang1

src/ntops/kernels/__init__.py

@@ -10,6 +10,7 @@
     clamp,
     conv2d,
     cos,
+    cosh,
     div,
     dropout,
     eq,
@@ -54,6 +55,7 @@
     "clamp",
     "conv2d",
     "cos",
+    "cosh",
     "div",
     "dropout",
     "eq",

src/ntops/kernels/cosh.py

@@ -0,0 +1,19 @@
+import functools
+
+import ninetoothed.language as ntl
+from ninetoothed import Tensor
+from ninetoothed.language import libdevice
+
+from ntops.kernels.element_wise import arrangement
+
+
+def application(input, output):
+    output = libdevice.cosh(ntl.cast(input, ntl.float32))  # noqa: F841
+
+
+def premake(ndim, dtype=None, block_size=None):
+    arrangement_ = functools.partial(arrangement, block_size=block_size)
+
+    tensors = (Tensor(ndim, dtype=dtype), Tensor(ndim, dtype=dtype))
+
+    return arrangement_, application, tensors

src/ntops/torch/__init__.py

@@ -9,6 +9,7 @@
 from ntops.torch.clamp import clamp
 from ntops.torch.conv2d import conv2d
 from ntops.torch.cos import cos
+from ntops.torch.cosh import cosh
 from ntops.torch.div import div
 from ntops.torch.dropout import dropout
 from ntops.torch.eq import eq
@@ -53,6 +54,7 @@
     "clamp",
     "conv2d",
     "cos",
+    "cosh",
     "div",
     "dropout",
     "eq",

src/ntops/torch/cosh.py

@@ -0,0 +1,15 @@
+import torch
+
+import ntops
+from ntops.torch.utils import _cached_make
+
+
+def cosh(input, *, out=None):
+    if out is None:
+        out = torch.empty_like(input)
+
+    kernel = _cached_make(ntops.kernels.cosh.premake, input.ndim)
+
+    kernel(input, out)
+
+    return out

tests/test_cosh.py

@@ -0,0 +1,17 @@
+import pytest
+import torch
+
+import ntops
+from tests.skippers import skip_if_cuda_not_available
+from tests.utils import generate_arguments
+
+
+@skip_if_cuda_not_available
+@pytest.mark.parametrize(*generate_arguments())
+def test_cosh(shape, dtype, device, rtol, atol):
+    input = torch.randn(shape, dtype=dtype, device=device)
+
+    ninetoothed_output = ntops.torch.cosh(input)
+    reference_output = torch.cosh(input)
+
+    assert torch.allclose(ninetoothed_output, reference_output, rtol=rtol, atol=atol)