test_apply_diffusion_to_vn (was made single precision ready before batch allocations)

Author: starkphi

model/atmosphere/diffusion/tests/diffusion/stencil_tests/test_apply_diffusion_to_vn.py

@@ -14,6 +14,7 @@
 from icon4py.model.atmosphere.diffusion.stencils.apply_diffusion_to_vn import apply_diffusion_to_vn
 from icon4py.model.common import dimension as dims
 from icon4py.model.common.grid import base, horizontal as h_grid
+from icon4py.model.common.type_alias import vpfloat, wpfloat
 from icon4py.model.common.utils import data_allocation as data_alloc
 from icon4py.model.testing.stencil_tests import StandardStaticVariants, StencilTest
 
@@ -25,6 +26,7 @@
 from .test_calculate_nabla4 import calculate_nabla4_numpy
 
 
+@pytest.mark.single_precision_ready
 @pytest.mark.uses_concat_where
 @pytest.mark.continuous_benchmarking
 class TestApplyDiffusionToVn(StencilTest):
@@ -117,25 +119,29 @@ def reference(
 
     @pytest.fixture
     def input_data(self, grid: base.Grid) -> dict:
-        u_vert = data_alloc.random_field(grid, dims.VertexDim, dims.KDim)
-        v_vert = data_alloc.random_field(grid, dims.VertexDim, dims.KDim)
+        u_vert = data_alloc.random_field(grid, dims.VertexDim, dims.KDim, dtype=vpfloat)
+        v_vert = data_alloc.random_field(grid, dims.VertexDim, dims.KDim, dtype=vpfloat)
 
-        primal_normal_vert_v1 = data_alloc.random_field(grid, dims.EdgeDim, dims.E2C2VDim)
-        primal_normal_vert_v2 = data_alloc.random_field(grid, dims.EdgeDim, dims.E2C2VDim)
+        primal_normal_vert_v1 = data_alloc.random_field(
+            grid, dims.EdgeDim, dims.E2C2VDim, dtype=wpfloat
+        )
+        primal_normal_vert_v2 = data_alloc.random_field(
+            grid, dims.EdgeDim, dims.E2C2VDim, dtype=wpfloat
+        )
 
-        inv_vert_vert_length = data_alloc.random_field(grid, dims.EdgeDim)
-        inv_primal_edge_length = data_alloc.random_field(grid, dims.EdgeDim)
+        inv_vert_vert_length = data_alloc.random_field(grid, dims.EdgeDim, dtype=wpfloat)
+        inv_primal_edge_length = data_alloc.random_field(grid, dims.EdgeDim, dtype=wpfloat)
 
-        area_edge = data_alloc.random_field(grid, dims.EdgeDim)
-        kh_smag_e = data_alloc.random_field(grid, dims.EdgeDim, dims.KDim)
-        z_nabla2_e = data_alloc.random_field(grid, dims.EdgeDim, dims.KDim)
-        diff_multfac_vn = data_alloc.random_field(grid, dims.KDim)
-        vn = data_alloc.random_field(grid, dims.EdgeDim, dims.KDim)
-        nudgecoeff_e = data_alloc.random_field(grid, dims.EdgeDim)
+        area_edge = data_alloc.random_field(grid, dims.EdgeDim, dtype=wpfloat)
+        kh_smag_e = data_alloc.random_field(grid, dims.EdgeDim, dims.KDim, dtype=vpfloat)
+        z_nabla2_e = data_alloc.random_field(grid, dims.EdgeDim, dims.KDim, dtype=wpfloat)
+        diff_multfac_vn = data_alloc.random_field(grid, dims.KDim, dtype=wpfloat)
+        vn = data_alloc.random_field(grid, dims.EdgeDim, dims.KDim, dtype=wpfloat)
+        nudgecoeff_e = data_alloc.random_field(grid, dims.EdgeDim, dtype=wpfloat)
 
         limited_area = grid.limited_area if hasattr(grid, "limited_area") else True
-        fac_bdydiff_v = 5.0
-        nudgezone_diff = 9.0
+        fac_bdydiff_v = wpfloat(5.0)
+        nudgezone_diff = vpfloat(9.0)
 
         edge_domain = h_grid.domain(dims.EdgeDim)
         start_2nd_nudge_line_idx_e = grid.start_index(edge_domain(h_grid.Zone.NUDGING_LEVEL_2))
@@ -164,3 +170,8 @@ def input_data(self, grid: base.Grid) -> dict:
             vertical_start=0,
             vertical_end=grid.num_levels,
         )
+
+
+@pytest.mark.continuous_benchmarking
+class TestApplyDiffusionToVnContinuousBenchmarking(TestApplyDiffusionToVn):
+    pass