feat(mge): add functional test

GitOrigin-RevId: aa0be626862782cf877adc4321f4eeda63d249f6

imperative/python/test/unit/functional/test_functional.py

@@ -15,6 +15,7 @@ import pytest
 from utils import opr_test
 
 import megengine.amp as amp
+import megengine.config as config
 import megengine.core.ops.builtin as builtin
 import megengine.core.tensor.dtype as dtype
 import megengine.functional as F
@@ -1258,3 +1259,34 @@ def test_pixel_shuffle_symbolic(is_symbolic):
         np.testing.assert_equal(out.numpy(), golden)
         if is_symbolic is None:
             break
+
+
+def test_set_conv2d_config():
+    """check setting config by contextmanager is equal to manually converted result"""
+    config._compute_mode = "float32"
+    inp = tensor(np.random.randn(1, 3, 224, 224), dtype=np.float16)
+    weight = tensor(np.random.randn(64, 3, 7, 7), dtype=np.float16)
+    config_out = F.conv2d(inp, weight, None, (2, 2), (3, 3), (1, 1), 1)
+    config._compute_mode = "default"
+    with config._override(compute_mode="float32"):
+        context_out = F.conv2d(inp, weight, None, (2, 2), (3, 3), (1, 1), 1)
+    expected = F.conv2d(
+        inp, weight, None, (2, 2), (3, 3), (1, 1), 1, compute_mode="float32",
+    )
+    np.testing.assert_allclose(config_out.numpy(), expected.numpy())
+    np.testing.assert_allclose(context_out.numpy(), expected.numpy())
+
+
+def test_set_warp_perspective_config():
+    config._conv_format = "NHWC"
+    inp_shape = (1, 1, 4, 4)
+    inp = Tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
+    M_shape = (1, 3, 3)
+    M = Tensor(np.random.randn(3, 3), dtype=np.float32).reshape(M_shape)
+    config_out = F.vision.warp_perspective(inp, M, (2, 2))
+    config._conv_format = "default"
+    with config._override(conv_format="NHWC"):
+        context_out = F.vision.warp_perspective(inp, M, (2, 2))
+    expected = F.vision.warp_perspective(inp, M, (2, 2), format="NHWC")
+    np.testing.assert_allclose(config_out.numpy(), expected.numpy())
+    np.testing.assert_allclose(context_out.numpy(), expected.numpy())