fp16 also works

tests/python/contrib/test_cudnn.py

@@ -264,7 +264,17 @@ def verify_conv2d_backward_data(data_dtype, conv_dtype, tensor_format=0, tol=1e-
     dy_np = np.random.uniform(-1, 1, oshape).astype(data_dtype)
     w_np = np.random.uniform(-1, 1, wshape).astype(data_dtype)
 
-    dx_np = ref_func(dy_np, w_np, (stride_h, stride_w), (pad_h, pad_w), (0, 0))
+    if data_dtype == "float16":
+        dx_np = ref_func(
+            dy_np.astype("float32"),
+            w_np.astype("float32"),
+            (stride_h, stride_w),
+            (pad_h, pad_w),
+            (0, 0),
+        )
+        dx_np = dx_np.astype("float16")
+    else:
+        dx_np = ref_func(dy_np, w_np, (stride_h, stride_w), (pad_h, pad_w), (0, 0))
 
     dy = te.placeholder(oshape, name="dy", dtype=data_dtype)
     w = te.placeholder(wshape, name="dw", dtype=data_dtype)
@@ -292,6 +302,7 @@ def verify_conv2d_backward_data(data_dtype, conv_dtype, tensor_format=0, tol=1e-
 
     f(dy, w, dx)
     print(np.max(np.abs(dx.numpy() - dx_np)))
+    print(np.mean(np.abs(dx.numpy() - dx_np)))
     tvm.testing.assert_allclose(dx.numpy(), dx_np, atol=tol, rtol=tol)
 
 
@@ -300,6 +311,9 @@ def verify_conv2d_backward_data(data_dtype, conv_dtype, tensor_format=0, tol=1e-
 def test_conv2d_backward_data():
     verify_conv2d_backward_data("float32", "float32", tensor_format=0, tol=1e-5)
     verify_conv2d_backward_data("float32", "float32", tensor_format=1, tol=1e-2)
+    # The scipy convolve function does not support fp16, so the reference will be computed with
+    # fp32. Use larger tolerance to be on the safe side (1e-2 also seems mostly ok).
+    verify_conv2d_backward_data("float16", "float16", tensor_format=1, tol=1e-1)
 
 
 test_kwargs_default_2d = {