!29265 fix resize_bilinear infer

Merge pull request !29265 from jiangzhenguang/resize_bilinear

mindspore/python/mindspore/ops/operations/_grad_ops.py

@@ -2339,7 +2339,7 @@ class ParallelResizeBilinearGrad(PrimitiveWithInfer):
         x_dtype = x['dtype']
         validator.check_tensor_dtype_valid("grad_dtype", grad_dtype, [mstype.float16, mstype.float32], self.name)
         validator.check_tensor_dtype_valid("x_dtype", x_dtype, [mstype.float16, mstype.float32], self.name)
-        if size_val is not None:
+        if size_val is None:
             raise ValueError("size should be const input")
         output_shape = [grad_shape[0], grad_shape[1], x_shape[2], x_shape[3]]
 

tests/ut/python/parallel/test_resizebilinear.py

@@ -239,3 +239,17 @@ def test_neighbor_auto_parallel():
     context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
     net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1)
     compile_net(net)
+
+
+def test_bilinear_shard_n_c_w():
+    """
+    Feature: test ResizeBilinear shard n/c/w
+    Description: shard n/c/w
+    Expectation: compile success
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=3)
+    strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
+    strategy2 = ((2, 2, 1, 2),)
+    net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
+              strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)