Add bias check for fused_layer_norm

Author: ooooo-create

paddle/phi/infermeta/multiary.cc

@@ -2647,6 +2647,17 @@ void FusedLayerNormInferMeta(const MetaTensor& x,
                             x_dims_vec[i],
                             residual_dims_vec[i]));
     }
+    if (config.is_runtime && bias && normalized_dims != 0) {
+      PADDLE_ENFORCE_EQ(bias.numel(),
+                        normalized_dims,
+                        common::errors::InvalidArgument(
+                            "The numel of Input(bias) must be equal to "
+                            "the normalized_dims of Input(X), but "
+                            "received numel of Input(bias) is [%d], "
+                            "received normalized_dims of Input(X) is [%d]",
+                            bias.numel(),
+                            normalized_dims));
+    }
   }
 
   int64_t rows = 1;

test/legacy_test/test_fused_layernorm_op.py

@@ -1229,5 +1229,28 @@ def setUp(self):
         self.quant_min_bound = -127
 
 
+@unittest.skipIf(
+    not core.is_compiled_with_cuda() and not paddle.is_compiled_with_rocm(),
+    "core is not compiled with CUDA or ROCM",
+)
+class TestFusedLayerNorm_ZeroSize_Error(unittest.TestCase):
+    def test_bias_error(self):
+        with paddle.base.dygraph.guard():
+            x = paddle.randn([16, 256], dtype="float32")
+            bias = paddle.randn([0], dtype="float32")
+            residual = paddle.rand([16, 256], "float32")
+            self.assertRaises(
+                ValueError,
+                paddle.incubate.nn.functional.fused_layer_norm,
+                x=x,
+                norm_weight=paddle.randn([256], dtype="float32"),
+                norm_bias=paddle.randn([256], dtype="float32"),
+                epsilon=1e-06,
+                begin_norm_axis=1,
+                bias=bias,
+                residual=residual,
+            )
+
+
 if __name__ == "__main__":
     unittest.main()