[0-size Tensor Job2 No.51] Add 0-size Tensor support for paddle.multiplex [fluid_ops]

paddle/phi/kernels/cpu/multiplex_kernel.cc

@@ -26,6 +26,7 @@ void MultiplexKernel(const Context& dev_ctx,
                      const DenseTensor& ids,
                      DenseTensor* out) {
   dev_ctx.template Alloc<T>(out);
+  if (out->numel() == 0) return;
   for (size_t i = 0; i < ins.size(); ++i) {
     PADDLE_ENFORCE_GT(
         ins[i]->numel(),

paddle/phi/kernels/gpu/multiplex_kernel.cu

@@ -27,6 +27,7 @@ void MultiplexKernel(const Context& dev_ctx,
                      const DenseTensor& ids,
                      DenseTensor* out) {
   dev_ctx.template Alloc<T>(out);
+  if (out->numel() == 0) return;
   for (size_t i = 0; i < ins.size(); ++i) {
     PADDLE_ENFORCE_GT(
         ins[i]->numel(),

test/legacy_test/test_multiplex_op.py

@@ -97,6 +97,7 @@ def init_dtype(self):
 class TestMultiplexOpError(unittest.TestCase):
 
     def test_errors(self):
+        paddle.enable_static()
         with base.program_guard(base.Program(), base.Program()):
             x1 = paddle.static.data(name='x1', shape=[None, 2], dtype='int64')
             x2 = paddle.static.data(name='x2', shape=[None, 2], dtype='int64')
@@ -198,5 +199,40 @@ def init_dtype(self):
         self.dtype = np.complex128
 
 
+class TestMultiplexOp_ZeroSize(OpTest):
+    def setUp(self):
+        self.op_type = "multiplex"
+        self.init_dtype()
+        self.python_api = paddle.tensor.multiplex
+        rows = 4
+        index = np.array([0, 2, 2, 3]).astype('int32')
+        np.random.shuffle(index)
+        index = np.reshape(index, (rows, 1))
+        ins1 = np.random.random((rows, 0)).astype(self.dtype)
+        ins2 = np.random.random((rows, 0)).astype(self.dtype)
+        ins3 = np.random.random((rows, 0)).astype(self.dtype)
+        ins4 = np.random.random((rows, 0)).astype(self.dtype)
+        self.inputs = {
+            'Ids': index,
+            'X': [('x1', ins1), ('x2', ins2), ('x3', ins3), ('x4', ins4)],
+        }
+        # multiplex output
+        output = np.zeros_like(ins1)
+        for i in range(0, rows):
+            k = index[i][0]
+            if self.inputs['X'][k][1][i].size != 0:
+                output[i] = self.inputs['X'][k][1][i]
+        self.outputs = {'Out': output}
+
+    def init_dtype(self):
+        self.dtype = 'float64'
+
+    def test_check_output(self):
+        self.check_output(check_pir=True)
+
+    def test_check_grad(self):
+        self.check_grad(['x1', 'x2', 'x3', 'x4'], 'Out', check_pir=True)
+
+
 if __name__ == '__main__':
     unittest.main()