Make assign op support LoDTensorArray and modify while_loop API (#22309) (#22525)

This PR makes assign op support LoDTensorArray and enable the loop_vars in
while_loop to support tuple or list.

paddle/fluid/framework/operator.cc

@@ -1234,7 +1234,7 @@ Scope* OperatorWithKernel::PrepareData(
 void OperatorWithKernel::ParseInputDataType(
     const ExecutionContext& ctx, const std::string& name,
     proto::VarType::Type* data_type) const {
-  proto::VarType::Type dafault_data_type =
+  proto::VarType::Type default_data_type =
       static_cast<proto::VarType::Type>(-1);
   const std::vector<Variable*> vars = ctx.MultiInputVar(name);
   for (size_t i = 0; i < vars.size(); ++i) {
@@ -1247,6 +1247,13 @@ void OperatorWithKernel::ParseInputDataType(
         t = &var->Get<LoDTensor>();
       } else if (var->IsType<SelectedRows>()) {
         t = &(var->Get<SelectedRows>().value());
+      } else if (var->IsType<LoDTensorArray>()) {
+        auto t_arr = var->Get<LoDTensorArray>();
+        for (size_t j = 0; j < t_arr.size(); j++) {
+          if (t_arr[j].IsInitialized()) {
+            t = &(t_arr[j]);
+          }
+        }
       }
       if (t != nullptr) {
         PADDLE_ENFORCE_EQ(
@@ -1257,7 +1264,7 @@ void OperatorWithKernel::ParseInputDataType(
                 Type(), name, ctx.InputNames(name).at(i)));
         proto::VarType::Type tmp = t->type();
         PADDLE_ENFORCE(
-            tmp == *data_type || *data_type == dafault_data_type,
+            tmp == *data_type || *data_type == default_data_type,
             platform::errors::InvalidArgument(
                 "The DataType of %s Op's duplicable Variable %s must be "
                 "consistent. The current variable type is (%s), but the "

python/paddle/fluid/layers/control_flow.py

@@ -1000,15 +1000,12 @@ def while_loop(cond, body, loop_vars, is_test=False, name=None):
     while_loop_block = While(pre_cond, is_test, name)
     with while_loop_block.block():
         output_vars = body(*loop_vars)
+        map_structure(assign, output_vars, loop_vars)
         if len(loop_vars) == 1:
-            assign(output_vars, loop_vars[0])
             now_cond = cond(output_vars)
         else:
-            for i in range(len(output_vars)):
-                assign(output_vars[i], loop_vars[i])
             now_cond = cond(*output_vars)
         assign(now_cond, pre_cond)
-
     return loop_vars
 
 

python/paddle/fluid/tests/unittests/test_while_loop_op.py

@@ -62,11 +62,7 @@ class TestApiWhileLoop(unittest.TestCase):
         with program_guard(main_program, startup_program):
             i = layers.zeros(shape=[1], dtype='int64')
             ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
-            mem = layers.data(
-                name='mem',
-                shape=[10],
-                dtype='float32',
-                append_batch_size=False)
+            mem = fluid.data(name='mem', shape=[10], dtype='float32')
             one = layers.fill_constant(shape=[10], dtype='float32', value=1)
             out = layers.while_loop(cond, body, [i, mem])
 
@@ -111,16 +107,8 @@ class TestApiWhileLoop_Nested(unittest.TestCase):
         with program_guard(main_program, startup_program):
             i = layers.zeros(shape=[1], dtype='int64')
             j = layers.zeros(shape=[1], dtype='int64')
-            init = layers.data(
-                name='init',
-                shape=[3, 3],
-                dtype='float32',
-                append_batch_size=False)
-            sums = layers.data(
-                name='sums',
-                shape=[3, 3],
-                dtype='float32',
-                append_batch_size=False)
+            init = fluid.data(name='init', shape=[3, 3], dtype='float32')
+            sums = fluid.data(name='sums', shape=[3, 3], dtype='float32')
             loop_len1 = layers.fill_constant(shape=[1], dtype='int64', value=2)
             loop_len2 = layers.fill_constant(shape=[1], dtype='int64', value=3)
             ones = layers.fill_constant(shape=[3, 3], dtype='float32', value=1)
@@ -159,13 +147,11 @@ class TestApiWhileLoop_Backward(unittest.TestCase):
         main_program = Program()
         startup_program = Program()
         with fluid.program_guard(main_program, startup_program):
-            i = layers.data(
-                name='i', shape=[1], dtype='float32', append_batch_size=False)
+            i = fluid.data(name='i', shape=[1], dtype='float32')
             i.stop_gradient = False
             eleven = layers.fill_constant(shape=[1], dtype='float32', value=11)
             one = layers.fill_constant(shape=[1], dtype='float32', value=1)
-            x = layers.data(
-                name='x', shape=[1], dtype='float32', append_batch_size=False)
+            x = fluid.data(name='x', shape=[1], dtype='float32')
             x.stop_gradient = False
 
             out = layers.while_loop(cond, body, [i, x])
@@ -189,63 +175,84 @@ class TestApiWhileLoop_Backward(unittest.TestCase):
         self.assertTrue(np.allclose(np.asarray(res[1]), i_grad))
 
 
-class TestApiWhileLoop_NestedWithBackward(unittest.TestCase):
-    def test_nested_net_with_backward(self):
-        def external_cond(i, x, y):
-            return layers.less_than(i, ten)
-
-        def external_body(i, x, y):
-            def internal_cond(i, x, y):
-                return layers.less_than(i, five)
-
-            def internal_body(i, x, y):
-                x = layers.elementwise_add(x=i, y=i)
-                i = layers.increment(i)
-                return [i, x, y]
-
-            temp = layers.while_loop(internal_cond, internal_body, [i, x, y])
-            y = layers.elementwise_add(x=temp[1], y=i)
-            i = layers.increment(i)
-            return [i, x, y]
+class TestApiWhileLoop_NestedWithBackwardAndLoDTensorArray(unittest.TestCase):
+    def test_nested_net_with_backward_and_lodtensor(self):
+        def external_cond(i, j, x, mem_array):
+            return layers.less_than(i, array_len)
+
+        def external_body(i, j, x, mem_array):
+            def internal_cond(j, x, mem_array):
+                return layers.less_than(j, array_len2)
+
+            def internal_body(j, x, mem_array):
+                inner_data = layers.array_read(array=data_array, i=j)
+                inner_prev = layers.array_read(array=mem_array, i=j)
+                inner_sum_0 = layers.elementwise_add(x=inner_data, y=inner_prev)
+                inner_sum_1 = layers.elementwise_add(x=x, y=inner_sum_0)
+                j = layers.increment(x=j, in_place=True)
+                layers.array_write(inner_sum_1, i=j, array=mem_array)
+                return [j, x, mem_array]
+
+            outer_data = layers.array_read(array=data_array, i=i)
+            outer_prev = layers.array_read(array=mem_array, i=i)
+            outer_sum_0 = layers.elementwise_add(x=outer_data, y=outer_prev)
+            outer_sum_1 = layers.elementwise_add(x=x, y=outer_sum_0)
+            i = layers.increment(x=i, in_place=True)
+            layers.array_write(outer_sum_1, i=i, array=mem_array)
+            j, x, mem_array = layers.while_loop(internal_cond, internal_body,
+                                                [j, x, mem_array])
+            return [i, j, x, mem_array]
 
         main_program = Program()
         startup_program = Program()
-
         with fluid.program_guard(main_program, startup_program):
-            i = layers.data(
-                name='i', shape=[1], dtype='float32', append_batch_size=False)
-            i.stop_gradient = False
-            ten = layers.fill_constant(shape=[1], dtype='float32', value=10)
-            five = layers.fill_constant(shape=[1], dtype='float32', value=5)
-            x = layers.data(
-                name='x', shape=[1], dtype='float32', append_batch_size=False)
+            d0 = fluid.data(name='d0', shape=[10], dtype='float32')
+            d1 = fluid.data(name='d1', shape=[10], dtype='float32')
+            d2 = fluid.data(name='d2', shape=[10], dtype='float32')
+            x = fluid.data(name='x', shape=[10], dtype='float32')
             x.stop_gradient = False
-            y = layers.data(
-                name='y', shape=[1], dtype='float32', append_batch_size=False)
-            y.stop_gradient = False
-            out = layers.while_loop(external_cond, external_body, [i, x, y])
+            i = layers.zeros(shape=[1], dtype='int64')
+            i.stop_gradient = True
+            init = layers.zeros(shape=[10], dtype='float32')
+            mem_array = layers.array_write(x=init, i=i)
+            data_array = layers.array_write(x=d0, i=i)
+            i = layers.increment(i)
+            layers.array_write(d1, i, array=data_array)
+            i = layers.increment(i)
+            layers.array_write(d2, i, array=data_array)
+            i = layers.zeros(shape=[1], dtype='int64')
+            i.stop_gradient = True
+            array_len = layers.fill_constant(shape=[1], dtype='int64', value=1)
+            j = layers.fill_constant(shape=[1], dtype='int64', value=1)
+            j.stop_gradient = True
+            array_len2 = layers.fill_constant(shape=[1], dtype='int64', value=3)
 
-            mean = layers.mean(out[2])
+            out = layers.while_loop(external_cond, external_body,
+                                    [i, j, x, mem_array])
+
+            sum_result = layers.array_read(array=mem_array, i=j)
+            mean = layers.mean(sum_result)
             append_backward(mean)
 
             place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
             ) else fluid.CPUPlace()
             exe = fluid.Executor(place)
 
-        data = np.asarray([17]).astype('float32')
-        feed_x = np.zeros(1).astype('float32')
-        feed_i = np.ones(1).astype('float32')
-        feed_y = np.zeros(1).astype('float32')
-        i_grad = np.asarray(13).astype('int32')
-
-        res = exe.run(main_program,
-                      feed={'i': feed_i,
-                            'x': feed_x,
-                            'y': feed_y},
-                      fetch_list=[mean.name, i.grad_name])
-
-        self.assertTrue(np.allclose(np.asarray(res[0]), data))
-        self.assertTrue(np.allclose(np.asarray(res[1]), i_grad))
+            d = []
+            for i in range(3):
+                d.append(np.random.random(size=[10]).astype('float32'))
+            feed_x = np.ones(10).astype('float32')
+            data_sum = d[0] + d[1] + d[2] + 3 * feed_x
+            x_grad = [0.3] * 10
+            res = exe.run(
+                main_program,
+                feed={'d0': d[0],
+                      'd1': d[1],
+                      'd2': d[2],
+                      'x': feed_x},
+                fetch_list=[sum_result.name, x.grad_name])
+            self.assertTrue(np.allclose(res[0], data_sum))
+            self.assertTrue(np.allclose(res[1], x_grad))
 
 
 class TestApiWhileLoopWithSwitchCase(unittest.TestCase):