support tensor array create read write in dygraph (#23200)

* support tensor array create read write in dygraph
test=develop

* minor fix
test=develop

* support tensor_array_to_tensor
test=develop

* add while loop case and err msg
test=develop

* refine ut
test=develop

python/paddle/fluid/layers/control_flow.py

@@ -1287,6 +1287,31 @@ def array_write(x, i, array=None):
             #       and '__int64' on Windows. They both represent 64-bit integer variables.
 
     """
+    if in_dygraph_mode():
+        assert isinstance(
+            x, Variable
+        ), "The input data 'x' in array_write must be Variable in dygraph mode"
+        assert isinstance(
+            i, Variable
+        ), "The index 'i' in array_write must be Variable in dygraph mode"
+        assert i.shape == [
+            1
+        ], "The shape of index 'i' should be [1] in dygraph mode"
+        i = i.numpy()[0]
+        if array is None:
+            array = create_array(x.dtype)
+        assert isinstance(
+            array,
+            list), "The 'array' in array_write must be a list in dygraph mode"
+        assert i <= len(
+            array
+        ), "The index 'i' should not be greater than the length of 'array' in dygraph mode"
+        if i < len(array):
+            array[i] = x
+        else:
+            array.append(x)
+        return array
+
     helper = LayerHelper('array_write', **locals())
     if array is None:
         array = helper.create_variable(
@@ -1322,6 +1347,9 @@ def create_array(dtype):
           data = fluid.layers.create_array(dtype='float32') # Create a float32 LoDTensorArray.
 
     """
+    if in_dygraph_mode():
+        return []
+
     helper = LayerHelper("array", **locals())
     return helper.create_variable(
         name="{0}.out".format(helper.name),
@@ -1643,6 +1671,19 @@ def array_read(array, i):
             #       so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux, 
             #       and '__int64' on Windows. They both represent 64-bit integer variables.
     """
+    if in_dygraph_mode():
+        assert isinstance(
+            array,
+            list), "The 'array' in array_read must be list in dygraph mode"
+        assert isinstance(
+            i, Variable
+        ), "The index 'i' in array_read must be Variable in dygraph mode"
+        assert i.shape == [
+            1
+        ], "The shape of index 'i' should be [1] in dygraph mode"
+        i = i.numpy()[0]
+        return array[i]
+
     helper = LayerHelper('array_read', **locals())
     if not isinstance(
             array,
@@ -1739,6 +1780,12 @@ def array_length(array):
             #       so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux, 
             #       and '__int64' on Windows. They both represent 64-bit integer variables.
     """
+    if in_dygraph_mode():
+        assert isinstance(
+            array,
+            list), "The 'array' in array_write must be a list in dygraph mode"
+        return len(array)
+
     helper = LayerHelper('array_length', **locals())
     tmp = helper.create_variable_for_type_inference(dtype='int64')
     tmp.stop_gradient = True

python/paddle/fluid/layers/tensor.py

@@ -381,6 +381,17 @@ def tensor_array_to_tensor(input, axis=1, name=None, use_stack=False):
             fluid.layers.array_write(x1, i + 1, array)
             output, output_index = fluid.layers.tensor_array_to_tensor(input=array)
     """
+    if in_dygraph_mode():
+        assert isinstance(
+            input, list), "The 'input' in tensor_array_to_tensor must be list"
+        from .nn import stack, concat
+        from ..dygraph import to_variable
+        op = stack if use_stack else concat
+        res = op(input, axis=axis)
+        sizes = to_variable(
+            numpy.array(list(map(lambda x: int(x.shape[axis]), input))))
+        return res, sizes
+
     helper = LayerHelper('tensor_array_to_tensor', **locals())
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     out_index = helper.create_variable_for_type_inference(dtype="int32")

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

@@ -15,6 +15,7 @@
 from __future__ import print_function
 
 import unittest
+import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.fluid.layers as layers
 from paddle.fluid.executor import Executor
@@ -23,18 +24,7 @@ from paddle.fluid.framework import default_main_program
 import numpy
 
 
-class TestArrayReadWrite(unittest.TestCase):
-    def test_read_write(self):
-        x = [
-            layers.data(
-                name='x0', shape=[100]), layers.data(
-                    name='x1', shape=[100]), layers.data(
-                        name='x2', shape=[100])
-        ]
-
-        for each_x in x:
-            each_x.stop_gradient = False
-
+def _test_read_write(x):
     i = layers.zeros(shape=[1], dtype='int64')
     i.stop_gradient = False
     arr = layers.array_write(x=x[0], i=i)
@@ -63,18 +53,30 @@ class TestArrayReadWrite(unittest.TestCase):
 
     x_sum = layers.sums(input=[mean_x0, mean_x1, mean_x2])
 
-        scope = core.Scope()
-        cpu = core.CPUPlace()
+    return a_sum, x_sum
 
-        exe = Executor(cpu)
+
+class TestArrayReadWrite(unittest.TestCase):
+    def test_read_write(self):
+        x = [
+            layers.data(
+                name='x0', shape=[100]), layers.data(
+                    name='x1', shape=[100]), layers.data(
+                        name='x2', shape=[100])
+        ]
+        for each_x in x:
+            each_x.stop_gradient = False
 
         tensor = numpy.random.random(size=(100, 100)).astype('float32')
+        a_sum, x_sum = _test_read_write(x)
 
+        place = core.CPUPlace()
+        exe = Executor(place)
         outs = exe.run(feed={'x0': tensor,
                              'x1': tensor,
                              'x2': tensor},
                        fetch_list=[a_sum, x_sum],
-                       scope=scope)
+                       scope=core.Scope())
         self.assertEqual(outs[0], outs[1])
 
         total_sum = layers.sums(input=[a_sum, x_sum])
@@ -100,6 +102,28 @@ class TestArrayReadWrite(unittest.TestCase):
         # the input gradient should also be 1
         self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
 
+        with fluid.dygraph.guard(place):
+            tensor1 = fluid.dygraph.to_variable(tensor)
+            tensor2 = fluid.dygraph.to_variable(tensor)
+            tensor3 = fluid.dygraph.to_variable(tensor)
+            x_dygraph = [tensor1, tensor2, tensor3]
+            for each_x in x_dygraph:
+                each_x.stop_gradient = False
+            a_sum_dygraph, x_sum_dygraph = _test_read_write(x_dygraph)
+            self.assertEqual(a_sum_dygraph, x_sum_dygraph)
+
+            total_sum_dygraph = layers.sums(
+                input=[a_sum_dygraph, x_sum_dygraph])
+            total_sum_scaled_dygraph = layers.scale(
+                x=total_sum_dygraph, scale=1 / 6.0)
+            total_sum_scaled_dygraph.backward()
+            g_out_dygraph = [
+                item._grad_ivar().numpy().sum() for item in x_dygraph
+            ]
+            g_out_sum_dygraph = numpy.array(g_out_dygraph).sum()
+
+            self.assertAlmostEqual(1.0, g_out_sum_dygraph, delta=0.1)
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -204,17 +204,61 @@ class TestLoDTensorArrayStack(unittest.TestCase):
 
 
 class TestTensorArrayToTensorAPI(unittest.TestCase):
-    def test_case(self):
-        x0 = fluid.layers.assign(numpy.random.rand(2, 3, 4).astype("float32"))
-        x1 = fluid.layers.assign(numpy.random.rand(2, 3, 4).astype("float32"))
+    def _test_case(self, inp1, inp2):
+        x0 = fluid.layers.assign(inp1)
+        x0.stop_gradient = False
+        x1 = fluid.layers.assign(inp2)
+        x1.stop_gradient = False
         i = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
         array = fluid.layers.create_array(dtype='float32')
         fluid.layers.array_write(x0, i, array)
         fluid.layers.array_write(x1, i + 1, array)
-        output, output_index = fluid.layers.tensor_array_to_tensor(
+        output_stack, output_index_stack = fluid.layers.tensor_array_to_tensor(
             input=array, axis=1, use_stack=True)
-        output, output_index = fluid.layers.tensor_array_to_tensor(
+        output_concat, output_index_concat = fluid.layers.tensor_array_to_tensor(
             input=array, axis=1, use_stack=False)
+        return output_stack, output_index_stack, output_concat, output_index_concat
+
+    def test_case(self):
+        inp0 = numpy.random.rand(2, 3, 4).astype("float32")
+        inp1 = numpy.random.rand(2, 3, 4).astype("float32")
+
+        _outs_static = self._test_case(inp0, inp1)
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        outs_static = exe.run(fetch_list=list(_outs_static))
+
+        with fluid.dygraph.guard(place):
+            outs_dynamic = self._test_case(inp0, inp1)
+
+        for s, d in zip(outs_static, outs_dynamic):
+            self.assertTrue(numpy.array_equal(s, d.numpy()))
+
+    def test_while_loop_case(self):
+        with fluid.dygraph.guard():
+            zero = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
+            ten = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
+            array = fluid.layers.create_array(dtype='float32')
+            inp0 = numpy.random.rand(2, 3, 4).astype("float32")
+            x0 = fluid.layers.assign(inp0)
+            fluid.layers.array_write(x0, zero, array)
+
+            def cond(i, end, array):
+                return fluid.layers.less_than(i, end)
+
+            def body(i, end, array):
+                prev = fluid.layers.array_read(array, i - 1)
+                fluid.layers.array_write(prev, i, array)
+                return i + 1, end, array
+
+            _, _, array = fluid.layers.while_loop(cond, body, [i, ten, array])
+
+            self.assertTrue(fluid.layers.array_length(array), 10)
+            last = fluid.layers.fill_constant(shape=[1], dtype='int64', value=9)
+            self.assertTrue(
+                numpy.array_equal(
+                    fluid.layers.array_read(array, last).numpy(), inp0))
 
 
 if __name__ == '__main__':