Feature/tensor array add python binding (#4616)

paddle/framework/tensor_array.h

@@ -26,6 +26,9 @@ namespace framework {
  * in original lod-tensor.
  */
 struct DySeqMeta {
+  DySeqMeta(size_t begin, size_t end, size_t ori_idx)
+      : begin(begin), end(end), ori_idx(ori_idx) {}
+
   size_t begin;
   size_t end;  // not included
   size_t ori_idx;

paddle/pybind/CMakeLists.txt

 if(WITH_PYTHON)
   cc_library(paddle_pybind SHARED
     SRCS pybind.cc exception.cc protobuf.cc
-    DEPS pybind python backward proto_desc
+    DEPS pybind python backward proto_desc tensor_array
     ${GLOB_OP_LIB})
 endif(WITH_PYTHON)

paddle/pybind/pybind.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include "paddle/framework/backward.h"
 #include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/tensor_array.h"
 #include "paddle/operators/cond_op.h"
 #include "paddle/operators/net_op.h"
 #include "paddle/operators/recurrent_op.h"
@@ -286,6 +287,56 @@ All parameter, weight, gradient are variables in Paddle.
         self->CompleteAddOp();
       });
 
+  py::class_<framework::TensorArray>(m, "TensorArray")
+      .def("__init__",
+           [](TensorArray &instance) { new (&instance) TensorArray(); })
+      .def("read",
+           [](TensorArray &self, size_t index) { return self.Read(index); })
+      .def("write", [](TensorArray &self, size_t index,
+                       LoDTensor &value) { self.Write(index, value); })
+      .def("write_shared",
+           [](TensorArray &self, size_t index, const LoDTensor &value) {
+             self.WriteShared(index, value);
+           })
+      .def("size", [](TensorArray &self) { return self.size(); })
+      .def("pack",
+           [](TensorArray &self, size_t level,
+              const std::vector<std::vector<size_t>> &meta_info,
+              const std::vector<std::vector<size_t>> &lod) {
+             std::vector<DySeqMeta> meta;
+             for (auto &info : meta_info) {
+               PADDLE_ENFORCE_EQ(info.size(), 3UL);
+               meta.emplace_back(info[0], info[1], info[2]);
+             }
+#ifndef PADDLE_WITH_CUDA
+             return self.Pack(level, meta, lod);
+#else
+             LoD new_lod;
+             new_lod.reserve(lod.size());
+             std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+             return self.Pack(level, meta, new_lod);
+#endif
+           })
+      .def("unpack",
+           [](TensorArray &self, const LoDTensor &source, int level,
+              bool length_descend) {
+             auto metas = self.Unpack(source, level, length_descend);
+             std::vector<std::vector<size_t>> meta_info;
+             for (auto meta : metas) {
+               meta_info.emplace_back(
+                   std::vector<size_t>({meta.begin, meta.end, meta.ori_idx}));
+             }
+             return meta_info;
+           })
+      .def("stack", [](TensorArray &self) { return self.Stack(); })
+      .def("unstack",
+           [](TensorArray &self, const LoDTensor &source) {
+             return self.Unstack(source);
+           })
+      .def("unstack_shared", [](TensorArray &self, const LoDTensor &source) {
+        return self.UnstackShared(source);
+      });
+
   // recurrent_op
   py::class_<operators::RecurrentOp, OperatorBase>(m, "RecurrentOp")
       .def_static(

python/paddle/v2/framework/tests/test_tensor_array.py

+import logging
+import paddle.v2.framework.core as core
+import unittest
+import numpy as np
+
+
+class TestTensorArray(unittest.TestCase):
+    def setUp(self):
+        self.ta = core.TensorArray()
+
+        self.batch_size = 10
+        self.dim = 2
+
+        # create a LoDTensor
+        self.scope = core.Scope()
+        var = self.scope.new_var("test_tensor")
+        self.place = core.CPUPlace()
+        tensor = var.get_tensor()
+        tensor.set_dims([self.batch_size, self.dim])
+        tensor.alloc_float(self.place)
+        tensor_array = np.array(tensor)
+        tensor_array[0, 0] = 0
+        tensor_array[1, 0] = 1
+        tensor_array[2, 0] = 2
+        tensor_array[3, 0] = 3
+        tensor_array[4, 0] = 4
+        tensor_array[5, 0] = 5
+        tensor_array[6, 0] = 6
+        tensor_array[7, 0] = 7
+        tensor_array[8, 0] = 8
+        tensor_array[9, 0] = 9
+
+        lod_py = [[0, 2, 5, 10]]
+        lod_tensor = core.LoDTensor(lod_py)
+        lod_tensor.set(tensor_array, self.place)
+
+        self.py_seq_meta = [[5, 10, 2], [2, 5, 1], [0, 2, 0]]
+
+        self.tensor = lod_tensor
+
+    def test_unstack(self):
+        self.ta.unstack(self.tensor)
+        self.assertEqual(self.tensor.get_dims()[0], self.ta.size())
+
+    def test_read(self):
+        self.ta.unstack(self.tensor)
+        for i in range(self.batch_size):
+            tensor = self.ta.read(i)
+
+    def test_write(self):
+        self.ta.unstack(self.tensor)
+
+        # create a tensor with shape of [1, self.dim]
+        var = self.scope.new_var("hell")
+        tensor = var.get_tensor()
+        tensor.set_dims([1, self.dim])
+        tensor.alloc_float(self.place)
+        tensor_array = np.array(tensor)
+        for i in range(self.dim):
+            tensor_array[0, i] = i
+        tensor.set(tensor_array, self.place)
+
+        self.ta.write(2, tensor)
+
+        ta_tensor = self.ta.read(2)
+        ta_tensor_array = np.array(ta_tensor)
+        self.assertEqual(ta_tensor.get_dims(), [1, self.dim])
+        self.assertTrue((tensor_array == ta_tensor_array).all())
+
+    def test_write_shared(self):
+        self.ta.unstack(self.tensor)
+
+        # create a tensor with shape of [1, self.dim]
+        var = self.scope.new_var("hell")
+        tensor = var.get_tensor()
+        tensor.set_dims([1, self.dim])
+        tensor.alloc_float(self.place)
+        tensor_array = np.array(tensor)
+        for i in range(self.dim):
+            tensor_array[0, i] = i
+        tensor.set(tensor_array, self.place)
+
+        self.ta.write_shared(2, tensor)
+
+        ta_tensor = self.ta.read(2)
+        ta_tensor_array = np.array(ta_tensor)
+        self.assertEqual(ta_tensor.get_dims(), [1, self.dim])
+        self.assertTrue((tensor_array == ta_tensor_array).all())
+
+    def test_unpack(self):
+        meta = self.ta.unpack(self.tensor, 0, True)
+        self.assertEqual(self.ta.size(), 5)
+        self.assertEqual(meta, self.py_seq_meta)
+
+    def test_pack(self):
+        meta = self.ta.unpack(self.tensor, 0, True)
+        print "meta", meta
+        tensor = self.ta.pack(0, meta, self.tensor.lod())
+        print np.array(self.tensor)
+        print np.array(tensor)
+        self.assertTrue((np.array(self.tensor) == np.array(tensor)).all())
+        self.assertTrue(tensor.lod(), self.tensor.lod())
+
+
+if __name__ == '__main__':
+    unittest.main()