Merge pull request #7251 from guoshengCS/enhance-reorderTensor

Enhance reorder_lod_tensor_by_rank_op to support Tensor

paddle/operators/reduce_op.cc

@@ -77,6 +77,7 @@ class ReduceGradOp : public framework::OperatorWithKernel {
     auto x_grad_name = framework::GradVarName("X");
     if (ctx->HasOutput(x_grad_name)) {
       ctx->SetOutputDim(x_grad_name, x_dims);
+      ctx->ShareLoD("X", /*->*/ x_grad_name);
     }
   }
 };

paddle/operators/reorder_lod_tensor_by_rank_op.cc

@@ -88,6 +88,17 @@ class ReorderLoDTensorByRankTableBase : public framework::OperatorBase {
   std::vector<AbsoluteRankTableItem> GetAbsoluteOffsetAndLengthByLoDRankTable(
       const framework::LoDTensor &x) const {
     std::vector<AbsoluteRankTableItem> absolute_table;
+
+    if (x.lod().empty()) {
+      // For Tensor without lod, such as the output of sequence_pool_op
+      size_t size = x.dims()[0];
+      absolute_table.reserve(size);
+      for (size_t i = 0; i < size; ++i) {
+        absolute_table.emplace_back();
+        absolute_table.back().length = 1;
+        absolute_table.back().offset = i;
+      }
+    } else {
       size_t level = 0;
       size_t size = x.lod()[level].size();
 
@@ -102,6 +113,8 @@ class ReorderLoDTensorByRankTableBase : public framework::OperatorBase {
         absolute_table.back().offset = offset.first;
         absolute_table.back().lod = lod_offset.first;
       }
+    }
+
     return absolute_table;
   }
 

python/paddle/v2/fluid/layers/control_flow.py

@@ -565,7 +565,7 @@ def lod_rank_table(x, level=0):
     """LoD Rank Table Operator. Given an input variable **x** and a level number
     of LoD, this layer creates a LodRankTable object. A LoDRankTable object
     contains a list of bi-element tuples. Each tuple consists of an index and
-    a length, both of which are int type. Reffering to specified level of LoD,
+    a length, both of which are int type. Refering to specified level of LoD,
     the index is the sequence index number and the length representes the
     sequence length. Please note that the list is ranked in descending order by
     the length. The following is an example:

python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py

 import unittest
 import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
 import numpy
 
 
 class TestReorderLoDTensor(unittest.TestCase):
-    def test_reorder(self):
-        dat = fluid.layers.data(name='input', shape=[1], lod_level=2)
+    num_seq = 5
+    # [name, dim, lod_level] pair indicating data info of source and target
+    data_desc = (['input', 9, 0], ['ref', 5, 1])
+
+    @classmethod
+    def setUpClass(cls):
+        cls.set_program()
+
+    @classmethod
+    def set_program(cls):
+        dat = fluid.layers.data(
+            name=cls.data_desc[0][0], shape=[cls.data_desc[0][1]])
         dat.stop_gradient = False
-        rank_dat = fluid.layers.data(name='ref', shape=[1], lod_level=1)
+        rank_dat = fluid.layers.data(
+            name=cls.data_desc[1][0], shape=[cls.data_desc[1][1]])
         table = fluid.layers.lod_rank_table(rank_dat)
         new_dat = fluid.layers.reorder_lod_tensor_by_rank(
             x=dat, rank_table=table)
-        loss = fluid.layers.mean(x=new_dat)
+        loss = fluid.layers.reduce_sum(new_dat)
         fluid.backward.append_backward(loss=loss)
+        cls.fetch_list = [new_dat, cls.data_desc[0][0] + '@GRAD']
+
+    def run_program(self):
+        outputs = []
+        input_grads = []
+        places = [core.CPUPlace()]
+        if core.is_compile_gpu():
+            places.append(core.CUDAPlace(0))
+        for place in places:
+            self.set_inputs(place)
+            exe = fluid.Executor(place)
+            output, input_grad = exe.run(fluid.default_main_program(),
+                                         feed=self.inputs,
+                                         fetch_list=self.fetch_list,
+                                         return_numpy=False)
+            outputs.append(output)
+            input_grads.append(input_grad)
+        self.actual_outputs = outputs
+        self.actual_grads = input_grads
+
+    def set_data(self):
+        self.data = {}
+        for desc in self.data_desc:
+            data_name = desc[0]
+            data_dim = desc[1]
+            data_lod_level = desc[2]
+            data_lod = []
+            for i in range(data_lod_level):
+                lod_level_i = numpy.random.randint(
+                    low=1,
+                    high=5,
+                    size=self.num_seq if i == 0 else lod_level_i[-1])
+                lod_level_i = [0] + numpy.cumsum(lod_level_i).tolist()
+                data_lod.append(lod_level_i)
+            data_value = numpy.random.random(size=[
+                data_lod[-1][-1] if data_lod else self.num_seq, data_dim
+            ]).astype('float32')
+            self.data[data_name] = (data_value, data_lod)
+
+    def set_inputs(self, place):
+        self.inputs = {}
+        for desc in self.data_desc:
+            tensor = fluid.Tensor()
+            tensor.set(self.data[desc[0]][0], place)
+            if self.data[desc[0]][1]:
+                tensor.set_lod(self.data[desc[0]][1])
+            self.inputs[desc[0]] = tensor
+
+    def reorder(self):
+        level = 0
+
+        # compute the rank_table according to ref_lod
+        ref_lod = self.data[self.data_desc[1][0]][1][level]
+        rank_table = []  # list of (index, length)
+        for i in range(len(ref_lod) - 1):
+            rank_table.append((i, ref_lod[i + 1] - ref_lod[i]))
+        rank_table = sorted(rank_table, lambda x, y: y[1] - x[1])
+
+        # compute the input sequence info according to input_lod
+        input_value, input_lod = self.data[self.data_desc[0][0]]
+
+        input_table = []  # list of (offset, length, sub_lod)
+        if input_lod:
+            for i in range(len(input_lod[level]) - 1):
+                start_idx = i
+                end_idx = i + 1
+                sub_lod = []
+                for lod_level_i in input_lod[level:]:
+                    sub_lod_i = []
+                    for idx in range(start_idx, end_idx):
+                        sub_lod_i.append(lod_level_i[idx + 1] - lod_level_i[
+                            idx])
+                    sub_lod.append(sub_lod_i)
+                    start_idx = lod_level_i[start_idx]
+                    end_idx = lod_level_i[end_idx]
+                input_table.append((start_idx, end_idx - start_idx, sub_lod))
+        else:
+            input_table = [(i, 1, []) for i in range(len(rank_table))]
+
+        # reorder by rank_table
+        output_value = numpy.zeros_like(input_value)
+        output_lod = []
+        offset = 0
+        for index, length in rank_table:
+            input_seq_start = input_table[index][0]
+            input_seq_len = input_table[index][1]
+            input_seq_end = input_seq_start + input_seq_len
+            output_value[offset:offset + input_seq_len] = input_value[
+                input_seq_start:input_seq_end]
+            offset += input_seq_len
+
+            input_seq_sub_lod = input_table[index][2]
+            if len(output_lod) == 0:
+                output_lod = [[0] for i in input_seq_sub_lod]
+            for i, sub_lod_i in enumerate(input_seq_sub_lod):
+                for idx_sub in sub_lod_i:
+                    output_lod[i].append(output_lod[i][-1] + idx_sub)
+        return output_value, output_lod
+
+    def test_reorder_lod_tensor(self):
+        self.data_desc[0][-1] = 2  # input is lod_tensor
+        self.set_data()
+        self.run_program()
+        # check output
+        expect_output, expect_output_lod = self.reorder()
+        for actual_output in self.actual_outputs:
+            self.assertTrue(
+                numpy.allclose(
+                    numpy.array(actual_output), expect_output, atol=0.001))
+            self.assertEqual(expect_output_lod, actual_output.lod())
+        # check gradient
+        expect_grad = numpy.ones_like(self.data[self.data_desc[0][0]][0])
+        expect_grad_lod = self.data[self.data_desc[0][0]][1]
+        for actual_grad in self.actual_grads:
+            self.assertTrue(
+                numpy.allclose(
+                    numpy.array(actual_grad), expect_grad, atol=0.001))
+            self.assertEqual(expect_grad_lod, actual_grad.lod())
+
+    def test_reorder_tensor(self):
+        self.data_desc[0][-1] = 0  # input is tensor
+        self.set_data()
+        self.run_program()
+        # check output
+        expect_output, expect_output_lod = self.reorder()
+        for actual_output in self.actual_outputs:
+            self.assertTrue(
+                numpy.allclose(
+                    numpy.array(actual_output), expect_output, atol=0.001))
+            self.assertEqual(expect_output_lod, actual_output.lod())
+        # check gradient
+        expect_grad = numpy.ones_like(self.data[self.data_desc[0][0]][0])
+        expect_grad_lod = self.data[self.data_desc[0][0]][1]
+        for actual_grad in self.actual_grads:
+            self.assertTrue(
+                numpy.allclose(
+                    numpy.array(actual_grad), expect_grad, atol=0.001))
+            self.assertEqual(expect_grad_lod, actual_grad.lod())
+        global outputs_from_tensor_implicit_lod
+        outputs_from_tensor_implicit_lod = self.actual_outputs
 
-        cpu = fluid.CPUPlace()
-        exe = fluid.Executor(cpu)
-        exe.run(fluid.default_startup_program())
-
-        ref = fluid.Tensor()
-        ref_lod = [0, 3, 4, 7, 8, 14]
-        ref.set_lod([ref_lod])
-
-        ref.set(numpy.random.random(size=[14, 1]).astype('float32'), cpu)
-        input = fluid.Tensor()
-        lod_level_0 = numpy.random.randint(low=1, high=5, size=5)
-        lod_level_0 = [0] + numpy.cumsum(lod_level_0).tolist()
-        lod_level_1 = numpy.random.randint(low=1, high=5, size=lod_level_0[-1])
-        lod_level_1 = [0] + numpy.cumsum(lod_level_1).tolist()
-
-        input.set_lod([lod_level_0, lod_level_1])
-        input.set(
-            numpy.random.random(size=[lod_level_1[-1], 1]).astype('float32'),
-            cpu)
-
-        ig = exe.run(fluid.default_main_program(),
-                     feed={'input': input,
-                           'ref': ref},
-                     fetch_list=['input@GRAD'],
-                     return_numpy=False)[0]
-        self.assertAlmostEqual(numpy.array(ig).sum(), 1.0, delta=0.001)
-        self.assertEqual(input.lod(), ig.lod())
+        # compare outputs between LodTensors with explicit and implicit lod
+        # use the same data but set the input lod explicitly
+        input_lod = [[
+            i for i in range(len(self.data[self.data_desc[0][0]][0]) + 1)
+        ]]
+        self.inputs[self.data_desc[0][0]].set_lod(input_lod)
+        # preserve the output of LodTensor with implicit lod to compare
+        expect_output = [
+            numpy.array(actual_output) for actual_output in self.actual_outputs
+        ]
+        self.run_program()
+        for actual_output in self.actual_outputs:
+            self.assertTrue(
+                numpy.allclose(
+                    numpy.array(actual_output), expect_output, atol=0.001))
 
 
 if __name__ == '__main__':