Fix instag (#22632)

* update

* update test=develop

* update compile set test=develop

* update compile set test=develop

* update test=develop

* update test=develop

* update test=develop

* update compile setting test=develop

* update compile setting test=develop

* update run demo test=develop

* update test=develop

* update test=develop

* fix test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update format test=develop

* update format test=develop

* update style test=develop

* update style test=develop

* change style test=develop

* change style test=develop

* change style test=develop

* add dataset unittest test=develop

* update test=develop

* update for record test=develop

* udpate style for record test=develop

* update for record test=develop

* update for record test=develop

* update for record test=develop

* fix format test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* update test=develop

* fix compile warning test=develop

* add attr default test=develop

* add unittest test=develop

* fix style test=develop

* fix style test=develop

* change out_val_ifempty to out_val_if_empty test=develop

paddle/fluid/operators/filter_by_instag_op.cc

@@ -60,6 +60,9 @@ class FilterByInstagOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Ins_tag", "(LoDTensor) ins tag list");
     AddInput("Filter_tag", "(1D Tensor) filter tag list");
     AddAttr<bool>("is_lod", "is Ins with LoD info or not, default True");
+    AddAttr<int64_t>("out_val_if_empty",
+                     "if the output after filter is empty, the output value")
+        .SetDefault(0);
     AddOutput("Out", "(LoDTensor) embeded tensor filtered by instag");
     AddOutput("LossWeight", "(Tensor) loss weight.");
     AddOutput("IndexMap", "(LoDTensor) mapping from Out rows to X1 rows");

paddle/fluid/operators/filter_by_instag_op.h

@@ -47,6 +47,7 @@ class FilterByInstagKernel : public framework::OpKernel<T> {
     // Dim [batch size, embedding size]
     auto* x1 = context.Input<LoDTensor>("Ins");
     bool is_x1_lod = context.Attr<bool>("is_lod");
+    int64_t out_val_if_empty = context.Attr<int64_t>("out_val_if_empty");
     // X2 is ins tag list
     // LoD [[0, Sum(ins1), Sum(ins1, ins2), ... ]]
     auto* x2 = context.Input<LoDTensor>("Ins_tag");
@@ -157,7 +158,15 @@ class FilterByInstagKernel : public framework::OpKernel<T> {
       std::vector<Vector<size_t>> out_lod_info;
       out_lod_info.push_back(out_lods);
       out->set_lod(out_lod_info);
-      memset(out_data, 0, out->numel() * sizeof(T));
+      for (int64_t oi = 0; oi < out->numel(); ++oi) {
+        if (std::is_same<T, int32_t>::value) {
+          out_data[oi] = (int32_t)out_val_if_empty;
+        } else if (std::is_same<T, int64_t>::value) {
+          out_data[oi] = (int64_t)out_val_if_empty;
+        } else {
+          out_data[oi] = static_cast<double>(out_val_if_empty);
+        }
+      }
       loss_weight_data[0] = 0;
     }
   }

paddle/fluid/operators/metrics/auc_op.h

@@ -102,9 +102,9 @@ class AucKernel : public framework::OpKernel<T> {
                               "The predict data must gather or equal 0."));
 
         uint32_t binIdx = static_cast<uint32_t>(predict_data * num_thresholds);
-        if (label_data[i]) {
+        if (label_data[i] > 0) {
           origin_stat_pos[binIdx] += 1;
-        } else {
+        } else if (label_data[i] == 0) {
           origin_stat_neg[binIdx] += 1;
         }
       }
@@ -142,9 +142,9 @@ class AucKernel : public framework::OpKernel<T> {
                             "The predict data must gather or equal 0."));
 
       uint32_t binIdx = static_cast<uint32_t>(predict_data * num_thresholds);
-      if (label_data[i]) {
+      if (label_data[i] > 0) {
         origin_stat_pos[cur_step_begin + binIdx] += 1;
-      } else {
+      } else if (label_data[i] == 0) {
         origin_stat_neg[cur_step_begin + binIdx] += 1;
       }
     }

python/paddle/fluid/layers/nn.py

@@ -9147,7 +9147,7 @@ def stack(x, axis=0):
 
 
 @templatedoc(op_type="filter_by_instag")
-def filter_by_instag(ins, ins_tag, filter_tag, is_lod):
+def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0):
     """
     **Filter By Instag Layer**
    
@@ -9184,6 +9184,8 @@ def filter_by_instag(ins, ins_tag, filter_tag, is_lod):
         filter_tag (Variable): Input Variable (1D Tensor/List), usually it is 
                         list that holds the tags.
         is_lod (Bool): Boolean value to indicate ins is lod tensor or not.
+        out_val_if_empty(Int64): If the output after filter is empty, this value
+                        will be set to Output tensor.
 
     Returns:
         Variable: filtered ins (LoDTensor) and loss weight (Tensor)
@@ -9211,7 +9213,8 @@ def filter_by_instag(ins, ins_tag, filter_tag, is_lod):
         outputs={'Out': out,
                  'LossWeight': loss_weight,
                  'IndexMap': mmap},
-        attrs={'is_lod': is_lod})
+        attrs={'is_lod': is_lod,
+               'out_val_if_empty': out_val_if_empty})
 
     return [out, loss_weight]
 

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

@@ -23,6 +23,7 @@ import paddle.fluid.layers as layers
 from op_test import OpTest
 import random
 from decorator_helper import prog_scope
+from paddle.fluid.op import Operator
 """This is Test Case 1"""
 
 
@@ -71,7 +72,7 @@ class TestFilterByInstagOp(OpTest):
             'IndexMap': (mmap, mmap_lod)
         }
 
-        self.attrs = {'is_lod': True}
+        self.attrs = {'is_lod': True, 'out_val_if_empty': 0}
 
     def test_check_output(self):
         self.check_output()
@@ -116,7 +117,7 @@ class TestFilterByInstagOp2(OpTest):
             'LossWeight': (loss_weight, mmap_lod),
             'IndexMap': (mmap, mmap_lod)
         }
-        self.attrs = {'is_lod': True, }
+        self.attrs = {'is_lod': True, 'out_val_if_empty': 0}
 
     def test_check_output(self):
         self.check_output()
@@ -158,7 +159,7 @@ class TestFilterByInstagOp3(OpTest):
             'LossWeight': (loss_weight, mmap_lod),
             'IndexMap': (mmap, mmap_lod)
         }
-        self.attrs = {'is_lod': True, }
+        self.attrs = {'is_lod': True, 'out_val_if_empty': 0}
 
     def test_check_output(self):
         self.check_output()
@@ -199,7 +200,7 @@ class TestFilterByInstagOp4(OpTest):
             'LossWeight': (loss_weight, mmap_lod),
             'IndexMap': (mmap, mmap_lod)
         }
-        self.attrs = {'is_lod': False, }
+        self.attrs = {'is_lod': False, 'out_val_if_empty': 0}
 
     def test_check_output(self):
         self.check_output()
@@ -209,5 +210,79 @@ class TestFilterByInstagOp4(OpTest):
             ['Ins'], 'Out', no_grad_set=set(['Ins_tag', 'Filter_tag']))
 
 
+class TestFilterByInstagOp6(OpTest):
+    def setUp(self):
+        self.op_type = 'filter_by_instag'
+
+        x1 = np.random.random((4, 36)).astype('int64')
+
+        x2 = np.array([[2], [1], [2], [1]]).astype('int64')
+        x2_lod = [[1, 1, 1, 1]]
+
+        x3 = np.array([3]).astype('int64')
+
+        out = np.zeros((1, 36)).astype('double')
+        out_lod = [[1]]
+
+        mmap = np.array([[0, 1, 1]]).astype('int64')
+        mmap_lod = [[1]]
+
+        loss_weight = np.array([[0]]).astype('double')
+        self.inputs = {
+            'Ins': x1,
+            'Ins_tag': (x2, x2_lod),
+            'Filter_tag': x3,
+        }
+        self.outputs = {
+            'Out': (out, out_lod),
+            'LossWeight': (loss_weight, mmap_lod),
+            'IndexMap': (mmap, mmap_lod)
+        }
+        self.attrs = {'is_lod': False, 'out_val_if_empty': 0}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        pass
+
+
+class TestFilterByInstagOp7(OpTest):
+    def setUp(self):
+        self.op_type = 'filter_by_instag'
+
+        x1 = np.random.random((4, 36)).astype('int32')
+
+        x2 = np.array([[2], [1], [2], [1]]).astype('int64')
+        x2_lod = [[1, 1, 1, 1]]
+
+        x3 = np.array([3]).astype('int64')
+
+        out = np.zeros((1, 36)).astype('double')
+        out_lod = [[1]]
+
+        mmap = np.array([[0, 1, 1]]).astype('int64')
+        mmap_lod = [[1]]
+
+        loss_weight = np.array([[0]]).astype('double')
+        self.inputs = {
+            'Ins': x1,
+            'Ins_tag': (x2, x2_lod),
+            'Filter_tag': x3,
+        }
+        self.outputs = {
+            'Out': (out, out_lod),
+            'LossWeight': (loss_weight, mmap_lod),
+            'IndexMap': (mmap, mmap_lod)
+        }
+        self.attrs = {'is_lod': False, 'out_val_if_empty': 0}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        pass
+
+
 if __name__ == '__main__':
     unittest.main()