Add ignore_index in cross_entropy op (#13217)

* add ignore index

* update api.spec

* enhance softmax_with_cross_entropy

paddle/fluid/API.spec

@@ -100,7 +100,7 @@ paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_att
 paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.crf_decoding ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label'], varargs=None, keywords=None, defaults=(False,))
+paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
 paddle.fluid.layers.square_error_cost ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None))
@@ -142,7 +142,7 @@ paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 's
 paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
-paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label'], varargs=None, keywords=None, defaults=(False,))
+paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
 paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))

paddle/fluid/operators/cross_entropy_op.cc

@@ -138,6 +138,11 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(bool, default false), a flag indicating whether to "
                   "interpretate the given labels as soft labels.")
         .SetDefault(false);
+    AddAttr<int>("ignore_index",
+                 "(int, default -100), Specifies a target value that is"
+                 "ignored and does not contribute to the input gradient."
+                 "Only valid if soft_label is set to False")
+        .SetDefault(-100);
     AddComment(R"DOC(
 CrossEntropy Operator.
 

paddle/fluid/operators/cross_entropy_op.h

@@ -40,7 +40,7 @@ class CrossEntropyOpKernel : public framework::OpKernel<T> {
 
     math::CrossEntropyFunctor<DeviceContext, T>()(
         ctx.template device_context<DeviceContext>(), &y_2d, &x_2d, &labels_2d,
-        ctx.Attr<bool>("soft_label"));
+        ctx.Attr<bool>("soft_label"), ctx.Attr<int>("ignore_index"));
   }
 };
 
@@ -74,14 +74,20 @@ class XeGradFunctor {
                 const T* dy,           // NOLINT
                 const T* x,            // NOLINT
                 const int64_t* label,  // NOLINT
-                size_t num_classes)
-      : dx_(dx), dy_(dy), x_(x), label_(label), num_classes_(num_classes) {}
+                size_t num_classes, size_t ignore_index)
+      : dx_(dx),
+        dy_(dy),
+        x_(x),
+        label_(label),
+        num_classes_(num_classes),
+        ignore_index_(ignore_index) {}
 
   HOSTDEVICE void operator()(size_t sample_id) {
     auto x_is_true_offset = sample_id * num_classes_ + label_[sample_id];
     for (size_t x_offset = sample_id * num_classes_;
          x_offset < (sample_id + 1) * num_classes_; ++x_offset) {
-      dx_[x_offset] = x_offset != x_is_true_offset
+      dx_[x_offset] =
+          (x_offset != x_is_true_offset || label_[sample_id] == ignore_index_)
               ? static_cast<T>(0)
               : -dy_[sample_id] / x_[x_offset];
     }
@@ -93,6 +99,7 @@ class XeGradFunctor {
   const T* x_;
   const int64_t* label_;
   size_t num_classes_;
+  size_t ignore_index_;
 };
 
 template <typename DeviceContext, typename T>
@@ -109,6 +116,7 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
     // unnecessary to convert tensors to 2-D views.
     int rank = x->dims().size();
     int64_t class_num = x->dims()[rank - 1];
+    int64_t ignore_index = ctx.Attr<int>("ignore_index");
     if (ctx.Attr<bool>("soft_label")) {
       XeSoftlabelGradFunctor<T> functor(dx_data, dy->data<T>(), x->data<T>(),
                                         label->data<T>(),
@@ -118,9 +126,9 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
           static_cast<size_t>(dx->numel()));
       for_range(functor);
     } else {
-      XeGradFunctor<T> functor(dx_data, dy->data<T>(), x->data<T>(),
-                               label->data<int64_t>(),
-                               static_cast<size_t>(class_num));
+      XeGradFunctor<T> functor(
+          dx_data, dy->data<T>(), x->data<T>(), label->data<int64_t>(),
+          static_cast<size_t>(class_num), static_cast<size_t>(ignore_index));
       platform::ForRange<DeviceContext> for_range(
           ctx.template device_context<DeviceContext>(),
           static_cast<size_t>(dy->numel()));

paddle/fluid/operators/math/cross_entropy.cc

@@ -28,7 +28,8 @@ class CrossEntropyFunctor<platform::CPUDeviceContext, T> {
  public:
   void operator()(const platform::CPUDeviceContext& ctx, framework::Tensor* out,
                   const framework::Tensor* prob,
-                  const framework::Tensor* labels, const bool softLabel) {
+                  const framework::Tensor* labels, const bool softLabel,
+                  const int ignore_index) {
     const int batch_size = prob->dims()[0];
     if (softLabel) {
       auto in = EigenMatrix<T>::From(*prob);
@@ -49,8 +50,12 @@ class CrossEntropyFunctor<platform::CPUDeviceContext, T> {
         int lbl = label_data[i];
         PADDLE_ENFORCE_GE(lbl, 0);
         PADDLE_ENFORCE_LT(lbl, class_num);
+        PADDLE_ENFORCE((lbl >= 0 && lbl < class_num) || lbl == ignore_index);
         int index = i * class_num + lbl;
-        loss_data[i] = -math::TolerableValue<T>()(std::log(prob_data[index]));
+        loss_data[i] =
+            lbl == ignore_index
+                ? 0
+                : -math::TolerableValue<T>()(std::log(prob_data[index]));
       }
     }
   }

paddle/fluid/operators/math/cross_entropy.cu

@@ -23,11 +23,14 @@ namespace math {
 namespace {
 template <typename T>
 __global__ void CrossEntropyKernel(T* Y, const T* X, const int64_t* label,
-                                   const int N, const int D) {
+                                   const int N, const int D,
+                                   const int ignore_index) {
   for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
        i += blockDim.x * gridDim.x) {
-    PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
-    Y[i] = -math::TolerableValue<T>()(log(X[i * D + label[i]]));
+    PADDLE_ASSERT(label[i] >= 0 && label[i] < D || label[i] == ignore_index);
+    Y[i] = ignore_index == label[i]
+               ? 0
+               : -math::TolerableValue<T>()(log(X[i * D + label[i]]));
   }
 }
 
@@ -57,7 +60,8 @@ class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
  public:
   void operator()(const platform::CUDADeviceContext& ctx,
                   framework::Tensor* out, const framework::Tensor* prob,
-                  const framework::Tensor* labels, bool softLabel) {
+                  const framework::Tensor* labels, bool softLabel,
+                  const int ignore_index) {
     const T* prob_data = prob->data<T>();
     T* loss_data = out->mutable_data<T>(ctx.GetPlace());
 
@@ -77,7 +81,8 @@ class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
       int block = 512;
       int grid = (batch_size + block - 1) / block;
       CrossEntropyKernel<T><<<grid, block, 0, ctx.stream()>>>(
-          loss_data, prob_data, label_data, batch_size, class_num);
+          loss_data, prob_data, label_data, batch_size, class_num,
+          ignore_index);
     }
   }
 };

paddle/fluid/operators/math/cross_entropy.h

@@ -38,7 +38,8 @@ class CrossEntropyFunctor {
  public:
   void operator()(const DeviceContext& context, framework::Tensor* out,
                   const framework::Tensor* prob,
-                  const framework::Tensor* labels, const bool softLabel);
+                  const framework::Tensor* labels, const bool softLabel,
+                  const int ignore_index);
 };
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/softmax_with_cross_entropy_op.cc

@@ -44,6 +44,12 @@ class SoftmaxWithCrossEntropyOpMaker
         "(bool, default: false), A flag to indicate whether to interpretate "
         "the given labels as soft labels.")
         .SetDefault(false);
+    AddAttr<int>(
+        "ignore_index",
+        "(int, default -100), Specifies a target value that is ignored and"
+        "does not contribute to the input gradient. Only valid if soft_label"
+        "is set to False")
+        .SetDefault(-100);
     AddComment(R"DOC(
 Softmax With Cross Entropy Operator.
 

paddle/fluid/operators/softmax_with_cross_entropy_op.cu

@@ -26,7 +26,8 @@ using Tensor = framework::Tensor;
 namespace {
 template <typename T>
 __global__ void CrossEntropyGrad(T* logit_grad, const int64_t* labels,
-                                 const int batch_size, const int class_num) {
+                                 const int batch_size, const int class_num,
+                                 const int ignore_index) {
   for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < batch_size;
        i += blockDim.x * gridDim.x) {
     int idx = i * class_num + labels[i];
@@ -260,6 +261,7 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
     auto* loss_data = loss->mutable_data<T>(context.GetPlace());
 
     auto soft_label = context.Attr<bool>("soft_label");
+    auto ignore_index = context.Attr<int>("ignore_index");
     if (soft_label) {
       int batch_size = logits->dims()[0];
       int feature_size = logits->dims()[1];
@@ -272,7 +274,8 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
       math::SoftmaxCUDNNFunctor<T>()(context.cuda_device_context(), logits,
                                      softmax);
       math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
-          context.cuda_device_context(), loss, softmax, labels, false);
+          context.cuda_device_context(), loss, softmax, labels, false,
+          ignore_index);
     }
   }
 };
@@ -295,7 +298,7 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
     const int class_num = logit_grad->dims()[1];
     int block = 512;
     auto stream = context.cuda_device_context().stream();
-
+    auto ignore_index = context.Attr<int>("ignore_index");
     if (context.Attr<bool>("soft_label")) {
       int grid = (batch_size * class_num + block - 1) / block;
       const T* label_data = labels->data<T>();
@@ -305,7 +308,7 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
       int grid = (batch_size + block - 1) / block;
       const int64_t* label_data = labels->data<int64_t>();
       CrossEntropyGrad<T><<<grid, block, 0, stream>>>(
-          logit_grad_data, label_data, batch_size, class_num);
+          logit_grad_data, label_data, batch_size, class_num, ignore_index);
       int num = batch_size * class_num;
       grid = (num + block - 1) / block;
       Scale<T><<<grid, block, 0, stream>>>(logit_grad_data, loss_grad_data, num,

paddle/fluid/operators/softmax_with_cross_entropy_op.h

@@ -45,7 +45,8 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
     math::SoftmaxFunctor<platform::CPUDeviceContext, T>()(dev_ctx, logits,
                                                           softmax);
     math::CrossEntropyFunctor<platform::CPUDeviceContext, T>()(
-        dev_ctx, loss, softmax, labels, context.Attr<bool>("soft_label"));
+        dev_ctx, loss, softmax, labels, context.Attr<bool>("soft_label"),
+        context.Attr<int>("ignore_index"));
   }
 };
 

python/paddle/fluid/layers/nn.py

@@ -968,7 +968,7 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
     return out
 
 
-def cross_entropy(input, label, soft_label=False):
+def cross_entropy(input, label, soft_label=False, ignore_index=-100):
     """
     **Cross Entropy Layer**
 
@@ -1012,7 +1012,10 @@ def cross_entropy(input, label, soft_label=False):
                                tensor<float/double> with shape [N x D].
         soft_label (bool): a flag indicating whether to
                                            interpretate the given labels as soft
-                                           labels, default `False`.
+                                           labels. Default: `False`.
+        ignore_index (int): Specifies a target value that is ignored and does 
+                            not contribute to the input gradient. Only valid 
+                            if soft_label is set to False. Default: -100
 
     Returns:
          A 2-D tensor with shape [N x 1], the cross entropy loss.
@@ -1037,7 +1040,8 @@ def cross_entropy(input, label, soft_label=False):
         inputs={'X': [input],
                 'Label': [label]},
         outputs={'Y': [out]},
-        attrs={"soft_label": soft_label})
+        attrs={"soft_label": soft_label,
+               "ignore_index": ignore_index})
     return out
 
 
@@ -4242,7 +4246,10 @@ def multiplex(inputs, index):
     return out
 
 
-def softmax_with_cross_entropy(logits, label, soft_label=False):
+def softmax_with_cross_entropy(logits,
+                               label,
+                               soft_label=False,
+                               ignore_index=-100):
     """
     **Softmax With Cross Entropy Operator.**
 
@@ -4284,6 +4291,10 @@ def softmax_with_cross_entropy(logits, label, soft_label=False):
             soft_label is set to true, Label is a Tensor<float/double> with
         soft_label (bool): A flag to indicate whether to interpretate the given
             labels as soft labels. By default, `soft_label` is set to False.
+        ignore_index (int): Specifies a target value that is ignored and does 
+                            not contribute to the input gradient. Only valid 
+                            if soft_label is set to False. Default: -100
+
     Returns:
         Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
 
@@ -4305,7 +4316,8 @@ def softmax_with_cross_entropy(logits, label, soft_label=False):
                 'Label': label},
         outputs={'Softmax': softmax,
                  'Loss': loss},
-        attrs={'soft_label': soft_label})
+        attrs={'soft_label': soft_label,
+               'ignore_index': ignore_index})
     return loss
 
 

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

@@ -209,5 +209,34 @@ class TestCrossEntropyOp6(OpTest):
             ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
 
 
+class TestCrossEntropyOp7(OpTest):
+    """Test cross-entropy with ignore index.
+    """
+
+    def setUp(self):
+        self.op_type = "cross_entropy"
+        batch_size = 30
+        class_num = 10
+        ignore_index = 3
+
+        X = randomize_probability(batch_size, class_num, dtype='float64')
+
+        label = np.random.randint(0, class_num, (batch_size, 1), dtype="int64")
+        cross_entropy = np.asmatrix(
+            [[-np.log(X[i][label[i][0]])]
+             if label[i][0] != ignore_index else [0]
+             for i in range(X.shape[0])],
+            dtype="float64")
+        self.inputs = {"X": X, "Label": label}
+        self.outputs = {"Y": cross_entropy}
+        self.attrs = {"soft_label": False, "ignore_index": ignore_index}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Y", numeric_grad_delta=0.001)
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -556,6 +556,15 @@ class TestBook(unittest.TestCase):
             out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)
         print(str(program))
 
+    def test_cross_entropy(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name="x", shape=[30, 10], dtype="float32")
+            label = layers.data(name="label", shape=[30, 1], dtype="int32")
+            mode = 'channel'
+            out = layers.cross_entropy(x, label, False, 4)
+            self.assertIsNotNone(out)
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -88,5 +88,40 @@ class TestSoftmaxWithCrossEntropyOp2(OpTest):
         self.check_grad(["Logits"], "Loss")
 
 
+class TestSoftmaxWithCrossEntropyOp3(OpTest):
+    """
+    Test softmax with cross entropy operator with ignore_index.
+    """
+
+    def setUp(self):
+        self.op_type = "softmax_with_cross_entropy"
+        batch_size = 41
+        class_num = 37
+
+        logits = np.random.uniform(0.1, 1.0,
+                                   [batch_size, class_num]).astype("float64")
+        softmax = np.apply_along_axis(stable_softmax, 1, logits)
+        labels = np.random.randint(0, class_num, [batch_size, 1], dtype="int64")
+        ignore_index = 7
+        cross_entropy = np.asmatrix(
+            [[-np.log(softmax[i][labels[i][0]])]
+             if labels[i] != ignore_index else [0]
+             for i in range(softmax.shape[0])],
+            dtype="float64")
+
+        self.inputs = {"Logits": logits, "Label": labels}
+        self.outputs = {
+            "Softmax": softmax.astype("float64"),
+            "Loss": cross_entropy.astype("float64")
+        }
+        self.attrs = {"ignore_index": ignore_index}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["Logits"], "Loss")
+
+
 if __name__ == "__main__":
     unittest.main()