Cherry pick from #21862 (#22194)

* Fix default label dim of label_smooth_op. test=develop (#21862)

* Fix unit tests of label_smooth_op's data size.

Author: guoshengCS

paddle/fluid/operators/label_smooth_op.cc

@@ -37,7 +37,7 @@ class LabelSmoothOp : public framework::OperatorWithKernel {
       auto noise_dims = ctx->GetInputDim("PriorDist");
       auto noise_numel = paddle::framework::product(noise_dims);
       PADDLE_ENFORCE(
-          in_dims[1] == noise_numel,
+          in_dims[in_dims.size() - 1] == noise_numel,
           "The number of elements in Input(PriorDist) must be equal to the "
           "dimension of each label.");
     }

paddle/fluid/operators/label_smooth_op.cu

@@ -34,7 +34,7 @@ __global__ void LabelSmoothRunDistKernel(const int N, const float epsilon,
                                          const T* dist_data, T* dst) {
   int idx = blockDim.x * blockIdx.x + threadIdx.x;
   for (; idx < N; idx += blockDim.x * gridDim.x) {
-    int dist_idx = idx - (idx / dist_numel) * dist_numel;
+    int dist_idx = idx % dist_numel;
     dst[idx] = static_cast<T>(1 - epsilon) * src[idx] +
                static_cast<T>(epsilon) * dist_data[dist_idx];
   }
@@ -56,7 +56,7 @@ class LabelSmoothGPUKernel : public framework::OpKernel<T> {
     auto* out_t = ctx.Output<framework::LoDTensor>("Out");
     auto* in_t = ctx.Input<framework::LoDTensor>("X");
     auto* dist_t = ctx.Input<framework::Tensor>("PriorDist");
-    auto label_dim = in_t->dims()[1];
+    auto label_dim = in_t->dims()[in_t->dims().size() - 1];
     auto epsilon = ctx.Attr<float>("epsilon");
     auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
     auto size_prob = in_t->numel();

paddle/fluid/operators/label_smooth_op.h

@@ -27,7 +27,7 @@ class LabelSmoothKernel : public framework::OpKernel<T> {
     auto* out_t = ctx.Output<framework::LoDTensor>("Out");
     auto* in_t = ctx.Input<framework::LoDTensor>("X");
     auto* dist_t = ctx.Input<framework::Tensor>("PriorDist");
-    auto label_dim = in_t->dims()[1];
+    auto label_dim = in_t->dims()[in_t->dims().size() - 1];
     out_t->mutable_data<T>(ctx.GetPlace());
 
     auto epsilon = ctx.Attr<float>("epsilon");
@@ -39,7 +39,7 @@ class LabelSmoothKernel : public framework::OpKernel<T> {
       out.device(dev) =
           static_cast<T>(1 - epsilon) * in +
           static_cast<T>(epsilon) *
-              dist.broadcast(Eigen::DSizes<int, 1>(in_t->numel()));
+              dist.broadcast(Eigen::DSizes<int, 1>(in_t->numel() / label_dim));
     } else {
       out.device(dev) = static_cast<T>(1 - epsilon) * in +
                         static_cast<T>(epsilon / label_dim);

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

@@ -23,7 +23,7 @@ class TestLabelSmoothOp(OpTest):
     def config(self):
         self.op_type = "label_smooth"
         self.epsilon = 0.1
-        batch_size, self.label_dim = 5, 10
+        batch_size, self.label_dim = 10, 12
         self.label = np.zeros((batch_size, self.label_dim)).astype("float64")
         nonzero_index = np.random.randint(self.label_dim, size=(batch_size))
         self.label[np.arange(batch_size), nonzero_index] = 1
@@ -53,5 +53,23 @@ def setUp(self):
         self.outputs = {'Out': smoothed_label}
 
 
+class TestLabelSmoothOp3D(TestLabelSmoothOp):
+    def setUp(self):
+        super(TestLabelSmoothOp3D, self).setUp()
+        self.inputs['X'] = self.inputs['X'].reshape(
+            [2, -1, self.inputs['X'].shape[-1]])
+        self.outputs['Out'] = self.outputs['Out'].reshape(self.inputs['X']
+                                                          .shape)
+
+
+class TestLabelSmoothOpWithPriorDist3D(TestLabelSmoothOpWithPriorDist):
+    def setUp(self):
+        super(TestLabelSmoothOpWithPriorDist3D, self).setUp()
+        self.inputs['X'] = self.inputs['X'].reshape(
+            [2, -1, self.inputs['X'].shape[-1]])
+        self.outputs['Out'] = self.outputs['Out'].reshape(self.inputs['X']
+                                                          .shape)
+
+
 if __name__ == '__main__':
     unittest.main()