test=develop

paddle/fluid/API.spec

@@ -103,7 +103,7 @@ 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', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
+paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode'], varargs=None, keywords=None, defaults=(False, -100, False))
 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/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<bool>(
+        "numeric_stable_mode",
+        "(bool, default: false), A flag to indicate whether to use more "
+        "numerically stable algorithm. This flag is only valid when "
+        "soft_label is false and GPU is used.")
+        .SetDefault(false);
     AddAttr<int>(
         "ignore_index",
         "(int, default -100), Specifies a target value that is ignored and"

paddle/fluid/operators/softmax_with_cross_entropy_op.cu

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <cub/cub.cuh>
 #include "paddle/fluid/operators/math/cross_entropy.h"
 #include "paddle/fluid/operators/softmax_with_cross_entropy_op.h"
+#include "paddle/fluid/platform/for_range.h"
 
 namespace paddle {
 namespace operators {
@@ -117,8 +118,8 @@ using BlockReduceTempStorage = typename BlockReduce<T, BlockDim>::TempStorage;
 // Make sure that BlockDim <= feature_size
 // This kernel is used to calculate the max element of each row
 template <typename T, int BlockDim>
-__global__ void RowReductionForMax(const T* logits_data, T* max_data,
-                                   int feature_size) {
+static __global__ void RowReductionForMax(const T* logits_data, T* max_data,
+                                          int feature_size) {
   __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
 
   auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
@@ -141,9 +142,10 @@ __global__ void RowReductionForMax(const T* logits_data, T* max_data,
 }
 
 // Make sure that BlockDim <= feature_size
-template <typename T, int BlockDim>
-__global__ void RowReductionForDiffMaxSum(const T* logits_data, T* max_data,
-                                          T* softmax, int feature_size) {
+template <typename T, int BlockDim, bool CalculateLogSoftmax = false>
+static __global__ void RowReductionForDiffMaxSum(const T* logits_data,
+                                                 T* max_data, T* softmax,
+                                                 int feature_size) {
   __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
 
   auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
@@ -153,24 +155,34 @@ __global__ void RowReductionForDiffMaxSum(const T* logits_data, T* max_data,
 
   softmax[beg_idx] = logits_data[beg_idx] - block_max;
   T diff_max_sum = real_exp(softmax[beg_idx]);
-  beg_idx += BlockDim;
-  while (beg_idx < end_idx) {
-    softmax[beg_idx] = logits_data[beg_idx] - block_max;
-    diff_max_sum += real_exp(softmax[beg_idx]);
-    beg_idx += BlockDim;
+  auto idx = beg_idx + BlockDim;
+  while (idx < end_idx) {
+    softmax[idx] = logits_data[idx] - block_max;
+    diff_max_sum += real_exp(softmax[idx]);
+    idx += BlockDim;
   }
 
   diff_max_sum =
       BlockReduce<T, BlockDim>(temp_storage).Reduce(diff_max_sum, cub::Sum());
   if (threadIdx.x == 0) max_data[blockIdx.x] = real_log(diff_max_sum);
+
+  if (!CalculateLogSoftmax) return;
+  __syncthreads();
+  diff_max_sum = max_data[blockIdx.x];
+  softmax[beg_idx] -= diff_max_sum;
+  beg_idx += BlockDim;
+  while (beg_idx < end_idx) {
+    softmax[beg_idx] -= diff_max_sum;
+    beg_idx += BlockDim;
+  }
+  if (threadIdx.x == 0) max_data[blockIdx.x] = 0;
 }
 
 // Make sure that BlockDim <= feature_size
 template <typename T, int BlockDim>
-__global__ void RowReductionForSoftmaxAndCrossEntropy(const T* logits_data,
-                                                      const T* labels_data,
-                                                      T* loss_data, T* softmax,
-                                                      int feature_size) {
+static __global__ void RowReductionForSoftmaxAndCrossEntropy(
+    const T* logits_data, const T* labels_data, T* loss_data, T* softmax,
+    int feature_size) {
   __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
 
   auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
@@ -194,11 +206,134 @@ __global__ void RowReductionForSoftmaxAndCrossEntropy(const T* logits_data,
 }
 
 template <typename T>
-__global__ void SetSoftmaxToOneWhenFeatureSizeIsOne(T* out, int batch_size) {
+struct HardLabelSoftmaxWithCrossEntropyFunctor {
+ public:
+  HardLabelSoftmaxWithCrossEntropyFunctor(const T* logits,
+                                          const int64_t* labels, T* loss,
+                                          T* log_softmax, int feature_size)
+      : logits_(logits),
+        labels_(labels),
+        loss_(loss),
+        log_softmax_(log_softmax),
+        feature_size_(feature_size) {}
+
+  __device__ void operator()(int idx) const {
+    auto row_idx = idx / feature_size_;
+    auto col_idx = idx % feature_size_;
+    if (col_idx != labels_[row_idx]) {
+      log_softmax_[idx] = real_exp(log_softmax_[idx]);
+    } else {
+      auto softmax = log_softmax_[idx];
+      log_softmax_[idx] = real_exp(softmax);
+      loss_[row_idx] = -softmax;
+    }
+  }
+
+ private:
+  const T* logits_;
+  const int64_t* labels_;
+  T* loss_;
+  T* log_softmax_;
+  int feature_size_;
+};
+
+template <typename T>
+struct HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx {
+ public:
+  HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx(const T* logits,
+                                                       const int64_t* labels,
+                                                       T* loss, T* log_softmax,
+                                                       int feature_size,
+                                                       int ignore_idx)
+      : logits_(logits),
+        labels_(labels),
+        loss_(loss),
+        log_softmax_(log_softmax),
+        feature_size_(feature_size),
+        ignore_idx_(ignore_idx) {}
+
+  __device__ void operator()(int idx) const {
+    auto row_idx = idx / feature_size_;
+    auto col_idx = idx % feature_size_;
+    if (col_idx != labels_[row_idx] || col_idx == ignore_idx_) {
+      log_softmax_[idx] = real_exp(log_softmax_[idx]);
+    } else {
+      auto softmax = log_softmax_[idx];
+      log_softmax_[idx] = real_exp(softmax);
+      loss_[row_idx] = -softmax;
+    }
+  }
+
+ private:
+  const T* logits_;
+  const int64_t* labels_;
+  T* loss_;
+  T* log_softmax_;
+  int feature_size_;
+  int ignore_idx_;
+};
+
+template <typename T>
+static __global__ void SetSoftmaxToOneWhenFeatureSizeIsOne(T* out,
+                                                           int batch_size) {
   auto idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < batch_size) out[idx] = static_cast<T>(1);
 }
 
+template <typename T>
+static void HardLabelSoftmaxWithCrossEntropy(
+    const platform::CUDADeviceContext& ctx, const T* logits_data,
+    const int64_t* labels_data, T* loss_data, T* softmax_data, int batch_size,
+    int feature_size, int ignore_idx) {
+  constexpr int kMaxBlockDim = 512;
+  int block_dim = feature_size >= kMaxBlockDim
+                      ? kMaxBlockDim
+                      : (1 << static_cast<int>(std::log2(feature_size)));
+  auto stream = ctx.stream();
+
+#define CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(BlockDim)    \
+  case BlockDim: {                                                           \
+    RowReductionForMax<T, BlockDim><<<batch_size, BlockDim, 0, stream>>>(    \
+        logits_data, loss_data, feature_size);                               \
+    RowReductionForDiffMaxSum<T, BlockDim,                                   \
+                              true><<<batch_size, BlockDim, 0, stream>>>(    \
+        logits_data, loss_data, softmax_data, feature_size);                 \
+    platform::ForRange<platform::CUDADeviceContext> for_range(               \
+        ctx, batch_size* feature_size);                                      \
+    if (ignore_idx >= 0 && ignore_idx < feature_size) {                      \
+      for_range(HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx<T>(     \
+          logits_data, labels_data, loss_data, softmax_data, feature_size,   \
+          ignore_idx));                                                      \
+    } else {                                                                 \
+      for_range(HardLabelSoftmaxWithCrossEntropyFunctor<T>(                  \
+          logits_data, labels_data, loss_data, softmax_data, feature_size)); \
+    }                                                                        \
+  } break
+
+  switch (block_dim) {
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(512);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(256);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(128);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(64);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(32);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(16);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(8);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(4);
+    CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(2);
+    case 1:
+      SetSoftmaxToOneWhenFeatureSizeIsOne<<<(batch_size + kMaxBlockDim - 1) /
+                                                kMaxBlockDim,
+                                            kMaxBlockDim, 0, stream>>>(
+          softmax_data, batch_size);
+      cudaMemsetAsync(loss_data, 0, batch_size * sizeof(T), stream);
+      break;
+    default:
+      PADDLE_THROW("BlockDim must be 2^n in softmax_with_cross_entropy_op");
+      break;
+  }
+#undef CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
+}
+
 template <typename T>
 static void SoftmaxWithCrossEntropyFusedKernel(const T* logits_data,
                                                const T* labels_data,
@@ -237,7 +372,7 @@ static void SoftmaxWithCrossEntropyFusedKernel(const T* logits_data,
                                                 kMaxBlockDim,
                                             kMaxBlockDim, 0, stream>>>(
           softmax_data, batch_size);
-      cudaMemsetAsync(loss_data, 0, batch_size, stream);
+      cudaMemsetAsync(loss_data, 0, batch_size * sizeof(T), stream);
       break;
     default:
       PADDLE_THROW("BlockDim must be 2^n in softmax_with_cross_entropy_op");
@@ -272,11 +407,21 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
           logits_data, labels_data, softmax_data, loss_data, batch_size,
           feature_size, context.cuda_device_context().stream());
     } else {
-      math::SoftmaxCUDNNFunctor<T>()(context.cuda_device_context(), logits,
-                                     softmax);
-      math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
-          context.cuda_device_context(), loss, softmax, labels, false,
-          ignore_index);
+      if (!context.Attr<bool>("numeric_stable_mode")) {
+        math::SoftmaxCUDNNFunctor<T>()(context.cuda_device_context(), logits,
+                                       softmax);
+        math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
+            context.cuda_device_context(), loss, softmax, labels, false,
+            ignore_index);
+      } else {
+        int batch_size = logits->dims()[0];
+        int feature_size = logits->dims()[1];
+        auto* logits_data = logits->data<T>();
+        auto* labels_data = labels->data<int64_t>();
+        HardLabelSoftmaxWithCrossEntropy<T>(
+            context.cuda_device_context(), logits_data, labels_data, loss_data,
+            softmax_data, batch_size, feature_size, ignore_index);
+      }
     }
   }
 };

python/paddle/fluid/layers/nn.py

@@ -4652,7 +4652,8 @@ def multiplex(inputs, index):
 def softmax_with_cross_entropy(logits,
                                label,
                                soft_label=False,
-                               ignore_index=-100):
+                               ignore_index=-100,
+                               numeric_stable_mode=False):
     """
     **Softmax With Cross Entropy Operator.**
 
@@ -4686,6 +4687,18 @@ def softmax_with_cross_entropy(logits,
         \\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
         \\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K
 
+    3) If numeric_stable_mode is True, softmax is calculated first by:
+
+    .. math::
+        
+        max_j = \\max_{i=0}^{K}{\\text{logit}_i}
+
+        log\\_max\\_sum_j = \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)
+
+        softmax_j = \\exp(logit_j - max_j - {log\\_max\\_sum}_j)
+
+    and then cross entropy loss is calculated by softmax and label.
+
     Args:
         logits (Variable): The unscaled log probabilities, which is a 2-D tensor
             with shape [N x K]. N is the batch_size, and K is the class number.
@@ -4697,6 +4710,13 @@ def softmax_with_cross_entropy(logits,
         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
+        numeric_stable_mode (bool): A flag to indicate whether to use a more
+                                    numerically stable algorithm. Only valid
+                                    when soft_label is False and GPU is used.
+                                    When soft_label is True or CPU is used, 
+                                    the algorithm is always numerically stable. 
+                                    Note that the speed may be slower when use 
+                                    stable algorithm. Default: False
 
     Returns:
         Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
@@ -4719,8 +4739,11 @@ def softmax_with_cross_entropy(logits,
                 'Label': label},
         outputs={'Softmax': softmax,
                  'Loss': loss},
-        attrs={'soft_label': soft_label,
-               'ignore_index': ignore_index})
+        attrs={
+            'soft_label': soft_label,
+            'ignore_index': ignore_index,
+            'numeric_stable_mode': numeric_stable_mode
+        })
     return loss
 
 

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

@@ -26,7 +26,11 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
     Test softmax with cross entropy operator with discreate one-hot labels.
     """
 
+    def initParams(self):
+        self.numeric_stable_mode = False
+
     def setUp(self):
+        self.initParams()
         self.op_type = "softmax_with_cross_entropy"
         batch_size = 41
         class_num = 37
@@ -46,6 +50,7 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
             "Softmax": softmax.astype("float64"),
             "Loss": cross_entropy.astype("float64")
         }
+        self.attrs = {"numeric_stable_mode": self.numeric_stable_mode}
 
     def test_check_output(self):
         self.check_output()
@@ -54,6 +59,11 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
         self.check_grad(["Logits"], "Loss")
 
 
+class TestSoftmaxWithCrossEntropyOpNoCudnn(TestSoftmaxWithCrossEntropyOp):
+    def initParams(self):
+        self.numeric_stable_mode = True
+
+
 class TestSoftmaxWithCrossEntropyOp2(OpTest):
     """
     Test softmax with cross entropy operator with soft labels.
@@ -93,7 +103,11 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
     Test softmax with cross entropy operator with ignore_index.
     """
 
+    def initParams(self):
+        self.numeric_stable_mode = False
+
     def setUp(self):
+        self.initParams()
         self.op_type = "softmax_with_cross_entropy"
         batch_size = 41
         class_num = 37
@@ -114,7 +128,10 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
             "Softmax": softmax.astype("float64"),
             "Loss": cross_entropy.astype("float64")
         }
-        self.attrs = {"ignore_index": ignore_index}
+        self.attrs = {
+            "ignore_index": ignore_index,
+            "numeric_stable_mode": self.numeric_stable_mode
+        }
 
     def test_check_output(self):
         self.check_output()
@@ -123,5 +140,10 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
         self.check_grad(["Logits"], "Loss")
 
 
+class TestSoftmaxWithCrossEntropyOp3NoCudnn(TestSoftmaxWithCrossEntropyOp3):
+    def initParams(self):
+        self.numeric_stable_mode = True
+
+
 if __name__ == "__main__":
     unittest.main()