Fix INF bug of softmax_cross_entropy_op, test=release/1.6 (#21283)

paddle/fluid/operators/softmax_with_cross_entropy_op.cu

@@ -150,10 +150,7 @@ static __global__ void RowReductionForMax(const T* logits_data, T* max_data,
 
   cur_max = BlockReduce<T, BlockDim>(temp_storage).Reduce(cur_max, cub::Max());
 
-  if (threadIdx.x == 0) {
-    max_data[blockIdx.x] =
-        cur_max < static_cast<T>(-64) ? static_cast<T>(-64) : cur_max;
-  }
+  if (threadIdx.x == 0) max_data[blockIdx.x] = cur_max;
 }
 
 // Make sure that BlockDim <= axis_dim
@@ -175,6 +172,12 @@ static __global__ void RowReductionForDiffMaxSum(const T* logits_data,
   auto block_max = max_data[blockIdx.x];
   int step = BlockDim * remain;
 
+  // In numeric stable mode softmax_with_loss, we calc loss with
+  // tmp_i_j = x_i_j - max_i - logDiffMaxSum_i, instead of
+  // log(exp(x_i_j - max_i)/DiffMaxSum_i). Therefore, log(0) will not occur.
+  // Also we calc softmax_i_j = e^{tmp_i_j}, the maximum and minimum value will
+  // be 1.0 and 0.0, represent prob is 1.0 and 0.0.
+  // So there is no need to clip on shift_softmax.
   softmax[beg_idx] = logits_data[beg_idx] - block_max;
   T diff_max_sum = exp_on_device(softmax[beg_idx]);
   auto idx = beg_idx + step;

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

@@ -25,7 +25,9 @@ np.random.random(123)
 
 def stable_softmax(x):
     """Compute the softmax of vector x in a numerically stable way."""
-    shiftx = x - np.max(x).clip(-64.)
+    # clip to shiftx, otherwise, when calc loss with
+    # log(exp(shiftx)), may get log(0)=INF
+    shiftx = (x - np.max(x)).clip(-64.)
     exps = np.exp(shiftx)
     return exps / np.sum(exps)
 

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

@@ -19,6 +19,14 @@ import copy
 from op_test import OpTest
 
 
+def softmax(x):
+    # clip to shiftx, otherwise, when calc loss with
+    # log(exp(shiftx)), may get log(0)=INF
+    shiftx = (x - np.max(x)).clip(-64.)
+    exps = np.exp(shiftx)
+    return exps / np.sum(exps)
+
+
 def iou(box_a, box_b, norm):
     """Apply intersection-over-union overlap between box_a and box_b
     """
@@ -254,11 +262,6 @@ class TestMulticlassNMSOp(OpTest):
 
         scores = np.random.random((N * M, C)).astype('float32')
 
-        def softmax(x):
-            shiftx = x - np.max(x).clip(-64.)
-            exps = np.exp(shiftx)
-            return exps / np.sum(exps)
-
         scores = np.apply_along_axis(softmax, 1, scores)
         scores = np.reshape(scores, (N, M, C))
         scores = np.transpose(scores, (0, 2, 1))
@@ -318,11 +321,6 @@ class TestMulticlassNMSLoDInput(OpTest):
 
         scores = np.random.random((M, C)).astype('float32')
 
-        def softmax(x):
-            shiftx = x - np.max(x).clip(-64.)
-            exps = np.exp(shiftx)
-            return exps / np.sum(exps)
-
         scores = np.apply_along_axis(softmax, 1, scores)
 
         boxes = np.random.random((M, C, BOX_SIZE)).astype('float32')
@@ -382,11 +380,6 @@ class TestMulticlassNMS2Op(TestMulticlassNMSOp):
 
         scores = np.random.random((N * M, C)).astype('float32')
 
-        def softmax(x):
-            shiftx = x - np.max(x).clip(-64.)
-            exps = np.exp(shiftx)
-            return exps / np.sum(exps)
-
         scores = np.apply_along_axis(softmax, 1, scores)
         scores = np.reshape(scores, (N, M, C))
         scores = np.transpose(scores, (0, 2, 1))
@@ -447,11 +440,6 @@ class TestMulticlassNMS2LoDInput(TestMulticlassNMSLoDInput):
 
         scores = np.random.random((M, C)).astype('float32')
 
-        def softmax(x):
-            shiftx = x - np.max(x).clip(-64.)
-            exps = np.exp(shiftx)
-            return exps / np.sum(exps)
-
         scores = np.apply_along_axis(softmax, 1, scores)
 
         boxes = np.random.random((M, C, BOX_SIZE)).astype('float32')

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

@@ -24,7 +24,9 @@ from paddle.fluid import compiler, Program, program_guard
 
 def stable_softmax(x):
     """Compute the softmax of vector x in a numerically stable way."""
-    shiftx = x - np.max(x).clip(-64.)
+    # clip to shiftx, otherwise, when calc loss with
+    # log(exp(shiftx)), may get log(0)=INF
+    shiftx = (x - np.max(x)).clip(-64.)
     exps = np.exp(shiftx)
     return exps / np.sum(exps)
 

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

@@ -58,7 +58,9 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
     def setUp(self):
         self.initParams()
 
-        logits = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
+        logits = getattr(
+            self, "logits",
+            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
         softmax = np.apply_along_axis(stable_softmax, self.axis, logits)
 
         if self.soft_label:
@@ -119,7 +121,9 @@ class TestSoftmaxWithCrossEntropyOpFp16(TestSoftmaxWithCrossEntropyOp):
         self.op_type = "softmax_with_cross_entropy"
 
         # NOTE: numpy float16 have very low accuracy, use float32 for numpy check.
-        logits = np.random.uniform(0.1, 1.0, self.shape).astype(np.float32)
+        logits = getattr(
+            self, "logits",
+            np.random.uniform(0.1, 1.0, self.shape).astype(np.float32))
         softmax = np.apply_along_axis(stable_softmax, self.axis, logits)
 
         axis_dim = self.shape[self.axis]
@@ -408,5 +412,40 @@ class TestSoftmaxWithCrossEntropyOpIgnoreIndexNoCudnnAxis4(
         self.dtype = np.float64
 
 
+class TestSoftmaxWithCrossEntropyOpBoundary0(TestSoftmaxWithCrossEntropyOp):
+    """
+    Test stable softmax with cross entropy operator will not product INF
+    with small logits value.
+    """
+
+    def initParams(self):
+        self.op_type = "softmax_with_cross_entropy"
+        self.numeric_stable_mode = True
+        self.soft_label = False
+        self.shape = [3, 5, 7, 11]
+        self.axis = -1
+        self.ignore_index = -1
+        self.dtype = np.float32
+        self.logits = np.full(self.shape, -500.0).astype(self.dtype)
+
+
+class TestSoftmaxWithCrossEntropyOpBoundary1(TestSoftmaxWithCrossEntropyOp):
+    """
+    Test stable softmax with cross entropy operator will not product INF
+    with small logits value.
+    """
+
+    def initParams(self):
+        self.op_type = "softmax_with_cross_entropy"
+        self.numeric_stable_mode = True
+        self.soft_label = False
+        self.shape = [3, 5, 7, 11]
+        self.axis = -1
+        self.ignore_index = -1
+        self.dtype = np.float32
+        self.logits = np.full(self.shape, 1000.0).astype(self.dtype)
+        self.logits[:, :, 0, :] = -1000.0
+
+
 if __name__ == "__main__":
     unittest.main()