diff --git a/paddle/fluid/operators/math/bert_encoder_functor.cu b/paddle/fluid/operators/math/bert_encoder_functor.cu
index c1c7d1edadca810b9d32e8326fd987127eccb02b..d3fccf697ab3caa06fc67b3db6adca8bdb61182e 100644
--- a/paddle/fluid/operators/math/bert_encoder_functor.cu
+++ b/paddle/fluid/operators/math/bert_encoder_functor.cu
@@ -378,7 +378,7 @@ __global__ void SoftmaxKernelWithEltaddForLarge(T *qk_buf,
   assert(blockDim.x % 32 == 0);
 
   T stride_max = -1e20f;
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     stride_max = qk_buf[threadIdx.x + i + qk_offset] +
                              bias_qk[threadIdx.x + i + qk_offset] >
                          stride_max
@@ -389,13 +389,13 @@ __global__ void SoftmaxKernelWithEltaddForLarge(T *qk_buf,
   T max_val = phi::funcs::blockReduceMax<T>(stride_max, mask);
 
   T stride_sum = 0.f;
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     stride_sum += __expf(qk_buf[threadIdx.x + i + qk_offset] +
                          bias_qk[threadIdx.x + i + qk_offset] - max_val);
   }
   T sum_val = phi::funcs::blockReduceSum<T>(stride_sum, mask);
 
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     qk_buf[threadIdx.x + i + qk_offset] =
         (T)(__expf(qk_buf[threadIdx.x + i + qk_offset] +
                    bias_qk[threadIdx.x + i + qk_offset] - max_val) /
@@ -417,7 +417,7 @@ __global__ void SoftmaxKernelWithEltaddForLarge(half *qk_buf,
   assert(blockDim.x % 32 == 0);
 
   float stride_max = -1e20f;
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float tmp = static_cast<float>(qk_buf[threadIdx.x + i + qk_offset] +
                                    bias_qk[threadIdx.x + i + qk_offset]);
     stride_max = tmp > stride_max ? tmp : stride_max;
@@ -425,14 +425,14 @@ __global__ void SoftmaxKernelWithEltaddForLarge(half *qk_buf,
   float max_val = phi::funcs::blockReduceMax<float>(stride_max, mask);
 
   float stride_sum = 0.f;
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float tmp = static_cast<float>(qk_buf[threadIdx.x + i + qk_offset] +
                                    bias_qk[threadIdx.x + i + qk_offset]);
     stride_sum += __expf(tmp - max_val);
   }
   float sum_val = phi::funcs::blockReduceSum<float>(stride_sum, mask);
 
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float tmp =
         __expf(static_cast<float>(qk_buf[threadIdx.x + i + qk_offset] +
                                   bias_qk[threadIdx.x + i + qk_offset]) -
@@ -454,7 +454,7 @@ __global__ void SoftmaxKernelWithEltaddForLarge2(T *qk_buf_,
   assert(blockDim.x % 32 == 0);
 
   float2 stride_max = make_float2(-1e20f, -1e20f);
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float2 cur = phi::funcs::ToFloat2<T>(qk_buf_[threadIdx.x + i + qk_offset] +
                                          bias_qk_[threadIdx.x + i + qk_offset]);
     stride_max.x = max(stride_max.x, cur.x);
@@ -464,7 +464,7 @@ __global__ void SoftmaxKernelWithEltaddForLarge2(T *qk_buf_,
       phi::funcs::blockReduceMax<float>(max(stride_max.x, stride_max.y), mask);
 
   float2 stride_sum = make_float2(0.f, 0.f);
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float2 cur = phi::funcs::ToFloat2<T>(qk_buf_[threadIdx.x + i + qk_offset] +
                                          bias_qk_[threadIdx.x + i + qk_offset]);
     stride_sum.x += __expf(cur.x - max_val);
@@ -475,7 +475,7 @@ __global__ void SoftmaxKernelWithEltaddForLarge2(T *qk_buf_,
       phi::funcs::blockReduceSum<float>(stride_sum.x + stride_sum.y, mask) +
       1e-6f;
 
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float2 cur = phi::funcs::ToFloat2<T>(qk_buf_[threadIdx.x + i + qk_offset] +
                                          bias_qk_[threadIdx.x + i + qk_offset]);
     qk_buf_[threadIdx.x + i + qk_offset] = phi::funcs::FloatsToPair<T>(
@@ -499,7 +499,7 @@ __global__ void SoftmaxKernelWithEltaddForLarge2(half2 *qk_buf_,
   assert(blockDim.x % 32 == 0);
 
   float2 stride_max = make_float2(-1e20f, -1e20f);
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float2 cur =
         phi::funcs::ToFloat2<half2>(qk_buf_[threadIdx.x + i + qk_offset] +
                                     bias_qk_[threadIdx.x + i + qk_offset]);
@@ -510,7 +510,7 @@ __global__ void SoftmaxKernelWithEltaddForLarge2(half2 *qk_buf_,
       phi::funcs::blockReduceMax<float>(max(stride_max.x, stride_max.y), mask);
 
   float2 stride_sum = make_float2(0.f, 0.f);
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float2 cur =
         phi::funcs::ToFloat2<half2>(qk_buf_[threadIdx.x + i + qk_offset] +
                                     bias_qk_[threadIdx.x + i + qk_offset]);
@@ -522,7 +522,7 @@ __global__ void SoftmaxKernelWithEltaddForLarge2(half2 *qk_buf_,
       phi::funcs::blockReduceSum<float>(stride_sum.x + stride_sum.y, mask) +
       1e-6f;
 
-  for (int i = 0; i < seq_len; i += blockDim.x) {
+  for (int i = 0; threadIdx.x + i < seq_len; i += blockDim.x) {
     float2 cur =
         phi::funcs::ToFloat2<half2>(qk_buf_[threadIdx.x + i + qk_offset] +
                                     bias_qk_[threadIdx.x + i + qk_offset]);