diff --git a/paddle/phi/kernels/gpudnn/softmax_gpudnn.h b/paddle/phi/kernels/gpudnn/softmax_gpudnn.h
index 75dfc8514abf8e5daf522fb1f9b89e50babddf36..a079827ca9fd70a2da367d2fbf25519976f9552e 100644
--- a/paddle/phi/kernels/gpudnn/softmax_gpudnn.h
+++ b/paddle/phi/kernels/gpudnn/softmax_gpudnn.h
@@ -346,28 +346,41 @@ template <template <typename, typename> class Reduction,
           typename AccT,
           int VecSize>
 __device__ __forceinline__ AccT
-ThreadVecReduce(const T* data,
+ThreadVecReduce(T* data,
                 int dim_size,
+                const int shift,
                 const Reduction<T, AccT>& functor,
                 AccT default_value) {
   using VecT = phi::AlignedVector<T, VecSize>;
   AccT thread_val = default_value;
+
+  // for memory align, handle the unaligned data in first block.
+  int offset = threadIdx.x;
+  if (shift > 0) {
+    data -= shift;
+    dim_size += shift;
+    if (offset >= shift) {
+      thread_val = functor(thread_val, data[offset]);
+    }
+    dim_size -= blockDim.x;
+    data += blockDim.x;
+  }
+
   const int last = dim_size % (VecSize * blockDim.x);
 
   T v[VecSize];
   VecT* value = reinterpret_cast<VecT*>(&v);
 
-  for (int offset = threadIdx.x; offset * VecSize < dim_size - last;
-       offset += blockDim.x) {
-    *value = reinterpret_cast<VecT*>(const_cast<T*>(data))[offset];
+  for (; offset * VecSize < dim_size - last; offset += blockDim.x) {
+    *value = reinterpret_cast<VecT*>(data)[offset];
 #pragma unroll
     for (int i = 0; i < VecSize; i++) {
       thread_val = functor(thread_val, v[i]);
     }
   }
 
-  for (int offset = dim_size - last + threadIdx.x; offset < dim_size;
-       offset += blockDim.x) {
+  offset = dim_size - last + threadIdx.x;
+  for (; offset < dim_size; offset += blockDim.x) {
     thread_val = functor(thread_val, data[offset]);
   }
   return thread_val;
@@ -377,12 +390,27 @@ template <template <typename, typename> class Reduction,
           typename T,
           typename AccT,
           int VecSize>
-__device__ __forceinline__ void ThreadVecWrite(T* out,
-                                               const T* input,
-                                               int dim_size,
-                                               Reduction<AccT, T> functor) {
+__device__ __forceinline__ void ThreadVecWriteVec(T* out,
+                                                  T* input,
+                                                  int dim_size,
+                                                  const int shift,
+                                                  Reduction<AccT, T> functor) {
   using VecT = phi::AlignedVector<T, VecSize>;
 
+  // for memory align, handle the unaligned data in first block.
+  int offset = threadIdx.x;
+  if (shift > 0) {
+    input -= shift;
+    out -= shift;
+    dim_size += shift;
+    if (offset >= shift) {
+      out[offset] = functor(static_cast<AccT>(input[offset]));
+    }
+    dim_size -= blockDim.x;
+    input += blockDim.x;
+    out += blockDim.x;
+  }
+
   const int last = dim_size % (VecSize * blockDim.x);
 
   T in_v[VecSize];
@@ -391,9 +419,8 @@ __device__ __forceinline__ void ThreadVecWrite(T* out,
   T out_v[VecSize];
   VecT* out_value = reinterpret_cast<VecT*>(&out_v);
 
-  for (int offset = threadIdx.x; offset * VecSize < dim_size - last;
-       offset += blockDim.x) {
-    *in_value = reinterpret_cast<VecT*>(const_cast<T*>(input))[offset];
+  for (; offset * VecSize < dim_size - last; offset += blockDim.x) {
+    *in_value = reinterpret_cast<VecT*>(input)[offset];
 #pragma unroll
     for (int i = 0; i < VecSize; i++) {
       out_v[i] = functor(static_cast<AccT>(in_v[i]));
@@ -401,6 +428,33 @@ __device__ __forceinline__ void ThreadVecWrite(T* out,
     reinterpret_cast<VecT*>(out)[offset] = *out_value;
   }
 
+  offset = dim_size - last + threadIdx.x;
+  // the tail
+  for (; offset < dim_size; offset += blockDim.x) {
+    out[offset] = functor(static_cast<AccT>(input[offset]));
+  }
+}
+
+template <template <typename, typename> class Reduction,
+          typename T,
+          typename AccT,
+          int VecSize>
+__device__ __forceinline__ void ThreadVecWrite(T* out,
+                                               T* input,
+                                               int dim_size,
+                                               Reduction<AccT, T> functor) {
+  const int last = dim_size % (VecSize * blockDim.x);
+
+  for (int offset = threadIdx.x; offset < dim_size - last;
+       offset += blockDim.x * VecSize) {
+#pragma unroll
+    for (int i = 0; i < VecSize; i++) {
+      out[offset + i * blockDim.x] =
+          functor(static_cast<AccT>(input[offset + i * blockDim.x]));
+    }
+  }
+
+  // the tail
   for (int offset = dim_size - last + threadIdx.x; offset < dim_size;
        offset += blockDim.x) {
     out[offset] = functor(static_cast<AccT>(input[offset]));
@@ -417,13 +471,19 @@ __global__ void KeMatrixSoftmaxForward(T* softmax, const T* src, int dim_size) {
   using VecT = phi::AlignedVector<T, VecSize>;
 
   int bid = blockIdx.x;
-  const T* batch_input = src + bid * dim_size;
+  T* batch_input = const_cast<T*>(src) + bid * dim_size;
   T* batch_output = softmax + bid * dim_size;
 
+  const int input_align_shift =
+      ((uint64_t)batch_input) % MATRIX_SOFTMAX_ALIGN_BYTES / sizeof(T);
+  const int output_align_shift =
+      ((uint64_t)batch_output) % MATRIX_SOFTMAX_ALIGN_BYTES / sizeof(T);
+
   // get max value
   AccT thread_max = ThreadVecReduce<MaxFunctor, T, AccT, VecSize>(
       batch_input,
       dim_size,
+      input_align_shift,
       MaxFunctor<T, AccT>(),
       std::numeric_limits<AccT>::min());
   BlockReduceMax<AccT>(&thread_max);
@@ -432,6 +492,7 @@ __global__ void KeMatrixSoftmaxForward(T* softmax, const T* src, int dim_size) {
   AccT thread_exp = ThreadVecReduce<SumExpFunctor, T, AccT, VecSize>(
       batch_input,
       dim_size,
+      input_align_shift,
       SumExpFunctor<T, AccT>(thread_max),
       static_cast<AccT>(0.));
   BlockReduceSum<AccT>(&thread_exp);
@@ -440,12 +501,22 @@ __global__ void KeMatrixSoftmaxForward(T* softmax, const T* src, int dim_size) {
   if (LogMode) {
     LogSoftmaxForwardFunctor<AccT, T> reduction(thread_max,
                                                 std::log(thread_exp));
-    ThreadVecWrite<LogSoftmaxForwardFunctor, T, AccT, VecSize>(
-        batch_output, batch_input, dim_size, reduction);
+    if (input_align_shift == output_align_shift) {
+      ThreadVecWriteVec<LogSoftmaxForwardFunctor, T, AccT, VecSize>(
+          batch_output, batch_input, dim_size, input_align_shift, reduction);
+    } else {
+      ThreadVecWrite<LogSoftmaxForwardFunctor, T, AccT, VecSize>(
+          batch_output, batch_input, dim_size, reduction);
+    }
   } else {
     SoftmaxForwardFunctor<AccT, T> reduction(thread_max, thread_exp);
-    ThreadVecWrite<SoftmaxForwardFunctor, T, AccT, VecSize>(
-        batch_output, batch_input, dim_size, reduction);
+    if (input_align_shift == output_align_shift) {
+      ThreadVecWriteVec<SoftmaxForwardFunctor, T, AccT, VecSize>(
+          batch_output, batch_input, dim_size, input_align_shift, reduction);
+    } else {
+      ThreadVecWrite<SoftmaxForwardFunctor, T, AccT, VecSize>(
+          batch_output, batch_input, dim_size, reduction);
+    }
   }
 }
 
@@ -1371,5 +1442,9 @@ void SoftmaxBackwardCUDAKernelDriver(const GPUContext& dev_ctx,
         dev_ctx, dx_data, dout.data<T>(), out.data<T>(), N, dim, D);
   }
 }
+#undef FIXED_BLOCK_DIM_BASE
+#undef FIXED_BLOCK_DIM
+#undef FIXED_VEC_SIZE_BASE
+#undef FIXED_VEC_SIZE
 
 }  // namespace phi