diff --git a/python/jittor/extern/cuda/cublas/ops/cublas_batched_matmul_op.cc b/python/jittor/extern/cuda/cublas/ops/cublas_batched_matmul_op.cc
index 47874ef3..6f93e343 100644
--- a/python/jittor/extern/cuda/cublas/ops/cublas_batched_matmul_op.cc
+++ b/python/jittor/extern/cuda/cublas/ops/cublas_batched_matmul_op.cc
@@ -89,7 +89,7 @@ void CublasBatchedMatmulOp::jit_prepare(JK& jk) {
     jk << _CS("[T:") << a->dtype();
     jk << _CS("][Trans_a:") << (trans_a ? 'T' : 'N');
     jk << _CS("][Trans_b:") << (trans_b ? 'T' : 'N');
-    jk << _CS("][op:") << (a->dtype().dsize() == 4 ? 'S' : 'D');
+    jk << _CS("][op:") << (a->dtype().dsize() == 2? 'H' : (a->dtype().dsize() == 4 ? 'S' : 'D'));
     jk << ']';
 }
 
@@ -128,6 +128,18 @@ void CublasBatchedMatmulOp::jit_run() {
         || b->dtype() == ns_float16 || c->dtype() == ns_float16) {
         computeType = CUBLAS_COMPUTE_16F;
     }
+    #else 
+    cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
+    cudaDataType_t computeType = CUDA_R_32F;
+    if (use_tensorcore) {
+        algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP;
+    }
+    if (a->dtype() == ns_float16
+        || b->dtype() == ns_float16 || c->dtype() == ns_float16) {
+        computeType = CUDA_R_16F;
+        algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP;
+    }
+    #endif
     checkCudaErrors(cublasGemmStridedBatchedEx(handle_,
     CUBLAS_OP_@Trans_b, CUBLAS_OP_@Trans_a,
     k, n, m, &alpha,
@@ -135,15 +147,13 @@ void CublasBatchedMatmulOp::jit_run() {
     a->ptr<T>(),get_dtype(a->dtype()), '@Trans_a' == 'N' ? m : n, n * m, &beta,
     c->ptr<T>(),get_dtype(c->dtype()), k, k * n,
     batch_size,computeType,algo));
-    #else
-    checkCudaErrors(cublas@op@@gemmStridedBatched(handle_,
-    CUBLAS_OP_@Trans_b, CUBLAS_OP_@Trans_a,
-    k, n, m, &alpha,
-    b->ptr<T>(), '@Trans_b' == 'N' ? k : m, k * m, 
-    a->ptr<T>(), '@Trans_a' == 'N' ? m : n, n * m, &beta,
-    c->ptr<T>(), k, k * n,
-    batch_size));
-    #endif
+    // checkCudaErrors(cublas@op@@gemmStridedBatched(handle_,
+    // CUBLAS_OP_@Trans_b, CUBLAS_OP_@Trans_a,
+    // k, n, m, &alpha,
+    // b->ptr<T>(), '@Trans_b' == 'N' ? k : m, k * m, 
+    // a->ptr<T>(), '@Trans_a' == 'N' ? m : n, n * m, &beta,
+    // c->ptr<T>(), k, k * n,
+    // batch_size));
 }
 #endif
 #endif // JIT
diff --git a/python/jittor/extern/cuda/cublas/ops/cublas_matmul_op.cc b/python/jittor/extern/cuda/cublas/ops/cublas_matmul_op.cc
index 95de20f7..a6708225 100644
--- a/python/jittor/extern/cuda/cublas/ops/cublas_matmul_op.cc
+++ b/python/jittor/extern/cuda/cublas/ops/cublas_matmul_op.cc
@@ -50,7 +50,7 @@ void CublasMatmulOp::jit_prepare(JK& jk) {
     jk << _CS("[T:") << a->dtype();
     jk << _CS("][Trans_a:") << (trans_a ? 'T' : 'N');
     jk << _CS("][Trans_b:") << (trans_b ? 'T' : 'N');
-    jk << _CS("][op:") << (a->dtype().dsize() == 4 ? 'S' : 'D');
+    jk << _CS("][op:") << (a->dtype().dsize() == 2? 'H' : (a->dtype().dsize() == 4 ? 'S' : 'D'));
     jk << ']';
 }
 
@@ -85,6 +85,18 @@ void CublasMatmulOp::jit_run() {
         || b->dtype() == ns_float16 || c->dtype() == ns_float16) {
         computeType = CUBLAS_COMPUTE_16F;
     }
+    #else
+    cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
+    cudaDataType_t computeType = CUDA_R_32F;
+    if (use_tensorcore) {
+        algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP;
+    }
+    if (a->dtype() == ns_float16
+        || b->dtype() == ns_float16 || c->dtype() == ns_float16) {
+        computeType = CUDA_R_16F;
+        algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP;
+    }
+    #endif
     checkCudaErrors(cublasGemmEx(handle_, 
     CUBLAS_OP_@Trans_b, CUBLAS_OP_@Trans_a, 
     k, n, m, &alpha, 
@@ -92,15 +104,13 @@ void CublasMatmulOp::jit_run() {
     a->ptr<T>(),get_dtype(a->dtype()), '@Trans_a' == 'N' ? m : n, &beta, 
     c->ptr<T>(),get_dtype(c->dtype()), k,
     computeType, algo));
-    #else
-    checkCudaErrors(cublas@op@@gemm(handle_, 
-    CUBLAS_OP_@Trans_b, CUBLAS_OP_@Trans_a, 
-    k, n, m, &alpha, 
-    b->ptr<T>(), '@Trans_b' == 'N' ? k : m, 
-    a->ptr<T>(), '@Trans_a' == 'N' ? m : n, &beta, 
-    c->ptr<T>(), k));
+    // checkCudaErrors(cublas@op@@gemm(handle_, 
+    // CUBLAS_OP_@Trans_b, CUBLAS_OP_@Trans_a, 
+    // k, n, m, &alpha, 
+    // b->ptr<T>(), '@Trans_b' == 'N' ? k : m, 
+    // a->ptr<T>(), '@Trans_a' == 'N' ? m : n, &beta, 
+    // c->ptr<T>(), k));
 
-    #endif
     
 }
 #endif // JIT
diff --git a/python/jittor/other/code_softmax.py b/python/jittor/other/code_softmax.py
index 8534f0cb..837bd648 100644
--- a/python/jittor/other/code_softmax.py
+++ b/python/jittor/other/code_softmax.py
@@ -100,7 +100,7 @@ def softmax_v1(a, log=False):
     {for_loop} 
         #pragma unroll
         for (int j=0; j<{ILP}; j++)
-            v1 += {"vy[i][j];" if log else "vx[i][j]*vy[i][j];"}
+            v1 += {"float(vy[i][j]);" if log else "float(vx[i][j]*vy[i][j]);"}
 
     typedef cub::BlockReduce<float, {tnum}> BlockReduce;
     __shared__ typename BlockReduce::TempStorage temp_storage;
@@ -114,8 +114,8 @@ def softmax_v1(a, log=False):
         #pragma unroll
         for (int j=0; j<{ILP}; j++)
             vx[i][j] = {
-                "vy[i][j] - expf(vx[i][j]) * reduce_var;" if log 
-                else "vx[i][j] * (vy[i][j] - reduce_var);"
+                "vy[i][j] - in0_type(expf(vx[i][j]) * reduce_var);" if log 
+                else "vx[i][j] * (vy[i][j] - in0_type(reduce_var));"
             }
 
     {for_loop}