fix tpp linear loop of first/next kernel (#2561)

csrc/cpu/tpp/kernels/TPPGEMMKrnl.h

@@ -16,7 +16,6 @@
 namespace torch_ipex {
 namespace tpp {
 
-static int large_cache_opt = false;
 static int use_at_vnni = false; // env2int("USE_AT_VNNI");
 static int FT_OPT_SIZE = env2int("FT_OPT_SIZE", 256);
 static int NCB_BLOCK_SIZE = env2int("NCB_BLOCK_SIZE", 64);
@@ -58,15 +57,16 @@ inline at::Tensor wt_tensor_for_first_token(at::Tensor& t) {
   if (dim < 5)
     return t;
   auto sizes = t.sizes();
-  constexpr long RBS = 2;
+  constexpr long RBS = 4;
   auto K1 = sizes[0];
   if (K1 % RBS != 0)
     return t;
   auto C1 = sizes[1];
   auto C2 = sizes[2];
   auto K2 = sizes[3];
   auto C3 = sizes[4];
-
+  if (K2 >= 32)
+    return t;
   auto t_new = t.new_empty({K1 / RBS, C1, C2, RBS * K2, C3});
   auto in = GetVLAPtr<T>(t, {RBS, C1, C2, K2 * C3});
   auto out = GetVLAPtr<T>(t_new, {C1, C2, RBS, K2 * C3});
@@ -96,6 +96,7 @@ inline void tpp_linear_bias(
   auto in_sizes = t_in.sizes();
   auto wt_sizes = t_wt_.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     if (wt_sizes[3] != 100) {
       t_wt_ = wt_tensor_for_first_token<T>(t_wt_);
@@ -183,13 +184,15 @@ inline void tpp_linear_no_bias(
   auto in_sizes = t_in.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
   auto wt_sizes = t_wt_.sizes();
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     if (wt_sizes[3] != 100) {
       t_wt_ = wt_tensor_for_first_token<T>(t_wt_);
       wt_sizes = t_wt_.sizes();
     }
     large_cache_opt = true;
   }
+
   auto C = in_sizes[2];
 
   auto Nc = wt_sizes[1];
@@ -254,10 +257,12 @@ inline void tpp_linear_mul(
   auto t_wt_ = t_wt;
   auto in_sizes = t_in.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     t_wt_ = wt_tensor_for_first_token<T>(t_wt_);
     large_cache_opt = true;
   }
+
   auto wt_sizes = t_wt_.sizes();
   auto C = in_sizes[2];
 
@@ -348,6 +353,7 @@ inline void tpp_linear_add_add(
   auto t_wt_ = t_wt;
   auto in_sizes = t_in.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     t_wt_ = wt_tensor_for_first_token<T>(t_wt_);
     large_cache_opt = true;
@@ -444,10 +450,12 @@ inline void tpp_linear_gelu(
   auto t_wt_ = t_wt;
   auto in_sizes = t_in.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     t_wt_ = wt_tensor_for_first_token<T>(t_wt_);
     large_cache_opt = true;
   }
+
   auto wt_sizes = t_wt_.sizes();
   auto C = in_sizes[2];
 
@@ -546,6 +554,7 @@ inline void tpp_fused_gate_up_proj(
   auto t_wt_up_ = t_wt_up;
   auto in_sizes = t_in.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     t_wt_gate_ = wt_tensor_for_first_token<T>(t_wt_gate_);
     t_wt_up_ = wt_tensor_for_first_token<T>(t_wt_up_);
@@ -670,10 +679,12 @@ inline void tpp_linear_add(
   auto t_wt_ = t_wt;
   auto in_sizes = t_in.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     t_wt_ = wt_tensor_for_first_token<T>(t_wt_);
     large_cache_opt = true;
   }
+
   auto wt_sizes = t_wt_.sizes();
   auto C = in_sizes[2];
 
@@ -761,10 +772,12 @@ inline void tpp_linear_silu(
   auto t_wt_ = t_wt;
   auto in_sizes = t_in.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     t_wt_ = wt_tensor_for_first_token<T>(t_wt_);
     large_cache_opt = true;
   }
+
   auto wt_sizes = t_wt_.sizes();
   auto C = in_sizes[2];
 
@@ -851,10 +864,12 @@ inline void tpp_linear_relu(
   auto t_wt_ = t_wt;
   auto in_sizes = t_in.sizes();
   auto BS = in_sizes[0] * in_sizes[1];
+  bool large_cache_opt = false;
   if (BS > FT_OPT_SIZE) { // first token compute
     t_wt_ = wt_tensor_for_first_token<T>(t_wt_);
     large_cache_opt = true;
   }
+
   auto wt_sizes = t_wt_.sizes();
   auto C = in_sizes[2];