Fixed issue where Pascal was not displaying proper error.

bitsandbytes/functional.py

@@ -185,14 +185,9 @@ def create_dynamic_map(signed=True, n=7):
 
 
 def get_special_format_str():
+    if not torch.cuda.is_available(): return 'col_turing'
     major, minor = torch.cuda.get_device_capability()
-    if major < 7:
-        print(
-            f"Device with CUDA capability of {major} not supported for 8-bit matmul. Device has no tensor cores!"
-        )
-        assert major >= 7
-
-    if major == 7:
+    if major <= 7:
         return "col_turing"
     elif major == 8:
         return "col_ampere"
@@ -1685,20 +1680,6 @@ def double_quant(
     return out_row, out_col, row_stats, col_stats, coo_tensor
 
 
-def get_special_format_str():
-    if not torch.cuda.is_available(): return 'col_turning'
-    major, minor = torch.cuda.get_device_capability()
-    if major < 7:
-        print(f"Device with CUDA capability of {major} not supported for 8-bit matmul. Device has no tensor cores!")
-        assert major >= 7
-
-    if major == 7: return 'col_turing'
-    elif major == 8: return 'col_ampere'
-    else: return 'col_turing'
-
-
-
-
 def transform(A, to_order, from_order='row', out=None, transpose=False, state=None, ld=None):
     prev_device = pre_call(A.device)
     if state is None: state = (A.shape, from_order)

csrc/ops.cu

@@ -371,7 +371,11 @@ template void transform<int32_t, COL32, ROW, false, 32>(cublasLtHandle_t ltHandl
 template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc) 
 {
 #ifdef NO_CUBLASLT
-  printf("ERROR: Your GPU does not support Int8 Matmul!");
+  cout << "" << endl;
+  cout << "=============================================" << endl;
+  cout << "ERROR: Your GPU does not support Int8 Matmul!" << endl;
+  cout << "=============================================" << endl;
+  cout << "" << endl;
   assert(false);
 
 	return 0;