Merge pull request #1 from TimDettmers/main

Update main branch

1 files changed, 2 insertions, 40 deletions

diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py
index 6637554..22200f2 100644
--- a/bitsandbytes/functional.py
+++ b/bitsandbytes/functional.py
@@ -5,7 +5,6 @@
 import ctypes as ct
 import operator
 import random
-import math
 import torch
 
 from typing import Tuple
@@ -185,14 +184,9 @@ 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"
@@ -248,23 +242,6 @@ def get_transform_func(dtype, orderA, orderOut, transpose=False):
         return getattr(lib, name)
 
 
-class GlobalData(object):
-    _instance = None
-
-    def __init__(self):
-        raise RuntimeError("Call get_instance() instead")
-
-    def initialize(self):
-        self.data = {}
-
-    @classmethod
-    def get_instance(cls):
-        if cls._instance is None:
-            cls._instance = cls.__new__(cls)
-            cls._instance.initialize()
-        return cls._instance
-
-
 def get_transform_buffer(
     shape, dtype, device, to_order, from_order="row", transpose=False
 ):
@@ -1685,21 +1662,6 @@ def double_quant(
     return out_row, out_col, row_stats, col_stats, coo_tensor
 
 
-def get_special_format_str():
-    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)