Added is_available_triton guard.

bitsandbytes/research/autograd/_functions.py

@@ -184,7 +184,7 @@ class MatMulFP8Global(torch.autograd.Function):
         return grad_A, grad_B, None, None, None, None, None
 
 
-class MatMul8bitMixed(torch.autograd.Function):
+class SwitchBackBnb(torch.autograd.Function):
     @staticmethod
     def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()):
         # default to pytorch behavior if inputs are empty
@@ -408,4 +408,4 @@ def switchback_bnb(
     state = state or MatmulLtState()
     if threshold > 0.0:
         state.threshold = threshold
-    return MatMul8bitMixed.apply(A, B, out, bias, state)
+    return SwitchBackBnb.apply(A, B, out, bias, state)

bitsandbytes/triton/dequantize_rowwise.py

@@ -1,6 +1,12 @@
 import math
 import torch
 import time
+from bitsandbytes.triton.triton_utils import is_triton_available
+
+if not is_triton_available():
+    def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor): return None
+else:
+
     import triton
     import triton.language as tl
     from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time

bitsandbytes/triton/int8_matmul_mixed_dequanitze.py

@@ -1,4 +1,9 @@
 import torch
+from bitsandbytes.triton.triton_utils import is_triton_available
+
+if not is_triton_available():
+    def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias): return None
+else:
 
     import triton
     import triton.language as tl

bitsandbytes/triton/int8_matmul_rowwise_dequantize.py

@@ -1,5 +1,10 @@
 import torch
 
+from bitsandbytes.triton.triton_utils import is_triton_available
+
+if not is_triton_available():
+    def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias): return None
+else:
     import triton
     import triton.language as tl
     from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time

bitsandbytes/triton/quantize_columnwise_and_transpose.py

@@ -1,6 +1,12 @@
 import math
 import torch
 import time
+from bitsandbytes.triton.triton_utils import is_triton_available
+
+if not is_triton_available():
+    def quantize_columnwise_and_transpose(x: torch.Tensor): return None
+else:
+
     import triton
     import triton.language as tl
     from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time

bitsandbytes/triton/quantize_global.py

@@ -1,6 +1,13 @@
 import math
 import torch
 import time
+from bitsandbytes.triton.triton_utils import is_triton_available
+
+if not is_triton_available():
+    def quantize_global_transpose(input): return None
+    def quantize_global(x: torch.Tensor): return None
+else:
+
     import triton
     import triton.language as tl
     from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time

bitsandbytes/triton/quantize_rowwise.py

@@ -1,6 +1,13 @@
 import math
 import torch
 import time
+
+from bitsandbytes.triton.triton_utils import is_triton_available
+
+if not is_triton_available():
+    def quantize_rowwise(x: torch.Tensor): return None
+else:
+
     import triton
     import triton.language as tl
     from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time