Removed rowscale (segfaults on ampere).

Makefile

@@ -27,7 +27,6 @@ COMPUTE_CAPABILITY += -gencode arch=compute_60,code=sm_60 # Pascal
 COMPUTE_CAPABILITY += -gencode arch=compute_61,code=sm_61 # Pascal
 COMPUTE_CAPABILITY += -gencode arch=compute_70,code=sm_70 # Volta
 COMPUTE_CAPABILITY += -gencode arch=compute_72,code=sm_72 # Volta 
-COMPUTE_CAPABILITY += -gencode arch=compute_72,code=sm_72 # Volta 
 
 # CUDA 9.2 supports CC 3.0, but CUDA >= 11.0 does not
 CC_CUDA92 := -gencode arch=compute_30,code=sm_30

bitsandbytes/functional.py

@@ -897,7 +897,7 @@ def batched_igemm(A: Tensor, B: Tensor, out: Tensor=None, transposed_A=False, tr
                ct.c_long(strideA), ct.c_long(strideB), ct.c_long(strideC), ct.c_uint32(num_batch))
     return out
 
-def igemmlt(A, B, SA, SB, out=None, Sout=None, row_scale=None, dtype=torch.int32):
+def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32):
     shapeA = SA[0]
     shapeB = SB[0]
     dimsA = len(shapeA)
@@ -917,7 +917,6 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, row_scale=None, dtype=torch.int32
     elif dimsA == 3 and out is None:
         out, Sout = get_transform_buffer((shapeA[0], shapeA[1], shapeB[0]), dtype, A.device, 'col32', 'row')
 
-    if row_scale is not None: assert row_scale.numel() == out.shape[0]
     assert dimsB != 3, 'len(B.shape)==3 not supported'
     assert A.device.type == 'cuda'
     assert B.device.type == 'cuda'
@@ -936,7 +935,6 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, row_scale=None, dtype=torch.int32
     ptrA = get_ptr(A)
     ptrB = get_ptr(B)
     ptrC = get_ptr(out)
-    ptrRowScale = get_ptr(row_scale)
 
     k = shapeA[-1]
     lda = ct.c_int32(m*32)
@@ -955,20 +953,17 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, row_scale=None, dtype=torch.int32
     k = ct.c_int32(k)
 
     has_error = 0
+    ptrRowScale = get_ptr(None)
     if formatB == 'col_turing':
         if dtype == torch.int32:
             has_error = lib.cigemmlt_turing_32(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc)
-        elif row_scale is None:
-            has_error = lib.cigemmlt_turing_8(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc)
         else:
-            has_error = lib.cigemmlt_turing_8_rowscale(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc)
+            has_error = lib.cigemmlt_turing_8(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc)
     elif formatB == 'col_ampere':
         if dtype == torch.int32:
             has_error = lib.cigemmlt_ampere_32(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc)
-        elif row_scale is None:
-            has_error = lib.cigemmlt_ampere_8(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc)
         else:
-            has_error = lib.cigemmlt_ampere_8_rowscale(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc)
+            has_error = lib.cigemmlt_ampere_8(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc)
 
     if has_error == 1:
         raise Exception('cublasLt ran into an error!')

tests/test_functional.py

@@ -992,6 +992,7 @@ inner = torch.randint(1,4*1024, size=(n,)).tolist()
 values = list(zip(dim1, dim4, inner))
 names = ['dim1_{0}_dim4_{1}_inner_{2}'.format(*vals) for vals in values]
 @pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
+@pytest.mark.skip("Row scale has some bugs for ampere")
 def test_igemmlt_row_scale(dim1, dim4, inner):
     formatB = F.get_special_format_str()
     err1, err2, err3 = [], [], []
@@ -1064,6 +1065,7 @@ dim4 = [12288, 4096]
 values = list(zip(dim1, dim4, inner))
 names = ['dim1_{0}_dim4_{1}_inner_{2}'.format(*vals) for vals in values]
 @pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
+@pytest.mark.skip("Row scale has some bugs for ampere")
 def test_row_scale_bench(dim1, dim4, inner):
     err1, err2, err3 = [], [], []
     relerr1, relerr2 = [], []