mrq/bitsandbytes-rocm
1
0
Fork 1
Code Issues 1 Pull Requests Packages Projects Releases Wiki Activity

Removed rowscale (segfaults on ampere).

Browse Source
This commit is contained in:
Tim Dettmers 2022-07-25 17:27:57 -07:00
parent 8b1fd32e3e
commit 1e88edd8c0
3 changed files with 6 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
Makefile
View File

@ -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_61,code=sm_61 # Pascal
COMPUTE_CAPABILITY += -gencode arch=compute_70,code=sm_70 # Volta 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
COMPUTE_CAPABILITY += -gencode arch=compute_72,code=sm_72 # Volta
# CUDA 9.2 supports CC 3.0, but CUDA >= 11.0 does not # CUDA 9.2 supports CC 3.0, but CUDA >= 11.0 does not
CC_CUDA92 := -gencode arch=compute_30,code=sm_30 CC_CUDA92 := -gencode arch=compute_30,code=sm_30

13
bitsandbytes/functional.py
View File

@ -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)) ct.c_long(strideA), ct.c_long(strideB), ct.c_long(strideC), ct.c_uint32(num_batch))
return out 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] shapeA = SA[0]
shapeB = SB[0] shapeB = SB[0]
dimsA = len(shapeA) 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: elif dimsA == 3 and out is None:
out, Sout = get_transform_buffer((shapeA[0], shapeA[1], shapeB[0]), dtype, A.device, 'col32', 'row') 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 dimsB != 3, 'len(B.shape)==3 not supported'
assert A.device.type == 'cuda' assert A.device.type == 'cuda'
assert B.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) ptrA = get_ptr(A)
ptrB = get_ptr(B) ptrB = get_ptr(B)
ptrC = get_ptr(out) ptrC = get_ptr(out)
ptrRowScale = get_ptr(row_scale)
k = shapeA[-1] k = shapeA[-1]
lda = ct.c_int32(m*32) 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) k = ct.c_int32(k)
has_error = 0 has_error = 0
ptrRowScale = get_ptr(None)
if formatB == 'col_turing': if formatB == 'col_turing':
if dtype == torch.int32: if dtype == torch.int32:
has_error = lib.cigemmlt_turing_32(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc) 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: 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': elif formatB == 'col_ampere':
if dtype == torch.int32: if dtype == torch.int32:
has_error = lib.cigemmlt_ampere_32(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc) 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: 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: if has_error == 1:
raise Exception('cublasLt ran into an error!') raise Exception('cublasLt ran into an error!')

2
tests/test_functional.py
View File

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