diff options
Diffstat (limited to 'bitsandbytes')
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 6 |
1 files changed, 2 insertions, 4 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 88a33a5..9928fbd 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -366,9 +366,8 @@ class MatMul8bitLt(torch.autograd.Function): CxAt, SAt = F.transform(CAt, formatB, transpose=True) C32grad, Sgrad = F.transform(Cgradt, "col32", transpose=True) gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt) - grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt).to(ctx.dtype_B) + grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt) if state.threshold > 0.0 and subA is not None: - assert False, idx grad_B[:, idx] += torch.matmul(grad_output.t(), subA) if req_gradA: @@ -382,8 +381,7 @@ class MatMul8bitLt(torch.autograd.Function): grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A) elif state.CB is not None: - CB = state.CB.to(ctx.dtype_B) - CB.mul_(state.SCB.unsqueeze(1).div_(127.0).to(CB.dtype)) + CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).div(127.0)) grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A) else: raise Exception('State must contain either CBt or CB matrix for backward') |