diff options
Diffstat (limited to 'bitsandbytes')
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 5499db9..93304f9 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -369,7 +369,7 @@ 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.B_dtype) + grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt).to(ctx.dtype_B) if state.threshold > 0.0 and subA is not None: grad_B[:, idx].addmm_(grad_output.t(), subA) @@ -381,12 +381,12 @@ class MatMul8bitLt(torch.autograd.Function): state.CBt, to_order=formatB, transpose=True ) gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt) - grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.A_dtype) + 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.B_dtype) - CB.mul_(state.SCB.unsqueeze(1).div_(127.0).to(ctx.B_dtype)) - grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.A_dtype) + CB = state.CB.to(ctx.dtype_B) + CB.mul_(state.SCB.unsqueeze(1).div_(127.0).to(CB.dtype)) + 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') |