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change typecast behavior

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justheuristic 2022-09-18 00:21:15 +03:00
parent 1da4880262
commit 5b169f18e4
1 changed files with 5 additions and 5 deletions
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10
bitsandbytes/autograd/_functions.py
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@ -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')