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memory efficient fp16 backward

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This commit is contained in:
dbaranchuk 2022-08-25 19:09:23 +03:00
parent ef2936a90d
commit 4d6174bc63
2 changed files with 6 additions and 41 deletions
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40
bitsandbytes/autograd/_functions.py
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@ -196,7 +196,6 @@ class MatmulLtState:
self.CxBt = None
self.SBt = None
self.CBt = None
class MatMul8bitLt(torch.autograd.Function):
@ -327,15 +326,12 @@ class MatMul8bitLt(torch.autograd.Function):
#clone_func = torch.clone
return clone_func(output.view(output_shape))
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:
bias_grad = (None if ctx.bias is None else torch.zeros_like(ctx.bias))
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
req_gradA, req_gradB, req_gradBias = ctx.req_grads
CAt, subA = ctx.tensors
SCAt, idx = ctx.tensor_states
formatB = ctx.formatB
assert not req_gradB, "TODO: support weight updates as well"
state = ctx.state
if len(grad_output.shape) == 3:
@ -345,37 +341,11 @@ class MatMul8bitLt(torch.autograd.Function):
grad_A = grad_B = grad_bias = None
Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output)
if req_gradB:
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)
if state.threshold > 0.0 and subA is not None:
grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
if req_gradA:
C32grad, Sgrad = F.transform(Cgrad, "col32")
if state.CxBt is None:
if state.has_fp16_weights:
CBt = state.CBt
else:
# Restore CBt from CB
assert state.CBt is None, "CBt should not be stored in state"
CB = state.CB.half()
SCB = state.SCB.unsqueeze(1).half()
SCBt = state.SCBt.unsqueeze(1).half()
Bt = (CB * SCB).t().contiguous()
CBt = (Bt / SCBt).t().to(torch.int8)
# intentionally, do not store CxBt in state
CxBt, SBt = F.transform(
CBt, to_order=formatB, transpose=True
)
else:
CxBt = state.CxBt
gradA32, SgradA32 = F.igemmlt(C32grad, CxBt, Sgrad, SBt)
grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape)
CB = state.CB.half()
SCB = state.SCB.unsqueeze(1).half()
B = (CB * SCB) / 127.0
grad_A = torch.mm(grad_output, B).view(ctx.grad_shape)
if req_gradBias:
grad_bias = grad_output.sum(0)

7
bitsandbytes/nn/modules.py
View File

@ -148,12 +148,10 @@ class Int8Params(torch.nn.Parameter):
has_fp16_weights=False,
CB=None,
SCB=None,
SCBt=None,
):
cls.has_fp16_weights = has_fp16_weights
cls.CB = None
cls.SCB = None
cls.SCBt = None
if data is None:
data = torch.empty(0)
return torch.Tensor._make_subclass(cls, data, requires_grad)
@ -167,10 +165,10 @@ class Int8Params(torch.nn.Parameter):
B = self.data.contiguous().half().cuda(device)
CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B)
del CBt
del SCBt
self.data = CB
setattr(self, "CB", CB)
setattr(self, "SCB", SCB)
setattr(self, "SCBt", SCBt)
return self
@ -212,7 +210,6 @@ class Int8Params(torch.nn.Parameter):
)
new_param.CB = self.CB
new_param.SCB = self.SCB
new_param.SCBt = self.SCBt
return new_param
@ -243,10 +240,8 @@ class Linear8bitLt(nn.Linear):
def init_8bit_state(self):
self.state.CB = self.weight.CB
self.state.SCB = self.weight.SCB
self.state.SCBt = self.weight.SCBt
self.weight.CB = None
self.weight.SCB = None
self.weight.SCBt = None
def forward(self, x):
self.state.is_training = self.training