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Update fast_lora.py

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Daniel Han-Chen 2024-03-10 19:32:02 +11:00
commit 23b7a5764c
1 changed files with 11 additions and 20 deletions
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31
unsloth/kernels/fast_lora.py
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@ -28,17 +28,14 @@ def matmul_lora(X, W, W_quant, A, B, s, out = None):
reshape = False
pass
A, B = A.t(), B.t()
W.addmm_(A.to(dtype), B.to(dtype), alpha = s)
out = torch.matmul(X, W, out = out)
if W_quant is not None: del W
# if A is not None:
# # LoRA is enabled
# A, B = A.t(), B.t()
# out += (X @ A.to(dtype)) @ (s * B.to(dtype))
# pass
if A is not None:
# LoRA is enabled
A, B = A.t(), B.t()
out += (X @ A.to(dtype)) @ (s * B.to(dtype))
pass
return out.view(batch, seq_len, -1) if reshape else out
pass
@ -151,16 +148,14 @@ class LoRA_MLP(torch.autograd.Function):
# dX = matmul_lora(df, upW.t(), upW_quant, upB, upA, upS)
# dX += matmul_lora(de, gateW.t(), gateW_quant, gateB, gateA, gateS)
upW = fast_dequantize(upW.t(), upW_quant)
upW.addmm_(upA.to(dtype), upB.to(dtype), alpha = upS)
dX = torch.matmul(df, upW.t(), out = X)
del upW
# dX += df @ upB.to(dtype).t() @ (upS * upA.to(dtype).t())
dX += df @ upB.to(dtype).t() @ (upS * upA.to(dtype).t())
gateW = fast_dequantize(gateW.t(), gateW_quant)
gateW.addmm_(gateA.to(dtype), gateB.to(dtype), alpha = gateS)
dX += de @ gateW.t()
del gateW
# dX += de @ gateB.to(dtype).t() @ (gateS * gateA.to(dtype).t())
dX += de @ gateB.to(dtype).t() @ (gateS * gateA.to(dtype).t())
# gateW, gateW_quant, gateA, gateB, gateS,
# upW, upW_quant, upA, upB, upS,
@ -308,24 +303,21 @@ class LoRA_QKV(torch.autograd.Function):
# Combine derivatives to find dX
# dQ
QW = fast_dequantize(QW.t(), QW_quant)
QW.addmm_(QA.to(dtype), QB.to(dtype), alpha = QS)
dX = torch.matmul(dQ, QW.t(), out = X)
del QW
# dX += (dQ @ QB.to(dtype).t() @ (QS * QA.to(dtype).t()))
dX += (dQ @ QB.to(dtype).t() @ (QS * QA.to(dtype).t()))
# dK
KW = fast_dequantize(KW.t(), KW_quant)
KW.addmm_(KA.to(dtype), KB.to(dtype), alpha = KS)
dX += dK @ KW.t()
del KW
# dX += dK @ KB.to(dtype).t() @ (KS * KA.to(dtype).t())
dX += dK @ KB.to(dtype).t() @ (KS * KA.to(dtype).t())
# dV
VW = fast_dequantize(VW.t(), VW_quant)
VW.addmm_(VA.to(dtype), VB.to(dtype), alpha = VS)
dX += dV @ VW.t()
del VW
# dX += dV @ VB.to(dtype).t() @ (VS * VA.to(dtype).t())
dX += dV @ VB.to(dtype).t() @ (VS * VA.to(dtype).t())
# QW, QW_quant, QA, QB, QS,
# KW, KW_quant, KA, KB, KS,
@ -411,10 +403,9 @@ class LoRA_W(torch.autograd.Function):
# Get derivative for dX
W = fast_dequantize(W.t(), W_quant)
W.addmm_(A.to(dtype), B.to(dtype), alpha = S)
dX = dY @ W.t()
del W
# dX += dY @ B.to(dtype).t() @ (S * A.to(dtype).t())
dX += dY @ B.to(dtype).t() @ (S * A.to(dtype).t())
# W, W_quant, A, B, S
return dX.view(batch, seq_len, hd), \