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Update fast_lora.py

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Daniel Han-Chen 2024-01-26 19:30:18 +11:00
commit e0a36b356b
1 changed files with 10 additions and 10 deletions
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unsloth/kernels/fast_lora.py
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@ -134,20 +134,20 @@ class LoRA_MLP(torch.autograd.Function):
# h, DW_f, DW_dfg = DW, e, g
# Down projection LoRA weights
d_downA = h.t() @ dY @ downB.t()
d_downB = downA.t() @ h.t() @ dY
d_downA = h.t() @ (dY @ downB.t())
d_downB = (downA.t() @ h.t()) @ dY
d_downA *= downS
d_downB *= downS
# Up projection LoRA weights
d_upA = X.t() @ DW_f @ upB.t()
d_upB = upA.t() @ X.t() @ DW_f
d_upA = X.t() @ (DW_f @ upB.t())
d_upB = (upA.t() @ X.t()) @ DW_f
d_upA *= upS
d_upB *= upS
# Gate projection LoRA weights
d_gateA = X.t() @ DW_dfg @ gateB.t()
d_gateB = gateA.t() @ X.t() @ DW_dfg
d_gateA = X.t() @ (DW_dfg @ gateB.t())
d_gateB = (gateA.t() @ X.t()) @ DW_dfg
d_gateA *= gateS
d_gateB *= gateS
@ -156,15 +156,15 @@ class LoRA_MLP(torch.autograd.Function):
# (D @ W.T * f) @ U.T
upW = fast_dequantize(upW.t(), upW_quant)
# (D @ W.T * f) @ (U.T + B.T @ A.T)
dX = torch.matmul(DW_f, upW.t())
dX = torch.matmul(DW_f, upW.t(), out = X)
del upW
dX += upS * (DW_f @ upB.to(dtype).t() @ (upA.to(dtype).t()))
# And add the derivative for the gate projection
gateW = fast_dequantize(gateW.t(), gateW_quant)
new_dX = DW_dfg @ gateW.t() + gateS * (DW_dfg @ gateB.to(dtype).t() @ (gateA.to(dtype).t()))
dX += DW_dfg @ gateW.t()
del gateW
dX += new_dX
dX += gateS * (DW_dfg @ gateB.to(dtype).t() @ (gateA.to(dtype).t()))
# gateW, gateW_quant, gateA, gateB, gateS,
# upW, upW_quant, upA, upB, upS,
@ -178,7 +178,7 @@ pass
from transformers.models.llama.modeling_llama import logger
def apply_lora_mlp(self, X):
logger.warning_once("Hello!3")
logger.warning_once("Hello!2")
# gate = self.gate_proj(X)
# up = self. up_proj(X)
# h = torch.nn.functional.silu(gate) * up