Update fast_lora.py
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Daniel Han-Chen
parent
363ffba1c2
commit
c74e1af85c
1 changed files with 6 additions and 111 deletions
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@ -90,8 +90,9 @@ class LoRA_MLP(torch.autograd.Function):
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e = matmul_lora(X, gateW, gateW_quant, gateA, gateB, gateS)
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g = matmul_lora(X, upW, upW_quant, upA, upB, upS)
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h = torch.nn.functional.silu(e) * g
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# h = swiglu_fg_kernel(e, g)
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# f = torch.nn.functional.silu(e)
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# h = f * g
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h = swiglu_fg_kernel(e, g)
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i = matmul_lora(h, downW, downW_quant, downA, downB, downS)
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ctx.custom_saved_tensors = (
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@ -104,6 +105,7 @@ class LoRA_MLP(torch.autograd.Function):
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return i
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pass
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@staticmethod
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@torch.cuda.amp.custom_bwd
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def backward(ctx, dY : torch.Tensor):
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@ -123,113 +125,6 @@ class LoRA_MLP(torch.autograd.Function):
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dtype = X.dtype
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DW = matmul_lora(dY, downW.t(), downW_quant, downB, downA, downS)
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se = 1 / (1 + torch.exp(-e.float()))
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f = torch.nn.functional.silu(e)
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h = f * g
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DW_f = (DW * f)
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DW_dfg = (DW * g).float() * se * (1.0 + e.float() * (1.0 - se))
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DW_dfg = DW_dfg.to(dtype)
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# f = e * se
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# h = f * g
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# df = se * (1 - f) + f
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# DW_f = DW * f
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# DW_dfg = DW * df * g
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# DW = matmul_lora(dY, downW.t(), downW_quant, downB, downA, downS)
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# DW, e, g = swiglu_DWf_DW_dfg_kernel(DW, e, g)
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# h, DW_f, DW_dfg = DW, e, g
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# Down projection LoRA weights
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d_downA = h.t() @ (dY @ (downS * downB).t())
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d_downB = ((downS * downA).t() @ h.t()) @ dY
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# d_downA *= downS
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# d_downB *= downS
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# Up projection LoRA weights
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d_upA = X.t() @ (DW_f @ (upS * upB).t())
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d_upB = ((upS * upA).t() @ X.t()) @ DW_f
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# d_upA *= upS
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# d_upB *= upS
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# Gate projection LoRA weights
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d_gateA = X.t() @ (DW_dfg @ (gateS * gateB).t())
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d_gateB = ((gateS * gateA).t() @ X.t()) @ DW_dfg
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# d_gateA *= gateS
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# d_gateB *= gateS
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# Final derivatives to backpropagate backwards.
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# See our blogpost for more details.
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# (D @ W.T * f) @ U.T
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upW = fast_dequantize(upW.t(), upW_quant)
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# (D @ W.T * f) @ (U.T + B.T @ A.T)
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dX = torch.matmul(DW_f, upW.t(), out = X)
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del upW
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dX += (DW_f @ upB.to(dtype).t() @ (upS * upA.to(dtype).t()))
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# And add the derivative for the gate projection
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gateW = fast_dequantize(gateW.t(), gateW_quant)
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# new_dX2 = DW_dfg @ gateW.t()
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dX += DW_dfg @ gateB.to(dtype).t() @ (gateS * gateA.to(dtype).t())
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dX += DW_dfg @ gateW.t()
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del gateW
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# gateW, gateW_quant, gateA, gateB, gateS,
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# upW, upW_quant, upA, upB, upS,
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# downW, downW_quant, downA, downB, downS,
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return dX.view(batch, seq_len, hd), \
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None, None, d_gateA.t(), d_gateB.t(), None, \
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None, None, d_upA.t(), d_upB.t(), None, \
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None, None, d_downA.t(), d_downB.t(), None,
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pass
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pass
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class LoRA_MLP_New(torch.autograd.Function):
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@classmethod
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@torch.cuda.amp.custom_fwd
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def forward(cls, ctx, X : torch.Tensor,
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gateW, gateW_quant, gateA, gateB, gateS,
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upW, upW_quant, upA, upB, upS,
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downW, downW_quant, downA, downB, downS):
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dtype = X.dtype
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e = matmul_lora(X, gateW, gateW_quant, gateA, gateB, gateS)
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g = matmul_lora(X, upW, upW_quant, upA, upB, upS)
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# f = torch.nn.functional.silu(e)
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# h = f * g
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h = swiglu_fg_kernel(e, g)
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i = matmul_lora(h, downW, downW_quant, downA, downB, downS)
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ctx.custom_saved_tensors = (
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gateW, gateW_quant, gateS,
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upW, upW_quant, upS,
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downW, downW_quant, downS,
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)
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ctx.save_for_backward(gateA, gateB, upA, upB, downA, downB,
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X, e, g)
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return i
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pass
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@classmethod
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@torch.cuda.amp.custom_bwd
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def backward(cls, ctx, dY : torch.Tensor):
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gateW, gateW_quant, gateS, upW, upW_quant, upS, downW, downW_quant, downS, = \
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ctx.custom_saved_tensors
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gateA, gateB, upA, upB, downA, downB, \
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X, e, g = ctx.saved_tensors
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gateA, gateB, upA, upB, downA, downB = \
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gateA.t(), gateB.t(), upA.t(), upB.t(), downA.t(), downB.t()
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batch, seq_len, hd = X.shape
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dY = dY.view(-1, dY.shape[-1])
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X = X .view(-1, X .shape[-1])
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e = e .view(-1, e .shape[-1])
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g = g .view(-1, g .shape[-1])
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dtype = X.dtype
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DW = matmul_lora(dY, downW.t(), downW_quant, downB, downA, downS)
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# e = e.float()
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# se = 1.0 / (1.0 + torch.exp(-e))
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# f = (se * e).to(dtype)
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@ -253,8 +148,8 @@ class LoRA_MLP_New(torch.autograd.Function):
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d_upB *= upS
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# Gate projection LoRA weights
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d_gateA = X.t() @ (de @ gateB.t())
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d_gateB = (gateA.t() @ X.t()) @ de
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d_gateA = X.t() @ (dg @ gateB.t())
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d_gateB = (gateA.t() @ X.t()) @ dg
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d_gateA *= gateS
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d_gateB *= gateS
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@ -281,7 +176,7 @@ class LoRA_MLP_New(torch.autograd.Function):
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pass
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pass
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from transformers.models.llama.modeling_llama import logger
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def apply_lora_mlp(self, X):
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# gate = self.gate_proj(X)
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# up = self. up_proj(X)
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@ -291,7 +186,7 @@ def apply_lora_mlp(self, X):
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gateW, gateW_quant, gateA, gateB, gateS = get_lora_parameters(self.gate_proj)
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upW, upW_quant, upA, upB, upS = get_lora_parameters(self. up_proj)
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downW, downW_quant, downA, downB, downS = get_lora_parameters(self.down_proj)
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out = LoRA_MLP_New.apply(X,
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out = LoRA_MLP.apply(X,
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gateW, gateW_quant, gateA, gateB, gateS,
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upW, upW_quant, upA, upB, upS,
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downW, downW_quant, downA, downB, downS)
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