make gate and out trainable (834aea0f54
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2f777f1286
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9
finetune/lora/v6/src/model.py
vendored
9
finetune/lora/v6/src/model.py
vendored
@ -29,7 +29,6 @@ LORA_CONFIG = {
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class LoraLinear(nn.Module):
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def __init__(self, in_features: int, out_features: int, bias: bool):
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super().__init__()
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@ -356,8 +355,8 @@ class RWKV_TimeMix_RWKV5(MyModule):
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self.key = make_linear_att(args.n_embd, args.dim_att, bias=False)
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self.value = make_linear_att(args.n_embd, args.dim_att, bias=False)
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self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
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self.gate = nn.Linear(args.n_embd, args.dim_att, bias=False)
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self.output = make_linear_att(args.dim_att, args.n_embd, bias=False)
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self.gate = make_linear_att(args.n_embd, args.dim_att, bias=False)
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self.ln_x = nn.GroupNorm(self.n_head, args.dim_att)
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@MyFunction
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@ -465,8 +464,8 @@ class RWKV_Tmix_x060(MyModule):
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self.receptance = make_linear_att(args.n_embd, args.dim_att, bias=False)
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self.key = make_linear_att(args.n_embd, args.dim_att, bias=False)
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self.value = make_linear_att(args.n_embd, args.dim_att, bias=False)
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self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
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self.gate = nn.Linear(args.n_embd, args.dim_att, bias=False)
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self.output = make_linear_att(args.dim_att, args.n_embd, bias=False)
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self.gate = make_linear_att(args.n_embd, args.dim_att, bias=False)
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self.ln_x = nn.GroupNorm(
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self.n_head, args.dim_att, eps=(1e-5) * (args.head_size_divisor**2)
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)
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