chatlearn/models/megatron/lora/layers.py [490:517]:
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        self.fuse_lora = False

    def eval(self):
        self.lora_dropout.eval()

    #   self.fuse_lora_weight()

    def train(self, mode=True):
        self.lora_dropout.train(mode)
        # self.unfuse_lora_weight()

    def reset_parameters(self):
        distributed_kaiming_uniform_(self.lora_right_weight, self.fan_in, a=math.sqrt(5))
        nn.init.zeros_(self.lora_left_weight)

    def fuse_lora_weight(self):
        if not self.fuse_lora:
            self.weight.data += self.lora_scaling * torch.matmul(
                self.lora_left_weight.t(), self.lora_right_weight.t())
        self.fuse_lora = True

    def unfuse_lora_weight(self):
        if self.fuse_lora:
            self.weight.data -= self.lora_scaling * torch.matmul(
                self.lora_left_weight.t(), self.lora_right_weight.t())
        self.fuse_lora = False

    def forward(self, input_):
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chatlearn/models/megatron/lora/layers.py [599:626]:
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        self.fuse_lora = False

    def eval(self):
        self.lora_dropout.eval()

    #   self.fuse_lora_weight()

    def train(self, mode=True):
        self.lora_dropout.train(mode)
        # self.unfuse_lora_weight()

    def reset_parameters(self):
        distributed_kaiming_uniform_(self.lora_right_weight, self.fan_in, a=math.sqrt(5))
        nn.init.zeros_(self.lora_left_weight)

    def fuse_lora_weight(self):
        if not self.fuse_lora:
            self.weight.data += self.lora_scaling * torch.matmul(
                self.lora_left_weight.t(), self.lora_right_weight.t())
        self.fuse_lora = True

    def unfuse_lora_weight(self):
        if self.fuse_lora:
            self.weight.data -= self.lora_scaling * torch.matmul(
                self.lora_left_weight.t(), self.lora_right_weight.t())
        self.fuse_lora = False

    def forward(self, input_):
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