03a57decde62c76783ef7e2288bd61bc87f6e266,fairseq/optim/fp16_optimizer.py,FP16Optimizer,step,#FP16Optimizer#Any#,168

Before Change

            if not p.requires_grad:
                continue
            numel = p.data.numel()
            p.data.copy_(self.fp32_params.data[offset:offset+numel].view_as(p.data))
            offset += numel

    def zero_grad(self):
        Clears the gradients of all optimized parameters.

After Change

        self.wrapped_optimizer.step(closure)

        // convert params back to FP16
        for group in self.wrapped_optimizer.optimizer.param_groups:
            group["params"] = group["params"].params  // unwrap from ConvertToFP32
            for p in group["params"]:
                p.data = p.data.half()
                if p.grad is not None:
                    p.grad.data = p.grad.data.half()

    def zero_grad(self):
        Clears the gradients of all optimized parameters.
        self.wrapped_optimizer.zero_grad()
        self._grads_are_scaled = False

Instances