Remove .data usages in optimizations.py (#23417)

Patched the optimizers

src/transformers/optimization.py

@@ -422,6 +422,7 @@ class AdamW(Optimizer):
         defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay, "correct_bias": correct_bias}
         super().__init__(params, defaults)
 
+    @torch.no_grad()
     def step(self, closure: Callable = None):
         """
         Performs a single optimization step.
@@ -437,7 +438,7 @@ class AdamW(Optimizer):
             for p in group["params"]:
                 if p.grad is None:
                     continue
-                grad = p.grad.data
+                grad = p.grad
                 if grad.is_sparse:
                     raise RuntimeError("Adam does not support sparse gradients, please consider SparseAdam instead")
 
@@ -447,9 +448,9 @@ class AdamW(Optimizer):
                 if len(state) == 0:
                     state["step"] = 0
                     # Exponential moving average of gradient values
-                    state["exp_avg"] = torch.zeros_like(p.data)
+                    state["exp_avg"] = torch.zeros_like(p)
                     # Exponential moving average of squared gradient values
-                    state["exp_avg_sq"] = torch.zeros_like(p.data)
+                    state["exp_avg_sq"] = torch.zeros_like(p)
 
                 exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                 beta1, beta2 = group["betas"]
@@ -468,7 +469,7 @@ class AdamW(Optimizer):
                     bias_correction2 = 1.0 - beta2 ** state["step"]
                     step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
 
-                p.data.addcdiv_(exp_avg, denom, value=-step_size)
+                p.addcdiv_(exp_avg, denom, value=-step_size)
 
                 # Just adding the square of the weights to the loss function is *not*
                 # the correct way of using L2 regularization/weight decay with Adam,
@@ -479,7 +480,7 @@ class AdamW(Optimizer):
                 # of the weights to the loss with plain (non-momentum) SGD.
                 # Add weight decay at the end (fixed version)
                 if group["weight_decay"] > 0.0:
-                    p.data.add_(p.data, alpha=(-group["lr"] * group["weight_decay"]))
+                    p.add_(p, alpha=(-group["lr"] * group["weight_decay"]))
 
         return loss
 
@@ -630,6 +631,7 @@ class Adafactor(Optimizer):
         c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt()
         return torch.mul(r_factor, c_factor)
 
+    @torch.no_grad()
     def step(self, closure=None):
         """
         Performs a single optimization step
@@ -646,7 +648,7 @@ class Adafactor(Optimizer):
             for p in group["params"]:
                 if p.grad is None:
                     continue
-                grad = p.grad.data
+                grad = p.grad
                 if grad.dtype in {torch.float16, torch.bfloat16}:
                     grad = grad.float()
                 if grad.is_sparse:
@@ -679,8 +681,8 @@ class Adafactor(Optimizer):
                     else:
                         state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
 
-                p_data_fp32 = p.data
-                if p.data.dtype in {torch.float16, torch.bfloat16}:
+                p_data_fp32 = p
+                if p.dtype in {torch.float16, torch.bfloat16}:
                     p_data_fp32 = p_data_fp32.float()
 
                 state["step"] += 1
@@ -718,8 +720,8 @@ class Adafactor(Optimizer):
 
                 p_data_fp32.add_(-update)
 
-                if p.data.dtype in {torch.float16, torch.bfloat16}:
-                    p.data.copy_(p_data_fp32)
+                if p.dtype in {torch.float16, torch.bfloat16}:
+                    p.copy_(p_data_fp32)
 
         return loss