LARC clipping+documentation (#6)

* Proper implementation of LARC clipping
 * Documentation of LARC class
 * Modification of FP16_Optimizer to absorb optimizer instance that's being wrapped instead of creating new optimizer instance of same class.

apex/fp16_utils/fp16_optimizer.py

@@ -184,7 +184,7 @@ class FP16_Optimizer(object):
             self.fp32_from_fp16_groups.append(fp32_from_fp16_params_this_group)
             self.fp32_from_fp32_groups.append(fp32_params_this_group)
 
-        self.optimizer = init_optimizer.__class__(init_optimizer.param_groups)
+        self.optimizer = optimizer
 
         if dynamic_loss_scale:
             self.dynamic_loss_scale = True

apex/parallel/LARC.py

@@ -4,11 +4,45 @@ from torch.autograd import Variable
 from torch.nn.parameter import Parameter
 
 class LARC(object):
-    def __init__(self, optimizer, trust_coefficient=0.02, epsilon=1e-8):
+    """
+    :class:`LARC` is a pytorch implementation of both the scaling and clipping varients of LARC,
+    in which the ratio between gradient and parameter magnitudes is used to calculate an adaptive 
+    local learning rate for each individual parameter. The algorithm is designed to improve
+    convergence of large batch training.
+     
+    See https://arxiv.org/abs/1708.03888 for calculation of the local learning rate.
+
+    In practice it modifies the gradients of parameters as a proxy for modifying the learning rate
+    of the parameters. This design allows it to be used as a wrapper around any torch.optim Optimizer.
+
+    ```
+    model = ...
+    optim = torch.optim.Adam(model.parameters(), lr=...)
+    optim = LARC(optim)
+    ```
+
+    It can even be used in conjunction with apex.fp16_utils.FP16_optimizer.
+
+    ```
+    model = ...
+    optim = torch.optim.Adam(model.parameters(), lr=...)
+    optim = LARC(optim)
+    optim = apex.fp16_utils.FP16_Optimizer(optim)
+    ```
+
+    Args:
+        optimizer: Pytorch optimizer to wrap and modify learning rate for.
+        trust_coefficient: Trust coefficient for calculating the lr. See https://arxiv.org/abs/1708.03888
+        clip: Decides between clipping or scaling mode of LARC. If `clip=True` the learning rate is set to `min(optimizer_lr, local_lr)` for each parameter. If `clip=False` the learning rate is set to `local_lr*optimizer_lr`.
+        eps: epsilon kludge to help with numerical stability while calculating adaotive_lr
+    """
+
+    def __init__(self, optimizer, trust_coefficient=0.02, clip=True, eps=1e-8):
         self.param_groups = optimizer.param_groups
         self.optim = optimizer
         self.trust_coefficient = trust_coefficient
-        self.eps = epsilon
+        self.eps = eps
+        self.clip = clip
 
     def __getstate__(self):
         return self.optim.__getstate__()
@@ -43,10 +77,20 @@ class LARC(object):
                     if p.grad is None:
                         continue
                     param_norm = torch.norm(p.data)
-                    # calculate adaptive lr + weight decay
-                    adaptive_lr = (param_norm + self.eps) / (torch.norm(p.grad.data) + param_norm * weight_decay + self.eps)
-                    p.grad.data += weight_decay * p.data
-                    p.grad.data *= self.trust_coefficient * adaptive_lr
+                    grad_norm = torch.norm(p.grad.data)
+
+                    if param_norm != 0 and grad_norm != 0:
+                        # calculate adaptive lr + weight decay
+                        adaptive_lr = self.trust_coefficient * (param_norm) / (grad_norm + param_norm * weight_decay + self.eps)
+
+                        # clip learning rate for LARC
+                        if self.clip:
+                            # calculation of adaptive_lr so that when multiplied by lr it equals `min(adaptive_lr, lr)`
+                            adaptive_lr = min(adaptive_lr/group['lr'], 1)
+
+                        p.grad.data += weight_decay * p.data
+                        p.grad.data *= adaptive_lr
+
         self.optim.step()
         # return weight decay control to optimizer
         for i, group in enumerate(self.optim.param_groups):