add choice to degenerated to sgd

add choice to degenerated to sgd or freeze

radam.py

@@ -4,7 +4,7 @@
 
 class RAdam(Optimizer):
 
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= eps:
@@ -13,7 +13,8 @@ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0
             raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
         if not 0.0 <= betas[1] < 1.0:
             raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-
+
+        self.degenerated_to_sgd = degenerated_to_sgd
         defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
         self.buffer = [[None, None, None] for ind in range(10)]
         super(RAdam, self).__init__(params, defaults)
@@ -68,27 +69,30 @@ def step(self, closure=None):
                     # more conservative since it's an approximated value
                     if N_sma >= 5:
                         step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
-                    else:
+                    elif self.degenerated_to_sgd:
                         step_size = 1.0 / (1 - beta1 ** state['step'])
+                    else:
+                        step_size = -1
                     buffered[2] = step_size
 
-                if group['weight_decay'] != 0:
-                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
-
                 # more conservative since it's an approximated value
-                if N_sma >= 5:            
+                if N_sma >= 5:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
                     denom = exp_avg_sq.sqrt().add_(group['eps'])
                     p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
-                else:
+                    p.data.copy_(p_data_fp32)
+                elif step_size > 0:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
                     p_data_fp32.add_(-step_size * group['lr'], exp_avg)
-
-                p.data.copy_(p_data_fp32)
+                    p.data.copy_(p_data_fp32)
 
         return loss
 
 class PlainRAdam(Optimizer):
 
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= eps:
@@ -97,7 +101,8 @@ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0
             raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
         if not 0.0 <= betas[1] < 1.0:
             raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-
+
+        self.degenerated_to_sgd = degenerated_to_sgd
         defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
 
         super(PlainRAdam, self).__init__(params, defaults)
@@ -143,19 +148,21 @@ def step(self, closure=None):
                 N_sma_max = 2 / (1 - beta2) - 1
                 N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
 
-                if group['weight_decay'] != 0:
-                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
 
                 # more conservative since it's an approximated value
-                if N_sma >= 5:                    
+                if N_sma >= 5:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
                     step_size = group['lr'] * math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
                     denom = exp_avg_sq.sqrt().add_(group['eps'])
                     p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
-                else:
+                    p.data.copy_(p_data_fp32)
+                elif self.degenerated_to_sgd:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
                     step_size = group['lr'] / (1 - beta1 ** state['step'])
                     p_data_fp32.add_(-step_size, exp_avg)
-
-                p.data.copy_(p_data_fp32)
+                    p.data.copy_(p_data_fp32)
 
         return loss