adds gradient_centralization2, fixes addcmul_ warning

ranger/ranger2020.py

@@ -0,0 +1,208 @@
+# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.
+
+# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
+# and/or
+# https://github.com/lessw2020/Best-Deep-Learning-Optimizers
+
+# Ranger has been used to capture 12 records on the FastAI leaderboard.
+
+# This version = 2020.9.4
+
+
+# Credits:
+# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github:  https://github.com/Yonghongwei/Gradient-Centralization
+# RAdam -->  https://github.com/LiyuanLucasLiu/RAdam
+# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.
+# Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610
+
+# summary of changes:
+# 9/4/20 - updated addcmul_ signature to avoid warning.  Integrates latest changes from GC developer (he did the work for this), and verified on performance on private dataset.
+# 4/11/20 - add gradient centralization option.  Set new testing benchmark for accuracy with it, toggle with use_gc flag at init.
+# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights),
+# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.
+# changes 8/31/19 - fix references to *self*.N_sma_threshold;
+# changed eps to 1e-5 as better default than 1e-8.
+
+import math
+import torch
+from torch.optim.optimizer import Optimizer, required
+
+
+def centralized_gradient(x, use_gc=True, gc_conv_only=False):
+    '''credit - https://github.com/Yonghongwei/Gradient-Centralization '''
+    if use_gc:
+        if gc_conv_only:
+            if len(list(x.size())) > 3:
+                x.add_(-x.mean(dim=tuple(range(1, len(list(x.size())))), keepdim=True))
+        else:
+            if len(list(x.size())) > 1:
+                x.add_(-x.mean(dim=tuple(range(1, len(list(x.size())))), keepdim=True))
+    return x
+
+
+class Ranger(Optimizer):
+
+    def __init__(self, params, lr=1e-3,                       # lr
+                 alpha=0.5, k=6, N_sma_threshhold=5,           # Ranger options
+                 betas=(.95, 0.999), eps=1e-5, weight_decay=0,  # Adam options
+                 # Gradient centralization on or off, applied to conv layers only or conv + fc layers
+                 use_gc=True, gc_conv_only=False, gc_loc=True
+                 ):
+
+        # parameter checks
+        if not 0.0 <= alpha <= 1.0:
+            raise ValueError(f'Invalid slow update rate: {alpha}')
+        if not 1 <= k:
+            raise ValueError(f'Invalid lookahead steps: {k}')
+        if not lr > 0:
+            raise ValueError(f'Invalid Learning Rate: {lr}')
+        if not eps > 0:
+            raise ValueError(f'Invalid eps: {eps}')
+
+        # parameter comments:
+        # beta1 (momentum) of .95 seems to work better than .90...
+        # N_sma_threshold of 5 seems better in testing than 4.
+        # In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.
+
+        # prep defaults and init torch.optim base
+        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas,
+                        N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay)
+        super().__init__(params, defaults)
+
+        # adjustable threshold
+        self.N_sma_threshhold = N_sma_threshhold
+
+        # look ahead params
+
+        self.alpha = alpha
+        self.k = k
+
+        # radam buffer for state
+        self.radam_buffer = [[None, None, None] for ind in range(10)]
+
+        # gc on or off
+        self.gc_loc = gc_loc
+        self.use_gc = use_gc
+        self.gc_conv_only = gc_conv_only
+        # level of gradient centralization
+        #self.gc_gradient_threshold = 3 if gc_conv_only else 1
+
+        print(
+            f"Ranger optimizer loaded. \nGradient Centralization usage = {self.use_gc}")
+        if (self.use_gc and self.gc_conv_only == False):
+            print(f"GC applied to both conv and fc layers")
+        elif (self.use_gc and self.gc_conv_only == True):
+            print(f"GC applied to conv layers only")
+
+    def __setstate__(self, state):
+        print("set state called")
+        super(Ranger, self).__setstate__(state)
+
+    def step(self, closure=None):
+        loss = None
+        # note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.
+        # Uncomment if you need to use the actual closure...
+
+        # if closure is not None:
+        #loss = closure()
+
+        # Evaluate averages and grad, update param tensors
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'Ranger optimizer does not support sparse gradients')
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]  # get state dict for this param
+
+                if len(state) == 0:  # if first time to run...init dictionary with our desired entries
+                    # if self.first_run_check==0:
+                    # self.first_run_check=1
+                    #print("Initializing slow buffer...should not see this at load from saved model!")
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+
+                    # look ahead weight storage now in state dict
+                    state['slow_buffer'] = torch.empty_like(p.data)
+                    state['slow_buffer'].copy_(p.data)
+
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
+                        p_data_fp32)
+
+                # begin computations
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                # GC operation for Conv layers and FC layers
+                # if grad.dim() > self.gc_gradient_threshold:
+                #    grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))
+                if self.gc_loc:
+                    grad = centralized_gradient(grad, use_gc=self.use_gc, gc_conv_only=self.gc_conv_only)
+
+                state['step'] += 1
+
+                # compute variance mov avg
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+                # compute mean moving avg
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+
+                buffered = self.radam_buffer[int(state['step'] % 10)]
+
+                if state['step'] == buffered[0]:
+                    N_sma, step_size = buffered[1], buffered[2]
+                else:
+                    buffered[0] = state['step']
+                    beta2_t = beta2 ** state['step']
+                    N_sma_max = 2 / (1 - beta2) - 1
+                    N_sma = N_sma_max - 2 * \
+                        state['step'] * beta2_t / (1 - beta2_t)
+                    buffered[1] = N_sma
+                    if N_sma > self.N_sma_threshhold:
+                        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:
+                        step_size = 1.0 / (1 - beta1 ** state['step'])
+                    buffered[2] = step_size
+
+                # if group['weight_decay'] != 0:
+                #    p_data_fp32.add_(-group['weight_decay']
+                #                     * group['lr'], p_data_fp32)
+
+                # apply lr
+                if N_sma > self.N_sma_threshhold:
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    G_grad = exp_avg / denom
+                else:
+                    G_grad = exp_avg
+
+                if group['weight_decay'] != 0:
+                    G_grad.add_(p_data_fp32, alpha=group['weight_decay'])
+                # GC operation
+                if self.gc_loc == False:
+                    G_grad = centralized_gradient(G_grad, use_gc=self.use_gc, gc_conv_only=self.gc_conv_only)
+
+                p_data_fp32.add_(G_grad, alpha=-step_size * group['lr'])
+                p.data.copy_(p_data_fp32)
+
+                # integrated look ahead...
+                # we do it at the param level instead of group level
+                if state['step'] % group['k'] == 0:
+                    # get access to slow param tensor
+                    slow_p = state['slow_buffer']
+                    # (fast weights - slow weights) * alpha
+                    slow_p.add_(p.data - slow_p, alpha=self.alpha)
+                    # copy interpolated weights to RAdam param tensor
+                    p.data.copy_(slow_p)
+
+        return loss