implement ema warmup from @crowsonkb (lucidrains#140)

dalle2_pytorch/trainer.py

@@ -175,12 +175,34 @@ def save_diffusion_model(save_path, model, optimizer, scaler, config, image_embe
 # exponential moving average wrapper
 
 class EMA(nn.Module):
+    """
+    Implements exponential moving average shadowing for your model.
+
+    Utilizes an inverse decay schedule to manage longer term training runs.
+    By adjusting the power, you can control how fast EMA will ramp up to your specified beta.
+
+    @crowsonkb's notes on EMA Warmup:
+    
+    If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are
+    good values for models you plan to train for a million or more steps (reaches decay
+    factor 0.999 at 31.6K steps, 0.9999 at 1M steps), gamma=1, power=3/4 for models
+    you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at
+    215.4k steps).
+    
+    Args:
+        inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
+        power (float): Exponential factor of EMA warmup. Default: 1.
+        min_value (float): The minimum EMA decay rate. Default: 0.
+    """
     def __init__(
         self,
         model,
         beta = 0.9999,
-        update_after_step = 1000,
+        update_after_step = 10000,
         update_every = 10,
+        inv_gamma = 1.0,
+        power = 2/3,
+        min_value = 0.0,
     ):
         super().__init__()
         self.beta = beta
@@ -190,6 +212,10 @@ def __init__(
         self.update_every = update_every
         self.update_after_step = update_after_step
 
+        self.inv_gamma = inv_gamma
+        self.power = power
+        self.min_value = min_value
+
         self.register_buffer('initted', torch.Tensor([False]))
         self.register_buffer('step', torch.tensor([0]))
 
@@ -201,6 +227,11 @@ def copy_params_from_model_to_ema(self):
         for ma_param, current_param in zip(list(self.ema_model.parameters()), list(self.online_model.parameters())):
             ma_param.data.copy_(current_param.data)
 
+    def get_current_decay(self):
+        epoch = max(0, self.step.item() - self.update_after_step - 1)
+        value = 1 - (1 + epoch / self.inv_gamma) ** - self.power
+        return 0. if epoch < 0 else min(self.beta, max(self.min_value, value))
+
     def update(self):
         step = self.step.item()
         self.step += 1
@@ -220,14 +251,16 @@ def update(self):
 
     @torch.no_grad()
     def update_moving_average(self, ma_model, current_model):
+        current_decay = self.get_current_decay()
+
         for current_params, ma_params in zip(list(current_model.parameters()), list(ma_model.parameters())):
             difference = ma_params.data - current_params.data
-            difference.mul_(1.0 - self.beta)
+            difference.mul_(1.0 - current_decay)
             ma_params.sub_(difference)
 
         for current_buffer, ma_buffer in zip(list(current_model.buffers()), list(ma_model.buffers())):
             difference = ma_buffer - current_buffer
-            difference.mul_(1.0 - self.beta)
+            difference.mul_(1.0 - current_decay)
             ma_buffer.sub_(difference)
 
     def __call__(self, *args, **kwargs):