[doc] improved docstring in the amp module (#857)

colossalai/amp/apex_amp/__init__.py

@@ -11,6 +11,9 @@ def convert_to_apex_amp(model: nn.Module, optimizer: Optimizer, amp_config):
         optimizer (:class:`torch.optim.Optimizer`): your optimizer object.
         amp_config (Union[:class:`colossalai.context.Config`, dict]): configuration for initializing apex_amp.
 
+    Returns:
+        Tuple: A tuple (model, optimizer).
+
     The ``amp_config`` should include parameters below:
     ::
 
@@ -27,9 +30,6 @@ def convert_to_apex_amp(model: nn.Module, optimizer: Optimizer, amp_config):
         min_loss_scale (float, default=None)
         max_loss_scale (float, default=2.**24)
 
-    Returns:
-        Tuples: A tuple (model, optimizer).
-
     More details about ``amp_config`` refer to `amp_config <https://nvidia.github.io/apex/amp.html?highlight=apex%20amp>`_.
     """
     import apex.amp as apex_amp

colossalai/amp/apex_amp/apex_amp.py

@@ -28,7 +28,7 @@ class ApexAMPOptimizer(ColossalaiOptimizer):
             scaled_loss.backward()
 
     def clip_grad_norm(self, model: nn.Module, max_norm: float):
-        """Clip gradients' norm
+        """Clip gradients by norm
 
         Args:
             model (torch.nn.Module): Your model object

colossalai/amp/naive_amp/__init__.py

@@ -17,6 +17,8 @@ def convert_to_naive_amp(model: nn.Module, optimizer: Optimizer, amp_config):
         optimizer (:class:`torch.optim.Optimizer`): your optimizer object
         amp_config (:class:`colossalai.context.Config` or dict): configuration for naive mode amp.
 
+    Returns:
+        Tuple: A tuple (model, optimizer)
 
     The ``amp_config`` should contain parameters below::
 
@@ -24,9 +26,6 @@ def convert_to_naive_amp(model: nn.Module, optimizer: Optimizer, amp_config):
         clip_grad_norm (float, optional): clip gradients with this global L2 norm (Default 0).
                                           Note that clipping is ignored if clip_grad == 0.
         dynamic_grad_scale (bool): whether to use dynamic grad scaler.
-
-    Returns:
-        Tuples: A tuple (model, optimizer)
     """
     if isinstance(model, nn.ModuleList):
         # interleaved pipeline

colossalai/amp/naive_amp/_fp16_optimizer.py

@@ -152,18 +152,39 @@ class FP16Optimizer(Optimizer):
 
     @property
     def grad_scaler(self):
+        """Returns the gradient scaler.
+
+        Returns:
+            :class:`BaseGradScaler`: gradient scaler.
+        """
+
         return self._grad_scaler
 
     @property
     def loss_scale(self):
+        """Returns the loss scale.
+
+        Returns:
+            int: loss scale.
+        """
         return self._grad_scaler.scale
 
     @property
     def optimizer(self):
+        """Returns the optimizer.
+
+        Returns:
+            :class:`torch.optim.Optimizer`: the optimizer object wrapped.
+        """
         return self._optimizer
 
     @property
     def defaults(self):
+        """Returns the default arguments of optimizer.
+
+        Returns:
+            dict: optimizer arguments saved in defaults of the optimizer wrapped.
+        """
         return self._defaults
 
     def _check_overflow(self):
@@ -188,6 +209,12 @@ class FP16Optimizer(Optimizer):
         return self._found_overflow.item() > 0
 
     def zero_grad(self, set_to_none=True):
+        """Set gradient to zero.
+
+        Args:
+            set_to_none (bool): Whether set the gradient to None.
+        """
+
         # set_to_none = True can save some memory space
         for param_group in self._optimizer.param_groups:
             zero_gard_by_list(param_group['params'], set_to_none=set_to_none)
@@ -222,6 +249,9 @@ class FP16Optimizer(Optimizer):
                                         overflow_buf=self._dummy_overflow_buf)
 
     def step(self):
+        """Update the model parameters.
+        """
+
         # Copy gradients from model params to main params.
         self._assign_grad_to_fp32_master_param()
         self._unscale_grads()
@@ -248,10 +278,19 @@ class FP16Optimizer(Optimizer):
         return True, grad_norm
 
     def backward(self, loss):
+        """Execute backward pass.
+
+        Args:
+            loss (:class:`torch.Tensor`): the loss value.
+        """
+
         scaled_loss = loss * self.grad_scaler.scale
         scaled_loss.backward()
 
     def state_dict(self):
+        """Returns the states of the fp16 optimizer as a dict object.
+        """
+
         state_dict = {}
         state_dict['optimizer'] = self._optimizer.state_dict()
         if self.grad_scaler:
@@ -260,6 +299,12 @@ class FP16Optimizer(Optimizer):
         return state_dict
 
     def load_state_dict(self, state_dict):
+        """Load the states of the fp16 optimizer from a dict object.
+
+        Args:
+            state_dict (dict): the states of the fp16 optimizer
+        """
+
         # Optimizer.
         self._optimizer.load_state_dict(state_dict['optimizer'])
 
@@ -275,6 +320,11 @@ class FP16Optimizer(Optimizer):
                     current_param.data.copy_(ckpt_param.data)
 
     def clip_grad_norm(self, clip_grad):
+        """Clip gradients by norm.
+
+        Args:
+            clip_grad (float): the max norm for clipping
+        """
         params = []
         for param_group in self._optimizer.param_groups:
             for param in param_group['params']:

colossalai/amp/naive_amp/_utils.py

@@ -3,6 +3,14 @@ from torch import Tensor
 
 
 def has_inf_or_nan(tensor):
+    """Check if tensor has inf or nan values.
+
+    Args:
+        tensor (:class:`torch.Tensor`): a torch tensor object
+
+    Returns:
+        bool: Whether the tensor has inf or nan. True for yes and False for no.
+    """
     try:
         # if tensor is half, the .float() incurs an additional deep copy, but it's necessary if
         # Pytorch's .sum() creates a one-element tensor of the same type as tensor
@@ -24,8 +32,8 @@ def has_inf_or_nan(tensor):
 
 
 def zero_gard_by_list(tensor_list: List[Tensor], set_to_none: bool = True) -> None:
-    """
+    """Clear the gradient of a list of tensors,
-    Clear the gradient of a list of tensors,
+
     Note: copied from torch.optim.optimizer.
     """
     for param in tensor_list:

colossalai/amp/naive_amp/grad_scaler/base_grad_scaler.py

@@ -11,6 +11,12 @@ __all__ = ['BaseGradScaler']
 
 
 class BaseGradScaler(ABC):
+    """A base class for the gradient scaler.
+
+    Args:
+        initial_scale (float): the initial loss scale
+        verbose (bool): whether to log messages
+    """
 
     def __init__(self, initial_scale: float, verbose: bool):
         assert initial_scale > 0
@@ -22,24 +28,53 @@ class BaseGradScaler(ABC):
 
     @property
     def scale(self) -> Tensor:
+        """Returns the loss scale.
+        """
+
         return self._scale
 
     @property
     def inv_scale(self) -> Tensor:
+        """Returns the inverse of the loss scale.
+        """
+
         return self._scale.double().reciprocal().float()
 
     def state_dict(self) -> Dict:
+        """Returns the states of the gradient scaler as a dict object.
+        """
+
         state_dict = dict()
         state_dict['scale'] = self.scale
         return state_dict
 
     def load_state_dict(self, state_dict: Dict) -> None:
+        """Load the states of the gradient scaler from a dict object.
+
+        Args:
+            state_dict (dict): the states of the gradient scaler
+        """
+
         self._scale = state_dict['scale']
 
     @abstractmethod
     def update(self, overflow: bool) -> None:
+        """Update the loss scale.
+
+        Args:
+            overflow (bool): whether overflow occurs
+        """
+
         pass
 
     def log(self, message, *args, **kwargs):
+        """Log messages.
+
+        Args:
+            message (str): the message to log
+            *args: positional arguments for :class:`colossalai.logging.DistributedLogger`
+            **kwargs: key-word arguments for :class:`colossalai.logging.DistributedLogger`
+        """
+
         if self._verbose:
             self._logger.info(message, *args, **kwargs)

colossalai/amp/naive_amp/grad_scaler/constant_grad_scaler.py

@@ -6,11 +6,21 @@ __all__ = ['ConstantGradScaler']
 
 
 class ConstantGradScaler(BaseGradScaler):
+    """A gradient scaler which uses constant loss scale
+
+    Args:
+        initial_scale (float): the initial loss scale
+        verbose (bool): whether to log messages
+    """
 
     def __init__(self, initial_scale: int, verbose: bool):
         super().__init__(initial_scale, verbose)
         self.log(f"Constant Gradient Scaler is initialized with scale {self.scale}", ranks=[0])
 
     def update(self, overflow: bool) -> None:
-        # do nothing to maintain the current scale value
+        """Do nothing to keep the loss scale constant.
+
+        Args:
+            overflow (bool): whether overflow occurs
+        """
         pass

colossalai/amp/naive_amp/grad_scaler/dynamic_grad_scaler.py

@@ -9,6 +9,18 @@ __all__ = ['DynamicGradScaler']
 
 
 class DynamicGradScaler(BaseGradScaler):
+    """A gradient scaler which uses dynamic loss scale
+
+    Args:
+        initial_scale (float): the initial loss scale, defaults to 2**16
+        growth_factor (float): the multiplication factor for increasing loss scale, defaults to 2
+        backoff_factor (float): the multiplication factor for decreasing loss scale, defaults to 0.5
+        growth_interval (int): the number of steps to increase loss scale when no overflow occurs, defaults to 1000
+        min_scale (float): the minimum loss scale, defaults to None
+        max_scale (float): the maximum loss scale, defaults to None
+        hysteresis (int):  the number of overflows before decreasing loss scale, defaults to 2
+        verbose (bool): whether to log messages, defaults to False
+    """
 
     def __init__(self,
                  initial_scale: float = 2**16,
@@ -39,6 +51,9 @@ class DynamicGradScaler(BaseGradScaler):
         self._sanity_checks()
 
     def _sanity_checks(self) -> None:
+        """Check if the arguments are correct.
+        """
+
         if self._min_scale:
             assert self._min_scale > 0, 'The minimum gradient scale cannot be zero or negative'
         if self._max_scale:
@@ -48,6 +63,11 @@ class DynamicGradScaler(BaseGradScaler):
         assert self._hysteresis >= 0, 'The hysteresis cannot be negative'
 
     def update(self, overflow: bool) -> None:
+        """Update the loss scale.
+
+        Args:
+            overflow (bool): whether overflow occurs
+        """
         if overflow:
             self._hysteresis_step += 1
             self._growth_step = 0
@@ -67,11 +87,17 @@ class DynamicGradScaler(BaseGradScaler):
                     ranks=[0])
 
     def _backoff_scale(self) -> None:
+        """Decrease the loss scale
+        """
+
         self._scale = self._scale * self._backoff_factor
         if self._min_scale:
             self._scale = torch.max(self._scale, self._min_scale)
 
     def _grow_scale(self) -> None:
+        """Increase the loss scale
+        """
+
         self._scale = self._scale * self._growth_factor
         if self._max_scale:
             self._scale = torch.min(self._scale, self._max_scale)

colossalai/amp/torch_amp/torch_amp.py

@@ -62,6 +62,9 @@ class TorchAMPOptimizer(ColossalaiOptimizer):
 class TorchAMPModel(nn.Module):
     """A wrapper class for a model object which executes forward with values automatically
     cast to fp16
+
+    Args:
+        model (:class:`torch.nn.Module`): a torch model instance
     """
 
     def __init__(self, model: nn.Module) -> None:
@@ -70,6 +73,9 @@ class TorchAMPModel(nn.Module):
 
     @torch_amp.autocast()
     def forward(self, *args, **kwargs):
+        """
+        Execute forward under the torch amp context
+        """
         return self.model(*args, **kwargs)
 
 
@@ -86,4 +92,7 @@ class TorchAMPLoss(nn.Module):
 
     @torch_amp.autocast()
     def forward(self, *args, **kwargs):
+        """
+        Execute forward under the torch amp context
+        """
         return self.loss(*args, **kwargs)