Update guide and reference of NAS (#1972)

src/sdk/pynni/nni/nas/pytorch/mutables.py

@@ -19,7 +19,19 @@ class Mutable(nn.Module):
     decisions among different mutables. In mutator's implementation, mutators should use the key to
     distinguish different mutables. Mutables that share the same key should be "similar" to each other.
 
-    Currently the default scope for keys is global.
+    Currently the default scope for keys is global. By default, the keys uses a global counter from 1 to
+    produce unique ids.
+
+    Parameters
+    ----------
+    key : str
+        The key of mutable.
+
+    Notes
+    -----
+    The counter is program level, but mutables are model level. In case multiple models are defined, and
+    you want to have `counter` starting from 1 in the second model, it's recommended to assign keys manually
+    instead of using automatic keys.
     """
 
     def __init__(self, key=None):
@@ -51,10 +63,16 @@ class Mutable(nn.Module):
 
     @property
     def key(self):
+        """
+        Read-only property of key.
+        """
         return self._key
 
     @property
     def name(self):
+        """
+        After the search space is parsed, it will be the module name of the mutable.
+        """
         return self._name if hasattr(self, "_name") else "_key"
 
     @name.setter
@@ -75,11 +93,23 @@ class Mutable(nn.Module):
 class MutableScope(Mutable):
     """
     Mutable scope marks a subgraph/submodule to help mutators make better decisions.
-    Mutators get notified when a mutable scope is entered and exited. Mutators can override ``enter_mutable_scope``
-    and ``exit_mutable_scope`` to catch corresponding events, and do status dump or update.
-    MutableScope are also mutables that are listed in the mutables (search space).
-    """
 
+    If not annotated with mutable scope, search space will be flattened as a list. However, some mutators might
+    need to leverage the concept of a "cell". So if a module is defined as a mutable scope, everything in it will
+    look like "sub-search-space" in the scope. Scopes can be nested.
+
+    There are two ways mutators can use mutable scope. One is to traverse the search space as a tree during initialization
+    and reset. The other is to implement `enter_mutable_scope` and `exit_mutable_scope`. They are called before and after
+    the forward method of the class inheriting mutable scope.
+
+    Mutable scopes are also mutables that are listed in the mutator.mutables (search space), but they are not supposed
+    to appear in the dict of choices.
+
+    Parameters
+    ----------
+    key : str
+        Key of mutable scope.
+    """
     def __init__(self, key):
         super().__init__(key=key)
 
@@ -93,6 +123,31 @@ class MutableScope(Mutable):
 
 
 class LayerChoice(Mutable):
+    """
+    Layer choice selects one of the ``op_candidates``, then apply it on inputs and return results.
+    In rare cases, it can also select zero or many.
+
+    Layer choice does not allow itself to be nested.
+
+    Parameters
+    ----------
+    op_candidates : list of nn.Module
+        A module list to be selected from.
+    reduction : str
+        ``mean``, ``concat``, ``sum`` or ``none``. Policy if multiples are selected.
+        If ``none``, a list is returned. ``mean`` returns the average. ``sum`` returns the sum.
+        ``concat`` concatenate the list at dimension 1.
+    return_mask : bool
+        If ``return_mask``, return output tensor and a mask. Otherwise return tensor only.
+    key : str
+        Key of the input choice.
+
+    Attributes
+    ----------
+    length : int
+        Number of ops to choose from.
+    """
+
     def __init__(self, op_candidates, reduction="sum", return_mask=False, key=None):
         super().__init__(key=key)
         self.length = len(op_candidates)
@@ -101,6 +156,12 @@ class LayerChoice(Mutable):
         self.return_mask = return_mask
 
     def forward(self, *inputs):
+        """
+        Returns
+        -------
+        tuple of tensors
+            Output and selection mask. If ``return_mask`` is ``False``, only output is returned.
+        """
         out, mask = self.mutator.on_forward_layer_choice(self, *inputs)
         if self.return_mask:
             return out, mask
@@ -109,42 +170,62 @@ class LayerChoice(Mutable):
 
 class InputChoice(Mutable):
     """
-    Input choice selects `n_chosen` inputs from `choose_from` (contains `n_candidates` keys). For beginners,
-    use `n_candidates` instead of `choose_from` is a safe option. To get the most power out of it, you might want to
-    know about `choose_from`.
+    Input choice selects ``n_chosen`` inputs from ``choose_from`` (contains ``n_candidates`` keys). For beginners,
+    use ``n_candidates`` instead of ``choose_from`` is a safe option. To get the most power out of it, you might want to
+    know about ``choose_from``.
 
-    The keys in `choose_from` can be keys that appear in past mutables, or ``NO_KEY`` if there are no suitable ones.
+    The keys in ``choose_from`` can be keys that appear in past mutables, or ``NO_KEY`` if there are no suitable ones.
     The keys are designed to be the keys of the sources. To help mutators make better decisions,
     mutators might be interested in how the tensors to choose from come into place. For example, the tensor is the
     output of some operator, some node, some cell, or some module. If this operator happens to be a mutable (e.g.,
     ``LayerChoice`` or ``InputChoice``), it has a key naturally that can be used as a source key. If it's a
-    module/submodule, it needs to be annotated with a key: that's where a ``MutableScope`` is needed.
-    """
+    module/submodule, it needs to be annotated with a key: that's where a :class:`MutableScope` is needed.
 
-    NO_KEY = ""
+    In the example below, ``input_choice`` is a 4-choose-any. The first 3 is semantically output of cell1, output of cell2,
+    output of cell3 with respectively. Notice that an extra max pooling is followed by cell1, indicating x1 is not
+    "actually" the direct output of cell1.
 
-    def __init__(self, n_candidates=None, choose_from=None, n_chosen=None,
-                 reduction="sum", return_mask=False, key=None):
-        """
-        Initialization.
+    .. code-block:: python
+
+        class Cell(MutableScope):
+            pass
+
+        class Net(nn.Module):
+            def __init__(self):
+                self.cell1 = Cell("cell1")
+                self.cell2 = Cell("cell2")
+                self.op = LayerChoice([conv3x3(), conv5x5()], key="op")
+                self.input_choice = InputChoice(choose_from=["cell1", "cell2", "op", InputChoice.NO_KEY])
+
+            def forward(self, x):
+                x1 = max_pooling(self.cell1(x))
+                x2 = self.cell2(x)
+                x3 = self.op(x)
+                x4 = torch.zeros_like(x)
+                return self.input_choice([x1, x2, x3, x4])
 
     Parameters
     ----------
     n_candidates : int
         Number of inputs to choose from.
     choose_from : list of str
-            List of source keys to choose from. At least of one of `choose_from` and `n_candidates` must be fulfilled.
-            If `n_candidates` has a value but `choose_from` is None, it will be automatically treated as `n_candidates`
+        List of source keys to choose from. At least of one of ``choose_from`` and ``n_candidates`` must be fulfilled.
+        If ``n_candidates`` has a value but ``choose_from`` is None, it will be automatically treated as ``n_candidates``
         number of empty string.
     n_chosen : int
         Recommended inputs to choose. If None, mutator is instructed to select any.
     reduction : str
-            `mean`, `concat`, `sum` or `none`.
+        ``mean``, ``concat``, ``sum`` or ``none``. See :class:`LayerChoice`.
     return_mask : bool
-            If `return_mask`, return output tensor and a mask. Otherwise return tensor only.
+        If ``return_mask``, return output tensor and a mask. Otherwise return tensor only.
     key : str
         Key of the input choice.
     """
+
+    NO_KEY = ""
+
+    def __init__(self, n_candidates=None, choose_from=None, n_chosen=None,
+                 reduction="sum", return_mask=False, key=None):
         super().__init__(key=key)
         # precondition check
         assert n_candidates is not None or choose_from is not None, "At least one of `n_candidates` and `choose_from`" \
@@ -172,12 +253,13 @@ class InputChoice(Mutable):
         ----------
         optional_inputs : list or dict
             Recommended to be a dict. As a dict, inputs will be converted to a list that follows the order of
-            `choose_from` in initialization. As a list, inputs must follow the semantic order that is the same as
-            `choose_from`.
+            ``choose_from`` in initialization. As a list, inputs must follow the semantic order that is the same as
+            ``choose_from``.
 
         Returns
         -------
-        tuple of torch.Tensor and torch.Tensor or torch.Tensor
+        tuple of tensors
+            Output and selection mask. If ``return_mask`` is ``False``, only output is returned.
         """
         optional_input_list = optional_inputs
         if isinstance(optional_inputs, dict):

src/sdk/pynni/nni/nas/pytorch/mutator.py

@@ -43,10 +43,6 @@ class Mutator(BaseMutator):
         """
         Reset the mutator by call the `sample_search` to resample (for search). Stores the result in a local
         variable so that `on_forward_layer_choice` and `on_forward_input_choice` can use the decision directly.
-
-        Returns
-        -------
-        None
         """
         self._cache = self.sample_search()
 
@@ -57,25 +53,28 @@ class Mutator(BaseMutator):
         Returns
         -------
         dict
+            A mapping from key of mutables to decisions.
         """
         return self.sample_final()
 
     def on_forward_layer_choice(self, mutable, *inputs):
         """
-        On default, this method calls :meth:`on_calc_layer_choice_mask` to get a mask on how to choose between layers
-        (either by switch or by weights), then it will reduce the list of all tensor outputs with the policy specified
-        in `mutable.reduction`. It will also cache the mask with corresponding `mutable.key`.
+        On default, this method retrieves the decision obtained previously, and select certain operations.
+        Only operations with non-zero weight will be executed. The results will be added to a list.
+        Then it will reduce the list of all tensor outputs with the policy specified in `mutable.reduction`.
 
         Parameters
         ----------
         mutable : LayerChoice
+            Layer choice module.
         inputs : list of torch.Tensor
+            Inputs
 
         Returns
         -------
         tuple of torch.Tensor and torch.Tensor
+            Output and mask.
         """
-
         def _map_fn(op, *inputs):
             return op(*inputs)
 
@@ -87,20 +86,20 @@ class Mutator(BaseMutator):
 
     def on_forward_input_choice(self, mutable, tensor_list):
         """
-        On default, this method calls :meth:`on_calc_input_choice_mask` with `tags`
-        to get a mask on how to choose between inputs (either by switch or by weights), then it will reduce
-        the list of all tensor outputs with the policy specified in `mutable.reduction`. It will also cache the
-        mask with corresponding `mutable.key`.
+        On default, this method retrieves the decision obtained previously, and select certain tensors.
+        Then it will reduce the list of all tensor outputs with the policy specified in `mutable.reduction`.
 
         Parameters
         ----------
         mutable : InputChoice
+            Input choice module.
         tensor_list : list of torch.Tensor
-        tags : list of string
+            Tensor list to apply the decision on.
 
         Returns
         -------
         tuple of torch.Tensor and torch.Tensor
+            Output and mask.
         """
         mask = self._get_decision(mutable)
         assert len(mask) == mutable.n_candidates, \

src/sdk/pynni/nni/nas/pytorch/random/mutator.py

@@ -6,7 +6,15 @@ from nni.nas.pytorch.mutables import LayerChoice, InputChoice
 
 
 class RandomMutator(Mutator):
+    """
+    Random mutator that samples a random candidate in the search space each time ``reset()``.
+    It uses random function in PyTorch, so users can set seed in PyTorch to ensure deterministic behavior.
+    """
+
     def sample_search(self):
+        """
+        Sample a random candidate.
+        """
         result = dict()
         for mutable in self.mutables:
             if isinstance(mutable, LayerChoice):
@@ -22,4 +30,7 @@ class RandomMutator(Mutator):
         return result
 
     def sample_final(self):
+        """
+        Same as :meth:`sample_search`.
+        """
         return self.sample_search()

src/sdk/pynni/nni/nas/pytorch/spos/evolution.py

@@ -16,11 +16,8 @@ _logger = logging.getLogger(__name__)
 
 
 class SPOSEvolution(Tuner):
-
-    def __init__(self, max_epochs=20, num_select=10, num_population=50, m_prob=0.1,
-                 num_crossover=25, num_mutation=25):
     """
-        Initialize SPOS Evolution Tuner.
+    SPOS evolution tuner.
 
     Parameters
     ----------
@@ -39,6 +36,9 @@ class SPOSEvolution(Tuner):
     num_mutation : int
         Number of candidates generated by mutation in each epoch.
     """
+
+    def __init__(self, max_epochs=20, num_select=10, num_population=50, m_prob=0.1,
+                 num_crossover=25, num_mutation=25):
         assert num_population >= num_select
         self.max_epochs = max_epochs
         self.num_select = num_select

src/sdk/pynni/nni/nas/pytorch/spos/mutator.py

@@ -10,9 +10,8 @@ _logger = logging.getLogger(__name__)
 
 
 class SPOSSupernetTrainingMutator(RandomMutator):
-    def __init__(self, model, flops_func=None, flops_lb=None, flops_ub=None,
-                 flops_bin_num=7, flops_sample_timeout=500):
     """
+    A random mutator with flops limit.
 
     Parameters
     ----------
@@ -31,6 +30,9 @@ class SPOSSupernetTrainingMutator(RandomMutator):
     flops_sample_timeout : int
         Maximum number of attempts to sample before giving up and use a random candidate.
     """
+    def __init__(self, model, flops_func=None, flops_lb=None, flops_ub=None,
+                 flops_bin_num=7, flops_sample_timeout=500):
+
         super().__init__(model)
         self._flops_func = flops_func
         if self._flops_func is not None:

src/sdk/pynni/nni/nas/pytorch/spos/trainer.py

@@ -15,13 +15,7 @@ logger = logging.getLogger(__name__)
 class SPOSSupernetTrainer(Trainer):
     """
     This trainer trains a supernet that can be used for evolution search.
-    """
 
-    def __init__(self, model, loss, metrics,
-                 optimizer, num_epochs, train_loader, valid_loader,
-                 mutator=None, batch_size=64, workers=4, device=None, log_frequency=None,
-                 callbacks=None):
-        """
     Parameters
     ----------
     model : nn.Module
@@ -52,6 +46,11 @@ class SPOSSupernetTrainer(Trainer):
     callbacks : list of Callback
         Callbacks to plug into the trainer. See Callbacks.
     """
+
+    def __init__(self, model, loss, metrics,
+                 optimizer, num_epochs, train_loader, valid_loader,
+                 mutator=None, batch_size=64, workers=4, device=None, log_frequency=None,
+                 callbacks=None):
         assert torch.cuda.is_available()
         super().__init__(model, mutator if mutator is not None else SPOSSupernetTrainingMutator(model),
                          loss, metrics, optimizer, num_epochs, None, None,

src/sdk/pynni/nni/nas/pytorch/trainer.py

@@ -24,10 +24,9 @@ class TorchTensorEncoder(json.JSONEncoder):
 
 
 class Trainer(BaseTrainer):
-    def __init__(self, model, mutator, loss, metrics, optimizer, num_epochs,
-                 dataset_train, dataset_valid, batch_size, workers, device, log_frequency, callbacks):
     """
-        Trainer initialization.
+    A trainer with some helper functions implemented. To implement a new trainer,
+    users need to implement :meth:`train_one_epoch`, :meth:`validate_one_epoch` and :meth:`checkpoint`.
 
     Parameters
     ----------
@@ -37,14 +36,22 @@ class Trainer(BaseTrainer):
         A mutator object that has been initialized with the model.
     loss : callable
         Called with logits and targets. Returns a loss tensor.
+        See `PyTorch loss functions`_ for examples.
     metrics : callable
-            Returns a dict that maps metrics keys to metrics data.
+        Called with logits and targets. Returns a dict that maps metrics keys to metrics data. For example,
+
+        .. code-block:: python
+
+            def metrics_fn(output, target):
+                return {"acc1": accuracy(output, target, topk=1), "acc5": accuracy(output, target, topk=5)}
+
     optimizer : Optimizer
         Optimizer that optimizes the model.
     num_epochs : int
         Number of epochs of training.
     dataset_train : torch.utils.data.Dataset
-            Dataset of training.
+        Dataset of training. If not otherwise specified, ``dataset_train`` and ``dataset_valid`` should be standard
+        PyTorch Dataset. See `torch.utils.data`_ for examples.
     dataset_valid : torch.utils.data.Dataset
         Dataset of validation/testing.
     batch_size : int
@@ -58,8 +65,13 @@ class Trainer(BaseTrainer):
         Number of mini-batches to log metrics.
     callbacks : list of Callback
         Callbacks to plug into the trainer. See Callbacks.
-        """
 
+
+    .. _`PyTorch loss functions`: https://pytorch.org/docs/stable/nn.html#loss-functions
+    .. _`torch.utils.data`: https://pytorch.org/docs/stable/data.html
+    """
+    def __init__(self, model, mutator, loss, metrics, optimizer, num_epochs,
+                 dataset_train, dataset_valid, batch_size, workers, device, log_frequency, callbacks):
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
         self.model = model
         self.mutator = mutator
@@ -84,13 +96,38 @@ class Trainer(BaseTrainer):
 
     @abstractmethod
     def train_one_epoch(self, epoch):
+        """
+        Train one epoch.
+
+        Parameters
+        ----------
+        epoch : int
+            Epoch number starting from 0.
+        """
         pass
 
     @abstractmethod
     def validate_one_epoch(self, epoch):
+        """
+        Validate one epoch.
+
+        Parameters
+        ----------
+        epoch : int
+            Epoch number starting from 0.
+        """
         pass
 
     def train(self, validate=True):
+        """
+        Train ``num_epochs``.
+        Trigger callbacks at the start and the end of each epoch.
+
+        Parameters
+        ----------
+        validate : bool
+            If ``true``, will do validation every epoch.
+        """
         for epoch in range(self.num_epochs):
             for callback in self.callbacks:
                 callback.on_epoch_begin(epoch)
@@ -108,12 +145,26 @@ class Trainer(BaseTrainer):
                 callback.on_epoch_end(epoch)
 
     def validate(self):
+        """
+        Do one validation.
+        """
         self.validate_one_epoch(-1)
 
     def export(self, file):
+        """
+        Call ``mutator.export()`` and dump the architecture to ``file``.
+
+        Parameters
+        ----------
+        file : str
+            A file path. Expected to be a JSON.
+        """
         mutator_export = self.mutator.export()
         with open(file, "w") as f:
             json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
 
     def checkpoint(self):
+        """
+        Return trainer checkpoint.
+        """
         raise NotImplementedError("Not implemented yet")

src/sdk/pynni/nni/nas/pytorch/utils.py

@@ -12,12 +12,18 @@ _logger = logging.getLogger(__name__)
 
 
 def global_mutable_counting():
+    """
+    A program level counter starting from 1.
+    """
     global _counter
     _counter += 1
     return _counter
 
 
 def to_device(obj, device):
+    """
+    Move a tensor, tuple, list, or dict onto device.
+    """
     if torch.is_tensor(obj):
         return obj.to(device)
     if isinstance(obj, tuple):
@@ -32,12 +38,18 @@ def to_device(obj, device):
 
 
 class AverageMeterGroup:
-    """Average meter group for multiple average meters"""
+    """
+    Average meter group for multiple average meters.
+    """
 
     def __init__(self):
         self.meters = OrderedDict()
 
     def update(self, data):
+        """
+        Update the meter group with a dict of metrics.
+        Non-exist average meters will be automatically created.
+        """
         for k, v in data.items():
             if k not in self.meters:
                 self.meters[k] = AverageMeter(k, ":4f")
@@ -53,15 +65,15 @@ class AverageMeterGroup:
         return "  ".join(str(v) for v in self.meters.values())
 
     def summary(self):
+        """
+        Return a summary string of group data.
+        """
         return "  ".join(v.summary() for v in self.meters.values())
 
 
 class AverageMeter:
-    """Computes and stores the average and current value"""
-
-    def __init__(self, name, fmt=':f'):
     """
-        Initialization of AverageMeter
+    Computes and stores the average and current value.
 
     Parameters
     ----------
@@ -70,17 +82,32 @@ class AverageMeter:
     fmt : str
         Format string to print the values.
     """
+
+    def __init__(self, name, fmt=':f'):
         self.name = name
         self.fmt = fmt
         self.reset()
 
     def reset(self):
+        """
+        Reset the meter.
+        """
         self.val = 0
         self.avg = 0
         self.sum = 0
         self.count = 0
 
     def update(self, val, n=1):
+        """
+        Update with value and weight.
+
+        Parameters
+        ----------
+        val : float or int
+            The new value to be accounted in.
+        n : int
+            The weight of the new value.
+        """
         if not isinstance(val, float) and not isinstance(val, int):
             _logger.warning("Values passed to AverageMeter must be number, not %s.", type(val))
         self.val = val
@@ -104,6 +131,11 @@ class StructuredMutableTreeNode:
     This tree can be seen as a "flattened" version of the module tree. Since nested mutable entity is not supported yet,
     the following must be true: each subtree corresponds to a ``MutableScope`` and each leaf corresponds to a
     ``Mutable`` (other than ``MutableScope``).
+
+    Parameters
+    ----------
+    mutable : nni.nas.pytorch.mutables.Mutable
+        The mutable that current node is linked with.
     """
 
     def __init__(self, mutable):
@@ -111,10 +143,16 @@ class StructuredMutableTreeNode:
         self.children = []
 
     def add_child(self, mutable):
+        """
+        Add a tree node to the children list of current node.
+        """
         self.children.append(StructuredMutableTreeNode(mutable))
         return self.children[-1]
 
     def type(self):
+        """
+        Return the ``type`` of mutable content.
+        """
         return type(self.mutable)
 
     def __iter__(self):