Extract base mutator/trainer and support ENAS micro search space (#1739)

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

+import json
+
+import torch
+
+from nni.nas.pytorch.mutator import Mutator
+
+
+class FixedArchitecture(Mutator):
+    def __init__(self, model, fixed_arc, strict=True):
+        """
+        Initialize a fixed architecture mutator.
+
+        Parameters
+        ----------
+        model: nn.Module
+            A mutable network.
+        fixed_arc: str or dict
+            Path to the architecture checkpoint (a string), or preloaded architecture object (a dict).
+        strict: bool
+            Force everything that appears in `fixed_arc` to be used at least once.
+        """
+        super().__init__(model)
+        if isinstance(fixed_arc, str):
+            with open(fixed_arc, "r") as f:
+                fixed_arc = json.load(f.read())
+        self._fixed_arc = fixed_arc
+        self._strict = strict
+
+    def _encode_tensor(self, data):
+        if isinstance(data, list):
+            if all(map(lambda o: isinstance(o, bool), data)):
+                return torch.tensor(data, dtype=torch.bool)  # pylint: disable=not-callable
+            else:
+                return torch.tensor(data, dtype=torch.float)  # pylint: disable=not-callable
+        if isinstance(data, dict):
+            return {k: self._encode_tensor(v) for k, v in data.items()}
+        return data
+
+    def before_pass(self):
+        self._unused_key = set(self._fixed_arc.keys())
+
+    def after_pass(self):
+        if self._strict:
+            if self._unused_key:
+                raise ValueError("{} are never used by the network. "
+                                 "Set strict=False if you want to disable this check.".format(self._unused_key))
+
+    def _check_key(self, key):
+        if key not in self._fixed_arc:
+            raise ValueError("\"{}\" is demanded by the network, but not found in saved architecture.".format(key))
+
+    def on_calc_layer_choice_mask(self, mutable):
+        self._check_key(mutable.key)
+        return self._fixed_arc[mutable.key]
+
+    def on_calc_input_choice_mask(self, mutable, tags):
+        self._check_key(mutable.key)
+        return self._fixed_arc[mutable.key]

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

@@ -3,7 +3,7 @@ import torch.nn as nn
 from nni.nas.utils import global_mutable_counting
 
 
-class PyTorchMutable(nn.Module):
+class Mutable(nn.Module):
     """
     Mutable is designed to function as a normal layer, with all necessary operators' weights.
     States and weights of architectures should be included in mutator, instead of the layer itself.
@@ -24,15 +24,11 @@ class PyTorchMutable(nn.Module):
             self._key = key
         else:
             self._key = self.__class__.__name__ + str(global_mutable_counting())
-        self.name = self.key
+        self.init_hook = self.forward_hook = None
 
     def __deepcopy__(self, memodict=None):
         raise NotImplementedError("Deep copy doesn't work for mutables.")
 
-    def __enter__(self):
-        self._check_built()
-        return super().__enter__()
-
     def __call__(self, *args, **kwargs):
         self._check_built()
         return super().__call__(*args, **kwargs)
@@ -47,8 +43,16 @@ class PyTorchMutable(nn.Module):
     def key(self):
         return self._key
 
+    @property
+    def name(self):
+        return self._name if hasattr(self, "_name") else "_key"
+
+    @name.setter
+    def name(self, name):
+        self._name = name
+
     def similar(self, other):
-        return self == other
+        return type(self) == type(other)
 
     def _check_built(self):
         if not hasattr(self, "mutator"):
@@ -56,8 +60,11 @@ class PyTorchMutable(nn.Module):
                 "Mutator not set for {}. Did you initialize a mutable on the fly in forward pass? Move to __init__"
                 "so that trainer can locate all your mutables. See NNI docs for more details.".format(self))
 
+    def __repr__(self):
+        return "{} ({})".format(self.name, self.key)
 
-class MutableScope(PyTorchMutable):
+
+class MutableScope(Mutable):
     """
     Mutable scope labels a subgraph 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
@@ -67,14 +74,18 @@ class MutableScope(PyTorchMutable):
     def __init__(self, key):
         super().__init__(key=key)
 
-    def __enter__(self):
-        self.mutator.enter_mutable_scope(self)
+    def build(self):
+        self.mutator.on_init_mutable_scope(self)
 
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.mutator.exit_mutable_scope(self)
+    def __call__(self, *args, **kwargs):
+        try:
+            self.mutator.enter_mutable_scope(self)
+            return super().__call__(*args, **kwargs)
+        finally:
+            self.mutator.exit_mutable_scope(self)
 
 
-class LayerChoice(PyTorchMutable):
+class LayerChoice(Mutable):
     def __init__(self, op_candidates, reduction="mean", return_mask=False, key=None):
         super().__init__(key=key)
         self.length = len(op_candidates)
@@ -83,10 +94,10 @@ class LayerChoice(PyTorchMutable):
         self.return_mask = return_mask
 
     def __len__(self):
-        return self.length
+        return len(self.choices)
 
     def forward(self, *inputs):
-        out, mask = self.mutator.on_forward(self, *inputs)
+        out, mask = self.mutator.on_forward_layer_choice(self, *inputs)
         if self.return_mask:
             return out, mask
         return out
@@ -95,7 +106,7 @@ class LayerChoice(PyTorchMutable):
         return type(self) == type(other) and self.length == other.length
 
 
-class InputChoice(PyTorchMutable):
+class InputChoice(Mutable):
     def __init__(self, n_candidates, n_selected=None, reduction="mean", return_mask=False, key=None):
         super().__init__(key=key)
         assert n_candidates > 0, "Number of candidates must be greater than 0."
@@ -104,16 +115,21 @@ class InputChoice(PyTorchMutable):
         self.reduction = reduction
         self.return_mask = return_mask
 
-    def forward(self, optional_inputs, semantic_labels=None):
+    def build(self):
+        self.mutator.on_init_input_choice(self)
+
+    def forward(self, optional_inputs, tags=None):
         assert len(optional_inputs) == self.n_candidates, \
             "Length of the input list must be equal to number of candidates."
-        if semantic_labels is None:
-            semantic_labels = ["default_label"] * self.n_candidates
-        out, mask = self.mutator.on_forward(self, optional_inputs, semantic_labels)
+        if tags is None:
+            tags = [""] * self.n_candidates
+        else:
+            assert len(tags) == self.n_candidates, "Length of tags must be equal to number of candidates."
+        out, mask = self.mutator.on_forward_input_choice(self, optional_inputs, tags)
         if self.return_mask:
             return out, mask
         return out
 
     def similar(self, other):
         return type(self) == type(other) and \
-            self.n_candidates == other.n_candidates and self.n_selected and other.n_selected
+               self.n_candidates == other.n_candidates and self.n_selected and other.n_selected

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

-import logging
 from contextlib import contextmanager
 
 import torch
 import torch.nn as nn
 
-from nni.nas.pytorch.mutables import PyTorchMutable
-from nni.nas.utils import to_snake_case
+from nni.nas.pytorch.base_mutator import BaseMutator
 
-logger = logging.getLogger(__name__)
 
+class Mutator(BaseMutator, nn.Module):
 
-class PyTorchMutator(nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.before_build(model)
-        self.parse_search_space(model)
-        self.after_build(model)
-
-    def before_build(self, model):
-        pass
-
-    def after_build(self, model):
-        pass
-
-    def named_mutables(self, model):
-        # if distinct is true, the method will filter out those with duplicated keys
-        key2module = dict()
-        for name, module in model.named_modules():
-            if isinstance(module, PyTorchMutable):
-                distinct = False
-                if module.key in key2module:
-                    assert key2module[module.key].similar(module), \
-                        "Mutable \"{}\" that share the same key must be similar to each other".format(module.key)
-                else:
-                    distinct = True
-                    key2module[module.key] = module
-                yield name, module, distinct
-
-    def __setattr__(self, key, value):
-        if key in ["model", "net", "network"]:
-            logger.warning("Think twice if you are including the network into mutator.")
-        return super().__setattr__(key, value)
-
-    def parse_search_space(self, model):
-        for name, mutable, distinct in self.named_mutables(model):
-            mutable.name = name
-            mutable.set_mutator(self)
-            if not distinct:
-                continue
-            init_method_name = "on_init_{}".format(to_snake_case(mutable.__class__.__name__))
-            if hasattr(self, init_method_name) and callable(getattr(self, init_method_name)):
-                getattr(self, init_method_name)(mutable)
-            else:
-                # fallback to general init
-                self.on_init_general(mutable)
-
-    def on_init_general(self, mutable):
-        pass
+    def export(self):
+        if self._in_forward_pass:
+            raise RuntimeError("Still in forward pass. Exporting might induce incompleteness.")
+        if not self._cache:
+            raise RuntimeError("No running history found. You need to call your model at least once before exporting. "
+                               "You might also want to check if there are no valid mutables in your model.")
+        return self._cache
 
     @contextmanager
     def forward_pass(self):
+        self._in_forward_pass = True
+        self._cache = dict()
         self.before_pass()
         try:
             yield self
         finally:
             self.after_pass()
+            self._in_forward_pass = False
 
     def before_pass(self):
-        self._in_forward_pass = True
-        self._cache = dict()
-
-    def after_pass(self):
-        self._in_forward_pass = False
-
-    def enter_mutable_scope(self, mutable_scope):
         pass
 
-    def exit_mutable_scope(self, mutable_scope):
+    def after_pass(self):
         pass
 
-    def forward(self, *inputs):
-        raise NotImplementedError("Mutator is not forward-able")
-
-    def on_forward(self, mutable, *inputs):
-        """Callback on forwarding a mutable"""
+    def _check_in_forward_pass(self):
         if not hasattr(self, "_in_forward_pass") or not self._in_forward_pass:
             raise ValueError("Not in forward pass. Did you forget to call mutator.forward_pass(), or forget to call "
                              "super().before_pass() and after_pass() in your override method?")
-        forward_method_name = "on_forward_{}".format(to_snake_case(mutable.__class__.__name__))
-        if hasattr(self, forward_method_name) and callable(getattr(self, forward_method_name)):
-            return getattr(self, forward_method_name)(mutable, *inputs)
-        else:
-            # fallback to general forward
-            return self.on_forward_general(mutable, *inputs)
-
-    def on_forward_general(self, mutable, *inputs):
-        raise NotImplementedError("Forward has to be implemented")
 
     def on_forward_layer_choice(self, mutable, *inputs):
         """
         Callback of layer choice forward. Override if you are an advanced user.
         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 speicified
+        (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`.
 
         Parameters
@@ -111,33 +52,38 @@ class PyTorchMutator(nn.Module):
 
         Returns
         -------
-        torch.Tensor
+        tuple of torch.Tensor and torch.Tensor
         """
+        self._check_in_forward_pass()
+
         def _map_fn(op, *inputs):
             return op(*inputs)
+
         mask = self._cache.setdefault(mutable.key, self.on_calc_layer_choice_mask(mutable))
         out = self._select_with_mask(_map_fn, [(choice, *inputs) for choice in mutable.choices], mask)
         return self._tensor_reduction(mutable.reduction, out), mask
 
-    def on_forward_input_choice(self, mutable, tensor_list, semantic_labels):
+    def on_forward_input_choice(self, mutable, tensor_list, tags):
         """
         Callback of input choice forward. Override if you are an advanced user.
-        On default, this method calls :meth:`on_calc_input_choice_mask` with `semantic_labels`
+        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 speicified in `mutable.reduction`. It will also cache the
+        the list of all tensor outputs with the policy specified in `mutable.reduction`. It will also cache the
         mask with corresponding `mutable.key`.
 
         Parameters
         ----------
         mutable: InputChoice
-        inputs: list of torch.Tensor
+        tensor_list: list of torch.Tensor
+        tags: list of string
 
         Returns
         -------
-        torch.Tensor
+        tuple of torch.Tensor and torch.Tensor
         """
-        mask = self._cache.setdefault(mutable.key, self.on_calc_input_choice_mask(mutable, semantic_labels))
-        out = self._select_with_mask(lambda x: x, [(t, ) for t in tensor_list], mask)
+        self._check_in_forward_pass()
+        mask = self._cache.setdefault(mutable.key, self.on_calc_input_choice_mask(mutable, tags))
+        out = self._select_with_mask(lambda x: x, [(t,) for t in tensor_list], mask)
         return self._tensor_reduction(mutable.reduction, out), mask
 
     def on_calc_layer_choice_mask(self, mutable):
@@ -157,7 +103,7 @@ class PyTorchMutator(nn.Module):
         """
         raise NotImplementedError("Layer choice mask calculation must be implemented")
 
-    def on_calc_input_choice_mask(self, mutable, semantic_labels):
+    def on_calc_input_choice_mask(self, mutable, tags):
         """
         Recommended to override. Calculate a mask tensor for a input choice.
 
@@ -165,7 +111,7 @@ class PyTorchMutator(nn.Module):
         ----------
         mutable: InputChoice
             Corresponding input choice object.
-        semantic_labels: list of string
+        tags: list of string
             The name of labels of input tensors given by user. Usually it's a
             :class:`~nni.nas.pytorch.mutables.MutableScope` marked by user.
 
@@ -179,7 +125,6 @@ class PyTorchMutator(nn.Module):
 
     def _select_with_mask(self, map_fn, candidates, mask):
         if "BoolTensor" in mask.type():
-            # print(candidates[0], len(mask))
             out = [map_fn(*cand) for cand, m in zip(candidates, mask) if m]
         elif "FloatTensor" in mask.type():
             out = [map_fn(*cand) * m for cand, m in zip(candidates, mask)]

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

@@ -33,13 +33,13 @@ class PdartsTrainer(Trainer):
         for epoch in range(self.pdarts_epoch):
 
             layers = self.layers+self.pdarts_num_layers[epoch]
-            model, loss, model_optim, lr_scheduler = self.model_creator(
+            model, loss, model_optim, _ = self.model_creator(
                 layers, n_nodes)
             mutator = PdartsMutator(
                 model, epoch, self.pdarts_num_to_drop, switches)
 
-            self.trainer = DartsTrainer(model, loss=loss, model_optim=model_optim,
-                                        lr_scheduler=lr_scheduler, mutator=mutator, **self.darts_parameters)
+            self.trainer = DartsTrainer(model, loss=loss, optimizer=model_optim,
+                                        mutator=mutator, **self.darts_parameters)
             print("start pdrats training %s..." % epoch)
 
             self.trainer.train()

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

-from abc import ABC, abstractmethod
+from abc import abstractmethod
 
+import torch
 
-class Trainer(ABC):
+from .base_trainer import BaseTrainer
+
+
+class Trainer(BaseTrainer):
+    def __init__(self, model, loss, metrics, optimizer, num_epochs,
+                 dataset_train, dataset_valid, batch_size, workers, device, log_frequency,
+                 mutator, callbacks):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
+        self.model = model
+        self.loss = loss
+        self.metrics = metrics
+        self.optimizer = optimizer
+        self.mutator = mutator
+
+        self.model.to(self.device)
+        self.loss.to(self.device)
+        self.mutator.to(self.device)
+
+        self.num_epochs = num_epochs
+        self.dataset_train = dataset_train
+        self.dataset_valid = dataset_valid
+        self.batch_size = batch_size
+        self.workers = workers
+        self.log_frequency = log_frequency
+        self.callbacks = callbacks if callbacks is not None else []
+        for callback in self.callbacks:
+            callback.build(self.model, self.mutator, self)
 
     @abstractmethod
-    def train(self):
-        raise NotImplementedError
+    def train_one_epoch(self, epoch):
+        pass
 
     @abstractmethod
-    def export(self):
-        raise NotImplementedError
+    def validate_one_epoch(self, epoch):
+        pass
+
+    def _train(self, validate):
+        for epoch in range(self.num_epochs):
+            for callback in self.callbacks:
+                callback.on_epoch_begin(epoch)
+
+            # training
+            print("Epoch {} Training".format(epoch))
+            self.train_one_epoch(epoch)
+
+            if validate:
+                # validation
+                print("Epoch {} Validating".format(epoch))
+                self.validate_one_epoch(epoch)
+
+            for callback in self.callbacks:
+                callback.on_epoch_end(epoch)
+
+    def train_and_validate(self):
+        self._train(True)
+
+    def train(self):
+        self._train(False)
+
+    def validate(self):
+        self.validate_one_epoch(-1)

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

-import re
 from collections import OrderedDict
 
-import torch
-
 _counter = 0
 
 
@@ -12,14 +9,6 @@ def global_mutable_counting():
     return _counter
 
 
-def to_snake_case(camel_case):
-    return re.sub('(?!^)([A-Z]+)', r'_\1', camel_case).lower()
-
-
-def auto_device():
-    return torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-
 class AverageMeterGroup(object):
 
     def __init__(self):