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Commit 69dfbf5e authored by chicm-ms's avatar chicm-ms Committed by GitHub
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Merge pull request #2020 from microsoft/dev-refactor-doc 

merge back to master: update doc index
parents eab0da15 889218bb
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src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
View file @ 69dfbf5e
......@@ -127,18 +127,15 @@ class RegularizedMutatorParallel(DistributedDataParallel):
class DartsDiscreteMutator(Mutator):
"""
A mutator that applies the final sampling result of a parent mutator on another model to train.
Parameters
----------
model : nn.Module
The model to apply the mutator.
parent_mutator : Mutator
The mutator that provides ``sample_final`` method, that will be called to get the architecture.
"""
def __init__(self, model, parent_mutator):
"""
Initialization.
Parameters
----------
model : nn.Module
The model to apply the mutator.
parent_mutator : Mutator
The mutator that provides ``sample_final`` method, that will be called to get the architecture.
"""
super().__init__(model)
self.__dict__["parent_mutator"] = parent_mutator # avoid parameters to be included
......
src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
View file @ 69dfbf5e
......@@ -32,73 +32,73 @@ class InteractiveKLLoss(nn.Module):
class CdartsTrainer(object):
"""
CDARTS trainer.
Parameters
----------
model_small : nn.Module
PyTorch model to be trained. This is the search network of CDARTS.
model_large : nn.Module
PyTorch model to be trained. This is the evaluation network of CDARTS.
criterion : callable
Receives logits and ground truth label, return a loss tensor, e.g., ``nn.CrossEntropyLoss()``.
loaders : list of torch.utils.data.DataLoader
List of train data and valid data loaders, for training weights and architecture weights respectively.
samplers : list of torch.utils.data.Sampler
List of train data and valid data samplers. This can be PyTorch standard samplers if not distributed.
In distributed mode, sampler needs to have ``set_epoch`` method. Refer to data utils in CDARTS example for details.
logger : logging.Logger
The logger for logging. Will use nni logger by default (if logger is ``None``).
regular_coeff : float
The coefficient of regular loss.
regular_ratio : float
The ratio of regular loss.
warmup_epochs : int
The epochs to warmup the search network
fix_head : bool
``True`` if fixing the paramters of auxiliary heads, else unfix the paramters of auxiliary heads.
epochs : int
Number of epochs planned for training.
steps_per_epoch : int
Steps of one epoch.
loss_alpha : float
The loss coefficient.
loss_T : float
The loss coefficient.
distributed : bool
``True`` if using distributed training, else non-distributed training.
log_frequency : int
Step count per logging.
grad_clip : float
Gradient clipping for weights.
interactive_type : string
``kl`` or ``smoothl1``.
output_path : string
Log storage path.
w_lr : float
Learning rate of the search network parameters.
w_momentum : float
Momentum of the search and the evaluation network.
w_weight_decay : float
The weight decay the search and the evaluation network parameters.
alpha_lr : float
Learning rate of the architecture parameters.
alpha_weight_decay : float
The weight decay the architecture parameters.
nasnet_lr : float
Learning rate of the evaluation network parameters.
local_rank : int
The number of thread.
share_module : bool
``True`` if sharing the stem and auxiliary heads, else not sharing these modules.
"""
def __init__(self, model_small, model_large, criterion, loaders, samplers, logger=None,
regular_coeff=5, regular_ratio=0.2, warmup_epochs=2, fix_head=True,
epochs=32, steps_per_epoch=None, loss_alpha=2, loss_T=2, distributed=True,
log_frequency=10, grad_clip=5.0, interactive_type='kl', output_path='./outputs',
w_lr=0.2, w_momentum=0.9, w_weight_decay=3e-4, alpha_lr=0.2, alpha_weight_decay=1e-4,
nasnet_lr=0.2, local_rank=0, share_module=True):
"""
Initialize a CdartsTrainer.
Parameters
----------
model_small : nn.Module
PyTorch model to be trained. This is the search network of CDARTS.
model_large : nn.Module
PyTorch model to be trained. This is the evaluation network of CDARTS.
criterion : callable
Receives logits and ground truth label, return a loss tensor, e.g., ``nn.CrossEntropyLoss()``.
loaders : list of torch.utils.data.DataLoader
List of train data and valid data loaders, for training weights and architecture weights respectively.
samplers : list of torch.utils.data.Sampler
List of train data and valid data samplers. This can be PyTorch standard samplers if not distributed.
In distributed mode, sampler needs to have ``set_epoch`` method. Refer to data utils in CDARTS example for details.
logger : logging.Logger
The logger for logging. Will use nni logger by default (if logger is ``None``).
regular_coeff : float
The coefficient of regular loss.
regular_ratio : float
The ratio of regular loss.
warmup_epochs : int
The epochs to warmup the search network
fix_head : bool
``True`` if fixing the paramters of auxiliary heads, else unfix the paramters of auxiliary heads.
epochs : int
Number of epochs planned for training.
steps_per_epoch : int
Steps of one epoch.
loss_alpha : float
The loss coefficient.
loss_T : float
The loss coefficient.
distributed : bool
``True`` if using distributed training, else non-distributed training.
log_frequency : int
Step count per logging.
grad_clip : float
Gradient clipping for weights.
interactive_type : string
``kl`` or ``smoothl1``.
output_path : string
Log storage path.
w_lr : float
Learning rate of the search network parameters.
w_momentum : float
Momentum of the search and the evaluation network.
w_weight_decay : float
The weight decay the search and the evaluation network parameters.
alpha_lr : float
Learning rate of the architecture parameters.
alpha_weight_decay : float
The weight decay the architecture parameters.
nasnet_lr : float
Learning rate of the evaluation network parameters.
local_rank : int
The number of thread.
share_module : bool
``True`` if sharing the stem and auxiliary heads, else not sharing these modules.
"""
if logger is None:
logger = logging.getLogger(__name__)
train_loader, valid_loader = loaders
......
src/sdk/pynni/nni/nas/pytorch/classic_nas/mutator.py
View file @ 69dfbf5e
......@@ -22,12 +22,21 @@ INPUT_CHOICE = "input_choice"
def get_and_apply_next_architecture(model):
"""
Wrapper of ClassicMutator to make it more meaningful,
similar to ```get_next_parameter``` for HPO.
Wrapper of :class:`~nni.nas.pytorch.classic_nas.mutator.ClassicMutator` to make it more meaningful,
similar to ``get_next_parameter`` for HPO.
Tt will generate search space based on ``model``.
If env ``NNI_GEN_SEARCH_SPACE`` exists, this is in dry run mode for
generating search space for the experiment.
If not, there are still two mode, one is nni experiment mode where users
use ``nnictl`` to start an experiment. The other is standalone mode
where users directly run the trial command, this mode chooses the first
one(s) for each LayerChoice and InputChoice.
Parameters
----------
model : pytorch model
user's model with search space (e.g., LayerChoice, InputChoice) embedded in it
model : nn.Module
User's model with search space (e.g., LayerChoice, InputChoice) embedded in it.
"""
ClassicMutator(model)
......@@ -36,23 +45,15 @@ class ClassicMutator(Mutator):
"""
This mutator is to apply the architecture chosen from tuner.
It implements the forward function of LayerChoice and InputChoice,
to only activate the chosen ones
to only activate the chosen ones.
Parameters
----------
model : nn.Module
User's model with search space (e.g., LayerChoice, InputChoice) embedded in it.
"""
def __init__(self, model):
"""
Generate search space based on ```model```.
If env ```NNI_GEN_SEARCH_SPACE``` exists, this is in dry run mode for
generating search space for the experiment.
If not, there are still two mode, one is nni experiment mode where users
use ```nnictl``` to start an experiment. The other is standalone mode
where users directly run the trial command, this mode chooses the first
one(s) for each LayerChoice and InputChoice.
Parameters
----------
model : PyTorch model
user's model with search space (e.g., LayerChoice, InputChoice) embedded in it
"""
super(ClassicMutator, self).__init__(model)
self._chosen_arch = {}
self._search_space = self._generate_search_space()
......@@ -121,9 +122,15 @@ class ClassicMutator(Mutator):
return torch.tensor(multihot_list, dtype=torch.bool) # pylint: disable=not-callable
def sample_search(self):
"""
See :meth:`sample_final`.
"""
return self.sample_final()
def sample_final(self):
"""
Convert the chosen arch and apply it on model.
"""
assert set(self._chosen_arch.keys()) == set(self._search_space.keys()), \
"Unmatched keys, expected keys '{}' from search space, found '{}'.".format(self._search_space.keys(),
self._chosen_arch.keys())
......
src/sdk/pynni/nni/nas/pytorch/darts/trainer.py
View file @ 69dfbf5e
......@@ -15,46 +15,46 @@ logger = logging.getLogger(__name__)
class DartsTrainer(Trainer):
"""
DARTS trainer.
Parameters
----------
model : nn.Module
PyTorch model to be trained.
loss : callable
Receives logits and ground truth label, return a loss tensor.
metrics : callable
Receives logits and ground truth label, return a dict of metrics.
optimizer : Optimizer
The optimizer used for optimizing the model.
num_epochs : int
Number of epochs planned for training.
dataset_train : Dataset
Dataset for training. Will be split for training weights and architecture weights.
dataset_valid : Dataset
Dataset for testing.
mutator : DartsMutator
Use in case of customizing your own DartsMutator. By default will instantiate a DartsMutator.
batch_size : int
Batch size.
workers : int
Workers for data loading.
device : torch.device
``torch.device("cpu")`` or ``torch.device("cuda")``.
log_frequency : int
Step count per logging.
callbacks : list of Callback
list of callbacks to trigger at events.
arc_learning_rate : float
Learning rate of architecture parameters.
unrolled : float
``True`` if using second order optimization, else first order optimization.
"""
def __init__(self, model, loss, metrics,
optimizer, num_epochs, dataset_train, dataset_valid,
mutator=None, batch_size=64, workers=4, device=None, log_frequency=None,
callbacks=None, arc_learning_rate=3.0E-4, unrolled=False):
"""
Initialize a DartsTrainer.
Parameters
----------
model : nn.Module
PyTorch model to be trained.
loss : callable
Receives logits and ground truth label, return a loss tensor.
metrics : callable
Receives logits and ground truth label, return a dict of metrics.
optimizer : Optimizer
The optimizer used for optimizing the model.
num_epochs : int
Number of epochs planned for training.
dataset_train : Dataset
Dataset for training. Will be split for training weights and architecture weights.
dataset_valid : Dataset
Dataset for testing.
mutator : DartsMutator
Use in case of customizing your own DartsMutator. By default will instantiate a DartsMutator.
batch_size : int
Batch size.
workers : int
Workers for data loading.
device : torch.device
``torch.device("cpu")`` or ``torch.device("cuda")``.
log_frequency : int
Step count per logging.
callbacks : list of Callback
list of callbacks to trigger at events.
arc_learning_rate : float
Learning rate of architecture parameters.
unrolled : float
``True`` if using second order optimization, else first order optimization.
"""
super().__init__(model, mutator if mutator is not None else DartsMutator(model),
loss, metrics, optimizer, num_epochs, dataset_train, dataset_valid,
batch_size, workers, device, log_frequency, callbacks)
......
src/sdk/pynni/nni/nas/pytorch/enas/mutator.py
View file @ 69dfbf5e
......@@ -28,38 +28,38 @@ class StackedLSTMCell(nn.Module):
class EnasMutator(Mutator):
"""
A mutator that mutates the graph with RL.
Parameters
----------
model : nn.Module
PyTorch model.
lstm_size : int
Controller LSTM hidden units.
lstm_num_layers : int
Number of layers for stacked LSTM.
tanh_constant : float
Logits will be equal to ``tanh_constant * tanh(logits)``. Don't use ``tanh`` if this value is ``None``.
cell_exit_extra_step : bool
If true, RL controller will perform an extra step at the exit of each MutableScope, dump the hidden state
and mark it as the hidden state of this MutableScope. This is to align with the original implementation of paper.
skip_target : float
Target probability that skipconnect will appear.
temperature : float
Temperature constant that divides the logits.
branch_bias : float
Manual bias applied to make some operations more likely to be chosen.
Currently this is implemented with a hardcoded match rule that aligns with original repo.
If a mutable has a ``reduce`` in its key, all its op choices
that contains `conv` in their typename will receive a bias of ``+self.branch_bias`` initially; while others
receive a bias of ``-self.branch_bias``.
entropy_reduction : str
Can be one of ``sum`` and ``mean``. How the entropy of multi-input-choice is reduced.
"""
def __init__(self, model, lstm_size=64, lstm_num_layers=1, tanh_constant=1.5, cell_exit_extra_step=False,
skip_target=0.4, temperature=None, branch_bias=0.25, entropy_reduction="sum"):
"""
Initialize a EnasMutator.
Parameters
----------
model : nn.Module
PyTorch model.
lstm_size : int
Controller LSTM hidden units.
lstm_num_layers : int
Number of layers for stacked LSTM.
tanh_constant : float
Logits will be equal to ``tanh_constant * tanh(logits)``. Don't use ``tanh`` if this value is ``None``.
cell_exit_extra_step : bool
If true, RL controller will perform an extra step at the exit of each MutableScope, dump the hidden state
and mark it as the hidden state of this MutableScope. This is to align with the original implementation of paper.
skip_target : float
Target probability that skipconnect will appear.
temperature : float
Temperature constant that divides the logits.
branch_bias : float
Manual bias applied to make some operations more likely to be chosen.
Currently this is implemented with a hardcoded match rule that aligns with original repo.
If a mutable has a ``reduce`` in its key, all its op choices
that contains `conv` in their typename will receive a bias of ``+self.branch_bias`` initially; while others
receive a bias of ``-self.branch_bias``.
entropy_reduction : str
Can be one of ``sum`` and ``mean``. How the entropy of multi-input-choice is reduced.
"""
super().__init__(model)
self.lstm_size = lstm_size
self.lstm_num_layers = lstm_num_layers
......
src/sdk/pynni/nni/nas/pytorch/enas/trainer.py
View file @ 69dfbf5e
......@@ -16,64 +16,64 @@ logger = logging.getLogger(__name__)
class EnasTrainer(Trainer):
"""
ENAS trainer.
Parameters
----------
model : nn.Module
PyTorch model to be trained.
loss : callable
Receives logits and ground truth label, return a loss tensor.
metrics : callable
Receives logits and ground truth label, return a dict of metrics.
reward_function : callable
Receives logits and ground truth label, return a tensor, which will be feeded to RL controller as reward.
optimizer : Optimizer
The optimizer used for optimizing the model.
num_epochs : int
Number of epochs planned for training.
dataset_train : Dataset
Dataset for training. Will be split for training weights and architecture weights.
dataset_valid : Dataset
Dataset for testing.
mutator : EnasMutator
Use when customizing your own mutator or a mutator with customized parameters.
batch_size : int
Batch size.
workers : int
Workers for data loading.
device : torch.device
``torch.device("cpu")`` or ``torch.device("cuda")``.
log_frequency : int
Step count per logging.
callbacks : list of Callback
list of callbacks to trigger at events.
entropy_weight : float
Weight of sample entropy loss.
skip_weight : float
Weight of skip penalty loss.
baseline_decay : float
Decay factor of baseline. New baseline will be equal to ``baseline_decay * baseline_old + reward * (1 - baseline_decay)``.
child_steps : int
How many mini-batches for model training per epoch.
mutator_lr : float
Learning rate for RL controller.
mutator_steps_aggregate : int
Number of steps that will be aggregated into one mini-batch for RL controller.
mutator_steps : int
Number of mini-batches for each epoch of RL controller learning.
aux_weight : float
Weight of auxiliary head loss. ``aux_weight * aux_loss`` will be added to total loss.
test_arc_per_epoch : int
How many architectures are chosen for direct test after each epoch.
"""
def __init__(self, model, loss, metrics, reward_function,
optimizer, num_epochs, dataset_train, dataset_valid,
mutator=None, batch_size=64, workers=4, device=None, log_frequency=None, callbacks=None,
entropy_weight=0.0001, skip_weight=0.8, baseline_decay=0.999, child_steps=500,
mutator_lr=0.00035, mutator_steps_aggregate=20, mutator_steps=50, aux_weight=0.4,
test_arc_per_epoch=1):
"""
Initialize an EnasTrainer.
Parameters
----------
model : nn.Module
PyTorch model to be trained.
loss : callable
Receives logits and ground truth label, return a loss tensor.
metrics : callable
Receives logits and ground truth label, return a dict of metrics.
reward_function : callable
Receives logits and ground truth label, return a tensor, which will be feeded to RL controller as reward.
optimizer : Optimizer
The optimizer used for optimizing the model.
num_epochs : int
Number of epochs planned for training.
dataset_train : Dataset
Dataset for training. Will be split for training weights and architecture weights.
dataset_valid : Dataset
Dataset for testing.
mutator : EnasMutator
Use when customizing your own mutator or a mutator with customized parameters.
batch_size : int
Batch size.
workers : int
Workers for data loading.
device : torch.device
``torch.device("cpu")`` or ``torch.device("cuda")``.
log_frequency : int
Step count per logging.
callbacks : list of Callback
list of callbacks to trigger at events.
entropy_weight : float
Weight of sample entropy loss.
skip_weight : float
Weight of skip penalty loss.
baseline_decay : float
Decay factor of baseline. New baseline will be equal to ``baseline_decay * baseline_old + reward * (1 - baseline_decay)``.
child_steps : int
How many mini-batches for model training per epoch.
mutator_lr : float
Learning rate for RL controller.
mutator_steps_aggregate : int
Number of steps that will be aggregated into one mini-batch for RL controller.
mutator_steps : int
Number of mini-batches for each epoch of RL controller learning.
aux_weight : float
Weight of auxiliary head loss. ``aux_weight * aux_loss`` will be added to total loss.
test_arc_per_epoch : int
How many architectures are chosen for direct test after each epoch.
"""
super().__init__(model, mutator if mutator is not None else EnasMutator(model),
loss, metrics, optimizer, num_epochs, dataset_train, dataset_valid,
batch_size, workers, device, log_frequency, callbacks)
......
src/sdk/pynni/nni/nas/pytorch/fixed.py
View file @ 69dfbf5e
......@@ -10,20 +10,20 @@ from nni.nas.pytorch.mutator import Mutator
class FixedArchitecture(Mutator):
"""
Fixed architecture mutator that always selects a certain graph.
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.
"""
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)
self._fixed_arc = fixed_arc
......@@ -35,9 +35,15 @@ class FixedArchitecture(Mutator):
raise RuntimeError("Missing keys in fixed architecture: {}.".format(mutable_keys - fixed_arc_keys))
def sample_search(self):
"""
Always returns the fixed architecture.
"""
return self._fixed_arc
def sample_final(self):
"""
Always returns the fixed architecture.
"""
return self._fixed_arc
......@@ -66,6 +72,7 @@ def apply_fixed_architecture(model, fixed_arc):
Returns
-------
FixedArchitecture
Mutator that is responsible for fixes the graph.
"""
if isinstance(fixed_arc, str):
......
src/sdk/pynni/nni/nas/pytorch/mutables.py
View file @ 69dfbf5e
......@@ -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.
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.
.. 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``
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``. See :class:`LayerChoice`.
return_mask : bool
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):
"""
Initialization.
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`
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`.
return_mask : bool
If `return_mask`, return output tensor and a mask. Otherwise return tensor only.
key : str
Key of the input choice.
"""
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
View file @ 69dfbf5e
......@@ -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
View file @ 69dfbf5e
......@@ -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
View file @ 69dfbf5e
......@@ -16,29 +16,29 @@ _logger = logging.getLogger(__name__)
class SPOSEvolution(Tuner):
"""
SPOS evolution tuner.
Parameters
----------
max_epochs : int
Maximum number of epochs to run.
num_select : int
Number of survival candidates of each epoch.
num_population : int
Number of candidates at the start of each epoch. If candidates generated by
crossover and mutation are not enough, the rest will be filled with random
candidates.
m_prob : float
The probability of mutation.
num_crossover : int
Number of candidates generated by crossover in each epoch.
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):
"""
Initialize SPOS Evolution Tuner.
Parameters
----------
max_epochs : int
Maximum number of epochs to run.
num_select : int
Number of survival candidates of each epoch.
num_population : int
Number of candidates at the start of each epoch. If candidates generated by
crossover and mutation are not enough, the rest will be filled with random
candidates.
m_prob : float
The probability of mutation.
num_crossover : int
Number of candidates generated by crossover in each epoch.
num_mutation : int
Number of candidates generated by mutation in each epoch.
"""
assert num_population >= num_select
self.max_epochs = max_epochs
self.num_select = num_select
......
src/sdk/pynni/nni/nas/pytorch/spos/mutator.py
View file @ 69dfbf5e
......@@ -10,27 +10,29 @@ _logger = logging.getLogger(__name__)
class SPOSSupernetTrainingMutator(RandomMutator):
"""
A random mutator with flops limit.
Parameters
----------
model : nn.Module
PyTorch model.
flops_func : callable
Callable that takes a candidate from `sample_search` and returns its candidate. When `flops_func`
is None, functions related to flops will be deactivated.
flops_lb : number
Lower bound of flops.
flops_ub : number
Upper bound of flops.
flops_bin_num : number
Number of bins divided for the interval of flops to ensure the uniformity. Bigger number will be more
uniform, but the sampling will be slower.
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):
"""
Parameters
----------
model : nn.Module
PyTorch model.
flops_func : callable
Callable that takes a candidate from `sample_search` and returns its candidate. When `flops_func`
is None, functions related to flops will be deactivated.
flops_lb : number
Lower bound of flops.
flops_ub : number
Upper bound of flops.
flops_bin_num : number
Number of bins divided for the interval of flops to ensure the uniformity. Bigger number will be more
uniform, but the sampling will be slower.
flops_sample_timeout : int
Maximum number of attempts to sample before giving up and use a random candidate.
"""
super().__init__(model)
self._flops_func = flops_func
if self._flops_func is not None:
......
src/sdk/pynni/nni/nas/pytorch/spos/trainer.py
View file @ 69dfbf5e
......@@ -15,43 +15,42 @@ logger = logging.getLogger(__name__)
class SPOSSupernetTrainer(Trainer):
"""
This trainer trains a supernet that can be used for evolution search.
Parameters
----------
model : nn.Module
Model with mutables.
mutator : Mutator
A mutator object that has been initialized with the model.
loss : callable
Called with logits and targets. Returns a loss tensor.
metrics : callable
Returns a dict that maps metrics keys to metrics data.
optimizer : Optimizer
Optimizer that optimizes the model.
num_epochs : int
Number of epochs of training.
train_loader : iterable
Data loader of training. Raise ``StopIteration`` when one epoch is exhausted.
dataset_valid : iterable
Data loader of validation. Raise ``StopIteration`` when one epoch is exhausted.
batch_size : int
Batch size.
workers: int
Number of threads for data preprocessing. Not used for this trainer. Maybe removed in future.
device : torch.device
Device object. Either ``torch.device("cuda")`` or ``torch.device("cpu")``. When ``None``, trainer will
automatic detects GPU and selects GPU first.
log_frequency : int
Number of mini-batches to log metrics.
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):
"""
Parameters
----------
model : nn.Module
Model with mutables.
mutator : Mutator
A mutator object that has been initialized with the model.
loss : callable
Called with logits and targets. Returns a loss tensor.
metrics : callable
Returns a dict that maps metrics keys to metrics data.
optimizer : Optimizer
Optimizer that optimizes the model.
num_epochs : int
Number of epochs of training.
train_loader : iterable
Data loader of training. Raise ``StopIteration`` when one epoch is exhausted.
dataset_valid : iterable
Data loader of validation. Raise ``StopIteration`` when one epoch is exhausted.
batch_size : int
Batch size.
workers: int
Number of threads for data preprocessing. Not used for this trainer. Maybe removed in future.
device : torch.device
Device object. Either ``torch.device("cuda")`` or ``torch.device("cpu")``. When ``None``, trainer will
automatic detects GPU and selects GPU first.
log_frequency : int
Number of mini-batches to log metrics.
callbacks : list of Callback
Callbacks to plug into the trainer. See Callbacks.
"""
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
View file @ 69dfbf5e
......@@ -24,42 +24,54 @@ class TorchTensorEncoder(json.JSONEncoder):
class Trainer(BaseTrainer):
"""
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
----------
model : nn.Module
Model with mutables.
mutator : BaseMutator
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
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. 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
Batch size.
workers : int
Number of workers used in data preprocessing.
device : torch.device
Device object. Either ``torch.device("cuda")`` or ``torch.device("cpu")``. When ``None``, trainer will
automatic detects GPU and selects GPU first.
log_frequency : int
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):
"""
Trainer initialization.
Parameters
----------
model : nn.Module
Model with mutables.
mutator : BaseMutator
A mutator object that has been initialized with the model.
loss : callable
Called with logits and targets. Returns a loss tensor.
metrics : callable
Returns a dict that maps metrics keys to metrics data.
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_valid : torch.utils.data.Dataset
Dataset of validation/testing.
batch_size : int
Batch size.
workers : int
Number of workers used in data preprocessing.
device : torch.device
Device object. Either ``torch.device("cuda")`` or ``torch.device("cpu")``. When ``None``, trainer will
automatic detects GPU and selects GPU first.
log_frequency : int
Number of mini-batches to log metrics.
callbacks : list of Callback
Callbacks to plug into the trainer. See 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
View file @ 69dfbf5e
......@@ -12,6 +12,9 @@ _logger = logging.getLogger(__name__)
def global_mutable_counting():
"""
A program level counter starting from 1.
"""
global _counter
_counter += 1
return _counter
......@@ -26,6 +29,9 @@ def _reset_global_mutable_counting():
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):
......@@ -40,12 +46,18 @@ 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")
......@@ -61,34 +73,49 @@ 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"""
"""
Computes and stores the average and current value.
Parameters
----------
name : str
Name to display.
fmt : str
Format string to print the values.
"""
def __init__(self, name, fmt=':f'):
"""
Initialization of AverageMeter
Parameters
----------
name : str
Name to display.
fmt : str
Format string to print the values.
"""
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
......@@ -112,6 +139,11 @@ 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):
......@@ -119,10 +151,16 @@ 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):
......
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