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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
......@@ -127,10 +127,6 @@ class RegularizedMutatorParallel(DistributedDataParallel):
class DartsDiscreteMutator(Mutator):
"""
A mutator that applies the final sampling result of a parent mutator on another model to train.
"""
def __init__(self, model, parent_mutator):
"""
Initialization.
Parameters
----------
......@@ -139,6 +135,7 @@ class DartsDiscreteMutator(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):
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
......@@ -32,14 +32,8 @@ class InteractiveKLLoss(nn.Module):
class CdartsTrainer(object):
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.
CDARTS trainer.
Parameters
----------
......@@ -99,6 +93,12 @@ class CdartsTrainer(object):
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):
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.
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
model : nn.Module
User's model with search space (e.g., LayerChoice, InputChoice) embedded in it.
"""
def __init__(self, model):
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
......@@ -15,12 +15,8 @@ logger = logging.getLogger(__name__)
class DartsTrainer(Trainer):
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.
DARTS trainer.
Parameters
----------
......@@ -55,6 +51,10 @@ class DartsTrainer(Trainer):
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):
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
......@@ -28,11 +28,8 @@ class StackedLSTMCell(nn.Module):
class EnasMutator(Mutator):
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.
A mutator that mutates the graph with RL.
Parameters
----------
......@@ -60,6 +57,9 @@ class EnasMutator(Mutator):
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"):
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
......@@ -16,14 +16,8 @@ logger = logging.getLogger(__name__)
class EnasTrainer(Trainer):
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.
ENAS trainer.
Parameters
----------
......@@ -74,6 +68,12 @@ class EnasTrainer(Trainer):
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):
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
......@@ -10,10 +10,8 @@ from nni.nas.pytorch.mutator import Mutator
class FixedArchitecture(Mutator):
def __init__(self, model, fixed_arc, strict=True):
"""
Initialize a fixed architecture mutator.
Fixed architecture mutator that always selects a certain graph.
Parameters
----------
......@@ -22,8 +20,10 @@ class FixedArchitecture(Mutator):
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.
Force everything that appears in ``fixed_arc`` to be used at least once.
"""
def __init__(self, model, fixed_arc, strict=True):
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.
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
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
......@@ -16,11 +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
View file @ 69dfbf5e
......@@ -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
View file @ 69dfbf5e
......@@ -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
View file @ 69dfbf5e
......@@ -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
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")
......@@ -61,15 +73,15 @@ 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
----------
......@@ -78,17 +90,32 @@ 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
......@@ -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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