[Retiarii] cross-graph optimization: device placement and input deduplication (#3202)

.gitignore

@@ -10,6 +10,8 @@
 /ts/nni_manager/exp_profile.json
 /ts/nni_manager/metrics.json
 /ts/nni_manager/trial_jobs.json
+/test/ut/retiarii/_debug_graph_data.json
+/test/ut/retiarii/out.tmp
 
 # Logs
 logs
@@ -105,5 +107,3 @@ venv.bak/
 .vscode
 .vs
 .history
-generated/
-test/ut/retiarii/_debug_graph_data.json

dependencies/recommended.txt

@@ -8,7 +8,7 @@ torch == 1.9.0+cpu ; sys_platform != "darwin"
 torch == 1.9.0 ; sys_platform == "darwin"
 torchvision == 0.10.0+cpu ; sys_platform != "darwin"
 torchvision == 0.10.0 ; sys_platform == "darwin"
-pytorch-lightning >= 1.1.1
+pytorch-lightning >= 1.2.8
 onnx
 peewee
 graphviz

dependencies/recommended_gpu.txt

@@ -5,7 +5,7 @@ tensorflow
 keras == 2.4.3
 torch == 1.9.0+cu111
 torchvision == 0.10.0+cu111
-pytorch-lightning >= 1.1.1
+pytorch-lightning >= 1.2.8
 onnx
 peewee
 graphviz

nni/common/device.py

@@ -9,7 +9,9 @@ class GPUDevice:
     status: Literal['idle', 'busy', 'unknown'] = 'idle'
 
     def __eq__(self, o) -> bool:
-        return self.node_id == o.node_id and self.gpu_id == o.gpu_id
+        if isinstance(o, GPUDevice):
+            return self.node_id == o.node_id and self.gpu_id == o.gpu_id
+        return False
 
     def __lt__(self, o) -> bool:
         if self.node_id < o.node_id:
@@ -23,7 +25,10 @@ class GPUDevice:
         return "{Environment %s, GPU %d, Status %s}" % (self.node_id, self.gpu_id, self.status)
 
     def __hash__(self) -> int:
-        return hash(self.node_id + '_' + self.gpu_id)
+        return hash(self.node_id + '_' + str(self.gpu_id))
 
     def set_status(self, status):
         self.status = status
+
+    def device_repr(self,):
+        return f"cuda:{self.gpu_id}"

nni/retiarii/codegen/pytorch.py

@@ -115,7 +115,7 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
             node_code = node.operation.to_init_code(_remove_prefix(node.name, graph_name))
             if node_code is not None:
                 if placement and node in placement and len(node_code) > 0:
-                    node_codes.append(f"{node_code}.to('{placement[node].device}')")
+                    node_codes.append(f"{node_code}.to('{placement[node].device_repr()}')")
                 else:
                     node_codes.append(node_code)
 

nni/retiarii/evaluator/pytorch/cgo/accelerator.py

+from typing import Any, Union, Optional, List
+import torch
+
+from pytorch_lightning.accelerators.accelerator import Accelerator
+from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
+from pytorch_lightning.trainer.connectors.accelerator_connector import AcceleratorConnector
+
+from pytorch_lightning.plugins import Plugin
+from pytorch_lightning.plugins.environments import ClusterEnvironment
+
+from ....serializer import serialize_cls
+
+
+class BypassPlugin(TrainingTypePlugin):
+    """ Plugin that handles communication on a single device. """
+
+    def __init__(self, device: str):
+        super().__init__()
+        self.device: str = device
+        self.global_rank = 0
+        self.local_rank = 0
+        self.world_size = 1
+
+    def connect(self, model: torch.nn.Module) -> torch.nn.Module:
+        self._model = model
+        self.model_to_device()
+        return self.model
+
+    @property
+    def on_tpu(self) -> bool:
+        return False
+
+    @property
+    def on_gpu(self) -> bool:
+        return "cuda" in self.device and torch.cuda.is_available()
+
+    def reduce(self, tensor: Union[Any, torch.Tensor], *args: Any, **kwargs: Any) -> Union[Any, torch.Tensor]:
+        """
+        Reduces a tensor from several distributed processes to one aggregated tensor.
+        As this plugin only operates with a single device, the reduction is simply the identity.
+
+        Args:
+            tensor: the tensor to sync and reduce
+            *args: ignored
+            **kwargs: ignored
+
+        Return:
+            the unmodified input as reduction is not needed for single process operation
+        """
+        return tensor
+
+    def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
+        """Perform a all_gather on all processes """
+        return tensor
+
+    @property
+    def root_device(self) -> torch.device:
+        return torch.device(self.device)
+
+    def model_to_device(self) -> None:
+        # bypass device placement from pytorch lightning
+        pass
+
+    def setup(self, model: torch.nn.Module) -> torch.nn.Module:
+        self.model_to_device()
+        return self.model
+
+    @property
+    def is_global_zero(self) -> bool:
+        return True
+
+    def barrier(self, *args, **kwargs) -> None:
+        pass
+
+    def broadcast(self, obj: object, src: int = 0) -> object:
+        return obj
+
+
+def get_accelerator_connector(
+        num_processes: int = 1,
+        tpu_cores: Optional[Union[List[int], str, int]] = None,
+        distributed_backend: Optional[str] = None,
+        auto_select_gpus: bool = False,
+        gpus: Optional[Union[List[int], str, int]] = None,
+        num_nodes: int = 1,
+        sync_batchnorm: bool = False,
+        benchmark: bool = False,
+        replace_sampler_ddp: bool = True,
+        deterministic: bool = False,
+        precision: int = 32,
+        amp_backend: str = 'native',
+        amp_level: str = 'O2',
+        plugins: Optional[Union[List[Union[Plugin, ClusterEnvironment, str]], Plugin, ClusterEnvironment, str]] = None):
+    return AcceleratorConnector(
+        num_processes, tpu_cores, distributed_backend, auto_select_gpus, gpus, num_nodes, sync_batchnorm, benchmark,
+        replace_sampler_ddp, deterministic, precision, amp_backend, amp_level, plugins
+    )
+
+
+@serialize_cls
+class BypassAccelerator(Accelerator):
+    def __init__(self, precision_plugin=None, device="cpu"):
+        if precision_plugin is None:
+            precision_plugin = get_accelerator_connector().precision_plugin
+        # pylint: disable=abstract-class-instantiated
+        super().__init__(precision_plugin=precision_plugin, training_type_plugin=BypassPlugin(device))

nni/retiarii/evaluator/pytorch/cgo/evaluator.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import warnings
+from typing import Dict, List, Optional, Union
+
+
+import torch.nn as nn
+import torch.optim as optim
+import pytorch_lightning as pl
+from torch.utils.data import DataLoader
+
+import nni
+
+from ..lightning import LightningModule, _AccuracyWithLogits, Lightning
+from .trainer import Trainer
+from ....serializer import serialize_cls
+
+
+@serialize_cls
+class _MultiModelSupervisedLearningModule(LightningModule):
+    def __init__(self, criterion: nn.Module, metrics: Dict[str, pl.metrics.Metric],
+                 n_models: int = 0,
+                 learning_rate: float = 0.001,
+                 weight_decay: float = 0.,
+                 optimizer: optim.Optimizer = optim.Adam):
+        super().__init__()
+        self.save_hyperparameters('criterion', 'optimizer', 'learning_rate', 'weight_decay')
+        self.criterion = criterion()
+        self.criterion_cls = criterion
+        self.optimizer = optimizer
+        self.metrics = nn.ModuleDict({name: cls() for name, cls in metrics.items()})
+        self.n_models = n_models
+
+    def forward(self, x):
+        y_hat = self.model(x)
+        return y_hat
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        multi_y_hat = self(x)
+        if isinstance(multi_y_hat, tuple):
+            assert len(multi_y_hat) == self.n_models
+        else:
+            assert self.n_models == 1
+            multi_y_hat = [multi_y_hat]
+        multi_loss = []
+        for idx, y_hat in enumerate(multi_y_hat):
+            loss = self.criterion(y_hat.to("cpu"), y.to("cpu"))
+            self.log(f'train_loss_{idx}', loss, prog_bar=True)
+            for name, metric in self.metrics.items():
+                self.log(f'train_{idx}_' + name, metric(y_hat.to("cpu"), y.to("cpu")), prog_bar=True)
+            multi_loss.append(loss)
+        return sum(multi_loss)
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        multi_y_hat = self(x)
+        if isinstance(multi_y_hat, tuple):
+            assert len(multi_y_hat) == self.n_models
+        else:
+            assert self.n_models == 1
+            multi_y_hat = [multi_y_hat]
+        for idx, y_hat in enumerate(multi_y_hat):
+            self.log(f'val_loss_{idx}', self.criterion(y_hat.to("cpu"), y.to("cpu")), prog_bar=True)
+            for name, metric in self.metrics.items():
+                self.log(f'val_{idx}_' + name, metric(y_hat.to("cpu"), y.to("cpu")), prog_bar=True)
+
+    def test_step(self, batch, batch_idx):
+        x, y = batch
+        multi_y_hat = self(x)
+        if isinstance(multi_y_hat, tuple):
+            assert len(multi_y_hat) == self.n_models
+        else:
+            assert self.n_models == 1
+            multi_y_hat = [multi_y_hat]
+        for idx, y_hat in enumerate(multi_y_hat):
+            self.log(f'test_loss_{idx}', self.criterion(y_hat.to("cpu"), y.to("cpu")), prog_bar=True)
+            for name, metric in self.metrics.items():
+                self.log(f'test_{idx}_' + name, metric(y_hat.to("cpu"), y.to("cpu")), prog_bar=True)
+
+    def configure_optimizers(self):
+        return self.optimizer(self.parameters(), lr=self.hparams.learning_rate, weight_decay=self.hparams.weight_decay)
+
+    def on_validation_epoch_end(self):
+        nni.report_intermediate_result(self._get_validation_metrics())
+
+    def teardown(self, stage):
+        if stage == 'fit':
+            nni.report_final_result(self._get_validation_metrics())
+
+    def _get_validation_metrics(self):
+        # TODO: split metric of multiple models?
+        if len(self.metrics) == 1:
+            metric_name = next(iter(self.metrics))
+            ret = []
+            for idx in range(self.n_models):
+                ret.append(self.trainer.callback_metrics[f'val_{idx}_' + metric_name].item())
+            return ret
+        else:
+            warnings.warn('Multiple metrics without "default" is not supported by current framework.')
+            return {name: self.trainer.callback_metrics['val_' + name].item() for name in self.metrics}
+
+
+class MultiModelSupervisedLearningModule(_MultiModelSupervisedLearningModule):
+    """
+    Lightning Module of SupervisedLearning for Cross-Graph Optimization.
+    Users who needs cross-graph optimization should use this module.
+
+    Parameters
+    ----------
+    criterion : nn.Module
+        Class for criterion module (not an instance). default: ``nn.CrossEntropyLoss``
+    learning_rate : float
+        Learning rate. default: 0.001
+    weight_decay : float
+        L2 weight decay. default: 0
+    optimizer : Optimizer
+        Class for optimizer (not an instance). default: ``Adam``
+    """
+
+    def __init__(self, criterion: nn.Module, metrics: Dict[str, pl.metrics.Metric],
+                 learning_rate: float = 0.001,
+                 weight_decay: float = 0.,
+                 optimizer: optim.Optimizer = optim.Adam):
+        super().__init__(criterion, metrics, learning_rate=learning_rate, weight_decay=weight_decay, optimizer=optimizer)
+
+
+@serialize_cls
+class _ClassificationModule(MultiModelSupervisedLearningModule):
+    def __init__(self, criterion: nn.Module = nn.CrossEntropyLoss,
+                 learning_rate: float = 0.001,
+                 weight_decay: float = 0.,
+                 optimizer: optim.Optimizer = optim.Adam):
+        super().__init__(criterion, {'acc': _AccuracyWithLogits},
+                         learning_rate=learning_rate, weight_decay=weight_decay, optimizer=optimizer)
+
+
+class Classification(Lightning):
+    """
+    Trainer that is used for classification.
+
+    Parameters
+    ----------
+    criterion : nn.Module
+        Class for criterion module (not an instance). default: ``nn.CrossEntropyLoss``
+    learning_rate : float
+        Learning rate. default: 0.001
+    weight_decay : float
+        L2 weight decay. default: 0
+    optimizer : Optimizer
+        Class for optimizer (not an instance). default: ``Adam``
+    train_dataloders : DataLoader
+        Used in ``trainer.fit()``. A PyTorch DataLoader with training samples.
+        If the ``lightning_module`` has a predefined train_dataloader method this will be skipped.
+    val_dataloaders : DataLoader or List of DataLoader
+        Used in ``trainer.fit()``. Either a single PyTorch Dataloader or a list of them, specifying validation samples.
+        If the ``lightning_module`` has a predefined val_dataloaders method this will be skipped.
+    trainer_kwargs : dict
+        Optional keyword arguments passed to trainer. See
+        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/trainer.html>`__ for details.
+    """
+
+    def __init__(self, criterion: nn.Module = nn.CrossEntropyLoss,
+                 learning_rate: float = 0.001,
+                 weight_decay: float = 0.,
+                 optimizer: optim.Optimizer = optim.Adam,
+                 train_dataloader: Optional[DataLoader] = None,
+                 val_dataloaders: Union[DataLoader, List[DataLoader], None] = None,
+                 **trainer_kwargs):
+        module = _ClassificationModule(criterion=criterion, learning_rate=learning_rate,
+                                       weight_decay=weight_decay, optimizer=optimizer)
+        super().__init__(module, Trainer(use_cgo=True, **trainer_kwargs),
+                         train_dataloader=train_dataloader, val_dataloaders=val_dataloaders)
+
+
+@serialize_cls
+class _RegressionModule(MultiModelSupervisedLearningModule):
+    def __init__(self, criterion: nn.Module = nn.MSELoss,
+                 learning_rate: float = 0.001,
+                 weight_decay: float = 0.,
+                 optimizer: optim.Optimizer = optim.Adam):
+        super().__init__(criterion, {'mse': pl.metrics.MeanSquaredError},
+                         learning_rate=learning_rate, weight_decay=weight_decay, optimizer=optimizer)
+
+
+class Regression(Lightning):
+    """
+    Trainer that is used for regression.
+
+    Parameters
+    ----------
+    criterion : nn.Module
+        Class for criterion module (not an instance). default: ``nn.MSELoss``
+    learning_rate : float
+        Learning rate. default: 0.001
+    weight_decay : float
+        L2 weight decay. default: 0
+    optimizer : Optimizer
+        Class for optimizer (not an instance). default: ``Adam``
+    train_dataloders : DataLoader
+        Used in ``trainer.fit()``. A PyTorch DataLoader with training samples.
+        If the ``lightning_module`` has a predefined train_dataloader method this will be skipped.
+    val_dataloaders : DataLoader or List of DataLoader
+        Used in ``trainer.fit()``. Either a single PyTorch Dataloader or a list of them, specifying validation samples.
+        If the ``lightning_module`` has a predefined val_dataloaders method this will be skipped.
+    trainer_kwargs : dict
+        Optional keyword arguments passed to trainer. See
+        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/trainer.html>`__ for details.
+    """
+
+    def __init__(self, criterion: nn.Module = nn.MSELoss,
+                 learning_rate: float = 0.001,
+                 weight_decay: float = 0.,
+                 optimizer: optim.Optimizer = optim.Adam,
+                 train_dataloader: Optional[DataLoader] = None,
+                 val_dataloaders: Union[DataLoader, List[DataLoader], None] = None,
+                 **trainer_kwargs):
+        module = _RegressionModule(criterion=criterion, learning_rate=learning_rate,
+                                   weight_decay=weight_decay, optimizer=optimizer)
+        super().__init__(module, Trainer(use_cgo=True, **trainer_kwargs),
+                         train_dataloader=train_dataloader, val_dataloaders=val_dataloaders)

nni/retiarii/evaluator/pytorch/cgo/trainer.py

+
+
+import pytorch_lightning as pl
+from ....serializer import serialize_cls
+from .accelerator import BypassAccelerator
+
+
+@serialize_cls
+class Trainer(pl.Trainer):
+    """
+    Trainer for cross-graph optimization.
+
+    Parameters
+    ----------
+    use_cgo : bool
+        Whether cross-graph optimization (CGO) is used.
+        If it is True, CGO will manage device placement.
+        Any device placement from pytorch lightning will be bypassed.
+        default: False
+    trainer_kwargs : dict
+        Optional keyword arguments passed to trainer. See
+        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/trainer.html>`__ for details.
+    """
+
+    def __init__(self, use_cgo=False, **trainer_kwargs):
+        if use_cgo:
+            if "accelerator" in trainer_kwargs:
+                raise ValueError("accelerator should not be set when cross-graph optimization is enabled.")
+            trainer_kwargs['accelerator'] = BypassAccelerator(device='cpu')
+
+        super().__init__(**trainer_kwargs)

nni/retiarii/evaluator/pytorch/lightning.py

@@ -12,6 +12,12 @@ import torch.optim as optim
 from torch.utils.data import DataLoader
 
 import nni
+try:
+    import nni.retiarii.evaluator.pytorch.cgo.trainer as cgo_trainer
+    cgo_import_failed = False
+except ImportError:
+    cgo_import_failed = True
+
 from ...graph import Evaluator
 from ...serializer import serialize_cls
 
@@ -36,7 +42,6 @@ class LightningModule(pl.LightningModule):
 Trainer = serialize_cls(pl.Trainer)
 DataLoader = serialize_cls(DataLoader)
 
-
 class Lightning(Evaluator):
     """
     Delegate the whole training to PyTorch Lightning.
@@ -67,7 +72,11 @@ class Lightning(Evaluator):
                  train_dataloader: Optional[DataLoader] = None,
                  val_dataloaders: Union[DataLoader, List[DataLoader], None] = None):
         assert isinstance(lightning_module, LightningModule), f'Lightning module must be an instance of {__name__}.LightningModule.'
-        assert isinstance(trainer, Trainer), f'Trainer must be imported from {__name__}.'
+        if cgo_import_failed:
+            assert isinstance(trainer, Trainer), f'Trainer must be imported from {__name__}'
+        else:
+            assert isinstance(trainer, Trainer) or isinstance(trainer, cgo_trainer.Trainer), \
+                f'Trainer must be imported from {__name__} or nni.retiarii.evaluator.pytorch.cgo.trainer'
         assert _check_dataloader(train_dataloader), f'Wrong dataloader type. Try import DataLoader from {__name__}.'
         assert _check_dataloader(val_dataloaders), f'Wrong dataloader type. Try import DataLoader from {__name__}.'
         self.module = lightning_module
@@ -91,7 +100,21 @@ class Lightning(Evaluator):
         return self.fit(model_cls)
 
     def __eq__(self, other):
-        return self.function == other.function and self.arguments == other.arguments
+        eq_func = False
+        eq_args = False
+        if other is None:
+            return False
+        if hasattr(self, "function") and hasattr(other, "function"):
+            eq_func = (self.function == other.function)
+        elif not (hasattr(self, "function") or hasattr(other, "function")):
+            eq_func = True
+
+        if hasattr(self, "arguments") and hasattr(other, "arguments"):
+            eq_args = (self.arguments == other.arguments)
+        elif not (hasattr(self, "arguments") or hasattr(other, "arguments")):
+            eq_args = True
+
+        return eq_func and eq_args
 
     def fit(self, model):
         """

nni/retiarii/execution/cgo_engine.py

@@ -2,14 +2,22 @@
 # Licensed under the MIT license.
 
 import logging
+import os
+import random
+import string
+import time
+import threading
 from typing import Iterable, List, Dict, Tuple
 
+from nni.common.device import GPUDevice
 from .interface import AbstractExecutionEngine, AbstractGraphListener, WorkerInfo
 from .. import codegen, utils
-from ..graph import Model, ModelStatus, MetricData
+from ..graph import Model, ModelStatus, MetricData, Node
 from ..integration_api import send_trial, receive_trial_parameters, get_advisor
-from .logical_optimizer.logical_plan import LogicalPlan, PhysicalDevice
+from .logical_optimizer.logical_plan import LogicalPlan, AbstractLogicalNode
 from .logical_optimizer.opt_dedup_input import DedupInputOptimizer
+from ..evaluator.pytorch.lightning import Lightning
+from ..evaluator.pytorch.cgo.evaluator import MultiModelSupervisedLearningModule, _MultiModelSupervisedLearningModule
 
 from .base import BaseGraphData
 
@@ -17,29 +25,93 @@ _logger = logging.getLogger(__name__)
 
 
 class CGOExecutionEngine(AbstractExecutionEngine):
-    def __init__(self, devices=None, n_model_per_graph=4) -> None:
+    """
+    The execution engine with Cross-Graph Optimization (CGO).
+
+    Only models using PyTorch Lighting and MultiModelSupervisedLearningModule as the evaluator can be optimized.
+    Otherwise, a model will be submitted independently without any cross-graph optimization.
+
+    Parameters
+    ----------
+    devices : List[str] or List[GPUDevice]
+        Available devices for execution.
+        If a list of str is provided, it will build a list of GPUDevice in a server named ``single_server``
+    max_concurrency : int
+        The maximum number of trials to run concurrently.
+    batch_waiting_time: int
+        Seconds to wait for each batch of trial submission.
+        The trials within one batch could apply cross-graph optimization.
+    """
+
+    def __init__(self, devices: List[GPUDevice] = None,
+                 max_concurrency: int = None,
+                 batch_waiting_time: int = 60,
+                 ) -> None:
         self._listeners: List[AbstractGraphListener] = []
         self._running_models: Dict[int, Model] = dict()
         self.logical_plan_counter = 0
-        self.n_model_per_graph = n_model_per_graph
+        self.available_devices: List[GPUDevice] = []
+        self.max_concurrency: int = max_concurrency
+        for device in devices:
+            self.available_devices.append(device)
+        self.all_devices = self.available_devices.copy()
+
+        self._batch_waiting_time = batch_waiting_time  # seconds to wait for all models in a batch to do cross-graph optimization
         self._optimizers = [DedupInputOptimizer()]
         self._original_models = {}
         self._original_model_to_multi_model = {}
-        self.devices = [] if devices is None else devices
+        self._trial_to_original_models = {}
+        self._trial_used_devices: Dict[int, List[GPUDevice]] = {}
+
+        self._history: List[Model] = []
+
+        self._queuing_jobs: List[Model] = []
+        self._queue_lock = threading.Lock()
 
         # register advisor callbacks
         advisor = get_advisor()
-        advisor.send_trial_callback = self._send_trial_callback
-        advisor.request_trial_jobs_callback = self._request_trial_jobs_callback
+        # advisor.send_trial_callback = self._send_trial_callback
+        # advisor.request_trial_jobs_callback = self._request_trial_jobs_callback
         advisor.trial_end_callback = self._trial_end_callback
         advisor.intermediate_metric_callback = self._intermediate_metric_callback
         advisor.final_metric_callback = self._final_metric_callback
 
+        self._stopped = False
+        self._consumer_thread = threading.Thread(target=self._consume_queue)
+        self._consumer_thread.start()
+
+    def join(self):
+        self._stopped = True
+        self._consumer_thread.join()
+
     def add_optimizer(self, opt):
         self._optimizers.append(opt)
 
     def submit_models(self, *models: List[Model]) -> None:
+        curr_time = time.time()
         _logger.info('%d models are submitted', len(models))
+        self._queue_lock.acquire()
+        self._queuing_jobs.extend([(curr_time, _) for _ in models])
+        self._queue_lock.release()
+
+    def _consume_queue(self):
+        # a thread to monitor self.queuing_jobs to consume them in batch
+        while not self._stopped:
+            if len(self._queuing_jobs) > 0:
+                curr_time = time.time()
+                self._queue_lock.acquire()
+                if (self.max_concurrency and len(self._queuing_jobs) >= self.max_concurrency):
+                    self._submit_models_in_batch(*[_[1] for _ in self._queuing_jobs[:self.max_concurrency]])
+                    self._queuing_jobs = self._queuing_jobs[self.max_concurrency:]
+                elif len(self.available_devices) <= len(self._queuing_jobs) or \
+                        (curr_time - self._queuing_jobs[0][0] > self._batch_waiting_time):
+                    self._submit_models_in_batch(*[_[1] for _ in self._queuing_jobs])
+                    self._queuing_jobs = []
+                self._queue_lock.release()
+            time.sleep(1)
+
+    def _submit_models_in_batch(self, *models: List[Model]) -> None:
+        _logger.info('%d models are submitted in batch', len(models))
         logical = self._build_logical(models)
 
         for opt in self._optimizers:
@@ -47,31 +119,51 @@ class CGOExecutionEngine(AbstractExecutionEngine):
 
         phy_models_and_placements = self._assemble(logical)
         for model, placement, grouped_models in phy_models_and_placements:
-            data = BaseGraphData(codegen.model_to_pytorch_script(model, placement=placement),
-                                 model.evaluator)
+            data = BaseGraphData(codegen.model_to_pytorch_script(model, placement=placement), model.evaluator)
+            trial_id = send_trial(data.dump())
+            # unique non-cpu devices used by the trial
+            self._trial_used_devices[trial_id] = list([_ for _ in set(placement.values()) if isinstance(_, GPUDevice)])
+
+            # currently, it is impossible for search strategy to submit models more than the number of available devices
+            for used_device in self._trial_used_devices[trial_id]:
+                self.available_devices.remove(used_device)  # used_device must be in self.available_devices
+            self._running_models[trial_id] = model
+
+            self._trial_to_original_models[trial_id] = []
             for m in grouped_models:
                 self._original_models[m.model_id] = m
                 self._original_model_to_multi_model[m.model_id] = model
-            self._running_models[send_trial(data.dump())] = model
-
-        # for model in models:
-        #     data = BaseGraphData(codegen.model_to_pytorch_script(model),
-        #                          model.config['trainer_module'], model.config['trainer_kwargs'])
-        #     self._running_models[send_trial(data.dump())] = model
+                self._trial_to_original_models[trial_id].append(m.model_id)
+                self._history.append(m)
 
     def list_models(self) -> Iterable[Model]:
-        raise NotImplementedError
-
-    def _assemble(self, logical_plan: LogicalPlan) -> List[Tuple[Model, PhysicalDevice]]:
-        # unique_models = set()
-        # for node in logical_plan.graph.nodes:
-        #     if node.graph.model not in unique_models:
-        #         unique_models.add(node.graph.model)
-        # return [m for m in unique_models]
-        grouped_models: List[Dict[Model, PhysicalDevice]] = AssemblePolicy().group(logical_plan)
+        return self._history
+
+    def _assemble(self, logical_plan: LogicalPlan) -> List[Tuple[Model, Dict[Node, GPUDevice], List[Model]]]:
+        # try to use the available_devices first so that it can be launched as early as possible
+        # if free devices are not enough to assemble all models in one trial, try all devices
+        if len(self.available_devices) > 0:
+            grouped_models: List[Dict[Model, GPUDevice]] = AssemblePolicy().group(logical_plan, self.available_devices)
+
+        if len(self.available_devices) == 0 or len(grouped_models) > 1:
+            grouped_models: List[Dict[Model, GPUDevice]] = AssemblePolicy().group(logical_plan, self.all_devices)
+
         phy_models_and_placements = []
         for multi_model in grouped_models:
             model, model_placement = logical_plan.assemble(multi_model)
+            assert isinstance(model.evaluator, Lightning), \
+                "cross-graph optimization only supports pytorch lighting as evaluator"
+            assert isinstance(model.evaluator.module, _MultiModelSupervisedLearningModule), \
+                "cross-graph optimization only support MultiModelSupervisedLearningModule"
+
+            # replace the module with a new instance whose n_models is set
+            # n_models must be set in __init__, otherwise it cannot be captured by serialize_cls
+            new_module_init_params = model.evaluator.module._init_parameters.copy()
+
+            # MultiModelSupervisedLearningModule hides n_models of _MultiModelSupervisedLearningModule from users
+            new_module_init_params['n_models'] = len(multi_model)
+            new_module = _MultiModelSupervisedLearningModule(**new_module_init_params)
+            model.evaluator.module = new_module
             phy_models_and_placements.append((model, model_placement, multi_model.keys()))
         return phy_models_and_placements
 
@@ -85,13 +177,14 @@ class CGOExecutionEngine(AbstractExecutionEngine):
     def register_graph_listener(self, listener: AbstractGraphListener) -> None:
         self._listeners.append(listener)
 
-    def _send_trial_callback(self, paramater: dict) -> None:
-        for listener in self._listeners:
-            listener.on_resource_used(0)  # FIXME: find the real resource id
+    # def _send_trial_callback(self, paramater: dict) -> None:
+    #     if len(self.available_devices) == 0:
+    #         _logger.warning('There is no available devices, but trial is submitted.')
+    #     _logger.debug('Resource used. Remaining: %d', len(self.available_devices))
 
-    def _request_trial_jobs_callback(self, num_trials: int) -> None:
-        for listener in self._listeners:
-            listener.on_resource_available([0] * num_trials)  # FIXME: find the real resource id
+    # def _request_trial_jobs_callback(self, num_trials: int) -> None:
+    #     self.resources += num_trials
+    #     _logger.info('on_resource_available: %d', self.resources)
 
     def _trial_end_callback(self, trial_id: int, success: bool) -> None:
         model = self._running_models[trial_id]
@@ -108,31 +201,40 @@ class CGOExecutionEngine(AbstractExecutionEngine):
                     original_model.status = ModelStatus.Failed
                 for listener in self._listeners:
                     listener.on_training_end(original_model, success)
+        self.available_devices.extend(self._trial_used_devices[trial_id])
+        self.available_devices = sorted(list(set(self.available_devices)))
+        del self._running_models[trial_id]
 
     def _intermediate_metric_callback(self, trial_id: int, metrics: MetricData) -> None:
-        # model = self._running_models[trial_id]
-        merged_metrics = dict(metrics)
+        merged_metrics = {}
+        for idx, _ in enumerate(metrics):
+            merged_metrics[self._trial_to_original_models[trial_id][idx]] = metrics[idx]
         for model_id in merged_metrics:
-            int_model_id = int(model_id)
-            self._original_models[int_model_id].intermediate_metrics.append(merged_metrics[model_id])
-            # model.intermediate_metrics.append(metrics)
+            self._original_models[model_id].intermediate_metrics.append(merged_metrics[model_id])
             for listener in self._listeners:
-                listener.on_intermediate_metric(self._original_models[int_model_id], merged_metrics[model_id])
+                listener.on_intermediate_metric(self._original_models[model_id], merged_metrics[model_id])
 
     def _final_metric_callback(self, trial_id: int, metrics: MetricData) -> None:
-        merged_metrics = dict(metrics)
-        for model_id in merged_metrics:
-            int_model_id = int(model_id)
-            self._original_models[int_model_id].intermediate_metrics.append(merged_metrics[model_id])
-            # model.intermediate_metrics.append(metrics)
-            for listener in self._listeners:
-                listener.on_metric(self._original_models[int_model_id], merged_metrics[model_id])
+        _logger.debug(metrics)
+
+        if isinstance(metrics, float):
+            self._listeners[0].on_metric(self._running_models[trial_id], metrics)
+        else:
+            merged_metrics = {}
+            for idx, _ in enumerate(metrics):
+                merged_metrics[self._trial_to_original_models[trial_id][idx]] = metrics[idx]
+            for model_id in merged_metrics:
+                self._original_models[model_id].metric = merged_metrics[model_id]
+                for listener in self._listeners:
+                    listener.on_metric(self._original_models[model_id], merged_metrics[model_id])
 
     def query_available_resource(self) -> List[WorkerInfo]:
-        raise NotImplementedError  # move the method from listener to here?
+        # the _queuing_jobs need to use available_devices first
+        return len(self.available_devices) - len(self._queuing_jobs)
 
     def budget_exhausted(self) -> bool:
-        raise NotImplementedError
+        advisor = get_advisor()
+        return advisor.stopping
 
     @classmethod
     def trial_execute_graph(cls) -> None:
@@ -141,20 +243,86 @@ class CGOExecutionEngine(AbstractExecutionEngine):
         """
         graph_data = BaseGraphData.load(receive_trial_parameters())
         _logger.info('CGO_ENGINE trial parameters received')
-        with open('_generated_model.py', 'w') as f:
+        random_str = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
+        file_name = f'_generated_model/{random_str}.py'
+        os.makedirs(os.path.dirname(file_name), exist_ok=True)
+        with open(file_name, 'w') as f:
             f.write(graph_data.model_script)
-        # with open('_debug_graph_data.json', 'w') as f:
-        #     json.dump(graph_data.dump(), f)
-        trainer_cls = utils.import_(graph_data.training_module)
-        model_cls = utils.import_(f"_generated_model.{graph_data.training_kwargs['model_cls']}")
-        trainer_instance = trainer_cls(model_cls(), graph_data.training_kwargs)
-        trainer_instance.fit()
+
+        trainer_instance = graph_data.evaluator
+        model_cls = utils.import_(f'_generated_model.{random_str}._model')
+
+        trainer_instance.fit(model_cls())
+        os.remove(file_name)
+
+
+def _remap_cuda_device(group_model: Dict[Model, GPUDevice]):
+    used_devices = {}
+    for m in group_model:
+        if group_model[m].node_id not in used_devices:
+            used_devices[group_model[m].node_id] = {}
+        if isinstance(group_model[m], GPUDevice):
+            if group_model[m].gpu_id not in used_devices[group_model[m].node_id]:
+                n_used_gpu_in_server = len(used_devices[group_model[m].node_id])
+                used_devices[group_model[m].node_id][group_model[m].gpu_id] = n_used_gpu_in_server
+            group_model[m].gpu_id = used_devices[group_model[m].node_id][group_model[m].gpu_id]
+    return group_model
 
 
 class AssemblePolicy:
     @staticmethod
-    def group(logical_plan):
+    def _is_related_node(model: Model, node: Node):
+        if isinstance(node, AbstractLogicalNode):
+            if model in node.related_models:
+                return True
+        else:
+            if model == node.graph.model:
+                return True
+        return False
+
+    @staticmethod
+    def _check_graph_connectivity(model: Model,
+                                  group_model: Dict[Model, GPUDevice],
+                                  logical_plan: LogicalPlan) -> bool:
+        for edge in logical_plan.logical_graph.edges:
+            if AssemblePolicy._is_related_node(model, edge.head) or \
+                    AssemblePolicy._is_related_node(model, edge.tail):
+                for grouped_model in group_model:
+                    if AssemblePolicy._is_related_node(grouped_model, edge.head) or \
+                            AssemblePolicy._is_related_node(grouped_model, edge.tail):
+                        return True
+        return False
+
+    @staticmethod
+    def _check_evaluator(new_model: Model, group_model: Dict[Model, GPUDevice]) -> bool:
+        if not (isinstance(new_model.evaluator, Lightning)
+                and isinstance(new_model.evaluator.module, MultiModelSupervisedLearningModule)):
+            return False
+        for m in group_model:
+            if not m.evaluator == new_model.evaluator:
+                return False
+        return True
+
+    @staticmethod
+    def group(logical_plan, available_devices):
+        # TODO: Packing multiple model in one GPU
+        # Currently, we only support one model per GPU
+        all_grouped_models = []
         group_model = {}
+        assert(len(available_devices) > 0)  # There should be at least 1 device, set in CGO_DEVICES
         for idx, m in enumerate(logical_plan.models):
-            group_model[m] = PhysicalDevice('server', f'cuda:{idx}')
-        return [group_model]
+            # models in one group should
+            # (1) not use more GPUs than available_devices
+            # (2) be connected in the logical plan (independent models should be assembled in multiple groups)
+            # (3) use same MultiModelSupervisedLearningModule
+            if len(group_model) > 0 and \
+                (AssemblePolicy._check_graph_connectivity(m, group_model, logical_plan) == False or
+                    AssemblePolicy._check_evaluator(m, group_model) == False):
+                all_grouped_models.append(_remap_cuda_device(group_model))
+                group_model = {}
+            group_model[m] = available_devices[idx % len(available_devices)]
+            if len(group_model) == len(available_devices) or \
+                    idx == len(logical_plan.models) - 1:
+                all_grouped_models.append(_remap_cuda_device(group_model))
+                group_model = {}
+        return all_grouped_models

nni/retiarii/execution/logical_optimizer/logical_plan.py

@@ -2,30 +2,30 @@
 # Licensed under the MIT license.
 
 import copy
-from typing import Dict, Tuple, List, Any
+from typing import Dict, Tuple, Any, Union
 
 from nni.retiarii.utils import uid
+from nni.common.device import GPUDevice
+
 from ...graph import Cell, Edge, Graph, Model, Node
 from ...operation import Operation, _IOPseudoOperation
 
 
-class PhysicalDevice:
-    def __init__(self, server: str, device: str):
-        self.server = server
-        self.device = device
-
-    def __eq__(self, o) -> bool:
-        return self.server == o.server and self.device == o.device
+class CPUDevice:
+    def __init__(self, node_id):
+        self.node_id = node_id
+        self.device = 'cpu'
 
-    def __hash__(self) -> int:
-        return hash(self.server + '_' + self.device)
+    def device_repr(self):
+        return "cpu"
 
 
 class AbstractLogicalNode(Node):
     def __init__(self, graph, node_id, name, operation, _internal=False):
         super().__init__(graph, node_id, name, operation, _internal=_internal)
+        self.related_models = []
 
-    def assemble(self, multi_model_placement: Dict[Model, PhysicalDevice]) -> Tuple[Node, PhysicalDevice]:
+    def assemble(self, multi_model_placement: Dict[Model, GPUDevice]) -> Tuple[Node, GPUDevice]:
         raise NotImplementedError
 
     def _fork_to(self, graph: Graph):
@@ -40,8 +40,7 @@ class LogicalGraph(Graph):
         nodes_dump = {}
         for node in self.hidden_nodes:
             if isinstance(node, OriginNode):
-                nodes_dump[f"{node.original_graph.model.model_id}_{node.name}"] = node._dump(
-                )
+                nodes_dump[f"{node.original_graph.model.model_id}_{node.name}"] = node._dump()
             else:
                 nodes_dump[f"{node.graph.model.model_id}_{node.name}"] = node._dump()
 
@@ -93,7 +92,7 @@ class OriginNode(AbstractLogicalNode):
         self.original_graph = original_graph
         self.original_node = original_node
 
-    def assemble(self, multi_model_placement: Dict[Model, PhysicalDevice]) -> Tuple[Node, PhysicalDevice]:
+    def assemble(self, multi_model_placement: Dict[Model, GPUDevice]) -> Tuple[Node, GPUDevice]:
         model_id = self.original_node.graph.model.model_id
         new_node = Node(self.original_node.graph, self.original_node.id,
                         f"M_{model_id}_" +
@@ -137,30 +136,32 @@ class LogicalPlan:
         for edge in from_graph.edges:
             new_head = id_to_new_node[edge.head.id]
             new_tail = id_to_new_node[edge.tail.id]
-            Edge((new_head, edge.head_slot), (new_tail,
-                                              edge.tail_slot), _internal=True)._register()
+            Edge((new_head, edge.head_slot), (new_tail, edge.tail_slot), _internal=True)._register()
 
-    def assemble(self, multi_model_placement: Dict[Model, PhysicalDevice]) \
-            -> Tuple[Model, Dict[Node, PhysicalDevice], List[Model]]:
-        phy_model = Model(_internal=True)  # self.lp_model.fork()
+    def assemble(self, multi_model_placement: Dict[Model, GPUDevice]) \
+            -> Tuple[Model, Dict[Node, Union[GPUDevice, CPUDevice]]]:
+        phy_model = Model(_internal=True)
         phy_graph = self.lp_model.root_graph._fork_to(phy_model)
-
-        # Add a flag to mark multi-model in graph json.
-        # Multi-model has a list of training configs in kwargs['model_kwargs']
-        if len(multi_model_placement) > 1:
-            phy_model.evaluator.kwargs['is_multi_model'] = True
-            phy_model.evaluator.kwargs['model_cls'] = phy_graph.name
-            phy_model.evaluator.kwargs['model_kwargs'] = []
-            # FIXME: allow user to specify
-            phy_model.evaluator.module = 'nni.retiarii.trainer.pytorch.PyTorchMultiModelTrainer'
+        phy_graph._rename_graph(phy_graph.name, "_model")
 
         # merge sub-graphs
         for model in multi_model_placement:
+            if phy_model.evaluator is None and model.evaluator is not None:
+                phy_model.evaluator = model.evaluator
             for graph_name in model.graphs:
                 if graph_name != model._root_graph_name:
-                    model.graphs[graph_name]._fork_to(
+                    new_graph = model.graphs[graph_name]._fork_to(
                         phy_model, name_prefix=f'M_{model.model_id}_')
 
+                    # prefix of M_ of hidden_nodes name in non-root graphs is added here
+                    for new_node in new_graph.hidden_nodes:
+                        if isinstance(new_node.operation, Cell):
+                            old_cell_name = new_node.operation.cell_name
+                            new_node.operation = copy.deepcopy(new_node.operation)
+                            new_node.operation.cell_name = f'M_{model.model_id}_{old_cell_name}'
+
+        assert(phy_model.evaluator is not None)
+
         # When replace logical nodes, merge the training configs when
         # input/output nodes are replaced.
         evaluator_slot = {}  # Model ID -> Slot ID
@@ -169,6 +170,9 @@ class LogicalPlan:
         # Replace all logical nodes to executable physical nodes
         hidden_nodes = phy_graph.hidden_nodes.copy()
         node_placements = {}
+
+        added_models = []
+
         for node in hidden_nodes:
             if isinstance(node, OriginNode):
                 model_id = node.original_graph.model.model_id
@@ -185,12 +189,9 @@ class LogicalPlan:
                 if isinstance(new_node.operation, _IOPseudoOperation):
                     model_id = new_node.graph.model.model_id
                     if model_id not in evaluator_slot:
-                        phy_model.evaluator.kwargs['model_kwargs'].append(new_node.graph.model.evaluator.kwargs.copy())
-                        evaluator_slot[model_id] = len(phy_model.evaluator.kwargs['model_kwargs']) - 1
+                        added_models.append(model_id)
+                        evaluator_slot[model_id] = len(added_models) - 1
                         slot = evaluator_slot[model_id]
-                        phy_model.evaluator.kwargs['model_kwargs'][slot]['model_id'] = model_id
-                        phy_model.evaluator.kwargs['model_kwargs'][slot]['use_input'] = False
-                        phy_model.evaluator.kwargs['model_kwargs'][slot]['use_output'] = False
                     else:
                         slot = evaluator_slot[model_id]
                     # If a model's inputs/outputs are not used in the multi-model
@@ -199,37 +200,47 @@ class LogicalPlan:
                     # an input/output of a model is used in a multi-model
                     if new_node.operation.type == '_inputs':
                         input_slot_mapping[new_node] = slot
-                        phy_model.evaluator.kwargs['model_kwargs'][slot]['use_input'] = True
                     if new_node.operation.type == '_outputs':
                         output_slot_mapping[new_node] = slot
-                        phy_model.evaluator.kwargs['model_kwargs'][slot]['use_output'] = True
 
                 self.node_replace(node, new_node)
 
+                # name prefix of M_ of cells in hidden_nodes of root graphs is added here
+                # FIXME: merge this rename with non-root graph, only do once.
                 if isinstance(new_node.operation, Cell):
                     old_cell_name = new_node.operation.cell_name
                     new_node.operation = copy.deepcopy(new_node.operation)
                     new_node.operation.cell_name = f'M_{model_id}_{old_cell_name}'
-                node_placements[new_node] = placement
+
+                # input should be at CPU, move it to GPU first if necessary
+                if isinstance(new_node.operation, _IOPseudoOperation) and new_node.operation.type == '_inputs':
+                    # hack: only support single_server
+                    node_placements[new_node] = CPUDevice(node_id=placement.node_id)
+                else:
+                    node_placements[new_node] = placement
 
                 node.remove()
 
         # If two nodes are placed on different devices, use ToDevice op to copy the node
         existing_edges = phy_graph.edges.copy()
         # Avoid a node is copied multiple times on the same device
-        copied_op: Dict[Tuple(Node, PhysicalDevice), Node] = {}
+        copied_op: Dict[Tuple(Node, Union[GPUDevice, CPUDevice]), Node] = {}
         for edge in existing_edges:
             head_placement = node_placements[edge.head]
             tail_placement = node_placements[edge.tail]
             if head_placement != tail_placement:
-                if head_placement.server != tail_placement.server:
+                if head_placement.node_id != tail_placement.node_id:
                     raise ValueError('Cross-server placement is not supported.')
                 # Same server different devices
                 if (edge.head, tail_placement) in copied_op:
                     to_node = copied_op[(edge.head, tail_placement)]
                 else:
-                    to_operation = Operation.new('ToDevice', {"device": tail_placement.device})
-                    to_node = Node(phy_graph, uid(), edge.head.name + "_to_" + edge.tail.name, to_operation)._register()
+                    dst_name = edge.head.name + "_to_" + edge.tail.name
+                    to_operation = Operation.new(
+                        'ToDevice', {
+                            "device": tail_placement.device_repr(), "src": (
+                                edge.head.name, edge.head_slot), "dst": dst_name})
+                    to_node = Node(phy_graph, uid(), dst_name, to_operation)._register()
                     Edge((edge.head, edge.head_slot), (to_node, None), _internal=True)._register()
                     copied_op[(edge.head, tail_placement)] = to_node
                 edge.head = to_node

nni/retiarii/execution/logical_optimizer/opt_dedup_input.py

@@ -4,23 +4,28 @@
 from typing import List, Dict, Tuple
 
 from nni.retiarii.utils import uid
+from nni.retiarii.evaluator.pytorch.cgo.evaluator import MultiModelSupervisedLearningModule
+from nni.common.device import GPUDevice
+
 from ...graph import Graph, Model, Node
 from .interface import AbstractOptimizer
 from .logical_plan import (AbstractLogicalNode, LogicalGraph, LogicalPlan,
-                           OriginNode, PhysicalDevice)
+                           OriginNode)
+
 
-_supported_training_modules = ['nni.retiarii.trainer.pytorch.PyTorchImageClassificationTrainer']
+_supported_evaluators = [MultiModelSupervisedLearningModule]
 
 
 class DedupInputNode(AbstractLogicalNode):
     def __init__(self, logical_graph: LogicalGraph, node_id: int,
                  nodes_to_dedup: List[Node], _internal=False):
         super().__init__(logical_graph, node_id,
-                         "Dedup_"+nodes_to_dedup[0].name,
+                         "Dedup_" + nodes_to_dedup[0].name,
                          nodes_to_dedup[0].operation)
         self.origin_nodes: List[OriginNode] = nodes_to_dedup.copy()
+        self.related_models = [_.original_graph.model for _ in self.origin_nodes]
 
-    def assemble(self, multi_model_placement: Dict[Model, PhysicalDevice]) -> Tuple[Node, PhysicalDevice]:
+    def assemble(self, multi_model_placement: Dict[Model, GPUDevice]) -> Tuple[Node, GPUDevice]:
         for node in self.origin_nodes:
             if node.original_graph.model in multi_model_placement:
                 new_node = Node(node.original_graph, node.id,
@@ -41,6 +46,12 @@ class DedupInputOptimizer(AbstractOptimizer):
     def __init__(self) -> None:
         pass
 
+    def _check_supported_evaluator(self, evaluator):
+        for e in _supported_evaluators:
+            if isinstance(evaluator, e):
+                return True
+        return False
+
     def _check_deduplicate_by_node(self, root_node, node_to_check):
         if root_node == node_to_check:
             return True
@@ -48,7 +59,7 @@ class DedupInputOptimizer(AbstractOptimizer):
             node_to_check.operation.type == '_inputs' and \
                 isinstance(root_node, OriginNode) and \
                 isinstance(node_to_check, OriginNode):
-            if root_node.original_graph.model.evaluator.module not in _supported_training_modules:
+            if self._check_supported_evaluator(root_node.original_graph.model.evaluator):
                 return False
             if root_node.original_graph.model.evaluator == node_to_check.original_graph.model.evaluator:
                 return True
@@ -68,7 +79,7 @@ class DedupInputOptimizer(AbstractOptimizer):
                     continue
                 root_node = node
                 break
-            if root_node == None:
+            if root_node is None:
                 break  # end of convert
             else:
                 nodes_to_dedup = []

nni/retiarii/experiment/pytorch.py

@@ -50,8 +50,11 @@ class RetiariiExeConfig(ConfigBase):
     trial_code_directory: PathLike = '.'
     trial_concurrency: int
     trial_gpu_number: int = 0
+    devices: Optional[List[Union[str, GPUDevice]]] = None
     max_experiment_duration: Optional[str] = None
     max_trial_number: Optional[int] = None
+    max_concurrency_cgo: Optional[int] = None
+    batch_waiting_time: Optional[int] = None
     nni_manager_ip: Optional[str] = None
     debug: bool = False
     log_level: Optional[str] = None
@@ -134,11 +137,12 @@ def preprocess_model(base_model, trainer, applied_mutators, full_ir=True, dummy_
 
     if mutators is not None and applied_mutators:
         raise RuntimeError('Have not supported mixed usage of LayerChoice/InputChoice and mutators, '
-                            'do not use mutators when you use LayerChoice/InputChoice')
+                           'do not use mutators when you use LayerChoice/InputChoice')
     if mutators is not None:
         applied_mutators = mutators
     return base_model_ir, applied_mutators
 
+
 def debug_mutated_model(base_model, trainer, applied_mutators):
     """
     Locally run only one trial without launching an experiment for debug purpose, then exit.
@@ -189,7 +193,7 @@ class RetiariiExperiment(Experiment):
         self.strategy.run(base_model_ir, self.applied_mutators)
         _logger.info('Strategy exit')
         # TODO: find out a proper way to show no more trial message on WebUI
-        #self._dispatcher.mark_experiment_as_ending()
+        # self._dispatcher.mark_experiment_as_ending()
 
     def start(self, port: int = 8080, debug: bool = False) -> None:
         """
@@ -205,14 +209,18 @@ class RetiariiExperiment(Experiment):
         """
         atexit.register(self.stop)
 
-        devices = self._construct_devices()
         # we will probably need a execution engine factory to make this clean and elegant
         if self.config.execution_engine == 'base':
             from ..execution.base import BaseExecutionEngine
             engine = BaseExecutionEngine()
         elif self.config.execution_engine == 'cgo':
             from ..execution.cgo_engine import CGOExecutionEngine
-            engine = CGOExecutionEngine(devices = devices)
+            # assert self.config.trial_gpu_number==1, "trial_gpu_number must be 1 to use CGOExecutionEngine"
+            assert self.config.batch_waiting_time is not None
+            devices = self._construct_devices()
+            engine = CGOExecutionEngine(devices,
+                                        max_concurrency=self.config.max_concurrency_cgo,
+                                        batch_waiting_time=self.config.batch_waiting_time)
         elif self.config.execution_engine == 'py':
             from ..execution.python import PurePythonExecutionEngine
             engine = PurePythonExecutionEngine()
@@ -315,7 +323,7 @@ class RetiariiExperiment(Experiment):
         if self._dispatcher_thread is not None:
             self._dispatcher.stopping = True
             self._dispatcher_thread.join(timeout=1)
- 
+
         if self.id is not None:
             nni.runtime.log.stop_experiment_log(self.id)
         if self._proc is not None:

nni/retiarii/graph.py

@@ -410,7 +410,7 @@ class Graph:
         return self is other
 
     def _fork_to(self, model: Model, name_prefix='') -> 'Graph':
-        new_graph = Graph(model, self.id, name_prefix+self.name, _internal=True)._register()
+        new_graph = Graph(model, self.id, name_prefix + self.name, _internal=True)._register()
         # TODO: use node copy instead
         new_graph.input_node.operation.io_names = self.input_node.operation.io_names
         new_graph.output_node.operation.io_names = self.output_node.operation.io_names
@@ -458,6 +458,11 @@ class Graph:
         self.model.graphs[self.name] = self
         return self
 
+    def _rename_graph(self, old_name, new_name):
+        self.model.graphs[old_name].name = new_name
+        self.model.graphs[new_name] = self.model.graphs[old_name]
+        del self.model.graphs[old_name]
+
     @staticmethod
     def _load(model: Model, name: str, ir: Any) -> 'Graph':
         graph = Graph(model, uid(), name, _internal=True)

nni/retiarii/integration.py

@@ -158,4 +158,4 @@ class RetiariiAdvisor(MsgDispatcherBase):
                 return value['default']
             else:
                 return value
-        return value
+        return value
\ No newline at end of file

nni/retiarii/operation.py

@@ -98,6 +98,10 @@ class PyTorchOperation(Operation):
             if hasattr(subclass, '_ori_type_name') and \
                 subclass_name in subclass._ori_type_name:
                 return subclass
+        for subclass in cls.__subclasses__():
+            if hasattr(subclass, '_artificial_op_name') and \
+                subclass_name in subclass._artificial_op_name:
+                return subclass
         return cls
 
     @classmethod

nni/retiarii/operation_def/torch_op_def.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-from typing import (Any, List)
+from typing import (Any, Dict, List)
 
 import torch
 
@@ -32,21 +32,27 @@ scalar_type_to_pytorch_type = [
     'torch.bool',         # 11
 ]
 
+
 class NoOpIdentity(PyTorchOperation):
     """
     this operator type is added by us
     """
     _ori_type_name = ['noop_identity']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = {", ".join(inputs)}'
 
+
 class ModuleOperator(PyTorchOperation):
     _ori_type_name = ['ModuleOperator', 'shared']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = self.{field}({", ".join(inputs)})'
 
+
 class FunctionalOperator(PyTorchOperation):
     _ori_type_name = ['FunctionalOperator']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         func_name = self.type[len('Function.'):]
         if not hasattr(torch.nn.functional, func_name):
@@ -54,8 +60,10 @@ class FunctionalOperator(PyTorchOperation):
                                f'{func_name} is not in it.')
         return f'{output} = F.{func_name}({", ".join(inputs)})'
 
+
 class PrimConstant(PyTorchOperation):
     _ori_type_name = ['prim::Constant']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         # TODO: refactor this part, maybe we can remove the code gen of prim::Constant
         # TODO: deal with all the types
@@ -75,63 +83,83 @@ class PrimConstant(PyTorchOperation):
         else:
             raise RuntimeError(f'unsupported type of prim::Constant: {self.parameters["type"]}')
 
+
 class PrimListConstruct(PyTorchOperation):
     _ori_type_name = ['prim::ListConstruct']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = [{", ".join(inputs)}]'
 
+
 class PrimListUnpack(PyTorchOperation):
     _ori_type_name = ['prim::ListUnpack']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = {inputs[0]}'
 
+
 class PrimTupleConstruct(PyTorchOperation):
     _ori_type_name = ['prim::TupleConstruct']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = ({", ".join(inputs)})'
 
+
 class PrimTupleUnpack(PyTorchOperation):
     _ori_type_name = ['prim::TupleUnpack']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         # have single output here, because the following code uses index to access the unpacked values
         assert len(inputs) == 1
         return f'{output} = {inputs[0]}'
 
+
 class PrimGetAttr(PyTorchOperation):
     _ori_type_name = ['prim::GetAttr']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         if self.parameters['value'] is not None:
             return f"{output} = {self.parameters['value']}"
         else:
             return f"{output} = {self.parameters['input']}.{self.parameters['name']}"
 
+
 class SimpleMember(PyTorchOperation):
     _ori_type_name = ['prim::is_cuda', 'prim::data']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         member_name = self.type.split('::')[-1]
         return f'{output} = {inputs[0]}.{member_name}'
 
+
 class AtenContiguous(PyTorchOperation):
     _ori_type_name = ['aten::contiguous']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         # defined in pytorch/c10/core/MemoryFormat.h
         assert inputs_value[1] in [0, 1, 2]
         return f'{output} = {inputs[0]}.contiguous(memory_format={mem_format[inputs_value[1]]})'
 
+
 class AtenGetitem(PyTorchOperation):
     _ori_type_name = ['aten::__getitem__']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         assert len(inputs) == 2
         return f'{output} = {inputs[0]}[{inputs[1]}]'
 
+
 class AtenAppend(PyTorchOperation):
     _ori_type_name = ['aten::append']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         assert len(inputs) == 2
         return f'_, {output} = {inputs[0]}.append({inputs[1]}), {inputs[0]}'
 
+
 class MergedSlice(PyTorchOperation):
     _ori_type_name = ['MergedSlice']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         if (len(inputs) - 1) % 4 == 0:
             slices = []
@@ -148,23 +176,30 @@ class MergedSlice(PyTorchOperation):
 
 # the following Aten classes means these aten ops are not in torch.Tensor
 
+
 class AtenBool(PyTorchOperation):
     _ori_type_name = ['aten::Bool']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = bool({inputs[0]})'
 
+
 class AtenNot(PyTorchOperation):
     _ori_type_name = ['aten::__not__']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = not {inputs[0]}'
 
+
 class AtenCat(PyTorchOperation):
     _ori_type_name = ['aten::cat']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         assert len(inputs) == 2
         return f'{output} = torch.cat({inputs[0]}, dim={inputs[1]})'
 
-#====================================
+# ====================================
+
 
 class AtenTensors(PyTorchOperation):
     _ori_type_name = ['aten::full', 'aten::full_like', 'aten::empty_like',
@@ -209,20 +244,26 @@ class AtenTensors(PyTorchOperation):
         else:
             return f'{output} = {inputs[0]}.{op_name}({", ".join(args_list[1:])})'
 
-#====================================
+# ====================================
+
 
 class AtenFloordiv(PyTorchOperation):
     _ori_type_name = ['aten::floordiv']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = {inputs[0]} // {inputs[1]}'
 
+
 class AtenLen(PyTorchOperation):
     _ori_type_name = ['aten::len']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = len({inputs[0]})'
 
+
 class AtenIntImplicit(PyTorchOperation):
     _ori_type_name = ['aten::IntImplicit', 'aten::Float', 'aten::Int', 'aten::ScalarImplicit']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         if self.type.endswith('Implicit'):
             return f'{output} = {inputs[0]}'
@@ -231,11 +272,14 @@ class AtenIntImplicit(PyTorchOperation):
         elif self.type == 'aten::Float':
             return f'{output} = float({inputs[0]})'
 
+
 class AtenIndex(PyTorchOperation):
     _ori_type_name = ['aten::index']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = {inputs[0]}[{inputs[1]}]'
 
+
 ManuallyChooseDef = {
     'aten::flatten': [('start_dim', 'int', '0'), ('end_dim', 'int', '-1')],
     'aten::split': [('split_size', 'int', 'None'), ('dim', 'int', '0')],
@@ -248,21 +292,24 @@ ManuallyChooseDef = {
 }
 
 TensorOpExceptions = {
-    'aten::sub': lambda output, inputs: f'{output} = {inputs[0]} - {inputs[1]}', # example: x.size(1) - 3
-    'aten::add': lambda output, inputs: f'{output} = {inputs[0]} + {inputs[1]}' # example: input.shape[0] + 5
+    'aten::sub': lambda output, inputs: f'{output} = {inputs[0]} - {inputs[1]}',  # example: x.size(1) - 3
+    'aten::add': lambda output, inputs: f'{output} = {inputs[0]} + {inputs[1]}'  # example: input.shape[0] + 5
 }
 
 TorchOpExclude = ['aten::Size', 'aten::as_tensor', 'aten::device',
                   'aten::manual_seed', 'aten::quantized_gru', 'aten::quantized_lstm',
                   'aten::save', 'aten::tensor', 'aten::wait'
-]
+                  ]
+
 
 def _hidden(name):
     return name.startswith('_') and not name.startswith('__')
 
+
 def _emit_args(args):
     # filter out the `out` argument here
-    return [(arg.name, str(arg.type), str(arg.default_value)) for arg in args] #  if arg.name != 'out'
+    return [(arg.name, str(arg.type), str(arg.default_value)) for arg in args]  # if arg.name != 'out'
+
 
 def _get_tensor_ops():
     def is_tensor_method(schema):
@@ -291,6 +338,7 @@ def _get_tensor_ops():
 
     return op_args.keys(), op_args
 
+
 def _get_torch_ops():
     torch_op_args = {}
     for mod in torch.jit._builtins._modules_containing_builtins:
@@ -316,6 +364,7 @@ def _get_torch_ops():
 
     return torch_op_args.keys(), torch_op_args
 
+
 def _get_torch_ops_exclude_tensor_ops():
     tensor_op_names, _ = _get_tensor_ops()
     torch_op_names, torch_ops = _get_torch_ops()
@@ -330,6 +379,7 @@ def _get_torch_ops_exclude_tensor_ops():
 
     return torch_exclude_ops.keys(), torch_exclude_ops
 
+
 class TensorOps(PyTorchOperation):
     """
     corresponding to _get_tensor_ops in torch.jit.supported_ops
@@ -346,7 +396,7 @@ class TensorOps(PyTorchOperation):
                 name = ','.join([arg[0] for arg in each])
                 concated_names.append(name)
             for i in range(len(concated_names) - 1):
-                if concated_names[i] != concated_names[i+1]:
+                if concated_names[i] != concated_names[i + 1]:
                     return False
             return True
 
@@ -383,6 +433,7 @@ class TensorOps(PyTorchOperation):
         args_str = ', '.join([f'{name}={inputs[i+1]}' for i, (name, t, default) in enumerate(matched_args)])
         return f'{output} = {inputs[0]}.{op_name}({args_str})'
 
+
 class TorchOps(PyTorchOperation):
     """
     corresponding to _get_nn_functional_ops in torch.jit.supported_ops
@@ -400,7 +451,7 @@ class TorchOps(PyTorchOperation):
                 name = ','.join([arg[0] for arg in each])
                 concated_names.append(name)
             for i in range(len(concated_names) - 1):
-                if concated_names[i] != concated_names[i+1]:
+                if concated_names[i] != concated_names[i + 1]:
                     return False
             return True
 
@@ -424,19 +475,36 @@ class TorchOps(PyTorchOperation):
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         matched_args = TorchOps._get_matched_args(self.type, inputs)
         op_name = self.type.split('::')[-1]
-        args_str = ', '.join([f'{name}={inputs[i]}' if t.startswith('Optional[') else f'{inputs[i]}' \
-            for i, (name, t, default) in enumerate(matched_args)])
+        args_str = ', '.join([f'{name}={inputs[i]}' if t.startswith('Optional[') else f'{inputs[i]}'
+                              for i, (name, t, default) in enumerate(matched_args)])
         return f'{output} = torch.{op_name}({args_str})'
 
+
 class AtenAvgpool2d(PyTorchOperation):
     # NOTE: it is not included in the above aten ops for unkown reason
     _ori_type_name = ['aten::avg_pool2d']
+
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
         return f'{output} = F.avg_pool2d({", ".join(inputs)})'
 
+
+class ToDevice(PyTorchOperation):
+    _artificial_op_name = "ToDevice"
+
+    def __init__(self, type_name: str, parameters: Dict[str, Any], _internal: bool = False):
+        self.type = "ToDevice"
+        self.device = parameters['device']
+        self.src = parameters['src']
+        self.dst = parameters['dst']
+
+    def __repr__(self):
+        return f'to("{self.device}")'
+
+    def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any]) -> str:
+        return f'{output} = {inputs[0]}.to("{self.device}")'
 class AtenDet(PyTorchOperation):
     # for torch 1.9
     # NOTE: it is not included in the above aten ops, maybe because torch.det is alias for torch.linalg.det
     _ori_type_name = ['aten::linalg_det']
     def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
-        return f'{output} = torch.det({inputs[0]})'
\ No newline at end of file
+        return f'{output} = torch.det({inputs[0]})'

test/retiarii_test/cgo/darts_model.py

+from collections import OrderedDict
+from typing import (List, Optional)
+
+import torch
+import torch.nn as torch_nn
+#sys.path.append(str(Path(__file__).resolve().parents[2]))
+
+import ops
+import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import basic_unit
+
+@basic_unit
+class AuxiliaryHead(nn.Module):
+    """ Auxiliary head in 2/3 place of network to let the gradient flow well """
+
+    def __init__(self, input_size, C, n_classes):
+        """ assuming input size 7x7 or 8x8 """
+        assert input_size in [7, 8]
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.ReLU(inplace=True),
+            nn.AvgPool2d(5, stride=input_size - 5, padding=0, count_include_pad=False),  # 2x2 out
+            nn.Conv2d(C, 128, kernel_size=1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 768, kernel_size=2, bias=False),  # 1x1 out
+            nn.BatchNorm2d(768),
+            nn.ReLU(inplace=True)
+        )
+        self.linear = nn.Linear(768, n_classes)
+
+    def forward(self, x):
+        out = self.net(x)
+        out = out.view(out.size(0), -1)  # flatten
+        logits = self.linear(out)
+        return logits
+
+class Node(nn.Module):
+    def __init__(self, node_id, num_prev_nodes, channels, num_downsample_connect):
+        super().__init__()
+        self.ops = nn.ModuleList()
+        choice_keys = []
+        for i in range(num_prev_nodes):
+            stride = 2 if i < num_downsample_connect else 1
+            choice_keys.append("{}_p{}".format(node_id, i))
+            self.ops.append(
+                nn.LayerChoice([
+                    ops.PoolBN('max', channels, 3, stride, 1, affine=False),
+                    ops.PoolBN('avg', channels, 3, stride, 1, affine=False),
+                    nn.Identity() if stride == 1 else ops.FactorizedReduce(channels, channels, affine=False),
+                    ops.SepConv(channels, channels, 3, stride, 1, affine=False),
+                    ops.SepConv(channels, channels, 5, stride, 2, affine=False),
+                    ops.DilConv(channels, channels, 3, stride, 2, 2, affine=False),
+                    ops.DilConv(channels, channels, 5, stride, 4, 2, affine=False)
+                ]))
+        self.drop_path = ops.DropPath()
+        self.input_switch = nn.InputChoice(n_candidates=num_prev_nodes, n_chosen=2)
+
+    def forward(self, prev_nodes: List['Tensor']) -> 'Tensor':
+        #assert self.ops.__len__() == len(prev_nodes)
+        #out = [op(node) for op, node in zip(self.ops, prev_nodes)]
+        out = []
+        for i, op in enumerate(self.ops):
+            out.append(op(prev_nodes[i]))
+        #out = [self.drop_path(o) if o is not None else None for o in out]
+        return self.input_switch(out)
+
+class Cell(nn.Module):
+
+    def __init__(self, n_nodes, channels_pp, channels_p, channels, reduction_p, reduction):
+        super().__init__()
+        self.reduction = reduction
+        self.n_nodes = n_nodes
+
+        # If previous cell is reduction cell, current input size does not match with
+        # output size of cell[k-2]. So the output[k-2] should be reduced by preprocessing.
+        if reduction_p:
+            self.preproc0 = ops.FactorizedReduce(channels_pp, channels, affine=False)
+        else:
+            self.preproc0 = ops.StdConv(channels_pp, channels, 1, 1, 0, affine=False)
+        self.preproc1 = ops.StdConv(channels_p, channels, 1, 1, 0, affine=False)
+
+        # generate dag
+        self.mutable_ops = nn.ModuleList()
+        for depth in range(2, self.n_nodes + 2):
+            self.mutable_ops.append(Node("{}_n{}".format("reduce" if reduction else "normal", depth),
+                                         depth, channels, 2 if reduction else 0))
+
+    def forward(self, s0, s1):
+        # s0, s1 are the outputs of previous previous cell and previous cell, respectively.
+        tensors = [self.preproc0(s0), self.preproc1(s1)]
+        new_tensors = []
+        for node in self.mutable_ops:
+            tmp = tensors + new_tensors
+            cur_tensor = node(tmp)
+            new_tensors.append(cur_tensor)
+
+        output = torch.cat(new_tensors, dim=1)
+        return output
+
+class CNN(nn.Module):
+
+    def __init__(self, input_size, in_channels, channels, n_classes, n_layers, n_nodes=4,
+                 stem_multiplier=3, auxiliary=False):
+        super().__init__()
+        self.in_channels = in_channels
+        self.channels = channels
+        self.n_classes = n_classes
+        self.n_layers = n_layers
+        self.aux_pos = 2 * n_layers // 3 if auxiliary else -1
+
+        c_cur = stem_multiplier * self.channels
+        self.stem = nn.Sequential(
+            nn.Conv2d(in_channels, c_cur, 3, 1, 1, bias=False),
+            nn.BatchNorm2d(c_cur)
+        )
+
+        # for the first cell, stem is used for both s0 and s1
+        # [!] channels_pp and channels_p is output channel size, but c_cur is input channel size.
+        channels_pp, channels_p, c_cur = c_cur, c_cur, channels
+
+        self.cells = nn.ModuleList()
+        reduction_p, reduction = False, False
+        for i in range(n_layers):
+            reduction_p, reduction = reduction, False
+            # Reduce featuremap size and double channels in 1/3 and 2/3 layer.
+            if i in [n_layers // 3, 2 * n_layers // 3]:
+                c_cur *= 2
+                reduction = True
+
+            cell = Cell(n_nodes, channels_pp, channels_p, c_cur, reduction_p, reduction)
+            self.cells.append(cell)
+            c_cur_out = c_cur * n_nodes
+            channels_pp, channels_p = channels_p, c_cur_out
+
+            #if i == self.aux_pos:
+            #    self.aux_head = AuxiliaryHead(input_size // 4, channels_p, n_classes)
+
+        self.gap = nn.AdaptiveAvgPool2d(1)
+        self.linear = nn.Linear(channels_p, n_classes)
+
+    def forward(self, x):
+        s0 = s1 = self.stem(x)
+
+        #aux_logits = None
+        for i, cell in enumerate(self.cells):
+            s0, s1 = s1, cell(s0, s1)
+            #if i == self.aux_pos and self.training:
+            #    aux_logits = self.aux_head(s1)
+
+        out = self.gap(s1)
+        out = out.view(out.size(0), -1)  # flatten
+        logits = self.linear(out)
+
+        #if aux_logits is not None:
+        #    return logits, aux_logits
+        return logits
+
+    def drop_path_prob(self, p):
+        for module in self.modules():
+            if isinstance(module, ops.DropPath):
+                module.p = p
+
+if __name__ == '__main__':
+    base_model = CNN(32, 3, 16, 10, 8)

test/retiarii_test/cgo/ops.py

+import torch
+import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import basic_unit
+
+@basic_unit
+class DropPath(nn.Module):
+    def __init__(self, p=0.):
+        """
+        Drop path with probability.
+        Parameters
+        ----------
+        p : float
+            Probability of an path to be zeroed.
+        """
+        super().__init__()
+        self.p = p
+
+    def forward(self, x):
+        if self.training and self.p > 0.:
+            keep_prob = 1. - self.p
+            # per data point mask
+            mask = torch.zeros((x.size(0), 1, 1, 1), device=x.device).bernoulli_(keep_prob)
+            return x / keep_prob * mask
+
+        return x
+
+@basic_unit
+class PoolBN(nn.Module):
+    """
+    AvgPool or MaxPool with BN. `pool_type` must be `max` or `avg`.
+    """
+    def __init__(self, pool_type, C, kernel_size, stride, padding, affine=True):
+        super().__init__()
+        if pool_type.lower() == 'max':
+            self.pool = nn.MaxPool2d(kernel_size, stride, padding)
+        elif pool_type.lower() == 'avg':
+            self.pool = nn.AvgPool2d(kernel_size, stride, padding, count_include_pad=False)
+        else:
+            raise ValueError()
+
+        self.bn = nn.BatchNorm2d(C, affine=affine)
+
+    def forward(self, x):
+        out = self.pool(x)
+        out = self.bn(out)
+        return out
+
+@basic_unit
+class StdConv(nn.Module):
+    """
+    Standard conv: ReLU - Conv - BN
+    """
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv2d(C_in, C_out, kernel_size, stride, padding, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+@basic_unit
+class FacConv(nn.Module):
+    """
+    Factorized conv: ReLU - Conv(Kx1) - Conv(1xK) - BN
+    """
+    def __init__(self, C_in, C_out, kernel_length, stride, padding, affine=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv2d(C_in, C_in, (kernel_length, 1), stride, padding, bias=False),
+            nn.Conv2d(C_in, C_out, (1, kernel_length), stride, padding, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+@basic_unit
+class DilConv(nn.Module):
+    """
+    (Dilated) depthwise separable conv.
+    ReLU - (Dilated) depthwise separable - Pointwise - BN.
+    If dilation == 2, 3x3 conv => 5x5 receptive field, 5x5 conv => 9x9 receptive field.
+    """
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv2d(C_in, C_in, kernel_size, stride, padding, dilation=dilation, groups=C_in,
+                      bias=False),
+            nn.Conv2d(C_in, C_out, 1, stride=1, padding=0, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+@basic_unit
+class SepConv(nn.Module):
+    """
+    Depthwise separable conv.
+    DilConv(dilation=1) * 2.
+    """
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            DilConv(C_in, C_in, kernel_size, stride, padding, dilation=1, affine=affine),
+            DilConv(C_in, C_out, kernel_size, 1, padding, dilation=1, affine=affine)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+@basic_unit
+class FactorizedReduce(nn.Module):
+    """
+    Reduce feature map size by factorized pointwise (stride=2).
+    """
+    def __init__(self, C_in, C_out, affine=True):
+        super().__init__()
+        self.relu = nn.ReLU()
+        self.conv1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
+        self.conv2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
+        self.bn = nn.BatchNorm2d(C_out, affine=affine)
+
+    def forward(self, x):
+        x = self.relu(x)
+        out = torch.cat([self.conv1(x), self.conv2(x[:, :, 1:, 1:])], dim=1)
+        out = self.bn(out)
+        return out