[Retiarii] Pure-python execution engine (#3605)

docs/en_US/NAS/retiarii/Advanced.rst

 Advanced Tutorial
 =================
 
-This document includes two parts. The first part explains the design decision of ``@basic_unit`` and ``serializer``. The second part is the tutorial of how to write a model space with mutators.
+Pure-python execution engine (experimental)
+-------------------------------------------
+
+If you are experiencing issues with TorchScript, or the generated model code by Retiarii, there is another execution engine called Pure-python execution engine which doesn't need the code-graph conversion. This should generally not affect models and strategies in most cases, but customized mutation might not be supported.
+
+This will come as the default execution engine in future version of Retiarii.
+
+Two steps are needed to enable this engine now.
+
+1. Add ``@nni.retiarii.model_wrapper`` decorator outside the whole PyTorch model.
+2. Add ``config.execution_engine = 'py'`` to ``RetiariiExeConfig``.
 
 ``@basic_unit`` and ``serializer``
 ----------------------------------

nni/retiarii/__init__.py

@@ -5,4 +5,4 @@ from .operation import Operation
 from .graph import *
 from .execution import *
 from .mutator import *
-from .serializer import basic_unit, json_dump, json_dumps, json_load, json_loads, serialize, serialize_cls
+from .serializer import basic_unit, json_dump, json_dumps, json_load, json_loads, serialize, serialize_cls, model_wrapper

nni/retiarii/execution/api.py

@@ -15,19 +15,18 @@ __all__ = ['get_execution_engine', 'get_and_register_default_listener',
            'list_models', 'submit_models', 'wait_models', 'query_available_resources',
            'set_execution_engine', 'is_stopped_exec', 'budget_exhausted']
 
-def set_execution_engine(engine) -> None:
+
+def set_execution_engine(engine: AbstractExecutionEngine) -> None:
     global _execution_engine
     if _execution_engine is None:
         _execution_engine = engine
     else:
-        raise RuntimeError('execution engine is already set')
+        raise RuntimeError('Execution engine is already set.')
 
 
 def get_execution_engine() -> AbstractExecutionEngine:
-    """
-    Currently we assume the default execution engine is BaseExecutionEngine.
-    """
     global _execution_engine
+    assert _execution_engine is not None, 'You need to set execution engine, before using it.'
     return _execution_engine
 
 

nni/retiarii/execution/base.py

@@ -5,7 +5,7 @@ import logging
 import os
 import random
 import string
-from typing import Dict, Iterable, List
+from typing import Any, Dict, Iterable, List
 
 from .interface import AbstractExecutionEngine, AbstractGraphListener
 from .. import codegen, utils
@@ -59,7 +59,7 @@ class BaseExecutionEngine(AbstractExecutionEngine):
 
     def submit_models(self, *models: Model) -> None:
         for model in models:
-            data = BaseGraphData(codegen.model_to_pytorch_script(model), model.evaluator)
+            data = self.pack_model_data(model)
             self._running_models[send_trial(data.dump())] = model
             self._history.append(model)
 
@@ -108,6 +108,10 @@ class BaseExecutionEngine(AbstractExecutionEngine):
         advisor = get_advisor()
         return advisor.stopping
 
+    @classmethod
+    def pack_model_data(cls, model: Model) -> Any:
+        return BaseGraphData(codegen.model_to_pytorch_script(model), model.evaluator)
+
     @classmethod
     def trial_execute_graph(cls) -> None:
         """

nni/retiarii/execution/python.py

+from typing import Dict, Any, List
+
+from ..graph import Evaluator, Model
+from ..integration_api import receive_trial_parameters
+from ..utils import ContextStack, import_, get_importable_name
+from .base import BaseExecutionEngine
+
+
+class PythonGraphData:
+    def __init__(self, class_name: str, init_parameters: Dict[str, Any],
+                 mutation: Dict[str, Any], evaluator: Evaluator) -> None:
+        self.class_name = class_name
+        self.init_parameters = init_parameters
+        self.mutation = mutation
+        self.evaluator = evaluator
+
+    def dump(self) -> dict:
+        return {
+            'class_name': self.class_name,
+            'init_parameters': self.init_parameters,
+            'mutation': self.mutation,
+            'evaluator': self.evaluator
+        }
+
+    @staticmethod
+    def load(data) -> 'PythonGraphData':
+        return PythonGraphData(data['class_name'], data['init_parameters'], data['mutation'], data['evaluator'])
+
+
+class PurePythonExecutionEngine(BaseExecutionEngine):
+    @classmethod
+    def pack_model_data(cls, model: Model) -> Any:
+        mutation = {mut.mutator.label: _unpack_if_only_one(mut.samples) for mut in model.history}
+        graph_data = PythonGraphData(get_importable_name(model.python_class, relocate_module=True),
+                                     model.python_init_params, mutation, model.evaluator)
+        return graph_data
+
+    @classmethod
+    def trial_execute_graph(cls) -> None:
+        graph_data = PythonGraphData.load(receive_trial_parameters())
+
+        class _model(import_(graph_data.class_name)):
+            def __init__(self):
+                super().__init__(**graph_data.init_parameters)
+
+        with ContextStack('fixed', graph_data.mutation):
+            graph_data.evaluator._execute(_model)
+
+
+def _unpack_if_only_one(ele: List[Any]):
+    if len(ele) == 1:
+        return ele[0]
+    return ele

nni/retiarii/experiment/pytorch.py

@@ -28,11 +28,11 @@ from nni.tools.nnictl.command_utils import kill_command
 
 from ..codegen import model_to_pytorch_script
 from ..converter import convert_to_graph
-from ..execution import list_models
+from ..execution import list_models, set_execution_engine
 from ..graph import Model, Evaluator
 from ..integration import RetiariiAdvisor
 from ..mutator import Mutator
-from ..nn.pytorch.mutator import process_inline_mutation
+from ..nn.pytorch.mutator import process_inline_mutation, extract_mutation_from_pt_module
 from ..strategy import BaseStrategy
 from ..oneshot.interface import BaseOneShotTrainer
 
@@ -43,7 +43,7 @@ _logger = logging.getLogger(__name__)
 class RetiariiExeConfig(ConfigBase):
     experiment_name: Optional[str] = None
     search_space: Any = ''  # TODO: remove
-    trial_command: str = 'python3 -m nni.retiarii.trial_entry'
+    trial_command: str = '_reserved'
     trial_code_directory: PathLike = '.'
     trial_concurrency: int
     trial_gpu_number: int = 0
@@ -55,21 +55,26 @@ class RetiariiExeConfig(ConfigBase):
     experiment_working_directory: PathLike = '~/nni-experiments'
     # remove configuration of tuner/assessor/advisor
     training_service: TrainingServiceConfig
+    execution_engine: str = 'base'
 
     def __init__(self, training_service_platform: Optional[str] = None, **kwargs):
         super().__init__(**kwargs)
         if training_service_platform is not None:
             assert 'training_service' not in kwargs
             self.training_service = util.training_service_config_factory(platform = training_service_platform)
+        self.__dict__['trial_command'] = 'python3 -m nni.retiarii.trial_entry base'
 
     def __setattr__(self, key, value):
         fixed_attrs = {'search_space': '',
-                       'trial_command': 'python3 -m nni.retiarii.trial_entry'}
+                       'trial_command': '_reserved'}
         if key in fixed_attrs and fixed_attrs[key] != value:
             raise AttributeError(f'{key} is not supposed to be set in Retiarii mode by users!')
         # 'trial_code_directory' is handled differently because the path will be converted to absolute path by us
         if key == 'trial_code_directory' and not (value == Path('.') or os.path.isabs(value)):
             raise AttributeError(f'{key} is not supposed to be set in Retiarii mode by users!')
+        if key == 'execution_engine':
+            assert value in ['base', 'py', 'cgo'], f'The specified execution engine "{value}" is not supported.'
+            self.__dict__['trial_command'] = 'python3 -m nni.retiarii.trial_entry ' + value
         self.__dict__[key] = value
 
     def validate(self, initialized_tuner: bool = False) -> None:
@@ -100,23 +105,27 @@ _validation_rules = {
     'training_service': lambda value: (type(value) is not TrainingServiceConfig, 'cannot be abstract base class')
 }
 
-def preprocess_model(base_model, trainer, applied_mutators):
+def preprocess_model(base_model, trainer, applied_mutators, full_ir=True):
+    # TODO: this logic might need to be refactored into execution engine
+    if full_ir:
         try:
             script_module = torch.jit.script(base_model)
         except Exception as e:
             _logger.error('Your base model cannot be parsed by torch.jit.script, please fix the following error:')
             raise e
         base_model_ir = convert_to_graph(script_module, base_model)
-        base_model_ir.evaluator = trainer
-
         # handle inline mutations
         mutators = process_inline_mutation(base_model_ir)
-        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')
-        if mutators is not None:
-            applied_mutators = mutators
-        return base_model_ir, applied_mutators
+    else:
+        base_model_ir, mutators = extract_mutation_from_pt_module(base_model)
+    base_model_ir.evaluator = trainer
+
+    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')
+    if mutators is not None:
+        applied_mutators = mutators
+    return base_model_ir, applied_mutators
 
 def debug_mutated_model(base_model, trainer, applied_mutators):
     """
@@ -160,7 +169,8 @@ class RetiariiExperiment(Experiment):
         self._pipe: Optional[Pipe] = None
 
     def _start_strategy(self):
-        base_model_ir, self.applied_mutators = preprocess_model(self.base_model, self.trainer, self.applied_mutators)
+        base_model_ir, self.applied_mutators = preprocess_model(
+            self.base_model, self.trainer, self.applied_mutators, full_ir=self.config.execution_engine != 'py')
 
         _logger.info('Start strategy...')
         self.strategy.run(base_model_ir, self.applied_mutators)
@@ -182,6 +192,18 @@ class RetiariiExperiment(Experiment):
         """
         atexit.register(self.stop)
 
+        # 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()
+        elif self.config.execution_engine == 'py':
+            from ..execution.python import PurePythonExecutionEngine
+            engine = PurePythonExecutionEngine()
+        set_execution_engine(engine)
+
         self.id = management.generate_experiment_id()
 
         if self.config.experiment_working_directory is not None:

nni/retiarii/graph.py

@@ -9,12 +9,12 @@ import abc
 import copy
 import json
 from enum import Enum
-from typing import (Any, Dict, Iterable, List, Optional, Tuple, Union, overload)
+from typing import (Any, Dict, Iterable, List, Optional, Tuple, Type, Union, overload)
 
 from .operation import Cell, Operation, _IOPseudoOperation
 from .utils import get_importable_name, import_, uid
 
-__all__ = ['Model', 'ModelStatus', 'Graph', 'Node', 'Edge', 'IllegalGraphError', 'MetricData']
+__all__ = ['Model', 'ModelStatus', 'Graph', 'Node', 'Edge', 'Mutation', 'IllegalGraphError', 'MetricData']
 
 
 MetricData = Any
@@ -80,6 +80,10 @@ class Model:
 
     Attributes
     ----------
+    python_class
+        Python class that base model is converted from.
+    python_init_params
+        Initialization parameters of python class.
     status
         See `ModelStatus`.
     root_graph
@@ -102,6 +106,8 @@ class Model:
     def __init__(self, _internal=False):
         assert _internal, '`Model()` is private, use `model.fork()` instead'
         self.model_id: int = uid('model')
+        self.python_class: Optional[Type] = None
+        self.python_init_params: Optional[Dict[str, Any]] = None
 
         self.status: ModelStatus = ModelStatus.Mutating
 
@@ -116,7 +122,8 @@ class Model:
 
     def __repr__(self):
         return f'Model(model_id={self.model_id}, status={self.status}, graphs={list(self.graphs.keys())}, ' + \
-            f'evaluator={self.evaluator}, metric={self.metric}, intermediate_metrics={self.intermediate_metrics})'
+            f'evaluator={self.evaluator}, metric={self.metric}, intermediate_metrics={self.intermediate_metrics}, ' + \
+            f'python_class={self.python_class})'
 
     @property
     def root_graph(self) -> 'Graph':
@@ -133,9 +140,12 @@ class Model:
         """
         new_model = Model(_internal=True)
         new_model._root_graph_name = self._root_graph_name
+        new_model.python_class = self.python_class
+        new_model.python_init_params = self.python_init_params
         new_model.graphs = {name: graph._fork_to(new_model) for name, graph in self.graphs.items()}
         new_model.evaluator = copy.deepcopy(self.evaluator)  # TODO this may be a problem when evaluator is large
-        new_model.history = self.history + [self]
+        new_model.history = [*self.history]
+        # Note: the history is not updated. It will be updated when the model is changed, that is in mutator.
         return new_model
 
     @staticmethod
@@ -167,8 +177,8 @@ class Model:
 
     def get_nodes_by_label(self, label: str) -> List['Node']:
         """
-        Traverse all the nodes to find the matched node(s) with the given name.
-        There could be multiple nodes with the same name. Name space name can uniquely
+        Traverse all the nodes to find the matched node(s) with the given label.
+        There could be multiple nodes with the same label. Name space name can uniquely
         identify a graph or node.
 
         NOTE: the implementation does not support the class abstration
@@ -493,6 +503,8 @@ class Node:
         If two models have nodes with same ID, they are semantically the same node.
     name
         Mnemonic name. It should have an one-to-one mapping with ID.
+    label
+        Optional. If two nodes have the same label, they are considered same by the mutator.
     operation
         ...
     cell
@@ -515,7 +527,7 @@ class Node:
         # TODO: the operation is likely to be considered editable by end-user and it will be hard to debug
         # maybe we should copy it here or make Operation class immutable, in next release
         self.operation: Operation = operation
-        self.label: str = None
+        self.label: Optional[str] = None
 
     def __repr__(self):
         return f'Node(id={self.id}, name={self.name}, label={self.label}, operation={self.operation})'
@@ -673,6 +685,37 @@ class Edge:
         }
 
 
+class Mutation:
+    """
+    An execution of mutation, which consists of four parts: a mutator, a list of decisions (choices),
+    the model that it comes from, and the model that it becomes.
+
+    In general cases, the mutation logs are not reliable and should not be replayed as the mutators can
+    be arbitrarily complex. However, for inline mutations, the labels correspond to mutator labels here,
+    this can be useful for metadata visualization and python execution mode.
+
+    Attributes
+    ----------
+    mutator
+        Mutator.
+    samples
+        Decisions/choices.
+    from_
+        Model that is comes from.
+    to
+        Model that it becomes.
+    """
+
+    def __init__(self, mutator: 'Mutator', samples: List[Any], from_: Model, to: Model):  # noqa: F821
+        self.mutator: 'Mutator' = mutator  # noqa: F821
+        self.samples: List[Any] = samples
+        self.from_: Model = from_
+        self.to: Model = to
+
+    def __repr__(self):
+        return f'Edge(mutator={self.mutator}, samples={self.samples}, from={self.from_}, to={self.to})'
+
+
 class IllegalGraphError(ValueError):
     def __init__(self, graph, *args):
         self._debug_dump_graph(graph)

nni/retiarii/integration.py

@@ -2,7 +2,6 @@
 # Licensed under the MIT license.
 
 import logging
-import os
 from typing import Any, Callable
 
 from nni.runtime.msg_dispatcher_base import MsgDispatcherBase
@@ -10,9 +9,6 @@ from nni.runtime.protocol import CommandType, send
 from nni.utils import MetricType
 
 from .graph import MetricData
-from .execution.base import BaseExecutionEngine
-from .execution.cgo_engine import CGOExecutionEngine
-from .execution.api import set_execution_engine
 from .integration_api import register_advisor
 from .serializer import json_dumps, json_loads
 
@@ -62,15 +58,6 @@ class RetiariiAdvisor(MsgDispatcherBase):
 
         self.parameters_count = 0
 
-        engine = self._create_execution_engine()
-        set_execution_engine(engine)
-
-    def _create_execution_engine(self):
-        if os.environ.get('CGO') == 'true':
-            return CGOExecutionEngine()
-        else:
-            return BaseExecutionEngine()
-
     def handle_initialize(self, data):
         """callback for initializing the advisor
         Parameters

nni/retiarii/mutator.py

@@ -3,7 +3,7 @@
 
 from typing import (Any, Iterable, List, Optional)
 
-from .graph import Model
+from .graph import Model, Mutation, ModelStatus
 
 
 __all__ = ['Sampler', 'Mutator']
@@ -40,10 +40,13 @@ class Mutator:
     and then use `Mutator.apply()` to mutate model.
     For certain mutator subclasses, strategy or sampler can use `Mutator.dry_run()` to predict choice candidates.
     # Method names are open for discussion.
+
+    If mutator has a label, in most cases, it means that this mutator is applied to nodes with this label.
     """
 
-    def __init__(self, sampler: Optional[Sampler] = None):
+    def __init__(self, sampler: Optional[Sampler] = None, label: Optional[str] = None):
         self.sampler: Optional[Sampler] = sampler
+        self.label: Optional[str] = label
         self._cur_model: Optional[Model] = None
         self._cur_choice_idx: Optional[int] = None
 
@@ -64,9 +67,12 @@ class Mutator:
         copy = model.fork()
         self._cur_model = copy
         self._cur_choice_idx = 0
+        self._cur_samples = []
         self.sampler.mutation_start(self, copy)
         self.mutate(copy)
         self.sampler.mutation_end(self, copy)
+        copy.history.append(Mutation(self, self._cur_samples, model, copy))
+        copy.status = ModelStatus.Frozen
         self._cur_model = None
         self._cur_choice_idx = None
         return copy
@@ -97,6 +103,7 @@ class Mutator:
         """
         assert self.sampler is not None and self._cur_model is not None and self._cur_choice_idx is not None
         ret = self.sampler.choice(list(candidates), self, self._cur_model, self._cur_choice_idx)
+        self._cur_samples.append(ret)
         self._cur_choice_idx += 1
         return ret
 

nni/retiarii/nn/pytorch/api.py

@@ -4,18 +4,32 @@
 import copy
 import warnings
 from collections import OrderedDict
-from typing import Any, List, Union, Dict
+from typing import Any, List, Union, Dict, Optional
 
 import torch
 import torch.nn as nn
 
 from ...serializer import Translatable, basic_unit
-from ...utils import uid
+from ...utils import uid, get_current_context
 
 
 __all__ = ['LayerChoice', 'InputChoice', 'ValueChoice', 'Placeholder', 'ChosenInputs']
 
 
+def _generate_new_label(label: Optional[str]):
+    if label is None:
+        return '_mutation_' + str(uid('mutation'))
+    return label
+
+
+def _get_fixed_value(label: str):
+    ret = get_current_context('fixed')
+    try:
+        return ret[_generate_new_label(label)]
+    except KeyError:
+        raise KeyError(f'Fixed context with {label} not found. Existing values are: {ret}')
+
+
 class LayerChoice(nn.Module):
     """
     Layer choice selects one of the ``candidates``, then apply it on inputs and return results.
@@ -55,6 +69,16 @@ class LayerChoice(nn.Module):
     ``self.op_choice[1] = nn.Conv3d(...)``. Adding more choices is not supported yet.
     """
 
+    def __new__(cls, candidates: Union[Dict[str, nn.Module], List[nn.Module]], label: str = None, **kwargs):
+        try:
+            chosen = _get_fixed_value(label)
+            if isinstance(candidates, list):
+                return candidates[int(chosen)]
+            else:
+                return candidates[chosen]
+        except AssertionError:
+            return super().__new__(cls)
+
     def __init__(self, candidates: Union[Dict[str, nn.Module], List[nn.Module]], label: str = None, **kwargs):
         super(LayerChoice, self).__init__()
         if 'key' in kwargs:
@@ -65,7 +89,7 @@ class LayerChoice(nn.Module):
         if 'reduction' in kwargs:
             warnings.warn(f'"reduction" is deprecated. Ignoring...')
         self.candidates = candidates
-        self._label = label if label is not None else f'layerchoice_{uid()}'
+        self._label = _generate_new_label(label)
 
         self.names = []
         if isinstance(candidates, OrderedDict):
@@ -163,6 +187,12 @@ class InputChoice(nn.Module):
         Identifier of the input choice.
     """
 
+    def __new__(cls, n_candidates: int, n_chosen: int = 1, reduction: str = 'sum', label: str = None, **kwargs):
+        try:
+            return ChosenInputs(_get_fixed_value(label), reduction=reduction)
+        except AssertionError:
+            return super().__new__(cls)
+
     def __init__(self, n_candidates: int, n_chosen: int = 1, reduction: str = 'sum', label: str = None, **kwargs):
         super(InputChoice, self).__init__()
         if 'key' in kwargs:
@@ -176,7 +206,7 @@ class InputChoice(nn.Module):
         self.n_chosen = n_chosen
         self.reduction = reduction
         assert self.reduction in ['mean', 'concat', 'sum', 'none']
-        self._label = label if label is not None else f'inputchoice_{uid()}'
+        self._label = _generate_new_label(label)
 
     @property
     def key(self):
@@ -265,10 +295,16 @@ class ValueChoice(Translatable, nn.Module):
         Identifier of the value choice.
     """
 
+    def __new__(cls, candidates: List[Any], label: str = None):
+        try:
+            return _get_fixed_value(label)
+        except AssertionError:
+            return super().__new__(cls)
+
     def __init__(self, candidates: List[Any], label: str = None):
         super().__init__()
         self.candidates = candidates
-        self._label = label if label is not None else f'valuechoice_{uid()}'
+        self._label = _generate_new_label(label)
         self._accessor = []
 
     @property
@@ -297,6 +333,14 @@ class ValueChoice(Translatable, nn.Module):
             raise KeyError(''.join([f'[{a}]' for a in self._accessor]) + f' does not work on {value}')
         return v
 
+    def __copy__(self):
+        return self
+
+    def __deepcopy__(self, memo):
+        new_item = ValueChoice(self.candidates, self.label)
+        new_item._accessor = [*self._accessor]
+        return new_item
+
     def __getitem__(self, item):
         """
         Get a sub-element of value choice.
@@ -331,9 +375,9 @@ class ChosenInputs(nn.Module):
     The already-chosen version of InputChoice.
     """
 
-    def __init__(self, chosen: List[int], reduction: str):
+    def __init__(self, chosen: Union[List[int], int], reduction: str):
         super().__init__()
-        self.chosen = chosen
+        self.chosen = chosen if isinstance(chosen, list) else [chosen]
         self.reduction = reduction
 
     def forward(self, candidate_inputs):

nni/retiarii/nn/pytorch/mutator.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+import inspect
 from typing import Any, List, Optional, Tuple
 
+import torch.nn as nn
+
 from ...mutator import Mutator
-from ...graph import Cell, Model, Node
-from .api import ValueChoice
+from ...graph import Cell, Graph, Model, ModelStatus, Node
+from ...utils import uid
+from .api import LayerChoice, InputChoice, ValueChoice, Placeholder
 
 
 class LayerChoiceMutator(Mutator):
@@ -40,7 +44,7 @@ class InputChoiceMutator(Mutator):
 
     def mutate(self, model):
         n_candidates = self.nodes[0].operation.parameters['n_candidates']
-        n_chosen =  self.nodes[0].operation.parameters['n_chosen']
+        n_chosen = self.nodes[0].operation.parameters['n_chosen']
         candidates = list(range(n_candidates))
         chosen = [self.choice(candidates) for _ in range(n_chosen)]
         for node in self.nodes:
@@ -116,12 +120,96 @@ def process_inline_mutation(model: Model) -> Optional[List[Mutator]]:
         mutator = LayerChoiceMutator(node_list)
         applied_mutators.append(mutator)
 
-
     if applied_mutators:
         return applied_mutators
     return None
 
 
+# The following are written for pure-python mode
+
+
+class ManyChooseManyMutator(Mutator):
+    """
+    Choose based on labels. Will not affect the model itself.
+    """
+
+    def __init__(self, label: Optional[str]):
+        super().__init__(label=label)
+
+    @staticmethod
+    def candidates(node):
+        if 'n_candidates' in node.operation.parameters:
+            return list(range(node.operation.parameters['n_candidates']))
+        else:
+            return node.operation.parameters['candidates']
+
+    @staticmethod
+    def number_of_chosen(node):
+        if 'n_chosen' in node.operation.parameters:
+            return node.operation.parameters['n_chosen']
+        return 1
+
+    def mutate(self, model: Model):
+        # this mutate does not have any effect, but it is recorded in the mutation history
+        for node in model.get_nodes_by_label(self.label):
+            for _ in range(self.number_of_chosen(node)):
+                self.choice(self.candidates(node))
+            break
+
+
+def extract_mutation_from_pt_module(pytorch_model: nn.Module) -> Tuple[Model, Optional[List[Mutator]]]:
+    model = Model(_internal=True)
+    graph = Graph(model, uid(), '_model', _internal=True)._register()
+    model.python_class = pytorch_model.__class__
+    if len(inspect.signature(model.python_class.__init__).parameters) > 1:
+        if not hasattr(pytorch_model, '_init_parameters'):
+            raise ValueError('Please annotate the model with @serialize decorator in python execution mode '
+                             'if your model has init parameters.')
+        model.python_init_params = pytorch_model._init_parameters
+    else:
+        model.python_init_params = {}
+
+    for name, module in pytorch_model.named_modules():
+        # tricky case: value choice that serves as parameters are stored in _init_parameters
+        if hasattr(module, '_init_parameters'):
+            for key, value in module._init_parameters.items():
+                if isinstance(value, ValueChoice):
+                    node = graph.add_node(name + '.init.' + key, 'ValueChoice', {'candidates': value.candidates})
+                    node.label = value.label
+
+        if isinstance(module, (LayerChoice, InputChoice, ValueChoice)):
+            # TODO: check the label of module and warn if it's auto-generated
+            pass
+        if isinstance(module, LayerChoice):
+            node = graph.add_node(name, 'LayerChoice', {'candidates': module.names})
+            node.label = module.label
+        if isinstance(module, InputChoice):
+            node = graph.add_node(name, 'InputChoice',
+                                  {'n_candidates': module.n_candidates, 'n_chosen': module.n_chosen})
+            node.label = module.label
+        if isinstance(module, ValueChoice):
+            node = graph.add_node(name, 'ValueChoice', {'candidates': module.candidates})
+            node.label = module.label
+        if isinstance(module, Placeholder):
+            raise NotImplementedError('Placeholder is not supported in python execution mode.')
+
+    model.status = ModelStatus.Frozen
+    if not graph.hidden_nodes:
+        return model, None
+
+    mutators = []
+    for nodes in _group_by_label_and_type(graph.hidden_nodes):
+        assert _is_all_equal(map(lambda n: n.operation.type, nodes)), \
+            f'Node with label "{nodes[0].label}" does not all have the same type.'
+        assert _is_all_equal(map(lambda n: n.operation.parameters, nodes)), \
+            f'Node with label "{nodes[0].label}" does not agree on parameters.'
+        mutators.append(ManyChooseManyMutator(nodes[0].label))
+    return model, mutators
+
+
+# utility functions
+
+
 def _is_all_equal(lst):
     last = None
     for x in lst:
@@ -131,6 +219,16 @@ def _is_all_equal(lst):
     return True
 
 
+def _group_by_label_and_type(nodes: List[Node]) -> List[List[Node]]:
+    result = {}
+    for node in nodes:
+        key = (node.label, node.operation.type)
+        if key not in result:
+            result[key] = []
+        result[key].append(node)
+    return list(result.values())
+
+
 def _group_by_label(nodes: List[Node]) -> List[List[Node]]:
     result = {}
     for node in nodes:

nni/retiarii/serializer.py

@@ -9,7 +9,7 @@ from typing import Any
 
 import json_tricks
 
-from .utils import get_importable_name, get_module_name, import_
+from .utils import get_importable_name, get_module_name, import_, reset_uid
 
 
 def get_init_parameters_or_fail(obj, silently=False):
@@ -83,9 +83,11 @@ class Translatable(abc.ABC):
         pass
 
 
-def _create_wrapper_cls(cls, store_init_parameters=True):
+def _create_wrapper_cls(cls, store_init_parameters=True, reset_mutation_uid=False):
     class wrapper(cls):
         def __init__(self, *args, **kwargs):
+            if reset_mutation_uid:
+                reset_uid('mutation')
             if store_init_parameters:
                 argname_list = list(inspect.signature(cls.__init__).parameters.keys())[1:]
                 full_args = {}
@@ -149,3 +151,15 @@ def basic_unit(cls):
     import torch.nn as nn
     assert issubclass(cls, nn.Module), 'When using @basic_unit, the class must be a subclass of nn.Module.'
     return serialize_cls(cls)
+
+
+def model_wrapper(cls):
+    """
+    Wrap the model if you are using pure-python execution engine.
+
+    The wrapper serves two purposes:
+
+        1. Capture the init parameters of python class so that it can be re-instantiated in another process.
+        2. Reset uid in `mutation` namespace so that each model counts from zero. Can be useful in unittest and other multi-model scenarios.
+    """
+    return _create_wrapper_cls(cls, reset_mutation_uid=True)

nni/retiarii/trial_entry.py

@@ -6,13 +6,20 @@ Entrypoint for trials.
 
 Assuming execution engine is BaseExecutionEngine.
 """
-import os
+import argparse
 
-from .execution.base import BaseExecutionEngine
-from .execution.cgo_engine import CGOExecutionEngine
 
 if __name__ == '__main__':
-    if os.environ.get('CGO') == 'true':
-        CGOExecutionEngine.trial_execute_graph()
-    else:
-        BaseExecutionEngine.trial_execute_graph()
+    parser = argparse.ArgumentParser()
+    parser.add_argument('exec', choices=['base', 'py', 'cgo'])
+    args = parser.parse_args()
+    if args.exec == 'base':
+        from .execution.base import BaseExecutionEngine
+        engine = BaseExecutionEngine
+    elif args.exec == 'cgo':
+        from .execution.cgo_engine import CGOExecutionEngine
+        engine = CGOExecutionEngine
+    elif args.exec == 'py':
+        from .execution.python import PurePythonExecutionEngine
+        engine = PurePythonExecutionEngine
+    engine.trial_execute_graph()

nni/retiarii/utils.py

@@ -4,7 +4,7 @@
 import inspect
 import warnings
 from collections import defaultdict
-from typing import Any
+from typing import Any, List, Dict
 from pathlib import Path
 
 
@@ -31,6 +31,10 @@ def uid(namespace: str = 'default') -> int:
     return _last_uid[namespace]
 
 
+def reset_uid(namespace: str = 'default') -> None:
+    _last_uid[namespace] = 0
+
+
 def get_module_name(cls_or_func):
     module_name = cls_or_func.__module__
     if module_name == '__main__':
@@ -61,3 +65,42 @@ def get_module_name(cls_or_func):
 def get_importable_name(cls, relocate_module=False):
     module_name = get_module_name(cls) if relocate_module else cls.__module__
     return module_name + '.' + cls.__name__
+
+
+class ContextStack:
+    """
+    This is to maintain a globally-accessible context envinronment that is visible to everywhere.
+
+    Use ``with ContextStack(namespace, value):`` to initiate, and use ``get_current_context(namespace)`` to
+    get the corresponding value in the namespace.
+    """
+
+    _stack: Dict[str, List[Any]] = defaultdict(list)
+
+    def __init__(self, key: str, value: Any):
+        self.key = key
+        self.value = value
+
+    def __enter__(self):
+        self.push(self.key, self.value)
+        return self
+
+    def __exit__(self, *args, **kwargs):
+        self.pop(self.key)
+
+    @classmethod
+    def push(cls, key: str, value: Any):
+        cls._stack[key].append(value)
+
+    @classmethod
+    def pop(cls, key: str) -> None:
+        cls._stack[key].pop()
+
+    @classmethod
+    def top(cls, key: str) -> Any:
+        assert cls._stack[key], 'Context is empty.'
+        return cls._stack[key][-1]
+
+
+def get_current_context(key: str) -> Any:
+    return ContextStack.top(key)

test/ut/retiarii/debug_mnist_pytorch.py

@@ -3,22 +3,24 @@ import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 
+import torch
+
 
 class _model(nn.Module):
     def __init__(self):
         super().__init__()
         self.stem = stem()
-        
-        self.fc1 = nn.Linear(1024, 256)
-        self.fc2 = nn.Linear(256, 10)
-        
+        self.flatten = torch.nn.Flatten()
+        self.fc1 = torch.nn.Linear(out_features=256, in_features=1024)
+        self.fc2 = torch.nn.Linear(out_features=10, in_features=256)
+        self.softmax = torch.nn.Softmax()
 
     def forward(self, image):
         stem = self.stem(image)
-        flatten = stem.view(stem.size(0), -1)
+        flatten = self.flatten(stem)
         fc1 = self.fc1(flatten)
         fc2 = self.fc2(fc1)
-        softmax = F.softmax(fc2, -1)
+        softmax = self.softmax(fc2)
         return softmax
 
 
@@ -26,10 +28,10 @@ class _model(nn.Module):
 class stem(nn.Module):
     def __init__(self):
         super().__init__()
-        self.conv1 = nn.Conv2d(out_channels=32, in_channels=1, kernel_size=5)
-        self.pool1 = nn.MaxPool2d(kernel_size=2)
-        self.conv2 = nn.Conv2d(out_channels=64, in_channels=32, kernel_size=5)
-        self.pool2 = nn.MaxPool2d(kernel_size=2)
+        self.conv1 = torch.nn.Conv2d(out_channels=32, in_channels=1, kernel_size=5)
+        self.pool1 = torch.nn.MaxPool2d(kernel_size=2)
+        self.conv2 = torch.nn.Conv2d(out_channels=64, in_channels=32, kernel_size=5)
+        self.pool2 = torch.nn.MaxPool2d(kernel_size=2)
 
     def forward(self, *_inputs):
         conv1 = self.conv1(_inputs[0])

test/ut/retiarii/mnist_pytorch.json

@@ -5,10 +5,10 @@
 
         "nodes": {
             "stem": {"operation": {"type": "_cell", "cell_name": "stem"}},
-            "flatten": {"operation": {"type": "Flatten"}},
-            "fc1": {"operation": {"type": "Dense", "parameters": {"out_features": 256, "in_features": 1024}}},
-            "fc2": {"operation": {"type": "Dense", "parameters": {"out_features": 10, "in_features": 256}}},
-            "softmax": {"operation": {"type": "Softmax"}}
+            "flatten": {"operation": {"type": "__torch__.torch.nn.Flatten"}},
+            "fc1": {"operation": {"type": "__torch__.torch.nn.Linear", "parameters": {"out_features": 256, "in_features": 1024}}},
+            "fc2": {"operation": {"type": "__torch__.torch.nn.Linear", "parameters": {"out_features": 10, "in_features": 256}}},
+            "softmax": {"operation": {"type": "__torch__.torch.nn.Softmax"}}
         },
 
         "edges": [
@@ -23,10 +23,10 @@
 
     "stem": {
         "nodes": {
-            "conv1": {"operation": {"type": "__torch__.Conv2d", "parameters": {"out_channels": 32, "in_channels": 1, "kernel_size": 5}}},
-            "pool1": {"operation": {"type": "__torch__.MaxPool2d", "parameters": {"kernel_size": 2}}},
-            "conv2": {"operation": {"type": "__torch__.Conv2d", "parameters": {"out_channels": 64, "in_channels": 32, "kernel_size": 5}}},
-            "pool2": {"operation": {"type": "__torch__.MaxPool2d", "parameters": {"kernel_size": 2}}}
+            "conv1": {"operation": {"type": "__torch__.torch.nn.Conv2d", "parameters": {"out_channels": 32, "in_channels": 1, "kernel_size": 5}}},
+            "pool1": {"operation": {"type": "__torch__.torch.nn.MaxPool2d", "parameters": {"kernel_size": 2}}},
+            "conv2": {"operation": {"type": "__torch__.torch.nn.Conv2d", "parameters": {"out_channels": 64, "in_channels": 32, "kernel_size": 5}}},
+            "pool2": {"operation": {"type": "__torch__.torch.nn.MaxPool2d", "parameters": {"kernel_size": 2}}}
         },
 
         "edges": [
@@ -36,26 +36,5 @@
             {"head": ["conv2", null], "tail": ["pool2", null]},
             {"head": ["pool2", null], "tail": ["_outputs", 0]}
         ]
-    },
-
-    "_evaluator": {
-        "module": "nni.retiarii.trainer.PyTorchImageClassificationTrainer",
-        "kwargs": {
-            "dataset_cls": "MNIST",
-            "dataset_kwargs": {
-                "root": "data/mnist",
-                "download": true
-            },
-            "dataloader_kwargs": {
-                "batch_size": 32
-            },
-            "optimizer_cls" : "SGD",
-            "optimizer_kwargs": {
-                "lr": 1e-3
-            },
-            "trainer_kwargs": {
-                "max_epochs": 1
-            }
-        }
     }
 }

test/ut/retiarii/test_engine.py

 import json
 import os
-import sys
-import threading
 import unittest
 from pathlib import Path
 
-import nni
+import nni.retiarii
 from nni.retiarii import Model, submit_models
 from nni.retiarii.codegen import model_to_pytorch_script
-from nni.retiarii.integration import RetiariiAdvisor, register_advisor
-from nni.retiarii.evaluator.pytorch import PyTorchImageClassificationTrainer
-from nni.retiarii.utils import import_
+from nni.retiarii.execution import set_execution_engine
+from nni.retiarii.execution.base import BaseExecutionEngine
+from nni.retiarii.execution.python import PurePythonExecutionEngine
+from nni.retiarii.integration import RetiariiAdvisor
 
 
-@unittest.skip('Skipped in this version')
-class CodeGenTest(unittest.TestCase):
-    def test_mnist_example_pytorch(self):
-        with open('mnist_pytorch.json') as f:
+class EngineTest(unittest.TestCase):
+    def test_codegen(self):
+        with open(self.enclosing_dir / 'mnist_pytorch.json') as f:
             model = Model._load(json.load(f))
             script = model_to_pytorch_script(model)
-        with open('debug_mnist_pytorch.py') as f:
+        with open(self.enclosing_dir / 'debug_mnist_pytorch.py') as f:
             reference_script = f.read()
         self.assertEqual(script.strip(), reference_script.strip())
 
+    def test_base_execution_engine(self):
+        advisor = RetiariiAdvisor()
+        set_execution_engine(BaseExecutionEngine())
+        with open(self.enclosing_dir / 'mnist_pytorch.json') as f:
+            model = Model._load(json.load(f))
+        submit_models(model, model)
 
-@unittest.skip('Skipped in this version')
-class TrainerTest(unittest.TestCase):
-    def test_trainer(self):
-        sys.path.insert(0, Path(__file__).parent.as_posix())
-        Model = import_('debug_mnist_pytorch._model')
-        trainer = PyTorchImageClassificationTrainer(
-            Model(),
-            dataset_kwargs={'root': (Path(__file__).parent / 'data' / 'mnist').as_posix(), 'download': True},
-            dataloader_kwargs={'batch_size': 32},
-            optimizer_kwargs={'lr': 1e-3},
-            trainer_kwargs={'max_epochs': 1}
-        )
-        trainer.fit()
-
-
-@unittest.skip('Skipped in this version')
-class EngineTest(unittest.TestCase):
+        advisor.stopping = True
+        advisor.default_worker.join()
+        advisor.assessor_worker.join()
 
-    def test_submit_models(self):
-        os.makedirs('generated', exist_ok=True)
-        from nni.runtime import protocol
-        protocol._out_file = open(Path(__file__).parent / 'generated/debug_protocol_out_file.py', 'wb')
+    def test_py_execution_engine(self):
+        
         advisor = RetiariiAdvisor()
-        with open('mnist_pytorch.json') as f:
-            model = Model._load(json.load(f))
+        set_execution_engine(PurePythonExecutionEngine())
+        model = Model._load({
+            '_model': {
+                'inputs': None,
+                'outputs': None,
+                'nodes': {
+                    'layerchoice_1': {
+                        'operation': {'type': 'LayerChoice', 'parameters': {'candidates': ['0', '1']}}
+                    }
+                },
+                'edges': []
+            }
+        })
+        model.python_class = object
         submit_models(model, model)
 
         advisor.stopping = True
         advisor.default_worker.join()
         advisor.assessor_worker.join()
 
-    def test_execution_engine(self):
-        pass
+    def setUp(self) -> None:
+        self.enclosing_dir = Path(__file__).parent
+        os.makedirs(self.enclosing_dir / 'generated', exist_ok=True)
+        from nni.runtime import protocol
+        protocol._out_file = open(self.enclosing_dir / 'generated/debug_protocol_out_file.py', 'wb')
+
+    def tearDown(self) -> None:
+        from nni.runtime import protocol
+        protocol._out_file.close()
+        nni.retiarii.execution.api._execution_engine = None
+        nni.retiarii.integration_api._advisor = None

test/ut/retiarii/test_highlevel_apis.py

@@ -8,7 +8,10 @@ import torch.nn.functional as F
 from nni.retiarii import Sampler, basic_unit
 from nni.retiarii.converter import convert_to_graph
 from nni.retiarii.codegen import model_to_pytorch_script
-from nni.retiarii.nn.pytorch.mutator import process_inline_mutation
+from nni.retiarii.execution.python import _unpack_if_only_one
+from nni.retiarii.nn.pytorch.mutator import process_inline_mutation, extract_mutation_from_pt_module
+from nni.retiarii.serializer import model_wrapper
+from nni.retiarii.utils import ContextStack
 
 
 class EnumerateSampler(Sampler):
@@ -44,7 +47,7 @@ class MutableConv(nn.Module):
             return self.conv2(x)
 
 
-class TestHighLevelAPI(unittest.TestCase):
+class GraphIR(unittest.TestCase):
 
     def _convert_to_ir(self, model):
         script_module = torch.jit.script(model)
@@ -56,7 +59,19 @@ class TestHighLevelAPI(unittest.TestCase):
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         return exec_vars['converted_model']
 
+    def _get_model_with_mutators(self, pytorch_model):
+        model = self._convert_to_ir(pytorch_model)
+        mutators = process_inline_mutation(model)
+        return model, mutators
+
+    def get_serializer(self):
+        def dummy(cls):
+            return cls
+
+        return dummy
+
     def test_layer_choice(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -68,8 +83,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.module(x)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -80,6 +94,7 @@ class TestHighLevelAPI(unittest.TestCase):
                          torch.Size([1, 5, 3, 3]))
 
     def test_input_choice(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -92,8 +107,7 @@ class TestHighLevelAPI(unittest.TestCase):
                 x2 = self.conv2(x)
                 return self.input([x1, x2])
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -104,6 +118,7 @@ class TestHighLevelAPI(unittest.TestCase):
                          torch.Size([1, 5, 3, 3]))
 
     def test_chosen_inputs(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self, reduction):
                 super().__init__()
@@ -117,8 +132,7 @@ class TestHighLevelAPI(unittest.TestCase):
                 return self.input([x1, x2])
 
         for reduction in ['none', 'sum', 'mean', 'concat']:
-            model = self._convert_to_ir(Net(reduction))
-            mutators = process_inline_mutation(model)
+            model, mutators = self._get_model_with_mutators(Net(reduction))
             self.assertEqual(len(mutators), 1)
             mutator = mutators[0].bind_sampler(EnumerateSampler())
             model = mutator.apply(model)
@@ -133,6 +147,7 @@ class TestHighLevelAPI(unittest.TestCase):
                 self.assertEqual(result.size(), torch.Size([1, 3, 3, 3]))
 
     def test_value_choice(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -142,8 +157,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.conv(x, self.index())
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -154,6 +168,7 @@ class TestHighLevelAPI(unittest.TestCase):
                          torch.Size([1, 5, 3, 3]))
 
     def test_value_choice_as_parameter(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -162,8 +177,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.conv(x)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -174,6 +188,7 @@ class TestHighLevelAPI(unittest.TestCase):
                          torch.Size([1, 5, 1, 1]))
 
     def test_value_choice_as_parameter(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -182,8 +197,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.conv(x)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -194,6 +208,7 @@ class TestHighLevelAPI(unittest.TestCase):
                          torch.Size([1, 5, 1, 1]))
 
     def test_value_choice_as_parameter(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -202,8 +217,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.conv(x)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 2)
         mutators[0].bind_sampler(EnumerateSampler())
         mutators[1].bind_sampler(EnumerateSampler())
@@ -214,6 +228,7 @@ class TestHighLevelAPI(unittest.TestCase):
                          torch.Size([1, 8, 1, 1]))
 
     def test_value_choice_as_parameter_shared(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -223,8 +238,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.conv1(x) + self.conv2(x)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -235,6 +249,7 @@ class TestHighLevelAPI(unittest.TestCase):
                          torch.Size([1, 8, 5, 5]))
 
     def test_value_choice_in_functional(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -243,8 +258,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return F.dropout(x, self.dropout_rate())
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -254,6 +268,7 @@ class TestHighLevelAPI(unittest.TestCase):
         self.assertAlmostEqual(self._get_converted_pytorch_model(model2)(torch.randn(1, 3, 3, 3)).abs().sum().item(), 0)
 
     def test_value_choice_in_layer_choice(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -265,8 +280,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.linear(x)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 3)
         sz_counter = Counter()
         sampler = RandomSampler()
@@ -278,6 +292,7 @@ class TestHighLevelAPI(unittest.TestCase):
         self.assertEqual(len(sz_counter), 4)
 
     def test_shared(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self, shared=True):
                 super().__init__()
@@ -294,16 +309,14 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.module1(x) + self.module2(x)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         sampler = RandomSampler()
         mutator = mutators[0].bind_sampler(sampler)
         self.assertEqual(self._get_converted_pytorch_model(mutator.apply(model))(torch.randn(1, 3, 3, 3)).size(0), 1)
         self.assertEqual(sampler.counter, 1)
 
-        model = self._convert_to_ir(Net(shared=False))
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net(shared=False))
         self.assertEqual(len(mutators), 2)
         sampler = RandomSampler()
         # repeat test. Expectation: sometimes succeeds, sometimes fails.
@@ -321,6 +334,7 @@ class TestHighLevelAPI(unittest.TestCase):
         self.assertLess(failed_count, 30)
 
     def test_valuechoice_access(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -330,8 +344,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return self.conv(x)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutators[0].bind_sampler(EnumerateSampler())
         input = torch.randn(1, 3, 5, 5)
@@ -340,6 +353,7 @@ class TestHighLevelAPI(unittest.TestCase):
         self.assertEqual(self._get_converted_pytorch_model(mutators[0].apply(model))(input).size(),
                          torch.Size([1, 8, 1, 1]))
 
+        @self.get_serializer()
         class Net2(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -354,14 +368,14 @@ class TestHighLevelAPI(unittest.TestCase):
                 x = self.conv(x)
                 return self.conv1(torch.cat((x, x), 1))
 
-        model = self._convert_to_ir(Net2())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net2())
         self.assertEqual(len(mutators), 1)
         mutators[0].bind_sampler(EnumerateSampler())
         input = torch.randn(1, 3, 5, 5)
         self._get_converted_pytorch_model(mutators[0].apply(model))(input)
 
     def test_valuechoice_access_functional(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -370,8 +384,7 @@ class TestHighLevelAPI(unittest.TestCase):
             def forward(self, x):
                 return F.dropout(x, self.dropout_rate()[0])
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -381,6 +394,7 @@ class TestHighLevelAPI(unittest.TestCase):
         self.assertAlmostEqual(self._get_converted_pytorch_model(model2)(torch.randn(1, 3, 3, 3)).abs().sum().item(), 0)
 
     def test_valuechoice_access_functional_expression(self):
+        @self.get_serializer()
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -391,8 +405,7 @@ class TestHighLevelAPI(unittest.TestCase):
                 # ValueError: dropout probability has to be between 0 and 1, but got 1.05
                 return F.dropout(x, self.dropout_rate()[0] - .1)
 
-        model = self._convert_to_ir(Net())
-        mutators = process_inline_mutation(model)
+        model, mutators = self._get_model_with_mutators(Net())
         self.assertEqual(len(mutators), 1)
         mutator = mutators[0].bind_sampler(EnumerateSampler())
         model1 = mutator.apply(model)
@@ -400,3 +413,29 @@ class TestHighLevelAPI(unittest.TestCase):
         self._get_converted_pytorch_model(model1)(torch.randn(1, 3, 3, 3))
         self.assertEqual(self._get_converted_pytorch_model(model1)(torch.randn(1, 3, 3, 3)).size(), torch.Size([1, 3, 3, 3]))
         self.assertAlmostEqual(self._get_converted_pytorch_model(model2)(torch.randn(1, 3, 3, 3)).abs().sum().item(), 0)
+
+
+class Python(GraphIR):
+    def _get_converted_pytorch_model(self, model_ir):
+        mutation = {mut.mutator.label: _unpack_if_only_one(mut.samples) for mut in model_ir.history}
+        with ContextStack('fixed', mutation):
+            model = model_ir.python_class(**model_ir.python_init_params)
+            return model
+
+    def _get_model_with_mutators(self, pytorch_model):
+        return extract_mutation_from_pt_module(pytorch_model)
+
+    def get_serializer(self):
+        return model_wrapper
+
+    @unittest.skip
+    def test_value_choice(self): ...
+
+    @unittest.skip
+    def test_value_choice_in_functional(self): ...
+
+    @unittest.skip
+    def test_valuechoice_access_functional(self): ...
+
+    @unittest.skip
+    def test_valuechoice_access_functional_expression(self): ...

test/ut/retiarii/test_mutator.py

@@ -60,7 +60,14 @@ def test_mutation():
     model2 = mutator.apply(model1)
     assert _get_pools(model2) == (global_pool, max_pool)
 
-    assert model2.history == [model0, model1]
+    assert len(model2.history) == 2
+    assert model2.history[0].from_ == model0
+    assert model2.history[0].to == model1
+    assert model2.history[1].from_ == model1
+    assert model2.history[1].to == model2
+    assert model2.history[0].mutator == mutator
+    assert model2.history[1].mutator == mutator
+
     assert _get_pools(model0) == (max_pool, max_pool)
     assert _get_pools(model1) == (avg_pool, global_pool)