Merge pull request #4670 from liuzhe-lz/doc-merge

nni/retiarii/codegen/pytorch.py

@@ -6,6 +6,7 @@ import re
 from typing import Dict, List, Tuple, Any
 
 from nni.retiarii.operation_def.torch_op_def import ToDevice
+from nni.retiarii.utils import STATE_DICT_PY_MAPPING
 from nni.common.device import Device, GPUDevice
 
 from ..graph import IllegalGraphError, Edge, Graph, Node, Model
@@ -97,7 +98,18 @@ def _format_variable_name(name: str, graph_name: str) -> str:
     name = name.replace('/', '__')
 
     # https://stackoverflow.com/questions/3303312/how-do-i-convert-a-string-to-a-valid-variable-name-in-python
-    return re.sub('\W|^(?=\d)','_', name)
+    name = re.sub('\W|^(?=\d)','_', name)
+
+    if name.startswith('__') and (len(name) > 2 and name[2] != '_'):
+        # name can't start with double underscore
+        # it's reserved in Python: https://stackoverflow.com/a/1301409/6837658
+        # but it's actually very common in our generated code
+        name = name[1:]
+    elif name.startswith('_'):
+        # to avoid conflicts between '_' and '__'
+        name = 'i' + name
+
+    return name
 
 
 def generate_cuda_mapping(placement: Dict[Node, Device]) -> Dict[Device, int]:
@@ -125,6 +137,7 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
     # only need to generate code for module here
     import_pkgs = set()
     node_codes = []
+    node_python_mappings = {}
     cuda_remapped_id = None
     if placement:
         cuda_remapped_id = generate_cuda_mapping(placement)
@@ -138,7 +151,9 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
             pkg_name = node.operation.get_import_pkg()
             if pkg_name is not None:
                 import_pkgs.add(pkg_name)
-            node_code = node.operation.to_init_code(_format_variable_name(node.name, graph_name))
+
+            py_variable_name = _format_variable_name(node.name, graph_name)
+            node_code = node.operation.to_init_code(py_variable_name)
             if node_code is not None:
                 if placement and node in placement and len(node_code) > 0:
                     if isinstance(placement[node], GPUDevice):
@@ -149,6 +164,11 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
                 else:
                     node_codes.append(node_code)
 
+                # Map to module hierarchies in original search space python code
+                node_python_mappings[py_variable_name] = node.python_name
+
+    node_codes.append(f'self.{STATE_DICT_PY_MAPPING} = {node_python_mappings}')
+
     if graph.input_node.operation.io_names is None:
         input_code = '*_inputs'
     else:

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

@@ -101,6 +101,7 @@ class _MultiModelSupervisedLearningModule(LightningModule):
             return {name: self.trainer.callback_metrics['val_' + name].item() for name in self.metrics}
 
 
+@nni.trace
 class MultiModelSupervisedLearningModule(_MultiModelSupervisedLearningModule):
     """
     Lightning Module of SupervisedLearning for Cross-Graph Optimization.

nni/retiarii/nn/pytorch/api.py

@@ -11,6 +11,7 @@ import torch.nn as nn
 
 from nni.common.serializer import Translatable
 from nni.retiarii.serializer import basic_unit
+from nni.retiarii.utils import STATE_DICT_PY_MAPPING_PARTIAL
 from .utils import Mutable, generate_new_label, get_fixed_value
 
 
@@ -82,9 +83,22 @@ class LayerChoice(Mutable):
                             label: Optional[str] = None, **kwargs):
         chosen = get_fixed_value(label)
         if isinstance(candidates, list):
-            return candidates[int(chosen)]
+            result = candidates[int(chosen)]
         else:
-            return candidates[chosen]
+            result = candidates[chosen]
+
+        # map the named hierarchies to support weight inheritance for python engine
+        if hasattr(result, STATE_DICT_PY_MAPPING_PARTIAL):
+            # handle cases where layer choices are nested
+            # already has a mapping, will merge with it
+            prev_mapping = getattr(result, STATE_DICT_PY_MAPPING_PARTIAL)
+            setattr(result, STATE_DICT_PY_MAPPING_PARTIAL, {k: f'{chosen}.{v}' for k, v in prev_mapping.items()})
+        else:
+            # "result" needs to know where to map itself.
+            # Ideally, we should put a _mapping_ in the module where "result" is located,
+            # but it's impossible to put mapping into parent module here.
+            setattr(result, STATE_DICT_PY_MAPPING_PARTIAL, {'__self__': str(chosen)})
+        return result
 
     def __init__(self, candidates: Union[Dict[str, nn.Module], List[nn.Module]], *,
                  prior: Optional[List[float]] = None, label: Optional[str] = None, **kwargs):

nni/retiarii/nn/pytorch/component.py

@@ -5,6 +5,8 @@ from typing import Callable, List, Union, Tuple, Optional
 import torch
 import torch.nn as nn
 
+from nni.retiarii.utils import STATE_DICT_PY_MAPPING_PARTIAL
+
 from .api import LayerChoice
 from .cell import Cell
 from .nasbench101 import NasBench101Cell, NasBench101Mutator
@@ -59,7 +61,15 @@ class Repeat(Mutable):
                                           List[nn.Module]],
                             depth: Union[int, Tuple[int, int]], *, label: Optional[str] = None):
         repeat = get_fixed_value(label)
-        return nn.Sequential(*cls._replicate_and_instantiate(blocks, repeat))
+        result = nn.Sequential(*cls._replicate_and_instantiate(blocks, repeat))
+
+        if hasattr(result, STATE_DICT_PY_MAPPING_PARTIAL):
+            # already has a mapping, will merge with it
+            prev_mapping = getattr(result, STATE_DICT_PY_MAPPING_PARTIAL)
+            setattr(result, STATE_DICT_PY_MAPPING_PARTIAL, {k: f'blocks.{v}' for k, v in prev_mapping.items()})
+        else:
+            setattr(result, STATE_DICT_PY_MAPPING_PARTIAL, {'__self__': 'blocks'})
+        return result
 
     def __init__(self,
                  blocks: Union[Callable[[int], nn.Module],

nni/retiarii/nn/pytorch/nasbench101.py

@@ -301,6 +301,8 @@ class NasBench101Cell(Mutable):
             [op_candidates[selected[f'{label}/op{i}']] for i in range(1, num_nodes - 1)],
             adjacency_list, in_features, out_features, num_nodes, projection)
 
+        # FIXME: weight inheritance on nasbench101 is not supported yet
+
     def __init__(self, op_candidates: Union[Dict[str, Callable[[int], nn.Module]], List[Callable[[int], nn.Module]]],
                  in_features: int, out_features: int, projection: Callable[[int, int], nn.Module],
                  max_num_nodes: int = 7, max_num_edges: int = 9, label: Optional[str] = None):

nni/retiarii/serializer.py

@@ -6,7 +6,7 @@ import os
 import warnings
 from typing import Any, TypeVar, Union
 
-from nni.common.serializer import Traceable, is_traceable, trace, _copy_class_wrapper_attributes
+from nni.common.serializer import Traceable, is_traceable, is_wrapped_with_trace, trace, _copy_class_wrapper_attributes
 from .utils import ModelNamespace
 
 __all__ = ['get_init_parameters_or_fail', 'serialize', 'serialize_cls', 'basic_unit', 'model_wrapper',
@@ -71,7 +71,8 @@ def basic_unit(cls: T, basic_unit_tag: bool = True) -> Union[T, Traceable]:
     if nni_trace_flag.lower() == 'disable':
         return cls
 
-    _check_wrapped(cls)
+    if _check_wrapped(cls, 'basic_unit'):
+        return cls
 
     import torch.nn as nn
     assert issubclass(cls, nn.Module), 'When using @basic_unit, the class must be a subclass of nn.Module.'
@@ -79,15 +80,7 @@ def basic_unit(cls: T, basic_unit_tag: bool = True) -> Union[T, Traceable]:
     cls = trace(cls)
     cls._nni_basic_unit = basic_unit_tag
 
-    # HACK: for torch script
-    # https://github.com/pytorch/pytorch/pull/45261
-    # https://github.com/pytorch/pytorch/issues/54688
-    # I'm not sure whether there will be potential issues
-    import torch
-    cls._get_nni_attr = torch.jit.ignore(cls._get_nni_attr)
-    cls.trace_symbol = torch.jit.unused(cls.trace_symbol)
-    cls.trace_args = torch.jit.unused(cls.trace_args)
-    cls.trace_kwargs = torch.jit.unused(cls.trace_kwargs)
+    _torchscript_patch(cls)
 
     return cls
 
@@ -116,12 +109,14 @@ def model_wrapper(cls: T) -> Union[T, Traceable]:
     if nni_trace_flag.lower() == 'disable':
         return cls
 
-    _check_wrapped(cls)
+    if _check_wrapped(cls, 'model_wrapper'):
+        return cls
 
     import torch.nn as nn
     assert issubclass(cls, nn.Module)
 
-    wrapper = trace(cls)
+    # subclass can still use trace info
+    wrapper = trace(cls, inheritable=True)
 
     class reset_wrapper(wrapper):
         def __init__(self, *args, **kwargs):
@@ -129,8 +124,12 @@ def model_wrapper(cls: T) -> Union[T, Traceable]:
                 super().__init__(*args, **kwargs)
 
     _copy_class_wrapper_attributes(wrapper, reset_wrapper)
-    reset_wrapper.__wrapped__ = wrapper.__wrapped__
+    reset_wrapper.__wrapped__ = getattr(wrapper, '__wrapped__', wrapper)
     reset_wrapper._nni_model_wrapper = True
+    reset_wrapper._traced = True
+
+    _torchscript_patch(cls)
+
     return reset_wrapper
 
 
@@ -146,6 +145,32 @@ def is_model_wrapped(cls_or_instance) -> bool:
     return getattr(cls_or_instance, '_nni_model_wrapper', False)
 
 
-def _check_wrapped(cls: T) -> bool:
-    if getattr(cls, '_traced', False) or getattr(cls, '_nni_model_wrapper', False):
-        raise TypeError(f'{cls} is already wrapped with trace wrapper (basic_unit / model_wrapper / trace). Cannot wrap again.')
+def _check_wrapped(cls: T, rewrap: str) -> bool:
+    wrapped = None
+    if is_model_wrapped(cls):
+        wrapped = 'model_wrapper'
+    elif is_basic_unit(cls):
+        wrapped = 'basic_unit'
+    elif is_wrapped_with_trace(cls):
+        wrapped = 'nni.trace'
+    if wrapped:
+        if wrapped != rewrap:
+            raise TypeError(f'{cls} is already wrapped with {wrapped}. Cannot rewrap with {rewrap}.')
+        return True
+    return False
+
+
+def _torchscript_patch(cls) -> None:
+    # HACK: for torch script
+    # https://github.com/pytorch/pytorch/pull/45261
+    # https://github.com/pytorch/pytorch/issues/54688
+    # I'm not sure whether there will be potential issues
+    import torch
+    if hasattr(cls, '_get_nni_attr'):  # could not exist on non-linux
+        cls._get_nni_attr = torch.jit.ignore(cls._get_nni_attr)
+    if hasattr(cls, 'trace_symbol'):
+        # these must all exist or all non-exist
+        cls.trace_symbol = torch.jit.unused(cls.trace_symbol)
+        cls.trace_args = torch.jit.unused(cls.trace_args)
+        cls.trace_kwargs = torch.jit.unused(cls.trace_kwargs)
+        cls.trace_copy = torch.jit.ignore(cls.trace_copy)

nni/retiarii/strategy/_rl_impl.py

@@ -43,6 +43,17 @@ class MultiThreadEnvWorker(EnvWorker):
     def reset(self):
         return self.env.reset()
 
+    def send(self, action):
+        # for tianshou >= 0.4.6
+        if action is None:
+            self.result = self.pool.apply_async(self.env.reset)
+        else:
+            self.send_action(action)
+
+    def recv(self):
+        # for tianshou >= 0.4.6
+        return self.result.get()
+
     @staticmethod
     def wait(*args, **kwargs):
         raise NotImplementedError('Async collect is not supported yet.')

nni/retiarii/utils.py

@@ -2,8 +2,10 @@
 # Licensed under the MIT license.
 
 import inspect
+import itertools
 import warnings
 from collections import defaultdict
+from contextlib import contextmanager
 from typing import Any, List, Dict
 from pathlib import Path
 
@@ -150,3 +152,119 @@ class ModelNamespace:
 
 def get_current_context(key: str) -> Any:
     return ContextStack.top(key)
+
+
+# map variables to prefix in the state dict
+# e.g., {'upsample': 'mynet.module.deconv2.upsample_layer'}
+STATE_DICT_PY_MAPPING = '_mapping_'
+
+# map variables to `prefix`.`value` in the state dict
+# e.g., {'upsample': 'choice3.upsample_layer'},
+# which actually means {'upsample': 'mynet.module.choice3.upsample_layer'},
+# and 'upsample' is also in `mynet.module`.
+STATE_DICT_PY_MAPPING_PARTIAL = '_mapping_partial_'
+
+
+@contextmanager
+def original_state_dict_hooks(model: Any):
+    """
+    Use this patch if you want to save/load state dict in the original state dict hierarchy.
+
+    For example, when you already have a state dict for the base model / search space (which often
+    happens when you have trained a supernet with one-shot strategies), the state dict isn't organized
+    in the same way as when a sub-model is sampled from the search space. This patch will help
+    the modules in the sub-model find the corresponding module in the base model.
+
+    The code looks like,
+
+    .. code-block:: python
+
+        with original_state_dict_hooks(model):
+            model.load_state_dict(state_dict_from_supernet, strict=False)  # supernet has extra keys
+
+    Or vice-versa,
+
+    .. code-block:: python
+
+        with original_state_dict_hooks(model):
+            supernet_style_state_dict = model.state_dict()
+    """
+
+    import torch.nn as nn
+    assert isinstance(model, nn.Module), 'PyTorch is the only supported framework for now.'
+
+    # the following are written for pytorch only
+
+    # first get the full mapping
+    full_mapping = {}
+
+    def full_mapping_in_module(src_prefix, tar_prefix, module):
+        if hasattr(module, STATE_DICT_PY_MAPPING):
+            # only values are complete
+            local_map = getattr(module, STATE_DICT_PY_MAPPING)
+        elif hasattr(module, STATE_DICT_PY_MAPPING_PARTIAL):
+            # keys and values are both incomplete
+            local_map = getattr(module, STATE_DICT_PY_MAPPING_PARTIAL)
+            local_map = {k: tar_prefix + v for k, v in local_map.items()}
+        else:
+            # no mapping
+            local_map = {}
+
+        if '__self__' in local_map:
+            # special case, overwrite prefix
+            tar_prefix = local_map['__self__'] + '.'
+
+        for key, value in local_map.items():
+            if key != '' and key not in module._modules:  # not a sub-module, probably a parameter
+                full_mapping[src_prefix + key] = value
+
+        if src_prefix != tar_prefix:  # To deal with leaf nodes.
+            for name, value in itertools.chain(module._parameters.items(), module._buffers.items()):  # direct children
+                if value is None or name in module._non_persistent_buffers_set:
+                    # it won't appear in state dict
+                    continue
+                if (src_prefix + name) not in full_mapping:
+                    full_mapping[src_prefix + name] = tar_prefix + name
+
+        for name, child in module.named_children():
+            # sub-modules
+            full_mapping_in_module(
+                src_prefix + name + '.',
+                local_map.get(name, tar_prefix + name) + '.',  # if mapping doesn't exist, respect the prefix
+                child
+            )
+
+    full_mapping_in_module('', '', model)
+
+    def load_state_dict_hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        reverse_mapping = defaultdict(list)
+        for src, tar in full_mapping.items():
+            reverse_mapping[tar].append(src)
+
+        transf_state_dict = {}
+        for src, tar_keys in reverse_mapping.items():
+            if src in state_dict:
+                value = state_dict.pop(src)
+                for tar in tar_keys:
+                    transf_state_dict[tar] = value
+            else:
+                missing_keys.append(src)
+        state_dict.update(transf_state_dict)
+
+    def state_dict_hook(module, destination, prefix, local_metadata):
+        result = {}
+        for src, tar in full_mapping.items():
+            if src in destination:
+                result[tar] = destination.pop(src)
+            else:
+                raise KeyError(f'"{src}" not in state dict, but found in mapping.')
+        destination.update(result)
+
+    try:
+        hooks = []
+        hooks.append(model._register_load_state_dict_pre_hook(load_state_dict_hook))
+        hooks.append(model._register_state_dict_hook(state_dict_hook))
+        yield
+    finally:
+        for hook in hooks:
+            hook.remove()

nni/tools/nnictl/legacy_launcher.py

@@ -70,21 +70,17 @@ def start_rest_server(port, platform, mode, experiment_id, foreground=False, log
         node_command = os.path.join(entry_dir, 'node')
     cmds = [node_command, '--max-old-space-size=4096', entry_file, '--port', str(port), '--mode', platform, \
             '--experiment_id', experiment_id]
-    if mode == 'view':
-        cmds += ['--start_mode', 'resume']
-        cmds += ['--readonly', 'true']
-    else:
-        cmds += ['--start_mode', mode]
+    cmds += ['--action', mode]
     if log_dir is not None:
-        cmds += ['--log_dir', log_dir]
+        cmds += ['--experiments-directory', log_dir]
     if log_level is not None:
-        cmds += ['--log_level', log_level]
+        cmds += ['--log-level', log_level]
     if foreground:
         cmds += ['--foreground', 'true']
     if url_prefix:
         _validate_prefix_path(url_prefix)
         set_prefix_url(url_prefix)
-        cmds += ['--url_prefix', url_prefix]
+        cmds += ['--url-prefix', url_prefix.strip('/')]
 
     stdout_full_path, stderr_full_path = get_log_path(experiment_id)
     with open(stdout_full_path, 'a+') as stdout_file, open(stderr_full_path, 'a+') as stderr_file:
@@ -520,9 +516,9 @@ def create_experiment(args):
 
     try:
         if schema == 1:
-            launch_experiment(args, config_v1, 'new', experiment_id, 1)
+            launch_experiment(args, config_v1, 'create', experiment_id, 1)
         else:
-            launch_experiment(args, config_v2, 'new', experiment_id, 2)
+            launch_experiment(args, config_v2, 'create', experiment_id, 2)
     except Exception as exception:
         restServerPid = Experiments().get_all_experiments().get(experiment_id, {}).get('pid')
         if restServerPid:

pipelines/fast-test.yml

@@ -177,7 +177,7 @@ stages:
   - job: windows
     pool:
       vmImage: windows-latest
-    timeoutInMinutes: 70
+    timeoutInMinutes: 75
 
     steps:
     - template: templates/install-dependencies.yml

test/ut/compression/v1/test_model_speedup.py

@@ -512,6 +512,46 @@ class SpeedupTestCase(TestCase):
         print("Fine-grained speeduped model")
         print(model)
 
+    def test_multiplication_speedup(self):
+        """
+        Model from issue 4540.
+        """
+        class Net(torch.nn.Module):
+            def __init__(self,):
+                super(Net, self).__init__()
+                self.avgpool = torch.nn.AdaptiveAvgPool2d(1)
+                self.input = torch.nn.Conv2d(3, 8, 3)
+                self.bn = torch.nn.BatchNorm2d(8)
+                self.fc1 = torch.nn.Conv2d(8, 16, 1)
+                self.fc2 = torch.nn.Conv2d(16, 8, 1)
+                self.activation = torch.nn.ReLU()
+                self.scale_activation = torch.nn.Hardsigmoid()
+                self.out = torch.nn.Conv2d(8, 12, 1)
+
+            def forward(self, input):
+                input = self.activation(self.bn(self.input(input)))
+                scale = self.avgpool(input)
+                out1 = self.activation(self.fc1(scale))
+                out1 = self.scale_activation(self.fc2(out1))
+                return self.out(out1 * input)
+
+        model = Net().to(device)
+        model.eval()
+        im = torch.ones(1, 3, 512, 512).to(device)
+        model(im)
+        cfg_list = []
+
+        for name, module in model.named_modules():
+            if isinstance(module, torch.nn.Conv2d):
+                cfg_list.append({'op_types':['Conv2d'], 'sparsity':0.3, 'op_names':[name]})
+
+        pruner = L1FilterPruner(model, cfg_list)
+        pruner.compress()
+        pruner.export_model(MODEL_FILE, MASK_FILE)
+        pruner._unwrap_model()
+        ms=ModelSpeedup(model, im, MASK_FILE)
+        ms.speedup_model()
+
     def tearDown(self):
         if os.path.exists(MODEL_FILE):
             os.remove(MODEL_FILE)

test/ut/compression/v2/test_iterative_pruner_torch.py

@@ -4,6 +4,7 @@
 import random
 import unittest
 
+import numpy
 import torch
 import torch.nn.functional as F
 
@@ -105,6 +106,17 @@ class IterativePrunerTestCase(unittest.TestCase):
         sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
         assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
 
+    def test_amc_pruner(self):
+        model = TorchModel()
+        config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.5, 'max_sparsity_per_layer': 0.8}]
+        dummy_input = torch.rand(10, 1, 28, 28)
+        ddpg_params = {'hidden1': 300, 'hidden2': 300, 'lr_c': 1e-3, 'lr_a': 1e-4, 'warmup': 5, 'discount': 1.,
+                       'bsize': 64, 'rmsize': 100, 'window_length': 1, 'tau': 0.01, 'init_delta': 0.5, 'delta_decay': 0.99,
+                       'max_episode_length': 1e9, 'epsilon': 50000}
+        pruner = AMCPruner(10, model, config_list, dummy_input, evaluator, finetuner=finetuner, ddpg_params=ddpg_params, target='flops', log_dir='../../../logs')
+        pruner.compress()
+
+class FixSeedPrunerTestCase(unittest.TestCase):
     def test_auto_compress_pruner(self):
         model = TorchModel()
         config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.8}]
@@ -126,15 +138,21 @@ class IterativePrunerTestCase(unittest.TestCase):
         print(sparsity_list)
         assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
 
-    def test_amc_pruner(self):
-        model = TorchModel()
-        config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.5, 'max_sparsity_per_layer': 0.8}]
-        dummy_input = torch.rand(10, 1, 28, 28)
-        ddpg_params = {'hidden1': 300, 'hidden2': 300, 'lr_c': 1e-3, 'lr_a': 1e-4, 'warmup': 5, 'discount': 1.,
-                       'bsize': 64, 'rmsize': 100, 'window_length': 1, 'tau': 0.01, 'init_delta': 0.5, 'delta_decay': 0.99,
-                       'max_episode_length': 1e9, 'epsilon': 50000}
-        pruner = AMCPruner(10, model, config_list, dummy_input, evaluator, finetuner=finetuner, ddpg_params=ddpg_params, target='flops', log_dir='../../../logs')
-        pruner.compress()
+    def setUp(self) -> None:
+        # fix seed in order to solve the random failure of ut
+        random.seed(1024)
+        numpy.random.seed(1024)
+        torch.manual_seed(1024)
+
+    def tearDown(self) -> None:
+        # reset seed
+        import time
+        now = int(time.time() * 100)
+        random.seed(now)
+        seed = random.randint(0, 2 ** 32 - 1)
+        random.seed(seed)
+        numpy.random.seed(seed)
+        torch.manual_seed(seed)
 
 if __name__ == '__main__':
     unittest.main()

test/ut/compression/v2/test_pruner_torch.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+import random
 import unittest
 
+import numpy
 import torch
 import torch.nn.functional as F
 
@@ -122,18 +124,6 @@ class PrunerTestCase(unittest.TestCase):
         sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
         assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
 
-    def test_activation_apoz_rank_pruner(self):
-        model = TorchModel()
-        config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
-        pruner = ActivationAPoZRankPruner(model=model, config_list=config_list, trainer=trainer,
-                                          traced_optimizer=get_optimizer(model), criterion=criterion, training_batches=5,
-                                          activation='relu', mode='dependency_aware',
-                                          dummy_input=torch.rand(10, 1, 28, 28))
-        pruned_model, masks = pruner.compress()
-        pruner._unwrap_model()
-        sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
-        assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
-
     def test_activation_mean_rank_pruner(self):
         model = TorchModel()
         config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
@@ -177,6 +167,34 @@ class PrunerTestCase(unittest.TestCase):
         sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
         assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
 
+class FixSeedPrunerTestCase(unittest.TestCase):
+    def test_activation_apoz_rank_pruner(self):
+        model = TorchModel()
+        config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
+        pruner = ActivationAPoZRankPruner(model=model, config_list=config_list, trainer=trainer,
+                                            traced_optimizer=get_optimizer(model), criterion=criterion, training_batches=5,
+                                            activation='relu', mode='dependency_aware',
+                                            dummy_input=torch.rand(10, 1, 28, 28))
+        pruned_model, masks = pruner.compress()
+        pruner._unwrap_model()
+        sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
+        assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
+
+    def setUp(self) -> None:
+        # fix seed in order to solve the random failure of ut
+        random.seed(1024)
+        numpy.random.seed(1024)
+        torch.manual_seed(1024)
+
+    def tearDown(self) -> None:
+        # reset seed
+        import time
+        now = int(time.time() * 100)
+        random.seed(now)
+        seed = random.randint(0, 2 ** 32 - 1)
+        random.seed(seed)
+        numpy.random.seed(seed)
+        torch.manual_seed(seed)
 
 if __name__ == '__main__':
     unittest.main()

test/ut/retiarii/debug_mnist_pytorch.py

@@ -16,6 +16,7 @@ class _model(nn.Module):
         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()
+        self._mapping_ = {'stem': None, 'flatten': None, 'fc1': None, 'fc2': None, 'softmax': None}
 
     def forward(self, image):
         stem = self.stem(image)
@@ -34,6 +35,7 @@ class stem(nn.Module):
         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)
+        self._mapping_ = {'conv1': None, 'pool1': None, 'conv2': None, 'pool2': None}
 
     def forward(self, *_inputs):
         conv1 = self.conv1(_inputs[0])

test/ut/retiarii/test_cgo_engine.py

@@ -9,6 +9,7 @@ from pytorch_lightning.utilities.seed import seed_everything
 from pathlib import Path
 
 import nni
+import nni.runtime.platform.test
 
 try:
     from nni.common.device import GPUDevice

test/ut/retiarii/test_convert.py

@@ -14,6 +14,7 @@ import torchvision
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii import basic_unit
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
@@ -50,16 +51,6 @@ class Linear(nn.Module):
         return out.view(size[0], size[1], -1)
 
 class TestConvert(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
 
     def checkExportImport(self, model, input):
         model_ir = self._convert_model(model, input)
@@ -68,9 +59,8 @@ class TestConvert(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(dict(model.state_dict()))
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)

test/ut/retiarii/test_convert_basic.py

@@ -12,20 +12,11 @@ from nni.retiarii import basic_unit
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 # following pytorch v1.7.1
 
 class TestConvert(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
 
     def checkExportImport(self, model, input, check_value=True):
         model_ir = self._convert_model(model, input)
@@ -35,9 +26,10 @@ class TestConvert(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(model.state_dict())
+
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)

test/ut/retiarii/test_convert_models.py

@@ -9,23 +9,13 @@ import torch.nn.functional as F
 import torchvision
 
 import nni.retiarii.nn.pytorch as nn
-from nni.retiarii import serialize
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
 
 class TestModels(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
 
     def run_test(self, model, input, check_value=True):
         model_ir = self._convert_model(model, input)
@@ -35,9 +25,10 @@ class TestModels(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(model.state_dict())
+
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)

test/ut/retiarii/test_convert_operators.py

@@ -16,6 +16,7 @@ import torchvision
 
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
@@ -23,16 +24,6 @@ from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
 
 class TestOperators(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
 
     def checkExportImport(self, model, input, check_value=True):
         model_ir = self._convert_model(model, input)
@@ -42,9 +33,10 @@ class TestOperators(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(model.state_dict())
+
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)

test/ut/retiarii/test_convert_pytorch.py

@@ -14,28 +14,17 @@ import torch.nn.functional as F
 import torchvision
 
 import nni.retiarii.nn.pytorch as nn
-from nni.retiarii import serialize
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
 
 class TestPytorch(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
-
-    def run_test(self, model, input, check_value=True):
+
+    def run_test(self, model, input, check_value=True, strict_load=True):
         model_ir = self._convert_model(model, input)
         model_code = model_to_pytorch_script(model_ir)
-        print(model_code)
 
         from .inject_nn import remove_inject_pytorch_nn
         remove_inject_pytorch_nn()
@@ -43,9 +32,10 @@ class TestPytorch(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(model.state_dict(), strict=strict_load)
+
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)
@@ -76,7 +66,8 @@ class TestPytorch(unittest.TestCase, ConvertMixin):
 
         model = LargeModel()
         x = torch.tensor([2], dtype=torch.long)
-        self.run_test(model, (x, ))
+        # emb and lin1 is actually not used so they won't appear in generated model
+        self.run_test(model, (x, ), strict_load=False)
 
     @unittest.skip('skip for now, as it needs inject_nn')
     def test_mobilenet_v2_with_external_data(self):