rm useless file (#5044)

nni/nas/oneshot/pytorch/base_lightning.py

@@ -374,11 +374,11 @@ class BaseOneShotLightningModule(pl.LightningModule):
     def on_fit_end(self):
         return self.model.on_fit_end()
 
-    def on_train_batch_start(self, batch, batch_idx, unused=0):
-        return self.model.on_train_batch_start(batch, batch_idx, unused)
+    def on_train_batch_start(self, batch, batch_idx, *args, **kwargs):
+        return self.model.on_train_batch_start(batch, batch_idx, *args, **kwargs)
 
-    def on_train_batch_end(self, outputs, batch, batch_idx, unused=0):
-        return self.model.on_train_batch_end(outputs, batch, batch_idx, unused)
+    def on_train_batch_end(self, outputs, batch, batch_idx, *args, **kwargs):
+        return self.model.on_train_batch_end(outputs, batch, batch_idx, *args, **kwargs)
 
     # Deprecated hooks in pytorch-lightning
     def on_epoch_start(self):

test/algo/nas/test_lightning_trainer.py

@@ -16,9 +16,9 @@ from torchvision.datasets import MNIST
 
 debug = False
 
-progress_bar_refresh_rate = 0
+enable_progress_bar = False
 if debug:
-    progress_bar_refresh_rate = 1
+    enable_progress_bar = True
 
 
 class MNISTModel(nn.Module):
@@ -96,7 +96,7 @@ def test_mnist():
     lightning = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
                                   val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
                                   max_epochs=2, limit_train_batches=0.25,  # for faster training
-                                  progress_bar_refresh_rate=progress_bar_refresh_rate)
+                                  enable_progress_bar=enable_progress_bar)
     lightning._execute(MNISTModel)
     assert _get_final_result() > 0.7
     _reset()
@@ -113,7 +113,7 @@ def test_diabetes():
                               train_dataloader=pl.DataLoader(train_dataset, batch_size=20),
                               val_dataloaders=pl.DataLoader(test_dataset, batch_size=20),
                               max_epochs=100,
-                              progress_bar_refresh_rate=progress_bar_refresh_rate)
+                              enable_progress_bar=enable_progress_bar)
     lightning._execute(FCNet(train_dataset.x.shape[1], 1))
     assert _get_final_result() < 2e4
     _reset()
@@ -134,7 +134,7 @@ def test_fit_api():
     def lightning(): return pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
                                               val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
                                               max_epochs=1, limit_train_batches=0.1,  # for faster training
-                                              progress_bar_refresh_rate=progress_bar_refresh_rate)
+                                              enable_progress_bar=enable_progress_bar)
     # Lightning will have some cache in models / trainers,
     # which is problematic if we call fit multiple times.
     lightning().fit(lambda: MNISTModel())

test/training_service/config/examples/classic-nas-pytorch-v2.yml

-experimentName: default_test
-searchSpaceFile: ni-nas-search-space.json
-trialCommand: python3 main.py --epochs 1 --batches 1
-trialCodeDirectory: ../../../../examples/nas/legacy/classic_nas
-trialGpuNumber: 0
-trialConcurrency: 1
-maxExperimentDuration: 15m
-maxTrialNumber: 1
-tuner:
-  name: PPOTuner
-  classArgs:
-    optimize_mode: maximize
-trainingService:
-  platform: local
-assessor:
-  name: Medianstop
-  classArgs:
-    optimize_mode: maximize

test/training_service/config/examples/classic-nas-pytorch.yml

-authorName: nni
-experimentName: default_test
-maxExecDuration: 10m
-maxTrialNum: 1
-trialConcurrency: 1
-searchSpacePath: nni-nas-search-space.json
-
-tuner:
-  builtinTunerName: PPOTuner
-  classArgs:
-    optimize_mode: maximize
-trial:
-  command: python3 mnist.py --epochs 1
-  codeDir: ../../../../examples/nas/legacy/classic_nas
-  gpuNum: 0
-
-useAnnotation: false
-multiPhase: false
-multiThread: false
-
-trainingServicePlatform: local
\ No newline at end of file

test/training_service/config/examples/classic-nas-tf2.yml

-authorName: nni
-experimentName: default_test
-maxExecDuration: 10m
-maxTrialNum: 1
-trialConcurrency: 1
-searchSpacePath: nni-nas-search-space-tf2.json
-
-tuner:
-  builtinTunerName: PPOTuner
-  classArgs:
-    optimize_mode: maximize
-trial:
-  command: python3 train.py --epochs 1
-  codeDir: ../../../../examples/nas/legacy/classic_nas-tf
-  gpuNum: 0
-
-useAnnotation: false
-multiPhase: false
-multiThread: false
-
-trainingServicePlatform: local
\ No newline at end of file

test/training_service/config/integration_tests.yml

@@ -114,20 +114,6 @@ testCases:
 #- name: nested-ss
 #  configFile: test/training_service/config/examples/mnist-nested-search-space.yml
 
-- name: classic-nas-gen-ss
-  configFile: test/training_service/config/examples/classic-nas-pytorch.yml
-  launchCommand: nnictl ss_gen --trial_command="python3 mnist.py --epochs 1" --trial_dir=../examples/nas/legacy/classic_nas --file=training_service/config/examples/nni-nas-search-space.json
-  stopCommand:
-  experimentStatusCheck: False
-  trainingService: local
-
-- name: classic-nas-pytorch
-  configFile: test/training_service/config/examples/classic-nas-pytorch.yml
-  # remove search space file
-  stopCommand: nnictl stop
-  onExitCommand: python3 -c "import os; os.remove('training_service/config/examples/nni-nas-search-space.json')"
-  trainingService: local
-
 #########################################################################
 # nni features test
 #########################################################################
@@ -247,8 +233,8 @@ testCases:
 #- name: tuner-metis
 #  configFile: test/training_service/config/tuners/metis.yml
 
-- name: tuner-regularized_evolution
-  configFile: test/training_service/config/tuners/regularized_evolution_tuner.yml
+# - name: tuner-regularized_evolution
+#   configFile: test/training_service/config/tuners/regularized_evolution_tuner.yml
 
 #########################################################################
 # nni customized-tuners test

test/training_service/config/integration_tests_config_v2.yml

@@ -38,20 +38,6 @@ testCases:
   configFile: test/training_service/config/examples/cifar10-pytorch-adl.yml
   trainingService: adl
 
-- name: classic-nas-gen-ss
-  configFile: test/training_service/config/examples/classic-nas-pytorch-v2.yml
-  launchCommand: nnictl ss_gen --trial_command="python3 mnist.py --epochs 1" --trial_dir=../examples/nas/legacy/classic_nas --file=training_service/config/examples/nni-nas-search-space.json
-  stopCommand:
-  experimentStatusCheck: False
-  trainingService: local
-
-- name: classic-nas-pytorch
-  configFile: test/training_service/config/examples/classic-nas-pytorch-v2.yml
-  # remove search space file
-  stopCommand: nnictl stop
-  onExitCommand: python3 -c "import os; os.remove('training_service/examples/nni-nas-search-space.json')"
-  trainingService: local
-
 #########################################################################
 # nni features test
 #########################################################################
@@ -124,8 +110,8 @@ testCases:
 #########################################################################
 # nni tuners test
 #########################################################################
-- name: tuner-regularized_evolution
-  configFile: test/training_service/config/tuners/regularized_evolution_tuner-v2.yml
+# - name: tuner-regularized_evolution
+#   configFile: test/training_service/config/tuners/regularized_evolution_tuner-v2.yml
 
 #########################################################################
 # nni customized-tuners test

test/training_service/config/integration_tests_tf2.yml

@@ -56,20 +56,6 @@ testCases:
   configFile: test/training_service/config/examples/cifar10-pytorch-adl.yml
   trainingService: adl
 
-- name: classic-nas-gen-ss
-  configFile: test/training_service/config/examples/classic-nas-tf2.yml
-  launchCommand: nnictl ss_gen --trial_command="python3 train.py --epochs 1" --trial_dir=../examples/nas/legacy/classic_nas-tf --file=training_service/config/examples/nni-nas-search-space-tf2.json
-  stopCommand:
-  experimentStatusCheck: False
-  trainingService: local
-
-- name: classic-nas-tensorflow2
-  configFile: test/training_service/config/examples/classic-nas-tf2.yml
-  # remove search space file
-  stopCommand: nnictl stop
-  onExitCommand: python3 -c 'import os; os.remove("training_service/config/examples/nni-nas-search-space-tf2.json")'
-  trainingService: local
-
 #########################################################################
 # nni features test
 #########################################################################

test/training_service/config/tuners/regularized_evolution_tuner-v2.yml

-experimentName: default_test
-searchSpaceFile: seach_space_classic_nas.json
-trialCommand: python3 mnist.py --epochs 1
-trialCodeDirectory: ../../../../examples/nas/legacy/classic_nas
-trialGpuNumber: 0
-trialConcurrency: 1
-maxExperimentDuration: 15m
-maxTrialNumber: 1
-tuner:
-  name: RegularizedEvolutionTuner
-  classArgs:
-    optimize_mode: maximize
-trainingService:
-  platform: local

test/training_service/config/tuners/regularized_evolution_tuner.yml

-authorName: nni
-experimentName: default_test
-maxExecDuration: 10m
-maxTrialNum: 1
-trialConcurrency: 1
-searchSpacePath: seach_space_classic_nas.json
-tuner:
-  builtinTunerName: RegularizedEvolutionTuner
-  classArgs:
-    optimize_mode: maximize
-trial:
-  codeDir: ../../../../examples/nas/legacy/classic_nas
-  command: python3 mnist.py --epochs 1
-  gpuNum: 0
-
-useAnnotation: false
-multiPhase: false
-multiThread: false
-
-trainingServicePlatform: local

test/ut/sdk/models/pytorch_models/__init__.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-
-from .mutable_scope import SpaceWithMutableScope
-from .naive import NaiveSearchSpace
-from .nested import NestedSpace

test/ut/sdk/models/pytorch_models/mutable_scope.py

-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from nni.nas.pytorch.mutables import LayerChoice, InputChoice, MutableScope
-
-
-class Cell(MutableScope):
-    def __init__(self, cell_name, prev_labels, channels):
-        super().__init__(cell_name)
-        self.input_choice = InputChoice(choose_from=prev_labels, n_chosen=1, return_mask=True,
-                                        key=cell_name + "_input")
-        self.op_choice = LayerChoice([
-            nn.Conv2d(channels, channels, 3, padding=1),
-            nn.Conv2d(channels, channels, 5, padding=2),
-            nn.MaxPool2d(3, stride=1, padding=1),
-            nn.AvgPool2d(3, stride=1, padding=1),
-            nn.Identity()
-        ], key=cell_name + "_op")
-
-    def forward(self, prev_layers):
-        chosen_input, chosen_mask = self.input_choice(prev_layers)
-        cell_out = self.op_choice(chosen_input)
-        return cell_out, chosen_mask
-
-
-class Node(MutableScope):
-    def __init__(self, node_name, prev_node_names, channels):
-        super().__init__(node_name)
-        self.cell_x = Cell(node_name + "_x", prev_node_names, channels)
-        self.cell_y = Cell(node_name + "_y", prev_node_names, channels)
-
-    def forward(self, prev_layers):
-        out_x, mask_x = self.cell_x(prev_layers)
-        out_y, mask_y = self.cell_y(prev_layers)
-        return out_x + out_y, mask_x | mask_y
-
-
-class Layer(nn.Module):
-    def __init__(self, num_nodes, channels):
-        super().__init__()
-        self.num_nodes = num_nodes
-        self.nodes = nn.ModuleList()
-        node_labels = [InputChoice.NO_KEY, InputChoice.NO_KEY]
-        for i in range(num_nodes):
-            node_labels.append("node_{}".format(i))
-            self.nodes.append(Node(node_labels[-1], node_labels[:-1], channels))
-        self.final_conv_w = nn.Parameter(torch.zeros(channels, self.num_nodes + 2, channels, 1, 1),
-                                         requires_grad=True)
-        self.bn = nn.BatchNorm2d(channels, affine=False)
-
-    def forward(self, pprev, prev):
-        prev_nodes_out = [pprev, prev]
-        nodes_used_mask = torch.zeros(self.num_nodes + 2, dtype=torch.bool, device=prev.device)
-        for i in range(self.num_nodes):
-            node_out, mask = self.nodes[i](prev_nodes_out)
-            nodes_used_mask[:mask.size(0)] |= mask.to(prev.device)
-            # NOTE: which device should we put mask on?
-            prev_nodes_out.append(node_out)
-
-        unused_nodes = torch.cat([out for used, out in zip(nodes_used_mask, prev_nodes_out) if not used], 1)
-        unused_nodes = F.relu(unused_nodes)
-        conv_weight = self.final_conv_w[:, ~nodes_used_mask, :, :, :]
-        conv_weight = conv_weight.view(conv_weight.size(0), -1, 1, 1)
-        out = F.conv2d(unused_nodes, conv_weight)
-        return prev, self.bn(out)
-
-
-class SpaceWithMutableScope(nn.Module):
-    def __init__(self, test_case, num_layers=4, num_nodes=5, channels=16, in_channels=3, num_classes=10):
-        super().__init__()
-        self.test_case = test_case
-        self.num_layers = num_layers
-
-        self.stem = nn.Sequential(
-            nn.Conv2d(in_channels, channels, 3, 1, 1, bias=False),
-            nn.BatchNorm2d(channels)
-        )
-
-        self.layers = nn.ModuleList()
-        for _ in range(self.num_layers + 2):
-            self.layers.append(Layer(num_nodes, channels))
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.dense = nn.Linear(channels, num_classes)
-
-    def forward(self, x):
-        prev = cur = self.stem(x)
-        for layer in self.layers:
-            prev, cur = layer(prev, cur)
-
-        cur = self.gap(F.relu(cur)).view(x.size(0), -1)
-        return self.dense(cur)

test/ut/sdk/models/pytorch_models/naive.py

-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from nni.nas.pytorch.mutables import LayerChoice, InputChoice
-
-
-class NaiveSearchSpace(nn.Module):
-    def __init__(self, test_case):
-        super().__init__()
-        self.test_case = test_case
-        self.conv1 = LayerChoice([nn.Conv2d(3, 6, 3, padding=1), nn.Conv2d(3, 6, 5, padding=2)])
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = LayerChoice([nn.Conv2d(6, 16, 3, padding=1), nn.Conv2d(6, 16, 5, padding=2)],
-                                 return_mask=True)
-        self.conv3 = nn.Conv2d(16, 16, 1)
-
-        self.skipconnect = InputChoice(n_candidates=1)
-        self.skipconnect2 = InputChoice(n_candidates=2, return_mask=True)
-        self.bn = nn.BatchNorm2d(16)
-
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Linear(16, 10)
-
-    def forward(self, x):
-        bs = x.size(0)
-
-        x = self.pool(F.relu(self.conv1(x)))
-        x0, mask = self.conv2(x)
-        self.test_case.assertEqual(mask.size(), torch.Size([2]))
-        x1 = F.relu(self.conv3(x0))
-
-        _, mask = self.skipconnect2([x0, x1])
-        x0 = self.skipconnect([x0])
-        if x0 is not None:
-            x1 += x0
-        x = self.pool(self.bn(x1))
-        self.test_case.assertEqual(mask.size(), torch.Size([2]))
-
-        x = self.gap(x).view(bs, -1)
-        x = self.fc(x)
-        return x

test/ut/sdk/models/pytorch_models/nested.py

-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import torch.nn as nn
-import torch.nn.functional as F
-
-from nni.nas.pytorch.mutables import LayerChoice, InputChoice
-
-
-class MutableOp(nn.Module):
-    def __init__(self, kernel_size):
-        super().__init__()
-        self.conv = nn.Conv2d(3, 120, kernel_size, padding=kernel_size // 2)
-        self.nested_mutable = InputChoice(n_candidates=10)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class NestedSpace(nn.Module):
-    # this doesn't pass tests
-    def __init__(self, test_case):
-        super().__init__()
-        self.test_case = test_case
-        self.conv1 = LayerChoice([MutableOp(3), MutableOp(5)])
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc1 = nn.Linear(120, 10)
-
-    def forward(self, x):
-        bs = x.size(0)
-        x = F.relu(self.conv1(x))
-        x = self.gap(x).view(bs, -1)
-        x = self.fc(x)
-        return x