Fix seed when test apoz pruner

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/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 @@ def test_simulated_annealing_pruner(self):
  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 @@ def test_auto_compress_pruner(self):
  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

@@ -1,8 +1,10 @@
 # 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 @@ def test_slim_pruner(self):
  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 @@ def test_movement_pruner(self):
  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()