Integration test

deployment/docker/Dockerfile → Dockerfile

@@ -3,12 +3,15 @@
 
 FROM nvidia/cuda:9.2-cudnn7-runtime-ubuntu18.04
 
+ARG NNI_RELEASE
+
 LABEL maintainer='Microsoft NNI Team<[email protected]>'
 
 ENV DEBIAN_FRONTEND=noninteractive 
 
-RUN apt-get -y update && \
-    apt-get -y install sudo \
+RUN apt-get -y update
+RUN apt-get -y install \
+    sudo \
     apt-utils \
     git \
     curl \
@@ -26,28 +29,27 @@ RUN apt-get -y update && \
     python3-dev \
     python3-pip \
     python3-tk \
-    libcupti-dev && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
+    libcupti-dev
+RUN apt-get clean
+RUN rm -rf /var/lib/apt/lists/*
 
 #
 # generate python script
 #
-RUN cp /usr/bin/python3 /usr/bin/python
+RUN ln -s python3 /usr/bin/python
 
 #
 # update pip
 #
-RUN python3 -m pip install --upgrade pip==20.0.2 setuptools==41.0.0
+RUN python3 -m pip install --upgrade pip==20.2.4 setuptools==50.3.2
 
 # numpy 1.14.3  scipy 1.1.0
-RUN python3 -m pip --no-cache-dir install \
-    numpy==1.14.3 scipy==1.1.0
+RUN python3 -m pip --no-cache-dir install numpy==1.14.3 scipy==1.1.0
 
 #
-# Tensorflow 1.15
+# TensorFlow
 #
-RUN python3 -m pip --no-cache-dir install tensorflow-gpu==1.15.0
+RUN python3 -m pip --no-cache-dir install tensorflow==2.3.1
 
 #
 # Keras 2.1.6
@@ -73,15 +75,15 @@ RUN python3 -m pip --no-cache-dir install pandas==0.23.4 lightgbm==2.2.2
 #
 # Install NNI
 #
-RUN python3 -m pip --no-cache-dir install nni
+COPY dist/nni-${NNI_RELEASE}-py3-none-manylinux1_x86_64.whl .
+RUN python3 -m pip install nni-${NNI_RELEASE}-py3-none-manylinux1_x86_64.whl
 
 #
 # install aml package
 #
 RUN python3 -m pip --no-cache-dir install azureml
 RUN python3 -m pip --no-cache-dir install azureml-sdk
 
-
 ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/root/.local/bin:/usr/bin:/bin:/sbin
 
 WORKDIR /root

examples/model_compress/BNN_quantizer_cifar10.py

@@ -5,7 +5,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from torchvision import datasets, transforms
-from nni.compression.torch import BNNQuantizer
+from nni.algorithms.compression.pytorch.quantization import BNNQuantizer
 
 
 class VGG_Cifar10(nn.Module):

examples/model_compress/DoReFaQuantizer_torch_mnist.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn.functional as F
 from torchvision import datasets, transforms
-from nni.compression.torch import DoReFaQuantizer
+from nni.algorithms.compression.pytorch.quantization import DoReFaQuantizer
 
 
 class Mnist(torch.nn.Module):
@@ -86,4 +86,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
+    main()

examples/model_compress/L1_torch_cifar10.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from torchvision import datasets, transforms
-from nni.compression.torch import L1FilterPruner
+from nni.algorithms.compression.pytorch.pruning import L1FilterPruner
 from models.cifar10.vgg import VGG
 
 

examples/model_compress/QAT_torch_quantizer.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn.functional as F
 from torchvision import datasets, transforms
-from nni.compression.torch import QAT_Quantizer
+from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
 
 
 class Mnist(torch.nn.Module):

examples/model_compress/amc/amc_search.py

@@ -8,7 +8,7 @@
 import torch
 import torch.nn as nn
 from torchvision.models import resnet
-from nni.compression.torch import AMCPruner
+from nni.algorithms.compression.pytorch.pruning import AMCPruner
 from data import get_split_dataset
 from utils import AverageMeter, accuracy
 

examples/model_compress/amc/amc_train.py

@@ -15,9 +15,9 @@
 from tensorboardX import SummaryWriter
 from torchvision.models import resnet
 
-from nni.compression.torch.pruning.amc.lib.net_measure import measure_model
-from nni.compression.torch.pruning.amc.lib.utils import get_output_folder
-from nni.compression.torch import ModelSpeedup
+from nni.algorithms.compression.pytorch.pruning.amc.lib.net_measure import measure_model
+from nni.algorithms.compression.pytorch.pruning.amc.lib.utils import get_output_folder
+from nni.compression.pytorch import ModelSpeedup
 
 from data import get_dataset
 from utils import AverageMeter, accuracy, progress_bar

examples/model_compress/auto_pruners_torch.py

@@ -14,10 +14,10 @@
 from models.mnist.lenet import LeNet
 from models.cifar10.vgg import VGG
 from models.cifar10.resnet import ResNet18, ResNet50
-from nni.compression.torch import L1FilterPruner, L2FilterPruner, FPGMPruner
-from nni.compression.torch import SimulatedAnnealingPruner, ADMMPruner, NetAdaptPruner, AutoCompressPruner
-from nni.compression.torch import ModelSpeedup
-from nni.compression.torch.utils.counter import count_flops_params
+from nni.algorithms.compression.pytorch.pruning import L1FilterPruner, L2FilterPruner, FPGMPruner
+from nni.algorithms.compression.pytorch.pruning import SimulatedAnnealingPruner, ADMMPruner, NetAdaptPruner, AutoCompressPruner
+from nni.compression.pytorch import ModelSpeedup
+from nni.compression.pytorch.utils.counter import count_flops_params
 
 
 def get_data(dataset, data_dir, batch_size, test_batch_size):

examples/model_compress/lottery_torch_mnist_fc.py

@@ -6,7 +6,7 @@
 import torch.utils.data
 import torchvision.datasets as datasets
 import torchvision.transforms as transforms
-from nni.compression.torch import LotteryTicketPruner
+from nni.algorithms.compression.pytorch.pruning import LotteryTicketPruner
 
 class fc1(nn.Module):
 

examples/model_compress/model_prune_torch.py

@@ -9,8 +9,16 @@
 
 from models.cifar10.vgg import VGG
 import nni
-from nni.compression.torch import LevelPruner, SlimPruner, FPGMPruner, L1FilterPruner, \
-    L2FilterPruner, AGPPruner, ActivationMeanRankFilterPruner, ActivationAPoZRankFilterPruner
+from nni.algorithms.compression.pytorch.pruning import (
+    LevelPruner,
+    SlimPruner,
+    FPGMPruner,
+    L1FilterPruner,
+    L2FilterPruner,
+    AGPPruner,
+    ActivationMeanRankFilterPruner,
+    ActivationAPoZRankFilterPruner
+)
 
 prune_config = {
     'level': {

examples/model_compress/model_speedup.py

@@ -6,7 +6,7 @@
 import torch.nn.functional as F
 from torchvision import datasets, transforms
 from models.cifar10.vgg import VGG
-from nni.compression.torch import apply_compression_results, ModelSpeedup
+from nni.compression.pytorch import apply_compression_results, ModelSpeedup
 
 torch.manual_seed(0)
 use_mask = True

examples/model_compress/pruning_kd.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from torchvision import datasets, transforms
-from nni.compression.torch import L1FilterPruner
+from nni.algorithms.compression.pytorch.pruning import L1FilterPruner
 from knowledge_distill.knowledge_distill import KnowledgeDistill
 from models.cifar10.vgg import VGG
 

examples/model_compress/slim_torch_cifar10.py

@@ -5,7 +5,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from torchvision import datasets, transforms
-from nni.compression.torch import SlimPruner
+from nni.algorithms.compression.pytorch.pruning import SlimPruner
 from models.cifar10.vgg import VGG
 
 def updateBN(model):

examples/nas/classic_nas/mnist.py

@@ -18,7 +18,7 @@
 from torchvision import datasets, transforms
 
 from nni.nas.pytorch.mutables import LayerChoice, InputChoice
-from nni.nas.pytorch.classic_nas import get_and_apply_next_architecture
+from nni.algorithms.nas.pytorch.classic_nas import get_and_apply_next_architecture
 
 
 logger = logging.getLogger('mnist_AutoML')

nni/algorithms/compression/pytorch/pruning/__init__.py

@@ -3,7 +3,6 @@
 
 from .finegrained_pruning import *
 from .structured_pruning import *
-from .apply_compression import apply_compression_results
 from .one_shot import *
 from .agp import *
 from .lottery_ticket import LotteryTicketPruner

nni/compression/pytorch/__init__.py

@@ -3,3 +3,4 @@
 
 from .speedup import ModelSpeedup
 from .compressor import Compressor, Pruner, Quantizer
+from .pruning import apply_compression_results

nni/compression/pytorch/pruning/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from .apply_compression import apply_compression_results

nni/compression/pytorch/pruning/apply_compression.py

@@ -0,0 +1,29 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import logging
+import torch
+
+logger = logging.getLogger('torch apply compression')
+
+def apply_compression_results(model, masks_file, map_location=None):
+    """
+    Apply the masks from ```masks_file``` to the model
+    Note: this API is for inference, because it simply multiplies weights with
+    corresponding masks when this API is called.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        The model to be compressed
+    masks_file : str
+        The path of the mask file
+    map_location : str
+        the device on which masks are placed, same to map_location in ```torch.load```
+    """
+    masks = torch.load(masks_file, map_location)
+    for name, module in model.named_modules():
+        if name in masks:
+            module.weight.data = module.weight.data.mul_(masks[name]['weight'])
+            if hasattr(module, 'bias') and module.bias is not None and 'bias' in masks[name]:
+                module.bias.data = module.bias.data.mul_(masks[name]['bias'])

nni/tools/nnictl/constants.py

@@ -64,21 +64,21 @@
 INSTALLABLE_PACKAGE_META = {
     'SMAC': {
         'type': 'tuner',
-        'class_name': 'nni.smac_tuner.smac_tuner.SMACTuner',
+        'class_name': 'nni.algorithms.hpo.smac_tuner.smac_tuner.SMACTuner',
         'code_sub_dir': 'smac_tuner',
-        'class_args_validator': 'nni.smac_tuner.smac_tuner.SMACClassArgsValidator'
+        'class_args_validator': 'nni.algorithms.hpo.smac_tuner.smac_tuner.SMACClassArgsValidator'
     },
     'BOHB': {
         'type': 'advisor',
-        'class_name': 'nni.bohb_advisor.bohb_advisor.BOHB',
+        'class_name': 'nni.algorithms.hpo.bohb_advisor.bohb_advisor.BOHB',
         'code_sub_dir': 'bohb_advisor',
-        'class_args_validator': 'nni.bohb_advisor.bohb_advisor.BOHBClassArgsValidator'
+        'class_args_validator': 'nni.algorithms.hpo.bohb_advisor.bohb_advisor.BOHBClassArgsValidator'
     },
     'PPOTuner': {
         'type': 'tuner',
-        'class_name': 'nni.ppo_tuner.ppo_tuner.PPOTuner',
+        'class_name': 'nni.algorithms.hpo.ppo_tuner.ppo_tuner.PPOTuner',
         'code_sub_dir': 'ppo_tuner',
-        'class_args_validator': 'nni.ppo_tuner.ppo_tuner.PPOClassArgsValidator'
+        'class_args_validator': 'nni.algorithms.hpo.ppo_tuner.ppo_tuner.PPOClassArgsValidator'
     }
 }
 

pipelines/full-test-linux.yml

@@ -0,0 +1,75 @@
+trigger: none
+pr: none
+schedules:
+- cron: 0 16 * * *
+  branches:
+    include: [ master ]
+
+jobs:
+- job: linux
+  pool: NNI CI GPU3
+  timeoutInMinutes: 120
+
+  steps:
+  - script: |
+      echo "##vso[task.setvariable variable=PATH]${PATH}:${HOME}/.local/bin"
+      echo "##vso[task.setvariable variable=NNI_RELEASE]999.$(date -u +%Y%m%d%H%M%S)"
+
+      python3 -m pip install -U --upgrade pip setuptools
+      python3 -m pip install -U pytest
+    displayName: Prepare
+
+  - script: |
+      set -e
+      python3 setup.py build_ts
+      python3 setup.py bdist_wheel -p manylinux1_x86_64
+      python3 -m pip install dist/nni-${NNI_RELEASE}-py3-none-manylinux1_x86_64.whl
+    displayName: Install NNI
+
+  - script: |
+      set -e
+      python3 -m pip install -U scikit-learn==0.23.2
+      python3 -m pip install -U torchvision==0.4.2
+      python3 -m pip install -U torch==1.3.1
+      python3 -m pip install -U keras==2.1.6
+      python3 -m pip install -U tensorflow==2.3.1 tensorflow-estimator==2.3.0
+      python3 -m pip install -U thop
+      sudo apt-get install swig -y
+      nnictl package install --name=SMAC
+      nnictl package install --name=BOHB
+      nnictl package install --name=PPOTuner
+    displayName: Install extra dependencies
+
+  - script: |
+      set -e
+      cd examples/tuners/customized_tuner
+      python3 setup.py develop --user
+      nnictl package install .
+    displayName: Install customized tuner
+
+  - script: |
+      set -e
+      (cd test && python3 -m pytest ut)
+      export PATH=$PATH:$PWD/toolchain/yarn/bin
+      export CI=true
+      (cd ts/nni_manager && yarn test)
+      (cd ts/nasui && yarn test)
+    displayName: Unit test
+    continueOnError: true
+
+  - script: |
+      cd test
+      python3 nni_test/nnitest/run_tests.py --config config/integration_tests.yml --ts local
+    displayName: Integration test
+    continueOnError: true
+
+  - script: |
+      cd test
+      source scripts/nas.sh
+    displayName: NAS test
+    continueOnError: true
+
+  - script: |
+      cd test
+      source scripts/model_compression.sh
+    displayName: Model compression test