From 67ea330469f22a0d8d352284e966af8ebd1df12d Mon Sep 17 00:00:00 2001 From: Yuge Zhang Date: Mon, 29 Jun 2020 07:26:47 +0800 Subject: [PATCH 01/17] Fix bugs in NAS UI (deferred from previous releases) (#2552) * Fix NASUI bugs * Add pipeline for NASUI * update log * Move graph utils test to test folder --- src/nasui/assets/darts/graph.json | 1 + src/nasui/assets/darts/log | 471 ++++++++++++++++++++++++++ src/nasui/assets/naive/graph.json | 1 + src/nasui/assets/naive/log | 404 ++++++++++++++++++++++ src/nasui/package.json | 1 + src/nasui/src/App.tsx | 25 +- src/nasui/src/Chart.tsx | 10 +- src/nasui/src/__tests__/graphUtils.ts | 26 ++ src/nasui/src/graphUtils.ts | 24 +- test/scripts/unittest.sh | 6 + 10 files changed, 953 insertions(+), 16 deletions(-) create mode 100644 src/nasui/assets/darts/graph.json create mode 100644 src/nasui/assets/darts/log create mode 100644 src/nasui/assets/naive/graph.json create mode 100644 src/nasui/assets/naive/log create mode 100644 src/nasui/src/__tests__/graphUtils.ts diff --git a/src/nasui/assets/darts/graph.json b/src/nasui/assets/darts/graph.json new file mode 100644 index 0000000000..578852363a --- /dev/null +++ b/src/nasui/assets/darts/graph.json @@ -0,0 +1 @@ +{"node": [{"name": "input/input.1", "op": "IO Node", "attr": {"attr": {"s": ""}, "_output_shapes": {"list": {"shape": [{"dim": [{"size": "2"}, {"size": "3"}, {"size": "32"}, {"size": "32"}]}]}}}}, {"name": "output/output.1", "op": "IO Node", "input": ["CNN/Linear[linear]/23753"], "attr": {"attr": {"s": ""}, "_output_shapes": {"list": {"shape": [{"dim": [{"size": "2"}, {"size": "10"}]}]}}}}, {"name": "CNN/Sequential[stem]/Conv2d[0]/weight/14553", "op": "prim::GetAttr", "input": ["CNN/Sequential[stem]/Conv2d[0]/weight/14552"], "attr": {"attr": {"s": "eyBuYW1lIDogIHdlaWdodCB9"}}}, {"name": "CNN/Sequential[stem]/Conv2d[0]/14554", "op": "prim::Constant", "attr": {"attr": {"s": "e30="}}}, {"name": "CNN/Sequential[stem]/Conv2d[0]/14555", "op": 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b/src/nasui/package.json @@ -20,6 +20,7 @@ "start": "react-scripts start", "build": "react-scripts build", "eject": "react-scripts eject", + "test": "react-scripts test", "backend": "node server.js" }, "eslintConfig": { diff --git a/src/nasui/src/App.tsx b/src/nasui/src/App.tsx index 31e35bfbca..ba5990f5a2 100644 --- a/src/nasui/src/App.tsx +++ b/src/nasui/src/App.tsx @@ -29,6 +29,7 @@ import ExpandMoreIcon from '@material-ui/icons/ExpandMore'; import List from '@material-ui/core/List'; import ListItem from '@material-ui/core/ListItem'; import Backdrop from '@material-ui/core/Backdrop'; +import Tooltip from '@material-ui/core/Tooltip'; import Chart from './Chart'; import { Graph } from './graphUtils'; @@ -272,15 +273,21 @@ class App extends React.Component { NNI NAS Board - - - - - - - - - + + + + + + + + + + + + + + + diff --git a/src/nasui/src/Chart.tsx b/src/nasui/src/Chart.tsx index 9bed42ed88..1c6cc78854 100644 --- a/src/nasui/src/Chart.tsx +++ b/src/nasui/src/Chart.tsx @@ -91,6 +91,7 @@ export default class Chart extends React.Component { expandSet: Set = new Set(); elemWeight: Map = new Map(); graphEl: any[] = []; + private firstUpdate = true; componentDidMount() { this.cyInstance = cytoscape({ @@ -149,7 +150,8 @@ export default class Chart extends React.Component { } } else { // re-calculate collapse - this.renderGraph(true); + this.renderGraph(true, this.firstUpdate); + this.firstUpdate = false; } } @@ -245,7 +247,7 @@ export default class Chart extends React.Component { setTimeout(_render, 100); } - private renderGraph(graphChanged: boolean) { + private renderGraph(graphChanged: boolean, fit: boolean = false) { const { graph } = this.props; if (graph === undefined) return; @@ -269,7 +271,9 @@ export default class Chart extends React.Component { this.cyInstance!.json({ elements: graphEl }); - layout.fit = false; + if (!fit) { + layout.fit = false; + } eles.layout(layout).run(); } } else { diff --git a/src/nasui/src/__tests__/graphUtils.ts b/src/nasui/src/__tests__/graphUtils.ts new file mode 100644 index 0000000000..ab0f7e7e1d --- /dev/null +++ b/src/nasui/src/__tests__/graphUtils.ts @@ -0,0 +1,26 @@ +import { Graph } from '../graphUtils'; +import * as fs from 'fs'; + +describe('Graph utils test', () => { + it('Graph constructor darts', () => { + const graph = new Graph(JSON.parse(fs.readFileSync('assets/darts/graph.json').toString()), true); + const activation = JSON.parse(fs.readFileSync('assets/darts/log').toString().split('\n')[0]); + expect(graph.nodes.length).toEqual(1842); + expect(graph.edges.length).toEqual(927); + const weights = graph.weightFromMutables(activation); + expect(weights.get('["CNN/ModuleList[cells]/Cell[1]/ModuleList[mutable_ops]/Node[0]/InputChoice[input_switch]/input.228",' + + '"CNN/ModuleList[cells]/Cell[1]/ModuleList[mutable_ops]/Node[1]/ModuleList[ops]/' + + 'LayerChoice[2]/PoolBN[maxpool]/MaxPool2d[pool]/input.229"]')).toBeCloseTo(0.125, 3); + }); + + it('Graph constructor naive', () => { + const graph = new Graph(JSON.parse(fs.readFileSync('assets/naive/graph.json').toString()), true); + expect(graph.nodes.length).toEqual(51); + expect(graph.edges.length).toEqual(37); + expect(graph.mutableEdges.get('LayerChoice1')![0].length).toEqual(5); + expect(graph.mutableEdges.get('LayerChoice1')![1].length).toEqual(5); + expect(graph.mutableEdges.get('LayerChoice2')![0].length).toEqual(5); + expect(graph.mutableEdges.get('LayerChoice2')![1].length).toEqual(5); + expect(graph.mutableEdges.get('InputChoice3')![0].length).toEqual(4); + }); +}); diff --git a/src/nasui/src/graphUtils.ts b/src/nasui/src/graphUtils.ts index 35b31b3647..b5b3ff64cd 100644 --- a/src/nasui/src/graphUtils.ts +++ b/src/nasui/src/graphUtils.ts @@ -47,6 +47,10 @@ export class NodeTs { isParent(): boolean { return this.children.length > 0; } + + toString(): string { + return `Node(id=${this.id})`; + } }; export class Edge { @@ -59,6 +63,10 @@ export class Edge { this.target = target; this.id = JSON.stringify([this.source.id, this.target.id]); } + + toString(): string { + return `Edge(${this.source} -> ${this.target})`; + } }; interface NodeSummary { @@ -76,12 +84,12 @@ export class Graph { nodes: NodeTs[]; edges: Edge[]; defaultExpandSet: Set; + mutableEdges: Map; private id2idx: Map; private edgeId2idx: Map; private forwardGraph: Map; private backwardGraph: Map; private node2edge: Map; - private mutableEdges: Map; private build() { this.id2idx.clear(); @@ -359,17 +367,25 @@ export class Graph { weightFromMutables(mutable: any): Map { const elemWeight = new Map(); Object.entries(mutable).forEach(entry => { + const elemWeightPartial = new Map(); const key = entry[0]; const weights = entry[1] as number[]; this.mutableEdges.get(key)!.forEach((edges: any, i: number) => { edges.forEach((edge: any) => { - if (elemWeight.has(edge.id)) { - elemWeight.set(edge.id, elemWeight.get(edge.id)! + weights[i]); + if (elemWeightPartial.has(edge.id)) { + elemWeightPartial.set(edge.id, elemWeightPartial.get(edge.id)! + weights[i]); } else { - elemWeight.set(edge.id, weights[i]); + elemWeightPartial.set(edge.id, weights[i]); } }) }); + elemWeightPartial.forEach((v, k) => { + if (elemWeight.has(k)) { + elemWeight.set(k, Math.min(elemWeight.get(k)!, v)); + } else { + elemWeight.set(k, v); + } + }); }); this.nodes.forEach(node => { const edges = this.connectedEdges(node.id); diff --git a/test/scripts/unittest.sh b/test/scripts/unittest.sh index 0c03b2ab77..ee66d86c85 100644 --- a/test/scripts/unittest.sh +++ b/test/scripts/unittest.sh @@ -26,6 +26,12 @@ echo "" echo "===========================Testing: nni_manager===========================" npm run test +# -------------For NASUI unittest------------- +cd ${CWD}/../src/nasui +echo "" +echo "===========================Testing: nasui===========================" +CI=true npm test + ## ------Run nnictl unit test------ echo "" echo "===========================Testing: nnictl===========================" From 3fb49d9bf4942c6bb21256881b089f5c8f64e2d0 Mon Sep 17 00:00:00 2001 From: chicm-ms <38930155+chicm-ms@users.noreply.github.com> Date: Mon, 29 Jun 2020 09:11:25 +0800 Subject: [PATCH 02/17] Fix it pipelines (#2605) --- test/config/integration_tests.yml | 9 +++------ test/config/integration_tests_tf2.yml | 9 +++------ test/config/pr_tests.yml | 1 + test/nni_test/nnitest/run_tests.py | 13 +++++++++++++ test/pipelines/pipelines-it-local-tf2.yml | 1 + test/pipelines/pipelines-it-local-windows.yml | 1 + test/pipelines/pipelines-it-local.yml | 1 + 7 files changed, 23 insertions(+), 12 deletions(-) diff --git a/test/config/integration_tests.yml b/test/config/integration_tests.yml index 1f5ccc35d8..3fb713cbe3 100644 --- a/test/config/integration_tests.yml +++ b/test/config/integration_tests.yml @@ -4,6 +4,7 @@ defaultTestCaseConfig: stopCommand: nnictl stop experimentStatusCheck: True platform: linux darwin win32 + trainingService: all testCases: ####################################################################### @@ -77,18 +78,14 @@ testCases: launchCommand: nnictl ss_gen --trial_command="python3 mnist.py --epochs 1" --trial_dir=../examples/nas/classic_nas --file=config/examples/nni-nas-search-space.json stopCommand: experimentStatusCheck: False - -- name: classic-nas-install-ppo - configFile: test/config/examples/classic-nas-pytorch.yml - launchCommand: nnictl package install --name=PPOTuner - stopCommand: - experimentStatusCheck: False + trainingService: local - name: classic-nas-pytorch configFile: test/config/examples/classic-nas-pytorch.yml # remove search space file stopCommand: nnictl stop onExitCommand: python3 -c 'import os; os.remove("config/examples/nni-nas-search-space.json")' + trainingService: local ######################################################################### # nni features test diff --git a/test/config/integration_tests_tf2.yml b/test/config/integration_tests_tf2.yml index 797c9c114c..e060511289 100644 --- a/test/config/integration_tests_tf2.yml +++ b/test/config/integration_tests_tf2.yml @@ -4,6 +4,7 @@ defaultTestCaseConfig: stopCommand: nnictl stop experimentStatusCheck: True platform: linux darwin win32 + trainingService: all testCases: ####################################################################### @@ -56,18 +57,14 @@ testCases: launchCommand: nnictl ss_gen --trial_command="python3 train.py --epochs 1" --trial_dir=../examples/nas/classic_nas-tf --file=config/examples/nni-nas-search-space-tf2.json stopCommand: experimentStatusCheck: False - -- name: classic-nas-install-ppo - configFile: test/config/examples/classic-nas-tf2.yml - launchCommand: nnictl package install --name=PPOTuner - stopCommand: - experimentStatusCheck: False + trainingService: local - name: classic-nas-tensorflow2 configFile: test/config/examples/classic-nas-tf2.yml # remove search space file stopCommand: nnictl stop onExitCommand: python3 -c 'import os; os.remove("config/examples/nni-nas-search-space-tf2.json")' + trainingService: local ######################################################################### # nni features test diff --git a/test/config/pr_tests.yml b/test/config/pr_tests.yml index aa3fc09ddc..d49bf9d7ec 100644 --- a/test/config/pr_tests.yml +++ b/test/config/pr_tests.yml @@ -4,6 +4,7 @@ defaultTestCaseConfig: stopCommand: nnictl stop experimentStatusCheck: True platform: linux darwin win32 + trainingService: all testCases: diff --git a/test/nni_test/nnitest/run_tests.py b/test/nni_test/nnitest/run_tests.py index 542a4a932d..1c31ffa89b 100644 --- a/test/nni_test/nnitest/run_tests.py +++ b/test/nni_test/nnitest/run_tests.py @@ -196,6 +196,15 @@ def case_included(name, cases): def match_platform(test_case_config): return sys.platform in test_case_config['platform'].split(' ') +def match_training_service(test_case_config, cur_training_service): + case_ts = test_case_config['trainingService'] + assert case_ts is not None + if case_ts == 'all': + return True + if cur_training_service in case_ts.split(' '): + return True + return False + def run(args): it_config = get_yml_content(args.config) @@ -217,6 +226,10 @@ def run(args): print('skipped {}, platform {} not match [{}]'.format(name, sys.platform, test_case_config['platform'])) continue + if not match_training_service(test_case_config, args.ts): + print('skipped {}, training service {} not match [{}]'.format(name, args.ts, test_case_config['trainingService'])) + continue + wait_for_port_available(8080, 30) print('{}Testing: {}{}'.format(GREEN, name, CLEAR)) begin_time = time.time() diff --git a/test/pipelines/pipelines-it-local-tf2.yml b/test/pipelines/pipelines-it-local-tf2.yml index b7eb5a6c7c..e208b33f11 100644 --- a/test/pipelines/pipelines-it-local-tf2.yml +++ b/test/pipelines/pipelines-it-local-tf2.yml @@ -17,6 +17,7 @@ jobs: sudo apt-get install swig -y PATH=$HOME/.local/bin:$PATH nnictl package install --name=SMAC PATH=$HOME/.local/bin:$PATH nnictl package install --name=BOHB + PATH=$HOME/.local/bin:$PATH nnictl package install --name=PPOTuner displayName: 'Install dependencies for integration tests' - script: | cd test diff --git a/test/pipelines/pipelines-it-local-windows.yml b/test/pipelines/pipelines-it-local-windows.yml index bb54e9421a..bfdf4eed21 100644 --- a/test/pipelines/pipelines-it-local-windows.yml +++ b/test/pipelines/pipelines-it-local-windows.yml @@ -11,6 +11,7 @@ jobs: python -m pip install keras==2.1.6 --user python -m pip install torchvision===0.4.1 torch===1.3.1 -f https://download.pytorch.org/whl/torch_stable.html --user python -m pip install tensorflow-gpu==1.15.2 tensorflow-estimator==1.15.1 --force --user + nnictl package install --name=PPOTuner displayName: 'Install dependencies for integration tests' - script: | cd test diff --git a/test/pipelines/pipelines-it-local.yml b/test/pipelines/pipelines-it-local.yml index 30d86ddb16..fd87e95d65 100644 --- a/test/pipelines/pipelines-it-local.yml +++ b/test/pipelines/pipelines-it-local.yml @@ -18,6 +18,7 @@ jobs: sudo apt-get install swig -y PATH=$HOME/.local/bin:$PATH nnictl package install --name=SMAC PATH=$HOME/.local/bin:$PATH nnictl package install --name=BOHB + PATH=$HOME/.local/bin:$PATH nnictl package install --name=PPOTuner displayName: 'Install dependencies for integration tests' - script: | cd test From b91aba399b0927e8850ed6fb253dc88439953a7c Mon Sep 17 00:00:00 2001 From: Yuge Zhang Date: Mon, 29 Jun 2020 10:31:34 +0800 Subject: [PATCH 03/17] NAS Benchmark (#2578) * Adding NAS Benchmark (201) * Add missing endline * Update script * Draft for NAS-Bench-101 * Update NAS-Bench-101 * Update constants * Add API * Update API * Fix typo * Draft for NDS * Fix issues in storing loss * Fix cell_spec problem * Finalize NDS * Update time consumption * Add nds query function * Update documentation for NAS-Bench-101 * Reformat generators * Add NAS-Bench-201 docs * Unite constant names * Update docstring * Update docstring * Update rst * Update scripts * Add git as dependency * Apt update * Update installation scripts * Fix dependency for pipeline * Fix NDS script * Fix NAS-Bench-201 installation * Add example notebook * Correct latency dimension * shortcuts -> query * Change run -> trial, ComputedStats -> TrialStats * ipynb needs re-generation * Fix NAS rst * Fix documentation and pylint * Fix pylint * Add pandoc as dependency * Update pandoc dependency * Fix documentation broken link --- azure-pipelines.yml | 7 +- docs/en_US/NAS/Benchmarks.md | 172 +++++++++++ docs/en_US/NAS/BenchmarksExample.ipynb | 277 ++++++++++++++++++ docs/en_US/Release.md | 4 +- docs/en_US/Tutorial/HowToDebug.md | 2 +- docs/en_US/Tutorial/InstallCustomizedAlgos.md | 2 +- docs/en_US/conf.py | 1 + docs/en_US/nas.rst | 1 + docs/requirements.txt | 2 + examples/nas/benchmarks/nasbench101.sh | 22 ++ examples/nas/benchmarks/nasbench201.sh | 19 ++ examples/nas/benchmarks/nds.sh | 19 ++ src/sdk/pynni/nni/nas/benchmarks/__init__.py | 0 src/sdk/pynni/nni/nas/benchmarks/constants.py | 4 + .../nas/benchmarks/nasbench101/__init__.py | 3 + .../nas/benchmarks/nasbench101/constants.py | 14 + .../nni/nas/benchmarks/nasbench101/db_gen.py | 53 ++++ .../nas/benchmarks/nasbench101/graph_util.py | 111 +++++++ .../nni/nas/benchmarks/nasbench101/model.py | 101 +++++++ .../nni/nas/benchmarks/nasbench101/query.py | 60 ++++ .../nas/benchmarks/nasbench201/__init__.py | 3 + .../nas/benchmarks/nasbench201/constants.py | 12 + .../nni/nas/benchmarks/nasbench201/db_gen.py | 106 +++++++ .../nni/nas/benchmarks/nasbench201/model.py | 159 ++++++++++ .../nni/nas/benchmarks/nasbench201/query.py | 57 ++++ .../pynni/nni/nas/benchmarks/nds/__init__.py | 3 + .../pynni/nni/nas/benchmarks/nds/constants.py | 16 + .../pynni/nni/nas/benchmarks/nds/db_gen.py | 153 ++++++++++ src/sdk/pynni/nni/nas/benchmarks/nds/model.py | 142 +++++++++ src/sdk/pynni/nni/nas/benchmarks/nds/query.py | 64 ++++ 30 files changed, 1582 insertions(+), 7 deletions(-) create mode 100644 docs/en_US/NAS/Benchmarks.md create mode 100644 docs/en_US/NAS/BenchmarksExample.ipynb create mode 100644 examples/nas/benchmarks/nasbench101.sh create mode 100644 examples/nas/benchmarks/nasbench201.sh create mode 100644 examples/nas/benchmarks/nds.sh create mode 100644 src/sdk/pynni/nni/nas/benchmarks/__init__.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/constants.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench101/__init__.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench101/constants.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench101/db_gen.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench101/graph_util.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench101/model.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench101/query.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench201/__init__.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench201/constants.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench201/db_gen.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench201/model.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nasbench201/query.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nds/__init__.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nds/constants.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nds/db_gen.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nds/model.py create mode 100644 src/sdk/pynni/nni/nas/benchmarks/nds/query.py diff --git a/azure-pipelines.yml b/azure-pipelines.yml index dc4206864d..7ad863336b 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -26,11 +26,12 @@ jobs: displayName: 'Run eslint' - script: | set -e + sudo apt-get install -y pandoc python3 -m pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html --user python3 -m pip install tensorflow==2.2.0 --user python3 -m pip install keras==2.4.2 --user - python3 -m pip install gym onnx --user - python3 -m pip install sphinx==1.8.3 sphinx-argparse==0.2.5 sphinx-markdown-tables==0.0.9 sphinx-rtd-theme==0.4.2 sphinxcontrib-websupport==1.1.0 recommonmark==0.5.0 --user + python3 -m pip install gym onnx peewee --user + python3 -m pip install sphinx==1.8.3 sphinx-argparse==0.2.5 sphinx-markdown-tables==0.0.9 sphinx-rtd-theme==0.4.2 sphinxcontrib-websupport==1.1.0 recommonmark==0.5.0 nbsphinx --user sudo apt-get install swig -y nnictl package install --name=SMAC nnictl package install --name=BOHB @@ -68,7 +69,7 @@ jobs: python3 -m pip install torch==1.3.1+cpu torchvision==0.4.2+cpu -f https://download.pytorch.org/whl/torch_stable.html --user python3 -m pip install tensorflow==1.15.2 --user python3 -m pip install keras==2.1.6 --user - python3 -m pip install gym onnx --user + python3 -m pip install gym onnx peewee --user sudo apt-get install swig -y nnictl package install --name=SMAC nnictl package install --name=BOHB diff --git a/docs/en_US/NAS/Benchmarks.md b/docs/en_US/NAS/Benchmarks.md new file mode 100644 index 0000000000..a5de456001 --- /dev/null +++ b/docs/en_US/NAS/Benchmarks.md @@ -0,0 +1,172 @@ +# NAS Benchmarks (experimental) + +```eval_rst +.. toctree:: + :hidden: + + Example Usages +``` + +## Prerequisites + +* Please prepare a folder to household all the benchmark databases. By default, it can be found at `${HOME}/.nni/nasbenchmark`. You can place it anywhere you like, and specify it in `NASBENCHMARK_DIR` before importing NNI. +* Please install `peewee` via `pip install peewee`, which NNI uses to connect to database. + +## Data Preparation + +To avoid storage and legal issues, we do not provide any prepared databases. We strongly recommend users to use docker to run the generation scripts, to ease the burden of installing multiple dependencies. Please follow the following steps. + +1. Clone NNI repo. Replace `${NNI_VERSION}` with a released version name or branch name, e.g., `v1.6`. + +```bash +git clone -b ${NNI_VERSION} https://github.com/microsoft/nni +``` + +2. Run docker. + +For NAS-Bench-101, + +```bash +docker run -v ${HOME}/.nni/nasbenchmark:/outputs -v /path/to/your/nni:/nni tensorflow/tensorflow:1.15.2-py3 /bin/bash /nni/examples/nas/benchmarks/nasbench101.sh +``` + +For NAS-Bench-201, + +```bash +docker run -v ${HOME}/.nni/nasbenchmark:/outputs -v /path/to/your/nni:/nni ufoym/deepo:pytorch-cpu /bin/bash /nni/examples/nas/benchmarks/nasbench201.sh +``` + +For NDS, + +```bash +docker run -v ${HOME}/.nni/nasbenchmark:/outputs -v /path/to/your/nni:/nni python:3.7 /bin/bash /nni/examples/nas/benchmarks/nds.sh +``` + +Please make sure there is at least 10GB free disk space and note that the conversion process can take up to hours to complete. + +## Example Usages + +Please refer to [examples usages of Benchmarks API](./BenchmarksExample). + +## NAS-Bench-101 + +[Paper link](https://arxiv.org/abs/1902.09635)     [Open-source](https://github.com/google-research/nasbench) + +NAS-Bench-101 contains 423,624 unique neural networks, combined with 4 variations in number of epochs (4, 12, 36, 108), each of which is trained 3 times. It is a cell-wise search space, which constructs and stacks a cell by enumerating DAGs with at most 7 operators, and no more than 9 connections. All operators can be chosen from `CONV3X3_BN_RELU`, `CONV1X1_BN_RELU` and `MAXPOOL3X3`, except the first operator (always `INPUT`) and last operator (always `OUTPUT`). + +Notably, NAS-Bench-101 eliminates invalid cells (e.g., there is no path from input to output, or there is redundant computation). Furthermore, isomorphic cells are de-duplicated, i.e., all the remaining cells are computationally unique. + +### API Documentation + +```eval_rst +.. autofunction:: nni.nas.benchmarks.nasbench101.query_nb101_trial_stats + +.. autoattribute:: nni.nas.benchmarks.nasbench101.INPUT + +.. autoattribute:: nni.nas.benchmarks.nasbench101.OUTPUT + +.. autoattribute:: nni.nas.benchmarks.nasbench101.CONV3X3_BN_RELU + +.. autoattribute:: nni.nas.benchmarks.nasbench101.CONV1X1_BN_RELU + +.. autoattribute:: nni.nas.benchmarks.nasbench101.MAXPOOL3X3 + +.. autoclass:: nni.nas.benchmarks.nasbench101.Nb101TrialConfig + +.. autoclass:: nni.nas.benchmarks.nasbench101.Nb101TrialStats + +.. autoclass:: nni.nas.benchmarks.nasbench101.Nb101IntermediateStats + +.. autofunction:: nni.nas.benchmarks.nasbench101.graph_util.nasbench_format_to_architecture_repr + +.. autofunction:: nni.nas.benchmarks.nasbench101.graph_util.infer_num_vertices + +.. autofunction:: nni.nas.benchmarks.nasbench101.graph_util.hash_module +``` + +## NAS-Bench-201 + +[Paper link](https://arxiv.org/abs/2001.00326)     [Open-source API](https://github.com/D-X-Y/NAS-Bench-201)    [Implementations](https://github.com/D-X-Y/AutoDL-Projects) + +NAS-Bench-201 is a cell-wise search space that views nodes as tensors and edges as operators. The search space contains all possible densely-connected DAGs with 4 nodes, resulting in 15,625 candidates in total. Each operator (i.e., edge) is selected from a pre-defined operator set (`NONE`, `SKIP_CONNECT`, `CONV_1X1`, `CONV_3X3` and `AVG_POOL_3X3`). Training appraoches vary in the dataset used (CIFAR-10, CIFAR-100, ImageNet) and number of epochs scheduled (12 and 200). Each combination of architecture and training approach is repeated 1 - 3 times with different random seeds. + +### API Documentation + + +```eval_rst +.. autofunction:: nni.nas.benchmarks.nasbench201.query_nb201_trial_stats + +.. autoattribute:: nni.nas.benchmarks.nasbench201.NONE + +.. autoattribute:: nni.nas.benchmarks.nasbench201.SKIP_CONNECT + +.. autoattribute:: nni.nas.benchmarks.nasbench201.CONV_1X1 + +.. autoattribute:: nni.nas.benchmarks.nasbench201.CONV_3X3 + +.. autoattribute:: nni.nas.benchmarks.nasbench201.AVG_POOL_3X3 + +.. autoclass:: nni.nas.benchmarks.nasbench201.Nb201TrialConfig + +.. autoclass:: nni.nas.benchmarks.nasbench201.Nb201TrialStats + +.. autoclass:: nni.nas.benchmarks.nasbench201.Nb201IntermediateStats +``` + +## NDS + +[Paper link](https://arxiv.org/abs/1905.13214)     [Open-source](https://github.com/facebookresearch/nds) + +_On Network Design Spaces for Visual Recognition_ released trial statistics of over 100,000 configurations (models + hyper-parameters) sampled from multiple model families, including vanilla (feedforward network loosely inspired by VGG), ResNet and ResNeXt (residual basic block and residual bottleneck block) and NAS cells (following popular design from NASNet, Ameoba, PNAS, ENAS and DARTS). Most configurations are trained only once with a fixed seed, except a few that are trained twice or three times. + +Instead of storing results obtained with different configurations in separate files, we dump them into one single database to enable comparison in multiple dimensions. Specifically, we use `model_family` to distinguish model types, `model_spec` for all hyper-parameters needed to build this model, `cell_spec` for detailed information on operators and connections if it is a NAS cell, `generator` to denote the sampling policy through which this configuration is generated. Refer to API documentation for details. + +## Available Operators + +Here is a list of available operators used in NDS. + +```eval_rst +.. autoattribute:: nni.nas.benchmarks.nds.constants.NONE + +.. autoattribute:: nni.nas.benchmarks.nds.constants.SKIP_CONNECT + +.. autoattribute:: nni.nas.benchmarks.nds.constants.AVG_POOL_3X3 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.MAX_POOL_3X3 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.MAX_POOL_5X5 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.MAX_POOL_7X7 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.CONV_1X1 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.CONV_3X3 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.CONV_3X1_1X3 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.CONV_7X1_1X7 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.DIL_CONV_3X3 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.DIL_CONV_5X5 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.SEP_CONV_3X3 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.SEP_CONV_5X5 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.SEP_CONV_7X7 + +.. autoattribute:: nni.nas.benchmarks.nds.constants.DIL_SEP_CONV_3X3 +``` + +### API Documentation + +```eval_rst +.. autofunction:: nni.nas.benchmarks.nds.query_nds_trial_stats + +.. autoclass:: nni.nas.benchmarks.nds.NdsTrialConfig + +.. autoclass:: nni.nas.benchmarks.nds.NdsTrialStats + +.. autoclass:: nni.nas.benchmarks.nds.NdsIntermediateStats +``` \ No newline at end of file diff --git a/docs/en_US/NAS/BenchmarksExample.ipynb b/docs/en_US/NAS/BenchmarksExample.ipynb new file mode 100644 index 0000000000..a808a118f1 --- /dev/null +++ b/docs/en_US/NAS/BenchmarksExample.ipynb @@ -0,0 +1,277 @@ +{ + "nbformat": 4, + "nbformat_minor": 2, + "metadata": { + "language_info": { + "name": "python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "version": "3.6.10-final" + }, + "orig_nbformat": 2, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "npconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3, + "kernelspec": { + "name": "python361064bitnnilatestcondabff8d66a619a4d26af34fe0fe687c7b0", + "display_name": "Python 3.6.10 64-bit ('nnilatest': conda)" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example Usages of NAS Benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pprint\n", + "import time\n", + "\n", + "from nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats\n", + "from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats\n", + "from nni.nas.benchmarks.nds import query_nds_trial_stats\n", + "\n", + "ti = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NAS-Bench-101" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "{'config': {'arch': {'input1': [0],\n 'input2': [1],\n 'input3': [2],\n 'input4': [0],\n 'input5': [0, 3, 4],\n 'input6': [2, 5],\n 'op1': 'conv3x3-bn-relu',\n 'op2': 'maxpool3x3',\n 'op3': 'conv3x3-bn-relu',\n 'op4': 'conv3x3-bn-relu',\n 'op5': 'conv1x1-bn-relu'},\n 'hash': '00005c142e6f48ac74fdcf73e3439874',\n 'id': 4,\n 'num_epochs': 108,\n 'num_vertices': 7},\n 'id': 10,\n 'parameters': 8.55553,\n 'test_acc': 92.11738705635071,\n 'train_acc': 100.0,\n 'training_time': 106147.67578125,\n 'valid_acc': 92.41786599159241}\n{'config': {'arch': {'input1': [0],\n 'input2': [1],\n 'input3': [2],\n 'input4': [0],\n 'input5': [0, 3, 4],\n 'input6': [2, 5],\n 'op1': 'conv3x3-bn-relu',\n 'op2': 'maxpool3x3',\n 'op3': 'conv3x3-bn-relu',\n 'op4': 'conv3x3-bn-relu',\n 'op5': 'conv1x1-bn-relu'},\n 'hash': '00005c142e6f48ac74fdcf73e3439874',\n 'id': 4,\n 'num_epochs': 108,\n 'num_vertices': 7},\n 'id': 11,\n 'parameters': 8.55553,\n 'test_acc': 91.90705418586731,\n 'train_acc': 100.0,\n 'training_time': 106095.05859375,\n 'valid_acc': 92.45793223381042}\n{'config': {'arch': {'input1': [0],\n 'input2': [1],\n 'input3': [2],\n 'input4': [0],\n 'input5': [0, 3, 4],\n 'input6': [2, 5],\n 'op1': 'conv3x3-bn-relu',\n 'op2': 'maxpool3x3',\n 'op3': 'conv3x3-bn-relu',\n 'op4': 'conv3x3-bn-relu',\n 'op5': 'conv1x1-bn-relu'},\n 'hash': '00005c142e6f48ac74fdcf73e3439874',\n 'id': 4,\n 'num_epochs': 108,\n 'num_vertices': 7},\n 'id': 12,\n 'parameters': 8.55553,\n 'test_acc': 92.15745329856873,\n 'train_acc': 100.0,\n 'training_time': 106138.55712890625,\n 'valid_acc': 93.04887652397156}\n" + } + ], + "source": [ + "arch = {\n", + " 'op1': 'conv3x3-bn-relu',\n", + " 'op2': 'maxpool3x3',\n", + " 'op3': 'conv3x3-bn-relu',\n", + " 'op4': 'conv3x3-bn-relu',\n", + " 'op5': 'conv1x1-bn-relu',\n", + " 'input1': [0],\n", + " 'input2': [1],\n", + " 'input3': [2],\n", + " 'input4': [0],\n", + " 'input5': [0, 3, 4],\n", + " 'input6': [2, 5]\n", + "}\n", + "for t in query_nb101_trial_stats(arch, 108):\n", + " pprint.pprint(t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NAS-Bench-201" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "{'config': {'arch': {'0_1': 'avg_pool_3x3',\n '0_2': 'conv_1x1',\n '0_3': 'conv_1x1',\n '1_2': 'skip_connect',\n '1_3': 'skip_connect',\n '2_3': 'skip_connect'},\n 'dataset': 'cifar100',\n 'id': 7,\n 'num_cells': 5,\n 'num_channels': 16,\n 'num_epochs': 200},\n 'flops': 15.65322,\n 'id': 3,\n 'latency': 0.013182918230692545,\n 'ori_test_acc': 53.11,\n 'ori_test_evaluation_time': 1.0195916947864352,\n 'ori_test_loss': 1.7307863704681397,\n 'parameters': 0.135156,\n 'seed': 999,\n 'test_acc': 53.07999995727539,\n 'test_evaluation_time': 0.5097958473932176,\n 'test_loss': 1.731276072692871,\n 'train_acc': 57.82,\n 'train_loss': 1.5116578379058838,\n 'training_time': 2888.4371995925903,\n 'valid_acc': 53.14000000610351,\n 'valid_evaluation_time': 0.5097958473932176,\n 'valid_loss': 1.7302966793060304}\n{'config': {'arch': {'0_1': 'avg_pool_3x3',\n '0_2': 'conv_1x1',\n '0_3': 'conv_1x1',\n '1_2': 'skip_connect',\n '1_3': 'skip_connect',\n '2_3': 'skip_connect'},\n 'dataset': 'cifar100',\n 'id': 7,\n 'num_cells': 5,\n 'num_channels': 16,\n 'num_epochs': 200},\n 'flops': 15.65322,\n 'id': 7,\n 'latency': 0.013182918230692545,\n 'ori_test_acc': 51.93,\n 'ori_test_evaluation_time': 1.0195916947864352,\n 'ori_test_loss': 1.7572312774658203,\n 'parameters': 0.135156,\n 'seed': 777,\n 'test_acc': 51.979999938964845,\n 'test_evaluation_time': 0.5097958473932176,\n 'test_loss': 1.7429540189743042,\n 'train_acc': 57.578,\n 'train_loss': 1.5114233912658692,\n 'training_time': 2888.4371995925903,\n 'valid_acc': 51.88,\n 'valid_evaluation_time': 0.5097958473932176,\n 'valid_loss': 1.7715086591720581}\n{'config': {'arch': {'0_1': 'avg_pool_3x3',\n '0_2': 'conv_1x1',\n '0_3': 'conv_1x1',\n '1_2': 'skip_connect',\n '1_3': 'skip_connect',\n '2_3': 'skip_connect'},\n 'dataset': 'cifar100',\n 'id': 7,\n 'num_cells': 5,\n 'num_channels': 16,\n 'num_epochs': 200},\n 'flops': 15.65322,\n 'id': 11,\n 'latency': 0.013182918230692545,\n 'ori_test_acc': 53.38,\n 'ori_test_evaluation_time': 1.0195916947864352,\n 'ori_test_loss': 1.7281623031616211,\n 'parameters': 0.135156,\n 'seed': 888,\n 'test_acc': 53.67999998779297,\n 'test_evaluation_time': 0.5097958473932176,\n 'test_loss': 1.7327697801589965,\n 'train_acc': 57.792,\n 'train_loss': 1.5091403088760376,\n 'training_time': 2888.4371995925903,\n 'valid_acc': 53.08000000610352,\n 'valid_evaluation_time': 0.5097958473932176,\n 'valid_loss': 1.7235548280715942}\n" + } + ], + "source": [ + "arch = {\n", + " '0_1': 'avg_pool_3x3',\n", + " '0_2': 'conv_1x1',\n", + " '1_2': 'skip_connect',\n", + " '0_3': 'conv_1x1',\n", + " '1_3': 'skip_connect',\n", + " '2_3': 'skip_connect'\n", + "}\n", + "for t in query_nb201_trial_stats(arch, 200, 'cifar100'):\n", + " pprint.pprint(t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NDS" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "{'best_test_acc': 90.48,\n 'best_train_acc': 96.356,\n 'best_train_loss': 0.116,\n 'config': {'base_lr': 0.1,\n 'cell_spec': {},\n 'dataset': 'cifar10',\n 'generator': 'random',\n 'id': 45505,\n 'model_family': 'residual_bottleneck',\n 'model_spec': {'bot_muls': [0.0, 0.25, 0.25, 0.25],\n 'ds': [1, 16, 1, 4],\n 'num_gs': [1, 2, 1, 2],\n 'ss': [1, 1, 2, 2],\n 'ws': [16, 64, 128, 16]},\n 'num_epochs': 100,\n 'proposer': 'resnext-a',\n 'weight_decay': 0.0005},\n 'final_test_acc': 90.39,\n 'final_train_acc': 96.298,\n 'final_train_loss': 0.116,\n 'flops': 69.890986,\n 'id': 45505,\n 'iter_time': 0.065,\n 'parameters': 0.083002,\n 'seed': 1}\n" + } + ], + "source": [ + "model_spec = {\n", + " 'bot_muls': [0.0, 0.25, 0.25, 0.25],\n", + " 'ds': [1, 16, 1, 4],\n", + " 'num_gs': [1, 2, 1, 2],\n", + " 'ss': [1, 1, 2, 2],\n", + " 'ws': [16, 64, 128, 16]\n", + "}\n", + "# Use none as a wildcard\n", + "for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):\n", + " pprint.pprint(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "{'best_test_acc': 93.58,\n 'best_train_acc': 99.772,\n 'best_train_loss': 0.011,\n 'config': {'base_lr': 0.1,\n 'cell_spec': {},\n 'dataset': 'cifar10',\n 'generator': 'random',\n 'id': 108998,\n 'model_family': 'residual_basic',\n 'model_spec': {'ds': [1, 12, 12, 12],\n 'ss': [1, 1, 2, 2],\n 'ws': [16, 24, 24, 40]},\n 'num_epochs': 100,\n 'proposer': 'resnet',\n 'weight_decay': 0.0005},\n 'final_test_acc': 93.49,\n 'final_train_acc': 99.772,\n 'final_train_loss': 0.011,\n 'flops': 184.519578,\n 'id': 108998,\n 'iter_time': 0.059,\n 'parameters': 0.594138,\n 'seed': 1}\n" + } + ], + "source": [ + "model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}\n", + "for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):\n", + " pprint.pprint(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "{'best_test_acc': 84.5,\n 'best_train_acc': 89.66499999999999,\n 'best_train_loss': 0.302,\n 'config': {'base_lr': 0.1,\n 'cell_spec': {},\n 'dataset': 'cifar10',\n 'generator': 'random',\n 'id': 139492,\n 'model_family': 'vanilla',\n 'model_spec': {'ds': [1, 12, 12, 12],\n 'ss': [1, 1, 2, 2],\n 'ws': [16, 24, 32, 40]},\n 'num_epochs': 100,\n 'proposer': 'vanilla',\n 'weight_decay': 0.0005},\n 'final_test_acc': 84.35,\n 'final_train_acc': 89.633,\n 'final_train_loss': 0.303,\n 'flops': 208.36393,\n 'id': 154692,\n 'iter_time': 0.058,\n 'parameters': 0.68977,\n 'seed': 1}\n" + } + ], + "source": [ + "# get the first one\n", + "pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "{'best_test_acc': 93.37,\n 'best_train_acc': 99.91,\n 'best_train_loss': 0.006,\n 'config': {'base_lr': 0.1,\n 'cell_spec': {'normal_0_input_x': 0,\n 'normal_0_input_y': 1,\n 'normal_0_op_x': 'avg_pool_3x3',\n 'normal_0_op_y': 'conv_7x1_1x7',\n 'normal_1_input_x': 2,\n 'normal_1_input_y': 0,\n 'normal_1_op_x': 'sep_conv_3x3',\n 'normal_1_op_y': 'sep_conv_5x5',\n 'normal_2_input_x': 2,\n 'normal_2_input_y': 2,\n 'normal_2_op_x': 'dil_sep_conv_3x3',\n 'normal_2_op_y': 'dil_sep_conv_3x3',\n 'normal_3_input_x': 4,\n 'normal_3_input_y': 4,\n 'normal_3_op_x': 'skip_connect',\n 'normal_3_op_y': 'dil_sep_conv_3x3',\n 'normal_4_input_x': 2,\n 'normal_4_input_y': 4,\n 'normal_4_op_x': 'conv_7x1_1x7',\n 'normal_4_op_y': 'sep_conv_3x3',\n 'normal_concat': [3, 5, 6],\n 'reduce_0_input_x': 0,\n 'reduce_0_input_y': 1,\n 'reduce_0_op_x': 'avg_pool_3x3',\n 'reduce_0_op_y': 'dil_sep_conv_3x3',\n 'reduce_1_input_x': 0,\n 'reduce_1_input_y': 0,\n 'reduce_1_op_x': 'sep_conv_3x3',\n 'reduce_1_op_y': 'sep_conv_3x3',\n 'reduce_2_input_x': 2,\n 'reduce_2_input_y': 0,\n 'reduce_2_op_x': 'skip_connect',\n 'reduce_2_op_y': 'sep_conv_7x7',\n 'reduce_3_input_x': 4,\n 'reduce_3_input_y': 4,\n 'reduce_3_op_x': 'conv_7x1_1x7',\n 'reduce_3_op_y': 'skip_connect',\n 'reduce_4_input_x': 0,\n 'reduce_4_input_y': 5,\n 'reduce_4_op_x': 'conv_7x1_1x7',\n 'reduce_4_op_y': 'conv_7x1_1x7',\n 'reduce_concat': [3, 6]},\n 'dataset': 'cifar10',\n 'generator': 'random',\n 'id': 1,\n 'model_family': 'nas_cell',\n 'model_spec': {'aux': False,\n 'depth': 12,\n 'drop_prob': 0.0,\n 'num_nodes_normal': 5,\n 'num_nodes_reduce': 5,\n 'width': 32},\n 'num_epochs': 100,\n 'proposer': 'amoeba',\n 'weight_decay': 0.0005},\n 'final_test_acc': 93.27,\n 'final_train_acc': 99.91,\n 'final_train_loss': 0.006,\n 'flops': 664.400586,\n 'id': 1,\n 'iter_time': 0.281,\n 'parameters': 4.190314,\n 'seed': 1}\n" + } + ], + "source": [ + "# count number\n", + "model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}\n", + "cell_spec = {\n", + " 'normal_0_op_x': 'avg_pool_3x3',\n", + " 'normal_0_input_x': 0,\n", + " 'normal_0_op_y': 'conv_7x1_1x7',\n", + " 'normal_0_input_y': 1,\n", + " 'normal_1_op_x': 'sep_conv_3x3',\n", + " 'normal_1_input_x': 2,\n", + " 'normal_1_op_y': 'sep_conv_5x5',\n", + " 'normal_1_input_y': 0,\n", + " 'normal_2_op_x': 'dil_sep_conv_3x3',\n", + " 'normal_2_input_x': 2,\n", + " 'normal_2_op_y': 'dil_sep_conv_3x3',\n", + " 'normal_2_input_y': 2,\n", + " 'normal_3_op_x': 'skip_connect',\n", + " 'normal_3_input_x': 4,\n", + " 'normal_3_op_y': 'dil_sep_conv_3x3',\n", + " 'normal_3_input_y': 4,\n", + " 'normal_4_op_x': 'conv_7x1_1x7',\n", + " 'normal_4_input_x': 2,\n", + " 'normal_4_op_y': 'sep_conv_3x3',\n", + " 'normal_4_input_y': 4,\n", + " 'normal_concat': [3, 5, 6],\n", + " 'reduce_0_op_x': 'avg_pool_3x3',\n", + " 'reduce_0_input_x': 0,\n", + " 'reduce_0_op_y': 'dil_sep_conv_3x3',\n", + " 'reduce_0_input_y': 1,\n", + " 'reduce_1_op_x': 'sep_conv_3x3',\n", + " 'reduce_1_input_x': 0,\n", + " 'reduce_1_op_y': 'sep_conv_3x3',\n", + " 'reduce_1_input_y': 0,\n", + " 'reduce_2_op_x': 'skip_connect',\n", + " 'reduce_2_input_x': 2,\n", + " 'reduce_2_op_y': 'sep_conv_7x7',\n", + " 'reduce_2_input_y': 0,\n", + " 'reduce_3_op_x': 'conv_7x1_1x7',\n", + " 'reduce_3_input_x': 4,\n", + " 'reduce_3_op_y': 'skip_connect',\n", + " 'reduce_3_input_y': 4,\n", + " 'reduce_4_op_x': 'conv_7x1_1x7',\n", + " 'reduce_4_input_x': 0,\n", + " 'reduce_4_op_y': 'conv_7x1_1x7',\n", + " 'reduce_4_input_y': 5,\n", + " 'reduce_concat': [3, 6]\n", + "}\n", + "\n", + "for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):\n", + " assert t['config']['model_spec'] == model_spec\n", + " assert t['config']['cell_spec'] == cell_spec\n", + " pprint.pprint(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "NDS (amoeba) count: 5107\n" + } + ], + "source": [ + "# count number\n", + "print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Elapsed time: 1.9107539653778076 seconds\n" + } + ], + "source": [ + "print('Elapsed time: ', time.time() - ti, 'seconds')" + ] + } + ] +} \ No newline at end of file diff --git a/docs/en_US/Release.md b/docs/en_US/Release.md index 6a23b5c3f4..412b1c7533 100644 --- a/docs/en_US/Release.md +++ b/docs/en_US/Release.md @@ -105,7 +105,7 @@ * WebUI refactor: adopt fabric framework #### Others -* Support running [NNI experiment at foreground](https://github.com/microsoft/nni/blob/v1.4/docs/en_US/Tutorial/Nnictl.md#manage-an-experiment), i.e., `--foreground` argument in `nnictl create/resume/view` +* Support running [NNI experiment at foreground](https://github.com/microsoft/nni/blob/v1.4/docs/en_US/Tutorial/Nnictl#manage-an-experiment), i.e., `--foreground` argument in `nnictl create/resume/view` * Support canceling the trials in UNKNOWN state * Support large search space whose size could be up to 50mb (thanks external contributor @Sundrops) @@ -338,7 +338,7 @@ * NNI running on windows for local mode * [New advisor: BOHB](Tuner/BohbAdvisor.md) * Support a new advisor BOHB, which is a robust and efficient hyperparameter tuning algorithm, combines the advantages of Bayesian optimization and Hyperband -* [Support import and export experiment data through nnictl](Tutorial/Nnictl.md#experiment) +* [Support import and export experiment data through nnictl](Tutorial/Nnictl.md) * Generate analysis results report after the experiment execution * Support import data to tuner and advisor for tuning * [Designated gpu devices for NNI trial jobs](Tutorial/ExperimentConfig.md#localConfig) diff --git a/docs/en_US/Tutorial/HowToDebug.md b/docs/en_US/Tutorial/HowToDebug.md index f94211ca2a..825042e734 100644 --- a/docs/en_US/Tutorial/HowToDebug.md +++ b/docs/en_US/Tutorial/HowToDebug.md @@ -86,4 +86,4 @@ A common example of this would be run the mnist example without installing tenso As it shows, every trial has a log path, where you can find trial's log and stderr. -In addition to experiment level debug, NNI also provides the capability for debugging a single trial without the need to start the entire experiment. Refer to [standalone mode](../TrialExample/Trials.md#standalone-mode-for-debug) for more information about debug single trial code. \ No newline at end of file +In addition to experiment level debug, NNI also provides the capability for debugging a single trial without the need to start the entire experiment. Refer to [standalone mode](../TrialExample/Trials#standalone-mode-for-debugging) for more information about debug single trial code. \ No newline at end of file diff --git a/docs/en_US/Tutorial/InstallCustomizedAlgos.md b/docs/en_US/Tutorial/InstallCustomizedAlgos.md index 63b61227f1..df2783df89 100644 --- a/docs/en_US/Tutorial/InstallCustomizedAlgos.md +++ b/docs/en_US/Tutorial/InstallCustomizedAlgos.md @@ -3,7 +3,7 @@ ## Overview -NNI provides a lot of [builtin tuners](../Tuner/BuiltinTuner.md), [advisors](../Tuner/BuiltinTuner.md#Hyperband) and [assessors](../Assessor/BuiltinAssessor.md) can be used directly for Hyper Parameter Optimization, and some extra algorithms can be installed via `nnictl package install --name ` after NNI is installed. You can check these extra algorithms via `nnictl package list` command. +NNI provides a lot of [builtin tuners](../Tuner/BuiltinTuner.md), [advisors](../Tuner/HyperbandAdvisor.md) and [assessors](../Assessor/BuiltinAssessor.md) can be used directly for Hyper Parameter Optimization, and some extra algorithms can be installed via `nnictl package install --name ` after NNI is installed. You can check these extra algorithms via `nnictl package list` command. NNI also provides the ability to build your own customized tuners, advisors and assessors. To use the customized algorithm, users can simply follow the spec in experiment config file to properly reference the algorithm, which has been illustrated in the tutorials of [customized tuners](../Tuner/CustomizeTuner.md)/[advisors](../Tuner/CustomizeAdvisor.md)/[assessors](../Assessor/CustomizeAssessor.md). diff --git a/docs/en_US/conf.py b/docs/en_US/conf.py index 0e0d3b8674..16808e2143 100644 --- a/docs/en_US/conf.py +++ b/docs/en_US/conf.py @@ -46,6 +46,7 @@ 'sphinxarg.ext', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode', + 'nbsphinx', ] # Add mock modules diff --git a/docs/en_US/nas.rst b/docs/en_US/nas.rst index c40224f681..280c4cad2c 100644 --- a/docs/en_US/nas.rst +++ b/docs/en_US/nas.rst @@ -23,4 +23,5 @@ For details, please refer to the following tutorials: One-shot NAS Customize a NAS Algorithm NAS Visualization + NAS Benchmarks API Reference diff --git a/docs/requirements.txt b/docs/requirements.txt index 8a976b3348..efa410404a 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -9,6 +9,8 @@ json_tricks numpy scipy coverage +peewee +nbsphinx schema tensorboard scikit-learn==0.20 diff --git a/examples/nas/benchmarks/nasbench101.sh b/examples/nas/benchmarks/nasbench101.sh new file mode 100644 index 0000000000..4b42628b81 --- /dev/null +++ b/examples/nas/benchmarks/nasbench101.sh @@ -0,0 +1,22 @@ +set -e +mkdir -p /outputs /tmp + +echo "Installing dependencies..." +apt update && apt install -y wget git +pip install --no-cache-dir tqdm peewee + +echo "Installing NNI..." +cd /nni && echo "y" | source install.sh + +cd /tmp + +echo "Installing NASBench..." +git clone https://github.com/google-research/nasbench +cd nasbench && pip install -e . && cd .. + +echo "Downloading NAS-Bench-101..." +wget https://storage.googleapis.com/nasbench/nasbench_full.tfrecord + +echo "Generating database..." +rm -f /outputs/nasbench101.db /outputs/nasbench101.db-journal +NASBENCHMARK_DIR=/outputs python -m nni.nas.benchmarks.nasbench101.db_gen nasbench_full.tfrecord diff --git a/examples/nas/benchmarks/nasbench201.sh b/examples/nas/benchmarks/nasbench201.sh new file mode 100644 index 0000000000..6121d95086 --- /dev/null +++ b/examples/nas/benchmarks/nasbench201.sh @@ -0,0 +1,19 @@ +set -e +mkdir -p /outputs /tmp + +echo "Installing dependencies..." +apt update && apt install -y wget +pip uninstall -y enum34 # https://github.com/iterative/dvc/issues/1995 +pip install --no-cache-dir gdown tqdm peewee + +echo "Installing NNI..." +cd /nni && echo "y" | source install.sh + +cd /tmp + +echo "Downloading NAS-Bench-201..." +gdown https://drive.google.com/uc\?id\=1OOfVPpt-lA4u2HJrXbgrRd42IbfvJMyE -O a.pth + +echo "Generating database..." +rm -f /outputs/nasbench201.db /outputs/nasbench201.db-journal +NASBENCHMARK_DIR=/outputs python -m nni.nas.benchmarks.nasbench201.db_gen a.pth diff --git a/examples/nas/benchmarks/nds.sh b/examples/nas/benchmarks/nds.sh new file mode 100644 index 0000000000..3750a34514 --- /dev/null +++ b/examples/nas/benchmarks/nds.sh @@ -0,0 +1,19 @@ +set -e +mkdir -p /outputs /tmp + +echo "Installing dependencies..." +apt update && apt install -y wget zip +pip install --no-cache-dir tqdm peewee + +echo "Installing NNI..." +cd /nni && echo "y" | source install.sh + +cd /tmp + +echo "Downloading NDS..." +wget https://dl.fbaipublicfiles.com/nds/data.zip -O data.zip +unzip data.zip + +echo "Generating database..." +rm -f /outputs/nds.db /outputs/nds.db-journal +NASBENCHMARK_DIR=/outputs python -m nni.nas.benchmarks.nds.db_gen nds_data diff --git a/src/sdk/pynni/nni/nas/benchmarks/__init__.py b/src/sdk/pynni/nni/nas/benchmarks/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/sdk/pynni/nni/nas/benchmarks/constants.py b/src/sdk/pynni/nni/nas/benchmarks/constants.py new file mode 100644 index 0000000000..4a52fa8d8f --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/constants.py @@ -0,0 +1,4 @@ +import os + + +DATABASE_DIR = os.environ.get("NASBENCHMARK_DIR", os.path.expanduser("~/.nni/nasbenchmark")) diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench101/__init__.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/__init__.py new file mode 100644 index 0000000000..e9aad0552c --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/__init__.py @@ -0,0 +1,3 @@ +from .constants import INPUT, OUTPUT, CONV3X3_BN_RELU, CONV1X1_BN_RELU, MAXPOOL3X3 +from .model import Nb101TrialStats, Nb101IntermediateStats, Nb101TrialConfig +from .query import query_nb101_trial_stats diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench101/constants.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/constants.py new file mode 100644 index 0000000000..c2769f497f --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/constants.py @@ -0,0 +1,14 @@ +INPUT = 'input' +OUTPUT = 'output' +CONV3X3_BN_RELU = 'conv3x3-bn-relu' +CONV1X1_BN_RELU = 'conv1x1-bn-relu' +MAXPOOL3X3 = 'maxpool3x3' + + +LABEL2ID = { + INPUT: -1, + OUTPUT: -2, + CONV3X3_BN_RELU: 0, + CONV1X1_BN_RELU: 1, + MAXPOOL3X3: 2 +} diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench101/db_gen.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/db_gen.py new file mode 100644 index 0000000000..04212ca270 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/db_gen.py @@ -0,0 +1,53 @@ +import argparse + +from tqdm import tqdm +from nasbench import api # pylint: disable=import-error + +from .model import db, Nb101TrialConfig, Nb101TrialStats, Nb101IntermediateStats +from .graph_util import nasbench_format_to_architecture_repr, hash_module + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('input_file', + help='Path to the file to be converted, e.g., nasbench_full.tfrecord') + args = parser.parse_args() + nasbench = api.NASBench(args.input_file) + with db: + db.create_tables([Nb101TrialConfig, Nb101TrialStats, Nb101IntermediateStats]) + for hashval in tqdm(nasbench.hash_iterator(), desc='Dumping data into database'): + metadata, metrics = nasbench.get_metrics_from_hash(hashval) + num_vertices, architecture = nasbench_format_to_architecture_repr( + metadata['module_adjacency'], metadata['module_operations']) + assert hashval == hash_module(architecture, num_vertices) + for epochs in [4, 12, 36, 108]: + trial_config = Nb101TrialConfig.create( + arch=architecture, + num_vertices=num_vertices, + hash=hashval, + num_epochs=epochs + ) + + for seed in range(3): + cur = metrics[epochs][seed] + trial = Nb101TrialStats.create( + config=trial_config, + train_acc=cur['final_train_accuracy'] * 100, + valid_acc=cur['final_validation_accuracy'] * 100, + test_acc=cur['final_test_accuracy'] * 100, + parameters=metadata['trainable_parameters'] / 1e6, + training_time=cur['final_training_time'] * 60 + ) + for t in ['halfway', 'final']: + Nb101IntermediateStats.create( + trial=trial, + current_epoch=epochs // 2 if t == 'halfway' else epochs, + training_time=cur[t + '_training_time'], + train_acc=cur[t + '_train_accuracy'] * 100, + valid_acc=cur[t + '_validation_accuracy'] * 100, + test_acc=cur[t + '_test_accuracy'] * 100 + ) + + +if __name__ == '__main__': + main() diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench101/graph_util.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/graph_util.py new file mode 100644 index 0000000000..10805685fe --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/graph_util.py @@ -0,0 +1,111 @@ +import hashlib + +import numpy as np + +from .constants import INPUT, LABEL2ID, OUTPUT + + +def _labeling_from_architecture(architecture, vertices): + return [INPUT] + [architecture['op{}'.format(i)] for i in range(1, vertices - 1)] + [OUTPUT] + + +def _adjancency_matrix_from_architecture(architecture, vertices): + matrix = np.zeros((vertices, vertices), dtype=np.bool) + for i in range(1, vertices): + for k in architecture['input{}'.format(i)]: + matrix[k, i] = 1 + return matrix + + +def nasbench_format_to_architecture_repr(adjacency_matrix, labeling): + """ + Computes a graph-invariance MD5 hash of the matrix and label pair. + Imported from NAS-Bench-101 repo. + + Parameters + ---------- + adjacency_matrix : np.ndarray + A 2D array of shape NxN, where N is the number of vertices. + ``matrix[u][v]`` is 1 if there is a direct edge from `u` to `v`, + otherwise it will be 0. + labeling : list of str + A list of str that starts with input and ends with output. The intermediate + nodes are chosen from candidate operators. + + Returns + ------- + tuple and int and dict + Converted number of vertices and architecture. + """ + num_vertices = adjacency_matrix.shape[0] + assert len(labeling) == num_vertices + architecture = {} + for i in range(1, num_vertices - 1): + architecture['op{}'.format(i)] = labeling[i] + assert labeling[i] not in [INPUT, OUTPUT] + for i in range(1, num_vertices): + architecture['input{}'.format(i)] = [k for k in range(i) if adjacency_matrix[k, i]] + return num_vertices, architecture + + +def infer_num_vertices(architecture): + """ + Infer number of vertices from an architecture dict. + + Parameters + ---------- + architecture : dict + Architecture in NNI format. + + Returns + ------- + int + Number of vertices. + """ + op_keys = set([k for k in architecture.keys() if k.startswith('op')]) + intermediate_vertices = len(op_keys) + assert op_keys == {'op{}'.format(i) for i in range(1, intermediate_vertices + 1)} + return intermediate_vertices + 2 + + +def hash_module(architecture, vertices): + """ + Computes a graph-invariance MD5 hash of the matrix and label pair. + This snippet is modified from code in NAS-Bench-101 repo. + + Parameters + ---------- + matrix : np.ndarray + Square upper-triangular adjacency matrix. + labeling : list of int + Labels of length equal to both dimensions of matrix. + + Returns + ------- + str + MD5 hash of the matrix and labeling. + """ + labeling = _labeling_from_architecture(architecture, vertices) + labeling = [LABEL2ID[t] for t in labeling] + matrix = _adjancency_matrix_from_architecture(architecture, vertices) + in_edges = np.sum(matrix, axis=0).tolist() + out_edges = np.sum(matrix, axis=1).tolist() + + assert len(in_edges) == len(out_edges) == len(labeling) + hashes = list(zip(out_edges, in_edges, labeling)) + hashes = [hashlib.md5(str(h).encode('utf-8')).hexdigest() for h in hashes] + # Computing this up to the diameter is probably sufficient but since the + # operation is fast, it is okay to repeat more times. + for _ in range(vertices): + new_hashes = [] + for v in range(vertices): + in_neighbors = [hashes[w] for w in range(vertices) if matrix[w, v]] + out_neighbors = [hashes[w] for w in range(vertices) if matrix[v, w]] + new_hashes.append(hashlib.md5( + (''.join(sorted(in_neighbors)) + '|' + + ''.join(sorted(out_neighbors)) + '|' + + hashes[v]).encode('utf-8')).hexdigest()) + hashes = new_hashes + fingerprint = hashlib.md5(str(sorted(hashes)).encode('utf-8')).hexdigest() + + return fingerprint diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench101/model.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/model.py new file mode 100644 index 0000000000..00a2596a5f --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/model.py @@ -0,0 +1,101 @@ +import os + +from peewee import CharField, FloatField, ForeignKeyField, IntegerField, Model +from playhouse.sqlite_ext import JSONField, SqliteExtDatabase + +from nni.nas.benchmarks.constants import DATABASE_DIR + +db = SqliteExtDatabase(os.path.join(DATABASE_DIR, 'nasbench101.db'), autoconnect=True) + + +class Nb101TrialConfig(Model): + """ + Trial config for NAS-Bench-101. + + Attributes + ---------- + arch : dict + A dict with keys ``op1``, ``op2``, ... and ``input1``, ``input2``, ... Vertices are + enumerate from 0. Since node 0 is input node, it is skipped in this dict. Each ``op`` + is one of :const:`nni.nas.benchmark.nasbench101.CONV3X3_BN_RELU`, + :const:`nni.nas.benchmark.nasbench101.CONV1X1_BN_RELU`, and :const:`nni.nas.benchmark.nasbench101.MAXPOOL3X3`. + Each ``input`` is a list of previous nodes. For example ``input5`` can be ``[0, 1, 3]``. + num_vertices : int + Number of vertices (nodes) in one cell. Should be less than or equal to 7 in default setup. + hash : str + Graph-invariant MD5 string for this architecture. + num_epochs : int + Number of epochs planned for this trial. Should be one of 4, 12, 36, 108 in default setup. + """ + + arch = JSONField(index=True) + num_vertices = IntegerField(index=True) + hash = CharField(max_length=64, index=True) + num_epochs = IntegerField(index=True) + + class Meta: + database = db + + +class Nb101TrialStats(Model): + """ + Computation statistics for NAS-Bench-101. Each corresponds to one trial. + Each config has multiple trials with different random seeds, but unfortunately seed for each trial is unavailable. + NAS-Bench-101 trains and evaluates on CIFAR-10 by default. The original training set is divided into + 40k training images and 10k validation images, and the original validation set is used for test only. + + Attributes + ---------- + config : Nb101TrialConfig + Setup for this trial data. + train_acc : float + Final accuracy on training data, ranging from 0 to 100. + valid_acc : float + Final accuracy on validation data, ranging from 0 to 100. + test_acc : float + Final accuracy on test data, ranging from 0 to 100. + parameters : float + Number of trainable parameters in million. + training_time : float + Duration of training in seconds. + """ + config = ForeignKeyField(Nb101TrialConfig, backref='trial_stats', index=True) + train_acc = FloatField() + valid_acc = FloatField() + test_acc = FloatField() + parameters = FloatField() + training_time = FloatField() + + class Meta: + database = db + + +class Nb101IntermediateStats(Model): + """ + Intermediate statistics for NAS-Bench-101. + + Attributes + ---------- + trial : Nb101TrialStats + The exact trial where the intermediate result is produced. + current_epoch : int + Elapsed epochs when evaluation is done. + train_acc : float + Intermediate accuracy on training data, ranging from 0 to 100. + valid_acc : float + Intermediate accuracy on validation data, ranging from 0 to 100. + test_acc : float + Intermediate accuracy on test data, ranging from 0 to 100. + training_time : float + Time elapsed in seconds. + """ + + trial = ForeignKeyField(Nb101TrialStats, backref='intermediates', index=True) + current_epoch = IntegerField(index=True) + train_acc = FloatField() + valid_acc = FloatField() + test_acc = FloatField() + training_time = FloatField() + + class Meta: + database = db diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench101/query.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/query.py new file mode 100644 index 0000000000..28c0dc03be --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench101/query.py @@ -0,0 +1,60 @@ +import functools + +from peewee import fn +from playhouse.shortcuts import model_to_dict +from .model import Nb101TrialStats, Nb101TrialConfig +from .graph_util import hash_module, infer_num_vertices + + +def query_nb101_trial_stats(arch, num_epochs, isomorphism=True, reduction=None): + """ + Query trial stats of NAS-Bench-101 given conditions. + + Parameters + ---------- + arch : dict or None + If a dict, it is in the format that is described in + :class:`nni.nas.benchmark.nasbench101.Nb101TrialConfig`. Only trial stats + matched will be returned. If none, architecture will be a wildcard. + num_epochs : int or None + If int, matching results will be returned. Otherwise a wildcard. + isomorphism : boolean + Whether to match essentially-same architecture, i.e., architecture with the + same graph-invariant hash value. + reduction : str or None + If 'none' or None, all trial stats will be returned directly. + If 'mean', fields in trial stats will be averaged given the same trial config. + + Returns + ------- + generator of dict + A generator of :class:`nni.nas.benchmark.nasbench101.Nb101TrialStats` objects, + where each of them has been converted into a dict. + """ + fields = [] + if reduction == 'none': + reduction = None + if reduction == 'mean': + for field_name in Nb101TrialStats._meta.sorted_field_names: + if field_name not in ['id', 'config']: + fields.append(fn.AVG(getattr(Nb101TrialStats, field_name)).alias(field_name)) + elif reduction is None: + fields.append(Nb101TrialStats) + else: + raise ValueError('Unsupported reduction: \'%s\'' % reduction) + query = Nb101TrialStats.select(*fields, Nb101TrialConfig).join(Nb101TrialConfig) + conditions = [] + if arch is not None: + if isomorphism: + num_vertices = infer_num_vertices(arch) + conditions.append(Nb101TrialConfig.hash == hash_module(arch, num_vertices)) + else: + conditions.append(Nb101TrialConfig.arch == arch) + if num_epochs is not None: + conditions.append(Nb101TrialConfig.num_epochs == num_epochs) + if conditions: + query = query.where(functools.reduce(lambda a, b: a & b, conditions)) + if reduction is not None: + query = query.group_by(Nb101TrialStats.config) + for k in query: + yield model_to_dict(k) diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench201/__init__.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/__init__.py new file mode 100644 index 0000000000..3ce3d0170d --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/__init__.py @@ -0,0 +1,3 @@ +from .constants import NONE, SKIP_CONNECT, CONV_1X1, CONV_3X3, AVG_POOL_3X3 +from .model import Nb201TrialStats, Nb201IntermediateStats, Nb201TrialConfig +from .query import query_nb201_trial_stats diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench201/constants.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/constants.py new file mode 100644 index 0000000000..c9d6bce5d1 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/constants.py @@ -0,0 +1,12 @@ +NONE = 'none' +SKIP_CONNECT = 'skip_connect' +CONV_1X1 = 'conv_1x1' +CONV_3X3 = 'conv_3x3' +AVG_POOL_3X3 = 'avg_pool_3x3' +PRIMITIVES = [ + NONE, + SKIP_CONNECT, + CONV_1X1, + CONV_3X3, + AVG_POOL_3X3, +] diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench201/db_gen.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/db_gen.py new file mode 100644 index 0000000000..e2202612b4 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/db_gen.py @@ -0,0 +1,106 @@ +import argparse +import re + +import tqdm +import torch + +from .constants import NONE, SKIP_CONNECT, CONV_1X1, CONV_3X3, AVG_POOL_3X3 +from .model import db, Nb201TrialConfig, Nb201TrialStats, Nb201IntermediateStats + + +def parse_arch_str(arch_str): + mp = { + 'none': NONE, + 'skip_connect': SKIP_CONNECT, + 'nor_conv_1x1': CONV_1X1, + 'nor_conv_3x3': CONV_3X3, + 'avg_pool_3x3': AVG_POOL_3X3 + } + m = re.match(r'\|(.*)~0\|\+\|(.*)~0\|(.*)~1\|\+\|(.*)~0\|(.*)~1\|(.*)~2\|', arch_str) + return { + '0_1': mp[m.group(1)], + '0_2': mp[m.group(2)], + '1_2': mp[m.group(3)], + '0_3': mp[m.group(4)], + '1_3': mp[m.group(5)], + '2_3': mp[m.group(6)] + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('input_file', + help='Path to the file to be converted, e.g., NAS-Bench-201-v1_1-096897.pth.') + args = parser.parse_args() + dataset_split = { + 'cifar10-valid': ['train', 'x-valid', 'ori-test', 'ori-test'], + 'cifar10': ['train', 'ori-test', 'ori-test', 'ori-test'], + 'cifar100': ['train', 'x-valid', 'x-test', 'ori-test'], + 'imagenet16-120': ['train', 'x-valid', 'x-test', 'ori-test'], + } + + with db: + db.create_tables([Nb201TrialConfig, Nb201TrialStats, Nb201IntermediateStats]) + print('Loading NAS-Bench-201 pickle...') + nb201_data = torch.load(args.input_file) + print('Dumping architectures...') + for arch_str in nb201_data['meta_archs']: + arch_json = parse_arch_str(arch_str) + for epochs in [12, 200]: + for dataset in Nb201TrialConfig.dataset.choices: + Nb201TrialConfig.create(arch=arch_json, num_epochs=epochs, dataset=dataset, + num_channels=16, num_cells=5) + for arch_info in tqdm.tqdm(nb201_data['arch2infos'].values(), + desc='Processing architecture statistics'): + for epochs_verb, d in arch_info.items(): + if epochs_verb == 'less': + epochs = 12 + else: + epochs = 200 + arch_json = parse_arch_str(d['arch_str']) + for (dataset, seed), r in d['all_results'].items(): + sp = dataset_split[dataset.lower()] + data_parsed = { + 'train_acc': r['train_acc1es'][epochs - 1], + 'valid_acc': r['eval_acc1es']['{}@{}'.format(sp[1], epochs - 1)], + 'test_acc': r['eval_acc1es']['{}@{}'.format(sp[2], epochs - 1)], + 'ori_test_acc': r['eval_acc1es']['{}@{}'.format(sp[3], epochs - 1)], + 'train_loss': r['train_losses'][epochs - 1], + 'valid_loss': r['eval_losses']['{}@{}'.format(sp[1], epochs - 1)], + 'test_loss': r['eval_losses']['{}@{}'.format(sp[2], epochs - 1)], + 'ori_test_loss': r['eval_losses']['{}@{}'.format(sp[3], epochs - 1)], + 'parameters': r['params'], + 'flops': r['flop'], + 'latency': r['latency'][0], + 'training_time': r['train_times'][epochs - 1] * epochs, + 'valid_evaluation_time': r['eval_times']['{}@{}'.format(sp[1], epochs - 1)], + 'test_evaluation_time': r['eval_times']['{}@{}'.format(sp[2], epochs - 1)], + 'ori_test_evaluation_time': r['eval_times']['{}@{}'.format(sp[3], epochs - 1)], + } + config = Nb201TrialConfig.get( + (Nb201TrialConfig.num_epochs == epochs) & + (Nb201TrialConfig.arch == arch_json) & + (Nb201TrialConfig.dataset == dataset.lower()) + ) + trial_stats = Nb201TrialStats.create(config=config, seed=seed, **data_parsed) + intermediate_stats = [] + for epoch in range(epochs): + data_parsed = { + 'train_acc': r['train_acc1es'].get(epoch), + 'valid_acc': r['eval_acc1es'].get('{}@{}'.format(sp[1], epoch)), + 'test_acc': r['eval_acc1es'].get('{}@{}'.format(sp[2], epoch)), + 'ori_test_acc': r['eval_acc1es'].get('{}@{}'.format(sp[3], epoch)), + 'train_loss': r['train_losses'].get(epoch), + 'valid_loss': r['eval_losses'].get('{}@{}'.format(sp[1], epoch)), + 'test_loss': r['eval_losses'].get('{}@{}'.format(sp[2], epoch)), + 'ori_test_loss': r['eval_losses'].get('{}@{}'.format(sp[3], epoch)), + } + if all([v is None for v in data_parsed.values()]): + continue + data_parsed.update(current_epoch=epoch + 1, trial=trial_stats) + intermediate_stats.append(data_parsed) + Nb201IntermediateStats.insert_many(intermediate_stats).execute(db) + + +if __name__ == '__main__': + main() diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench201/model.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/model.py new file mode 100644 index 0000000000..3e3322f921 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/model.py @@ -0,0 +1,159 @@ +import os + +from peewee import CharField, FloatField, ForeignKeyField, IntegerField, Model +from playhouse.sqlite_ext import JSONField, SqliteExtDatabase + +from nni.nas.benchmarks.constants import DATABASE_DIR + +db = SqliteExtDatabase(os.path.join(DATABASE_DIR, 'nasbench201.db'), autoconnect=True) + + +class Nb201TrialConfig(Model): + """ + Trial config for NAS-Bench-201. + + Attributes + ---------- + arch : dict + A dict with keys ``0_1``, ``0_2``, ``0_3``, ``1_2``, ``1_3``, ``2_3``, each of which + is an operator chosen from :const:`nni.nas.benchmark.nasbench201.NONE`, + :const:`nni.nas.benchmark.nasbench201.SKIP_CONNECT`, + :const:`nni.nas.benchmark.nasbench201.CONV_1X1`, + :const:`nni.nas.benchmark.nasbench201.CONV_3X3` and :const:`nni.nas.benchmark.nasbench201.AVG_POOL_3X3`. + num_epochs : int + Number of epochs planned for this trial. Should be one of 12 and 200. + num_channels: int + Number of channels for initial convolution. 16 by default. + num_cells: int + Number of cells per stage. 5 by default. + dataset: str + Dataset used for training and evaluation. NAS-Bench-201 provides the following 4 options: + ``cifar10-valid`` (training data is splited into 25k for training and 25k for validation, + validation data is used for test), ``cifar10`` (training data is used in training, validation + data is splited into 25k for validation and 25k for testing), ``cifar100`` (same protocol as ``cifar10``), + and ``imagenet16-120`` (a subset of 120 classes in ImageNet, downscaled to 16x16, using training data + for training, 6k images from validation set for validation and the other 6k for testing). + """ + + arch = JSONField(index=True) + num_epochs = IntegerField(index=True) + num_channels = IntegerField() + num_cells = IntegerField() + dataset = CharField(max_length=20, index=True, choices=[ + 'cifar10-valid', # 25k+25k+10k + 'cifar10', # 50k+5k+5k + 'cifar100', # 50k+5k+5k + 'imagenet16-120', + ]) + + class Meta: + database = db + + +class Nb201TrialStats(Model): + """ + Computation statistics for NAS-Bench-201. Each corresponds to one trial. + + Attributes + ---------- + config : Nb201TrialConfig + Setup for this trial data. + seed : int + Random seed selected, for reproduction. + train_acc : float + Final accuracy on training data, ranging from 0 to 100. + valid_acc : float + Final accuracy on validation data, ranging from 0 to 100. + test_acc : float + Final accuracy on test data, ranging from 0 to 100. + ori_test_acc : float + Test accuracy on original validation set (10k for CIFAR and 12k for Imagenet16-120), + ranging from 0 to 100. + train_loss : float or None + Final cross entropy loss on training data. Note that loss could be NaN, in which case + this attributed will be None. + valid_loss : float or None + Final cross entropy loss on validation data. + test_loss : float or None + Final cross entropy loss on test data. + ori_test_loss : float or None + Final cross entropy loss on original validation set. + parameters : float + Number of trainable parameters in million. + latency : float + Latency in seconds. + flops : float + FLOPs in million. + training_time : float + Duration of training in seconds. + valid_evaluation_time : float + Time elapsed to evaluate on validation set. + test_evaluation_time : float + Time elapsed to evaluate on test set. + ori_test_evaluation_time : float + Time elapsed to evaluate on original test set. + """ + config = ForeignKeyField(Nb201TrialConfig, backref='trial_stats', index=True) + seed = IntegerField() + train_acc = FloatField() + valid_acc = FloatField() + test_acc = FloatField() + ori_test_acc = FloatField() # test accuracy of the original test set + train_loss = FloatField(null=True) # possibly nan + valid_loss = FloatField(null=True) + test_loss = FloatField(null=True) + ori_test_loss = FloatField(null=True) + parameters = FloatField() # parameters in million + latency = FloatField() # latency in milliseconds + flops = FloatField() # flops in million + training_time = FloatField() + valid_evaluation_time = FloatField() + test_evaluation_time = FloatField() + ori_test_evaluation_time = FloatField() + + class Meta: + database = db + + +class Nb201IntermediateStats(Model): + """ + Intermediate statistics for NAS-Bench-201. + + Attributes + ---------- + trial : Nb201TrialStats + Corresponding trial. + current_epoch : int + Elapsed epochs. + train_acc : float + Current accuracy on training data, ranging from 0 to 100. + valid_acc : float + Current accuracy on validation data, ranging from 0 to 100. + test_acc : float + Current accuracy on test data, ranging from 0 to 100. + ori_test_acc : float + Test accuracy on original validation set (10k for CIFAR and 12k for Imagenet16-120), + ranging from 0 to 100. + train_loss : float or None + Current cross entropy loss on training data. + valid_loss : float or None + Current cross entropy loss on validation data. + test_loss : float or None + Current cross entropy loss on test data. + ori_test_loss : float or None + Current cross entropy loss on original validation set. + """ + + trial = ForeignKeyField(Nb201TrialStats, backref='intermediates', index=True) + current_epoch = IntegerField(index=True) + train_acc = FloatField(null=True) + valid_acc = FloatField(null=True) + test_acc = FloatField(null=True) + ori_test_acc = FloatField(null=True) + train_loss = FloatField(null=True) + valid_loss = FloatField(null=True) + test_loss = FloatField(null=True) + ori_test_loss = FloatField(null=True) + + class Meta: + database = db diff --git a/src/sdk/pynni/nni/nas/benchmarks/nasbench201/query.py b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/query.py new file mode 100644 index 0000000000..3e5a29dea0 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nasbench201/query.py @@ -0,0 +1,57 @@ +import functools + +from peewee import fn +from playhouse.shortcuts import model_to_dict +from .model import Nb201TrialStats, Nb201TrialConfig + + +def query_nb201_trial_stats(arch, num_epochs, dataset, reduction=None): + """ + Query trial stats of NAS-Bench-201 given conditions. + + Parameters + ---------- + arch : dict or None + If a dict, it is in the format that is described in + :class:`nni.nas.benchmark.nasbench201.Nb201TrialConfig`. Only trial stats + matched will be returned. If none, architecture will be a wildcard. + num_epochs : int or None + If int, matching results will be returned. Otherwise a wildcard. + dataset : str or None + If specified, can be one of the dataset available in :class:`nni.nas.benchmark.nasbench201.Nb201TrialConfig`. + Otherwise a wildcard. + reduction : str or None + If 'none' or None, all trial stats will be returned directly. + If 'mean', fields in trial stats will be averaged given the same trial config. + + Returns + ------- + generator of dict + A generator of :class:`nni.nas.benchmark.nasbench201.Nb201TrialStats` objects, + where each of them has been converted into a dict. + """ + fields = [] + if reduction == 'none': + reduction = None + if reduction == 'mean': + for field_name in Nb201TrialStats._meta.sorted_field_names: + if field_name not in ['id', 'config', 'seed']: + fields.append(fn.AVG(getattr(Nb201TrialStats, field_name)).alias(field_name)) + elif reduction is None: + fields.append(Nb201TrialStats) + else: + raise ValueError('Unsupported reduction: \'%s\'' % reduction) + query = Nb201TrialStats.select(*fields, Nb201TrialConfig).join(Nb201TrialConfig) + conditions = [] + if arch is not None: + conditions.append(Nb201TrialConfig.arch == arch) + if num_epochs is not None: + conditions.append(Nb201TrialConfig.num_epochs == num_epochs) + if dataset is not None: + conditions.append(Nb201TrialConfig.dataset == dataset) + if conditions: + query = query.where(functools.reduce(lambda a, b: a & b, conditions)) + if reduction is not None: + query = query.group_by(Nb201TrialStats.config) + for k in query: + yield model_to_dict(k) diff --git a/src/sdk/pynni/nni/nas/benchmarks/nds/__init__.py b/src/sdk/pynni/nni/nas/benchmarks/nds/__init__.py new file mode 100644 index 0000000000..9d393b86e1 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nds/__init__.py @@ -0,0 +1,3 @@ +from .constants import * +from .model import NdsTrialConfig, NdsTrialStats, NdsIntermediateStats +from .query import query_nds_trial_stats diff --git a/src/sdk/pynni/nni/nas/benchmarks/nds/constants.py b/src/sdk/pynni/nni/nas/benchmarks/nds/constants.py new file mode 100644 index 0000000000..164f094f3a --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nds/constants.py @@ -0,0 +1,16 @@ +NONE = 'none' +SKIP_CONNECT = 'skip_connect' +AVG_POOL_3X3 = 'avg_pool_3x3' +MAX_POOL_3X3 = 'max_pool_3x3' +MAX_POOL_5X5 = 'max_pool_5x5' +MAX_POOL_7X7 = 'max_pool_7x7' +CONV_1X1 = 'conv_1x1' +CONV_3X3 = 'conv_3x3' +CONV_3X1_1X3 = 'conv_3x1_1x3' +CONV_7X1_1X7 = 'conv_7x1_1x7' +DIL_CONV_3X3 = 'dil_conv_3x3' +DIL_CONV_5X5 = 'dil_conv_5x5' +SEP_CONV_3X3 = 'sep_conv_3x3' +SEP_CONV_5X5 = 'sep_conv_5x5' +SEP_CONV_7X7 = 'sep_conv_7x7' +DIL_SEP_CONV_3X3 = 'dil_sep_conv_3x3' diff --git a/src/sdk/pynni/nni/nas/benchmarks/nds/db_gen.py b/src/sdk/pynni/nni/nas/benchmarks/nds/db_gen.py new file mode 100644 index 0000000000..d4df09d4e3 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nds/db_gen.py @@ -0,0 +1,153 @@ +import json +import argparse +import os + +import numpy as np +import tqdm + +from .model import db, NdsTrialConfig, NdsTrialStats, NdsIntermediateStats + + +def inject_item(db, item, proposer, dataset, generator): + if 'genotype' in item['net']: + model_family = 'nas_cell' + num_nodes_normal = len(item['net']['genotype']['normal']) // 2 + num_nodes_reduce = len(item['net']['genotype']['reduce']) // 2 + model_spec = { + 'num_nodes_normal': num_nodes_normal, + 'num_nodes_reduce': num_nodes_reduce, + 'depth': item['net']['depth'], + 'width': item['net']['width'], + 'aux': item['net']['aux'], + 'drop_prob': item['net']['drop_prob'], + } + cell_spec = {} + for cell_type in ['normal', 'reduce']: + for i in range(num_nodes_normal): + for j, label in enumerate(['x', 'y']): + cell_spec['{}_{}_op_{}'.format(cell_type, i, label)] = \ + item['net']['genotype'][cell_type][i * 2 + j][0] + cell_spec['{}_{}_input_{}'.format(cell_type, i, label)] = \ + item['net']['genotype'][cell_type][i * 2 + j][1] + cell_spec['{}_concat'.format(cell_type)] = item['net']['genotype']['{}_concat'.format(cell_type)] + else: + if item['net']['block_type'].startswith('res_bottleneck'): + model_family = 'residual_bottleneck' + elif item['net']['block_type'].startswith('res_basic'): + model_family = 'residual_basic' + elif item['net']['block_type'].startswith('double_plain'): + model_family = 'vanilla' + else: + raise ValueError('Unrecognized block type') + model_spec = {k: v for k, v in item['net'].items() if v and k != 'block_type'} + cell_spec = {} + trial_config, _ = NdsTrialConfig.get_or_create( + model_family=model_family, + model_spec=model_spec, + cell_spec=cell_spec, + proposer=proposer, + base_lr=item['optim']['base_lr'], + weight_decay=item['optim']['wd'], + num_epochs=item['optim']['max_ep'], + dataset=dataset, + generator=generator + ) + assert len(item['train_ep_top1']) == len(item['test_ep_top1']) == trial_config.num_epochs + trial = NdsTrialStats.create( + config=trial_config, + seed=item['rng_seed'], + final_train_acc=100 - item['train_ep_top1'][-1], + final_train_loss=item['train_ep_loss'][-1], + final_test_acc=100 - item['test_ep_top1'][-1], + best_train_acc=100 - min(item['train_ep_top1']), + best_train_loss=np.nanmin(item['train_ep_loss']).item(), + best_test_acc=100 - min(item['test_ep_top1']), + parameters=item['params'] / 1e6, + flops=item['flops'] / 1e6, + iter_time=item['iter_time'] + ) + intermediate_stats = [] + for i in range(trial_config.num_epochs): + intermediate_stats.append({ + 'trial': trial, + 'current_epoch': i + 1, + 'train_loss': item['train_ep_loss'][i], + 'train_acc': 100 - item['train_ep_top1'][i], + 'test_acc': 100 - item['test_ep_top1'][i] + }) + NdsIntermediateStats.insert_many(intermediate_stats).execute(db) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('input_dir', help='Path to extracted NDS data dir.') + args = parser.parse_args() + + sweep_list = [ + 'Amoeba.json', + 'Amoeba_in.json', + 'DARTS.json', + 'DARTS_fix-w-d.json', + 'DARTS_in.json', + 'DARTS_lr-wd.json', + 'DARTS_lr-wd_in.json', + 'ENAS.json', + 'ENAS_fix-w-d.json', + 'ENAS_in.json', + 'NASNet.json', + 'NASNet_in.json', + 'PNAS.json', + 'PNAS_fix-w-d.json', + 'PNAS_in.json', + 'ResNeXt-A.json', + 'ResNeXt-A_in.json', + 'ResNeXt-B.json', + 'ResNeXt-B_in.json', + 'ResNet-B.json', + 'ResNet.json', + 'ResNet_lr-wd.json', + 'ResNet_lr-wd_in.json', + 'ResNet_reruns.json', + 'ResNet_rng1.json', + 'ResNet_rng2.json', + 'ResNet_rng3.json', + 'Vanilla.json', + 'Vanilla_lr-wd.json', + 'Vanilla_lr-wd_in.json', + 'Vanilla_reruns.json', + 'Vanilla_rng1.json', + 'Vanilla_rng2.json', + 'Vanilla_rng3.json' + ] + + with db: + db.create_tables([NdsTrialConfig, NdsTrialStats, NdsIntermediateStats]) + for json_idx, json_file in enumerate(sweep_list, start=1): + if 'fix-w-d' in json_file: + generator = 'fix_w_d' + elif 'lr-wd' in json_file: + generator = 'tune_lr_wd' + else: + generator = 'random' + if '_in' in json_file: + dataset = 'imagenet' + else: + dataset = 'cifar10' + proposer = json_file.split(".")[0].split("_")[0].lower() + with open(os.path.join(args.input_dir, json_file), 'r') as f: + data = json.load(f) + if 'top' in data and 'mid' in data: + for t in tqdm.tqdm(data['top'], + desc='[{}/{}] Processing {} (top)'.format(json_idx, len(sweep_list), json_file)): + inject_item(db, t, proposer, dataset, generator) + for t in tqdm.tqdm(data['mid'], + desc='[{}/{}] Processing {} (mid)'.format(json_idx, len(sweep_list), json_file)): + inject_item(db, t, proposer, dataset, generator) + else: + for job in tqdm.tqdm(data, + desc='[{}/{}] Processing {}'.format(json_idx, len(sweep_list), json_file)): + inject_item(db, job, proposer, dataset, generator) + + +if __name__ == '__main__': + main() diff --git a/src/sdk/pynni/nni/nas/benchmarks/nds/model.py b/src/sdk/pynni/nni/nas/benchmarks/nds/model.py new file mode 100644 index 0000000000..d7f6894da1 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nds/model.py @@ -0,0 +1,142 @@ +import os + +from peewee import CharField, FloatField, ForeignKeyField, IntegerField, Model +from playhouse.sqlite_ext import JSONField, SqliteExtDatabase + +from nni.nas.benchmarks.constants import DATABASE_DIR + +db = SqliteExtDatabase(os.path.join(DATABASE_DIR, 'nds.db'), autoconnect=True) + + +class NdsTrialConfig(Model): + """ + Trial config for NDS. + + Attributes + ---------- + model_family : str + Could be ``nas_cell``, ``residual_bottleneck``, ``residual_basic`` or ``vanilla``. + model_spec : dict + If ``model_family`` is ``nas_cell``, it contains ``num_nodes_normal``, ``num_nodes_reduce``, ``depth``, + ``width``, ``aux`` and ``drop_prob``. If ``model_family`` is ``residual_bottleneck``, it contains ``bot_muls``, + ``ds`` (depths), ``num_gs`` (number of groups) and ``ss`` (strides). If ``model_family`` is ``residual_basic`` or + ``vanilla``, it contains ``ds``, ``ss`` and ``ws``. + cell_spec : dict + If ``model_family`` is not ``nas_cell`` it will be an empty dict. Otherwise, it specifies + ``___``, where i ranges from 0 to ``num_nodes_ - 1``. + If it is an ``op``, the value is chosen from the constants specified previously like :const:`nni.nas.benchmark.nds.CONV_1X1`. + If it is i's ``input``, the value range from 0 to ``i + 1``, as ``nas_cell`` uses previous two nodes as inputs, and + node 0 is actually the second node. Refer to NASNet paper for details. Finally, another two key-value pairs + ``normal_concat`` and ``reduce_concat`` specify which nodes are eventually concatenated into output. + dataset : str + Dataset used. Could be ``cifar10`` or ``imagenet``. + generator : str + Can be one of ``random`` which generates configurations at random, while keeping learning rate and weight decay fixed, + ``fix_w_d`` which further keeps ``width`` and ``depth`` fixed, only applicable for ``nas_cell``. ``tune_lr_wd`` which + further tunes learning rate and weight decay. + proposer : str + Paper who has proposed the distribution for random sampling. Available proposers include ``nasnet``, ``darts``, ``enas``, + ``pnas``, ``amoeba``, ``vanilla``, ``resnext-a``, ``resnext-b``, ``resnet``, ``resnet-b`` (ResNet with bottleneck). + See NDS paper for details. + base_lr : float + Initial learning rate. + weight_decay : float + L2 weight decay applied on weights. + num_epochs : int + Number of epochs scheduled, during which learning rate will decay to 0 following cosine annealing. + """ + + model_family = CharField(max_length=20, index=True, choices=[ + 'nas_cell', + 'residual_bottleneck', + 'residual_basic', + 'vanilla', + ]) + model_spec = JSONField(index=True) + cell_spec = JSONField(index=True, null=True) + dataset = CharField(max_length=15, index=True, choices=['cifar10', 'imagenet']) + generator = CharField(max_length=15, index=True, choices=[ + 'random', + 'fix_w_d', + 'tune_lr_wd', + ]) + proposer = CharField(max_length=15, index=True) + base_lr = FloatField() + weight_decay = FloatField() + num_epochs = IntegerField() + + class Meta: + database = db + + +class NdsTrialStats(Model): + """ + Computation statistics for NDS. Each corresponds to one trial. + + Attributes + ---------- + config : NdsTrialConfig + Corresponding config for trial. + seed : int + Random seed selected, for reproduction. + final_train_acc : float + Final accuracy on training data, ranging from 0 to 100. + final_train_loss : float or None + Final cross entropy loss on training data. Could be NaN (None). + final_test_acc : float + Final accuracy on test data, ranging from 0 to 100. + best_train_acc : float + Best accuracy on training data, ranging from 0 to 100. + best_train_loss : float or None + Best cross entropy loss on training data. Could be NaN (None). + best_test_acc : float + Best accuracy on test data, ranging from 0 to 100. + parameters : float + Number of trainable parameters in million. + flops : float + FLOPs in million. + iter_time : float + Seconds elapsed for each iteration. + """ + config = ForeignKeyField(NdsTrialConfig, backref='trial_stats', index=True) + seed = IntegerField() + final_train_acc = FloatField() + final_train_loss = FloatField(null=True) + final_test_acc = FloatField() + best_train_acc = FloatField() + best_train_loss = FloatField(null=True) + best_test_acc = FloatField() + parameters = FloatField() + flops = FloatField() + iter_time = FloatField() + + class Meta: + database = db + + +class NdsIntermediateStats(Model): + """ + Intermediate statistics for NDS. + + Attributes + ---------- + trial : NdsTrialStats + Corresponding trial. + current_epoch : int + Elapsed epochs. + train_loss : float or None + Current cross entropy loss on training data. Can be NaN (None). + train_acc : float + Current accuracy on training data, ranging from 0 to 100. + test_acc : float + Current accuracy on test data, ranging from 0 to 100. + """ + + trial = ForeignKeyField(NdsTrialStats, backref='intermediates', index=True) + current_epoch = IntegerField(index=True) + train_loss = FloatField(null=True) + train_acc = FloatField() + test_acc = FloatField() + + class Meta: + database = db diff --git a/src/sdk/pynni/nni/nas/benchmarks/nds/query.py b/src/sdk/pynni/nni/nas/benchmarks/nds/query.py new file mode 100644 index 0000000000..618b9e57b3 --- /dev/null +++ b/src/sdk/pynni/nni/nas/benchmarks/nds/query.py @@ -0,0 +1,64 @@ +import functools + +from peewee import fn +from playhouse.shortcuts import model_to_dict +from .model import NdsTrialStats, NdsTrialConfig + + +def query_nds_trial_stats(model_family, proposer, generator, model_spec, cell_spec, dataset, + num_epochs=None, reduction=None): + """ + Query trial stats of NDS given conditions. + + Parameters + ---------- + model_family : str or None + If str, can be one of the model families available in :class:`nni.nas.benchmark.nds.NdsTrialConfig`. + Otherwise a wildcard. + proposer : str or None + If str, can be one of the proposers available in :class:`nni.nas.benchmark.nds.NdsTrialConfig`. Otherwise a wildcard. + generator : str or None + If str, can be one of the generators available in :class:`nni.nas.benchmark.nds.NdsTrialConfig`. Otherwise a wildcard. + model_spec : dict or None + If specified, can be one of the model spec available in :class:`nni.nas.benchmark.nds.NdsTrialConfig`. + Otherwise a wildcard. + cell_spec : dict or None + If specified, can be one of the cell spec available in :class:`nni.nas.benchmark.nds.NdsTrialConfig`. + Otherwise a wildcard. + dataset : str or None + If str, can be one of the datasets available in :class:`nni.nas.benchmark.nds.NdsTrialConfig`. Otherwise a wildcard. + num_epochs : float or None + If int, matching results will be returned. Otherwise a wildcard. + reduction : str or None + If 'none' or None, all trial stats will be returned directly. + If 'mean', fields in trial stats will be averaged given the same trial config. + + Returns + ------- + generator of dict + A generator of :class:`nni.nas.benchmark.nds.NdsTrialStats` objects, + where each of them has been converted into a dict. + """ + fields = [] + if reduction == 'none': + reduction = None + if reduction == 'mean': + for field_name in NdsTrialStats._meta.sorted_field_names: + if field_name not in ['id', 'config', 'seed']: + fields.append(fn.AVG(getattr(NdsTrialStats, field_name)).alias(field_name)) + elif reduction is None: + fields.append(NdsTrialStats) + else: + raise ValueError('Unsupported reduction: \'%s\'' % reduction) + query = NdsTrialStats.select(*fields, NdsTrialConfig).join(NdsTrialConfig) + conditions = [] + for field_name in ['model_family', 'proposer', 'generator', 'model_spec', 'cell_spec', + 'dataset', 'num_epochs']: + if locals()[field_name] is not None: + conditions.append(getattr(NdsTrialConfig, field_name) == locals()[field_name]) + if conditions: + query = query.where(functools.reduce(lambda a, b: a & b, conditions)) + if reduction is not None: + query = query.group_by(NdsTrialStats.config) + for k in query: + yield model_to_dict(k) From b82bad0fe6861ce85807c39d67990b539820aa81 Mon Sep 17 00:00:00 2001 From: Lijiaoa <61399850+Lijiaoa@users.noreply.github.com> Date: Mon, 29 Jun 2020 10:35:16 +0800 Subject: [PATCH 04/17] Show final result keys in hyper-parameter graph (#2586) --- .../src/components/trial-detail/Para.tsx | 76 ++++++++++++++++--- .../src/components/trial-detail/TableList.tsx | 25 +----- src/webui/src/static/model/trial.ts | 5 ++ src/webui/src/static/model/trialmanager.ts | 11 ++- src/webui/src/static/style/para.scss | 4 + 5 files changed, 87 insertions(+), 34 deletions(-) diff --git a/src/webui/src/components/trial-detail/Para.tsx b/src/webui/src/components/trial-detail/Para.tsx index 0a4cd8b34e..bf334c3fb4 100644 --- a/src/webui/src/components/trial-detail/Para.tsx +++ b/src/webui/src/components/trial-detail/Para.tsx @@ -1,7 +1,7 @@ import * as React from 'react'; import ReactEcharts from 'echarts-for-react'; import { filterByStatus } from '../../static/function'; -import { EXPERIMENT } from '../../static/datamodel'; +import { EXPERIMENT, TRIALS } from '../../static/datamodel'; import { Stack, PrimaryButton, Dropdown, IDropdownOption } from 'office-ui-fabric-react'; import { ParaObj, Dimobj, TableObj } from '../../static/interface'; import 'echarts/lib/chart/parallel'; @@ -30,6 +30,8 @@ interface ParaState { swapyAxis?: string[]; // yAxis Selector paraYdataNested: number[][]; isNested: false; + showFinalMetricKey: string; + metricType: string; } interface ParaProps { @@ -72,7 +74,9 @@ class Para extends React.Component { isLoadConfirm: false, swapyAxis: [], paraYdataNested: [], - isNested: false + isNested: false, + showFinalMetricKey: "default", + metricType: 'numberType' }; } @@ -101,7 +105,7 @@ class Para extends React.Component { } else { paraYdata = this.state.paraYdataNested; } - // add acc + // add metric value Object.keys(paraYdata).map(item => { paraYdata[item].push(accPara[item]); }); @@ -305,9 +309,11 @@ class Para extends React.Component { } this.setState({ dimName: dimName }); } + // metric yAxis + const { showFinalMetricKey } = this.state; parallelAxis.push({ dim: i, - name: 'default metric', + name: showFinalMetricKey, scale: true, nameTextStyle: { fontWeight: 700 @@ -364,9 +370,20 @@ class Para extends React.Component { eachTrialParams.push(trial.description.parameters); // may be a succeed trial hasn't final result // all detail page may be break down if havn't if + // metric yAxis data if (trial.acc !== undefined) { - if (trial.acc.default !== undefined) { - accPara.push(JSON.parse(trial.acc.default)); + const val = trial.acc[showFinalMetricKey]; + if (val !== undefined) { + const typeOfVal = typeof val; + if(typeOfVal === 'number'){ + this.setState(() => ({metricType: 'numberType'})); + } else { + // string type + parallelAxis[parallelAxis.length - 1].type = 'category'; + parallelAxis[parallelAxis.length - 1].data = [val]; + this.setState(() => ({metricType: 'stringType'})); + } + accPara.push(val); } } }); @@ -400,7 +417,7 @@ class Para extends React.Component { } this.setState({ paraYdataNested: renderDataSource }); } - // if not return final result + const maxVal = accPara.length === 0 ? 1 : Math.max(...accPara); const minVal = accPara.length === 0 ? 1 : Math.min(...accPara); this.setState({ max: maxVal, min: minVal }, () => { @@ -424,7 +441,7 @@ class Para extends React.Component { // deal with response data into pic data getOption = (dataObj: ParaObj, lengthofTrials: number): void => { // dataObj [[y1], [y2]... [default metric]] - const { max, min } = this.state; + const { max, min, metricType } = this.state; const parallelAxis = dataObj.parallelAxis; const paralleData = dataObj.data; let visualMapObj = {}; @@ -472,7 +489,7 @@ class Para extends React.Component { }, } }, - visualMap: visualMapObj, + visualMap: metricType === 'numberType' ? visualMapObj : null, series: { type: 'parallel', smooth: true, @@ -629,6 +646,13 @@ class Para extends React.Component { }); } + // select all final keys + updateEntries = (event: React.FormEvent, item: any): void => { + if (item !== undefined) { + this.setState({ showFinalMetricKey: item.key }, () => { this.reInit() }); + } + } + componentDidMount(): void { this.reInit(); } @@ -644,6 +668,7 @@ class Para extends React.Component { render(): React.ReactNode { const { option, paraNodata, dimName, isLoadConfirm, selectedItem, swapyAxis } = this.state; + return (
@@ -659,9 +684,10 @@ class Para extends React.Component { { key: '0.8', text: '80%' }, { key: '1', text: '100%' }, ]} - styles={{ dropdown: { width: 300 } }} + styles={{ dropdown: { width: 120 } }} className="para-filter-percent" /> + {this.finalKeysDropdown()} { }; }) } - styles={{ dropdown: { width: 300 } }} + styles={{ dropdown: { width: 240 } }} /> {
); } + + private finalKeysDropdown = (): any => { + const { showFinalMetricKey } = this.state; + if (TRIALS.finalKeys().length === 1) { + return null; + } else { + const finalKeysDropdown: any = []; + TRIALS.finalKeys().forEach(item => { + finalKeysDropdown.push({ + key: item, text: item + }); + }); + return ( +
+ Metrics + +
+ ); + } + + }; + } export default Para; diff --git a/src/webui/src/components/trial-detail/TableList.tsx b/src/webui/src/components/trial-detail/TableList.tsx index d8648f15ed..5233801faa 100644 --- a/src/webui/src/components/trial-detail/TableList.tsx +++ b/src/webui/src/components/trial-detail/TableList.tsx @@ -422,28 +422,9 @@ class TableList extends React.Component { parameterStr.push(`${value} (search space)`); }); } - let allColumnList = COLUMNPro.concat(parameterStr); - - // only succeed trials have final keys - if (tableSource.filter(record => record.status === 'SUCCEEDED').length >= 1) { - const temp = tableSource.filter(record => record.status === 'SUCCEEDED')[0].accDictionary; - if (temp !== undefined && typeof temp === 'object') { - // concat default column and finalkeys - const item = Object.keys(temp); - // item: ['default', 'other-keys', 'maybe loss'] - if (item.length > 1) { - const want: string[] = []; - item.forEach(value => { - if (value !== 'default') { - want.push(value); - } - }); - allColumnList = allColumnList.concat(want); - } - } - } - - return allColumnList; + // concat trial all final keys and remove dup "default" val, return list + return (COLUMNPro.concat(parameterStr)).concat(Array.from(new Set(TRIALS.finalKeys()))); + } // get IColumn[] diff --git a/src/webui/src/static/model/trial.ts b/src/webui/src/static/model/trial.ts index da3b982377..adbeed5f03 100644 --- a/src/webui/src/static/model/trial.ts +++ b/src/webui/src/static/model/trial.ts @@ -160,6 +160,11 @@ class Trial implements TableObj { return undefined; } + public finalKeys(): string[] { + // eslint-disable-next-line @typescript-eslint/no-non-null-assertion + return Object.keys(this.acc!); + } + /* table obj end */ public initialized(): boolean { diff --git a/src/webui/src/static/model/trialmanager.ts b/src/webui/src/static/model/trialmanager.ts index 9b2f964511..7ef5603196 100644 --- a/src/webui/src/static/model/trialmanager.ts +++ b/src/webui/src/static/model/trialmanager.ts @@ -91,6 +91,15 @@ class TrialManager { return this.filter(trial => trial.status === 'SUCCEEDED'); } + public finalKeys(): string[] { + const succeedTrialsList = this.filter(trial => trial.status === 'SUCCEEDED'); + if (succeedTrialsList !== undefined && succeedTrialsList[0] !== undefined) { + return succeedTrialsList[0].finalKeys(); + } else { + return ["default"]; + } + } + public sort(): Trial[] { // eslint-disable-next-line @typescript-eslint/no-non-null-assertion return this.filter(trial => trial.sortable).sort((trial1, trial2) => trial1.compareAccuracy(trial2)!); @@ -230,7 +239,7 @@ class TrialManager { updated = true; }); - return updated; + return updated; } private async updateMetrics(lastTime?: boolean): Promise { diff --git a/src/webui/src/static/style/para.scss b/src/webui/src/static/style/para.scss index dc5ed8848c..aca75281a7 100644 --- a/src/webui/src/static/style/para.scss +++ b/src/webui/src/static/style/para.scss @@ -14,6 +14,10 @@ &-percent{ margin-right: 10px; } + + &-middle{ + vertical-align: top; + } } .meline{ From e60e183846ddad0ccaa9f92249b0f5b46aa9e83e Mon Sep 17 00:00:00 2001 From: chicm-ms <38930155+chicm-ms@users.noreply.github.com> Date: Mon, 29 Jun 2020 10:48:25 +0800 Subject: [PATCH 05/17] Update lottery ticket example (#2559) --- .../model_compress/lottery_torch_mnist_fc.py | 60 ++++++++++++++++++- .../torch/pruning/lottery_ticket.py | 4 +- test/scripts/model_compression.sh | 4 +- 3 files changed, 62 insertions(+), 6 deletions(-) diff --git a/examples/model_compress/lottery_torch_mnist_fc.py b/examples/model_compress/lottery_torch_mnist_fc.py index 24242380b1..77c928c571 100644 --- a/examples/model_compress/lottery_torch_mnist_fc.py +++ b/examples/model_compress/lottery_torch_mnist_fc.py @@ -1,3 +1,5 @@ +import argparse +import copy import torch import torch.nn as nn import torch.nn.functional as F @@ -53,6 +55,31 @@ def test(model, test_loader, criterion): if __name__ == '__main__': + """ + THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS (https://arxiv.org/pdf/1803.03635.pdf) + + The Lottery Ticket Hypothesis. A randomly-initialized, dense neural network contains a subnetwork that is + initialized such that—when trained in isolation—it can match the test accuracy of the original network after + training for at most the same number of iterations. + + Identifying winning tickets. We identify a winning ticket by training a network and pruning its + smallest-magnitude weights. The remaining, unpruned connections constitute the architecture of the + winning ticket. Unique to our work, each unpruned connection’s value is then reset to its initialization + from original network before it was trained. This forms our central experiment: + 1. Randomly initialize a neural network f(x; θ0) (where θ0 ∼ Dθ). + 2. Train the network for j iterations, arriving at parameters θj . + 3. Prune p% of the parameters in θj , creating a mask m. + 4. Reset the remaining parameters to their values in θ0, creating the winning ticket f(x; m θ0). + As described, this pruning approach is one-shot: the network is trained once, p% of weights are + pruned, and the surviving weights are reset. However, in this paper, we focus on iterative pruning, + which repeatedly trains, prunes, and resets the network over n rounds; each round prunes p**(1/n) % of + the weights that survive the previous round. Our results show that iterative pruning finds winning tickets + that match the accuracy of the original network at smaller sizes than does one-shot pruning. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--train_epochs", type=int, default=10, help="training epochs") + args = parser.parse_args() + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) traindataset = datasets.MNIST('./data', train=True, download=True, transform=transform) testdataset = datasets.MNIST('./data', train=False, transform=transform) @@ -63,6 +90,20 @@ def test(model, test_loader, criterion): optimizer = torch.optim.Adam(model.parameters(), lr=1.2e-3) criterion = nn.CrossEntropyLoss() + # Record the random intialized model weights + orig_state = copy.deepcopy(model.state_dict()) + + # train the model to get unpruned metrics + for epoch in range(args.train_epochs): + train(model, train_loader, optimizer, criterion) + orig_accuracy = test(model, test_loader, criterion) + print('unpruned model accuracy: {}'.format(orig_accuracy)) + + # reset model weights and optimizer for pruning + model.load_state_dict(orig_state) + optimizer = torch.optim.Adam(model.parameters(), lr=1.2e-3) + + # Prune the model to find a winning ticket configure_list = [{ 'prune_iterations': 5, 'sparsity': 0.96, @@ -71,14 +112,29 @@ def test(model, test_loader, criterion): pruner = LotteryTicketPruner(model, configure_list, optimizer) pruner.compress() + best_accuracy = 0. + best_state_dict = None for i in pruner.get_prune_iterations(): pruner.prune_iteration_start() loss = 0 accuracy = 0 - for epoch in range(10): + for epoch in range(args.train_epochs): loss = train(model, train_loader, optimizer, criterion) accuracy = test(model, test_loader, criterion) print('current epoch: {0}, loss: {1}, accuracy: {2}'.format(epoch, loss, accuracy)) + if accuracy > best_accuracy: + best_accuracy = accuracy + # state dict of weights and masks + best_state_dict = copy.deepcopy(model.state_dict()) print('prune iteration: {0}, loss: {1}, accuracy: {2}'.format(i, loss, accuracy)) - pruner.export_model('model.pth', 'mask.pth') + + if best_accuracy > orig_accuracy: + # load weights and masks + pruner.bound_model.load_state_dict(best_state_dict) + # reset weights to original untrained model and keep masks unchanged to export winning ticket + pruner.load_model_state_dict(orig_state) + pruner.export_model('model_winning_ticket.pth', 'mask_winning_ticket.pth') + print('winning ticket has been saved: model_winning_ticket.pth, mask_winning_ticket.pth') + else: + print('winning ticket is not found in this run, you can run it again.') diff --git a/src/sdk/pynni/nni/compression/torch/pruning/lottery_ticket.py b/src/sdk/pynni/nni/compression/torch/pruning/lottery_ticket.py index 4b65fe99da..ace2b36154 100644 --- a/src/sdk/pynni/nni/compression/torch/pruning/lottery_ticket.py +++ b/src/sdk/pynni/nni/compression/torch/pruning/lottery_ticket.py @@ -83,12 +83,12 @@ def _calc_sparsity(self, sparsity): return max(1 - curr_keep_ratio, 0) def _calc_mask(self, wrapper, sparsity): - weight = wrapper.weight.data + weight = wrapper.module.weight.data if self.curr_prune_iteration == 0: mask = {'weight_mask': torch.ones(weight.shape).type_as(weight)} else: curr_sparsity = self._calc_sparsity(sparsity) - mask = self.masker.calc_mask(wrapper, curr_sparsity) + mask = self.masker.calc_mask(sparsity=curr_sparsity, wrapper=wrapper) return mask def calc_mask(self, wrapper, **kwargs): diff --git a/test/scripts/model_compression.sh b/test/scripts/model_compression.sh index 3083493495..afc21cc1f2 100644 --- a/test/scripts/model_compression.sh +++ b/test/scripts/model_compression.sh @@ -28,8 +28,8 @@ python3 model_prune_torch.py --pruner_name agp --pretrain_epochs 1 --prune_epoch echo 'testing mean_activation pruning' python3 model_prune_torch.py --pruner_name mean_activation --pretrain_epochs 1 --prune_epochs 1 -#echo "testing lottery ticket pruning..." -#python3 lottery_torch_mnist_fc.py +echo "testing lottery ticket pruning..." +python3 lottery_torch_mnist_fc.py --train_epochs 1 echo "" echo "===========================Testing: quantizers===========================" From 5a805e36bf31870801f04ce9bd67c88ed12686ba Mon Sep 17 00:00:00 2001 From: Tab Zhang Date: Mon, 29 Jun 2020 11:06:14 +0800 Subject: [PATCH 06/17] format nnictl tutorial (#2599) --- docs/en_US/Tutorial/Nnictl.md | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/docs/en_US/Tutorial/Nnictl.md b/docs/en_US/Tutorial/Nnictl.md index 16b8ad734e..ed5c9761e1 100644 --- a/docs/en_US/Tutorial/Nnictl.md +++ b/docs/en_US/Tutorial/Nnictl.md @@ -28,7 +28,7 @@ nnictl support commands: ### Manage an experiment -![](https://placehold.it/15/1589F0/000000?text=+) `nnictl create` +### nnictl create * Description @@ -79,7 +79,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `nnictl resume` +### nnictl resume * Description @@ -110,7 +110,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `nnictl view` +### nnictl view * Description @@ -138,7 +138,7 @@ Debug mode will disable version check function in Trialkeeper. ``` -![](https://placehold.it/15/1589F0/000000?text=+) `nnictl stop` +### nnictl stop * Description @@ -190,7 +190,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `nnictl update` +### nnictl update * __nnictl update searchspace__ * Description @@ -299,7 +299,7 @@ Debug mode will disable version check function in Trialkeeper. ``` -![](https://placehold.it/15/1589F0/000000?text=+) `nnictl trial` +### nnictl trial * __nnictl trial ls__ @@ -348,7 +348,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `nnictl top` +### nnictl top * Description @@ -368,7 +368,7 @@ Debug mode will disable version check function in Trialkeeper. |--time, -t| False| |The interval to update the experiment status, the unit of time is second, and the default value is 3 second.| -![](https://placehold.it/15/1589F0/000000?text=+) `Manage experiment information` +### Manage experiment information * __nnictl experiment show__ @@ -532,7 +532,7 @@ Debug mode will disable version check function in Trialkeeper. ``` -![](https://placehold.it/15/1589F0/000000?text=+) `Manage platform information` +### Manage platform information * __nnictl platform clean__ @@ -558,7 +558,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `nnictl config show` +### nnictl config show * Description @@ -572,7 +572,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `Manage log` +### Manage log * __nnictl log stdout__ @@ -643,12 +643,12 @@ Debug mode will disable version check function in Trialkeeper. |--trial_id, -T| False| |ID of the trial to be found the log path, required when id is not empty.| -![](https://placehold.it/15/1589F0/000000?text=+) `Manage webui` +### Manage webui * __nnictl webui url__ -![](https://placehold.it/15/1589F0/000000?text=+) `Manage tensorboard` +### Manage tensorboard * __nnictl tensorboard start__ @@ -697,7 +697,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `Manage package` +### Manage package * __nnictl package install__ * Description @@ -806,7 +806,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `Generate search space` +### Generate search space * __nnictl ss_gen__ * Description @@ -837,7 +837,7 @@ Debug mode will disable version check function in Trialkeeper. -![](https://placehold.it/15/1589F0/000000?text=+) `Check NNI version` +### Check NNI version * __nnictl --version__ From 93d9b67437ad266f1d417d63cb20215156742786 Mon Sep 17 00:00:00 2001 From: Vaggelis Gkiastas <50487017+egkiastas@users.noreply.github.com> Date: Mon, 29 Jun 2020 06:07:43 +0300 Subject: [PATCH 07/17] Update README.md (#2596) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 10c3391fe7..dbce191cac 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ The tool manages automated machine learning (AutoML) experiments, **dispatches a ## **NNI capabilities in a glance** -NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiments. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in stat-of-the-art AutoML algorithms and out of box support for popular training platforms. +NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiments. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in state-of-the-art AutoML algorithms and out of box support for popular training platforms. Within the following table, we summarized the current NNI capabilities, we are gradually adding new capabilities and we'd love to have your contribution. From ab6406340c71b835bd98eb3572d0bc315e30a94c Mon Sep 17 00:00:00 2001 From: Tab Zhang Date: Mon, 29 Jun 2020 11:08:01 +0800 Subject: [PATCH 08/17] add description about update_search_space (#2597) --- docs/en_US/Tuner/CustomizeTuner.md | 40 +++++++++++++++++++----------- 1 file changed, 25 insertions(+), 15 deletions(-) diff --git a/docs/en_US/Tuner/CustomizeTuner.md b/docs/en_US/Tuner/CustomizeTuner.md index 0e7b40d533..42209ba7e8 100644 --- a/docs/en_US/Tuner/CustomizeTuner.md +++ b/docs/en_US/Tuner/CustomizeTuner.md @@ -7,7 +7,7 @@ NNI provides state-of-the-art tuning algorithm in builtin-tuners. NNI supports t If you want to implement your own tuning algorithm, you can implement a customized Tuner, there are three things to do: 1. Inherit the base Tuner class -1. Implement receive_trial_result and generate_parameter function +1. Implement receive_trial_result, generate_parameter and update_search_space function 1. Configure your customized tuner in experiment YAML config file Here is an example: @@ -22,7 +22,7 @@ class CustomizedTuner(Tuner): ... ``` -**2. Implement receive_trial_result and generate_parameter function** +**2. Implement receive_trial_result, generate_parameter and update_search_space function** ```python from nni.tuner import Tuner @@ -32,22 +32,32 @@ class CustomizedTuner(Tuner): ... def receive_trial_result(self, parameter_id, parameters, value, **kwargs): - ''' - Receive trial's final result. - parameter_id: int - parameters: object created by 'generate_parameters()' - value: final metrics of the trial, including default metric - ''' - # your code implements here. + ''' + Receive trial's final result. + parameter_id: int + parameters: object created by 'generate_parameters()' + value: final metrics of the trial, including default metric + ''' + # your code implements here. ... def generate_parameters(self, parameter_id, **kwargs): - ''' - Returns a set of trial (hyper-)parameters, as a serializable object - parameter_id: int - ''' - # your code implements here. - return your_parameters + ''' + Returns a set of trial (hyper-)parameters, as a serializable object + parameter_id: int + ''' + # your code implements here. + return your_parameters + ... + + def update_search_space(self, search_space): + ''' + Tuners are advised to support updating search space at run-time. + If a tuner can only set search space once before generating first hyper-parameters, + it should explicitly document this behaviour. + search_space: JSON object created by experiment owner + ''' + # your code implements here. ... ``` From 813e41156a835efeb1c2b963cf61e726791ed038 Mon Sep 17 00:00:00 2001 From: Tab Zhang Date: Mon, 29 Jun 2020 11:11:44 +0800 Subject: [PATCH 09/17] format tuner and accessor README (#2588) --- docs/en_US/Assessor/BuiltinAssessor.md | 4 ++-- docs/en_US/Tuner/BuiltinTuner.md | 28 +++++++++++++------------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/docs/en_US/Assessor/BuiltinAssessor.md b/docs/en_US/Assessor/BuiltinAssessor.md index 7374d6665d..ad8a88ad76 100644 --- a/docs/en_US/Assessor/BuiltinAssessor.md +++ b/docs/en_US/Assessor/BuiltinAssessor.md @@ -19,7 +19,7 @@ Note: Please follow the provided format when writing your `config.yml` file. -![](https://placehold.it/15/1589F0/000000?text=+) `Median Stop Assessor` +### Median Stop Assessor > Builtin Assessor Name: **Medianstop** @@ -47,7 +47,7 @@ assessor: -![](https://placehold.it/15/1589F0/000000?text=+) `Curve Fitting Assessor` +### Curve Fitting Assessor > Builtin Assessor Name: **Curvefitting** diff --git a/docs/en_US/Tuner/BuiltinTuner.md b/docs/en_US/Tuner/BuiltinTuner.md index 516b5c2adc..1c3f77c825 100644 --- a/docs/en_US/Tuner/BuiltinTuner.md +++ b/docs/en_US/Tuner/BuiltinTuner.md @@ -31,7 +31,7 @@ Note: Please follow the format when you write your `config.yml` file. Some built -![](https://placehold.it/15/1589F0/000000?text=+) `TPE` +### TPE > Built-in Tuner Name: **TPE** @@ -60,7 +60,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `Random Search` +### Random Search > Built-in Tuner Name: **Random** @@ -80,7 +80,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `Anneal` +### Anneal > Built-in Tuner Name: **Anneal** @@ -107,7 +107,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `Naïve Evolution` +### Naïve Evolution > Built-in Tuner Name: **Evolution** @@ -136,7 +136,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `SMAC` +### SMAC > Built-in Tuner Name: **SMAC** @@ -173,7 +173,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `Batch Tuner` +### Batch Tuner > Built-in Tuner Name: BatchTuner @@ -212,7 +212,7 @@ The search space file should include the high-level key `combine_params`. The ty -![](https://placehold.it/15/1589F0/000000?text=+) `Grid Search` +### Grid Search > Built-in Tuner Name: **Grid Search** @@ -234,7 +234,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `Hyperband` +### Hyperband > Built-in Advisor Name: **Hyperband** @@ -264,7 +264,7 @@ advisor: -![](https://placehold.it/15/1589F0/000000?text=+) `Network Morphism` +### Network Morphism > Built-in Tuner Name: **NetworkMorphism** @@ -302,7 +302,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `Metis Tuner` +### Metis Tuner > Built-in Tuner Name: **MetisTuner** @@ -330,7 +330,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `BOHB Advisor` +### BOHB Advisor > Built-in Tuner Name: **BOHB** @@ -375,7 +375,7 @@ advisor: -![](https://placehold.it/15/1589F0/000000?text=+) `GP Tuner` +### GP Tuner > Built-in Tuner Name: **GPTuner** @@ -417,7 +417,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `PPO Tuner` +### PPO Tuner > Built-in Tuner Name: **PPOTuner** @@ -453,7 +453,7 @@ tuner: -![](https://placehold.it/15/1589F0/000000?text=+) `PBT Tuner` +### PBT Tuner > Built-in Tuner Name: **PBTTuner** From 92b5fa145da1bef6576b2cc2ca327477f566c4c4 Mon Sep 17 00:00:00 2001 From: SparkSnail Date: Mon, 29 Jun 2020 11:34:54 +0800 Subject: [PATCH 10/17] Fix pai pipeline (#2607) --- test/config/training_service.yml | 1 + test/pipelines/pipelines-it-pai-windows.yml | 2 +- test/pipelines/pipelines-it-pai.yml | 2 +- tools/nni_cmd/config_schema.py | 2 +- 4 files changed, 4 insertions(+), 3 deletions(-) diff --git a/test/config/training_service.yml b/test/config/training_service.yml index ff0d2cb047..0ae24d1aaf 100644 --- a/test/config/training_service.yml +++ b/test/config/training_service.yml @@ -90,6 +90,7 @@ pai: cpuNum: 1 image: memoryMB: 8192 + virtualCluster: default nniManagerNFSMountPath: containerNFSMountPath: paiStorageConfigName: diff --git a/test/pipelines/pipelines-it-pai-windows.yml b/test/pipelines/pipelines-it-pai-windows.yml index f8590d27a0..3bdb4ee69b 100644 --- a/test/pipelines/pipelines-it-pai-windows.yml +++ b/test/pipelines/pipelines-it-pai-windows.yml @@ -69,6 +69,6 @@ jobs: set PATH=$(ENV_PATH) python --version mount -o anon $(pai_nfs_uri) $(local_nfs_uri) - python nni_test/nnitest/generate_ts_config.py --ts pai --pai_token $(pai_token) --pai_host $(pai_host) --pai_user $(pai_user) --nni_docker_image $(docker_image) --pai_storage_config_name $(pai_storage_config_name) --nni_manager_nfs_mount_path $(nni_manager_nfs_mount_path) --container_nfs_mount_path $(container_nfs_mount_path) --nni_manager_ip $(nni_manager_ip) + python nni_test/nnitest/generate_ts_config.py --ts pai --pai_token $(pai_token) --pai_host $(pai_host) --pai_user $(pai_user) --nni_docker_image $(docker_image) --pai_storage_config_name $(pai_storage_config_name) --nni_manager_nfs_mount_path $(nni_manager_nfs_mount_path) --container_nfs_mount_path $(container_nfs_mount_path) --nni_manager_ip $(nni_manager_ip) --vc $(virtual_cluster) python nni_test/nnitest/run_tests.py --config config/integration_tests.yml --ts pai displayName: 'Examples and advanced features tests on pai' \ No newline at end of file diff --git a/test/pipelines/pipelines-it-pai.yml b/test/pipelines/pipelines-it-pai.yml index f2b428ecc6..6e0ea46403 100644 --- a/test/pipelines/pipelines-it-pai.yml +++ b/test/pipelines/pipelines-it-pai.yml @@ -58,6 +58,6 @@ jobs: echo "TEST_IMG:$TEST_IMG" cd test python3 nni_test/nnitest/generate_ts_config.py --ts pai --pai_host $(pai_host) --pai_user $(pai_user) --nni_docker_image $TEST_IMG --pai_storage_config_name $(pai_storage_config_name)\ - --pai_token $(pai_token) --nni_manager_nfs_mount_path $(nni_manager_nfs_mount_path) --container_nfs_mount_path $(container_nfs_mount_path) --nni_manager_ip $(nni_manager_ip) + --pai_token $(pai_token) --nni_manager_nfs_mount_path $(nni_manager_nfs_mount_path) --container_nfs_mount_path $(container_nfs_mount_path) --nni_manager_ip $(nni_manager_ip) --vc $(virtual_cluster) PATH=$HOME/.local/bin:$PATH python3 nni_test/nnitest/run_tests.py --config config/integration_tests.yml --ts pai displayName: 'integration test' diff --git a/tools/nni_cmd/config_schema.py b/tools/nni_cmd/config_schema.py index 27f6e7af13..b1b903530d 100644 --- a/tools/nni_cmd/config_schema.py +++ b/tools/nni_cmd/config_schema.py @@ -463,7 +463,7 @@ def validate_pai_config_path(self, experiment_config): if not taskRoles_dict: raise SchemaError('Please set taskRoles in paiConfigPath config file!') else: - pai_trial_fields_required_list = ['image', 'gpuNum', 'cpuNum', 'memoryMB', 'paiStoragePlugin', 'command'] + pai_trial_fields_required_list = ['image', 'gpuNum', 'cpuNum', 'memoryMB', 'paiStorageConfigName', 'command'] for trial_field in pai_trial_fields_required_list: if experiment_config['trial'].get(trial_field) is None: raise SchemaError('Please set {0} in trial configuration,\ From 0fbaff6cddb1d68823fc39878b2962460073343c Mon Sep 17 00:00:00 2001 From: Chi Song <27178119+squirrelsc@users.noreply.github.com> Date: Mon, 29 Jun 2020 15:54:35 +0800 Subject: [PATCH 11/17] Remove duplicate data under /tmp folder, and other small changes. (#2484) --- examples/nas/classic_nas/mnist.py | 3 +- .../trials/mnist-pbt-tuner-pytorch/mnist.py | 6 +-- examples/trials/mnist-pytorch/mnist.py | 4 +- test/config/integration_tests.yml | 4 +- test/nni_test/nnitest/run_tests.py | 48 ++++++++++++------- test/pipelines/pipelines-it-local-tf2.yml | 2 +- test/pipelines/pipelines-it-local.yml | 2 +- 7 files changed, 43 insertions(+), 26 deletions(-) diff --git a/examples/nas/classic_nas/mnist.py b/examples/nas/classic_nas/mnist.py index d3831b1742..faf32247b1 100644 --- a/examples/nas/classic_nas/mnist.py +++ b/examples/nas/classic_nas/mnist.py @@ -109,8 +109,7 @@ def main(args): kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {} - #data_dir = os.path.join(args['data_dir'], nni.get_trial_id()) - data_dir = os.path.join(args['data_dir'], 'data') + data_dir = args['data_dir'] train_loader = torch.utils.data.DataLoader( datasets.MNIST(data_dir, train=True, download=True, diff --git a/examples/trials/mnist-pbt-tuner-pytorch/mnist.py b/examples/trials/mnist-pbt-tuner-pytorch/mnist.py index 3403b185ae..417e702b6b 100644 --- a/examples/trials/mnist-pbt-tuner-pytorch/mnist.py +++ b/examples/trials/mnist-pbt-tuner-pytorch/mnist.py @@ -88,7 +88,7 @@ def main(args): kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {} - data_dir = os.path.join(args['data_dir'], nni.get_trial_id()) + data_dir = args['data_dir'] train_loader = torch.utils.data.DataLoader( datasets.MNIST(data_dir, train=True, download=True, @@ -144,7 +144,7 @@ def get_params(): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument("--data_dir", type=str, - default='/tmp/pytorch/mnist/input_data', help="data directory") + default='./data', help="data directory") parser.add_argument('--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--lr', type=float, default=0.01, metavar='LR', @@ -180,4 +180,4 @@ def get_params(): main(params) except Exception as exception: logger.exception(exception) - raise \ No newline at end of file + raise diff --git a/examples/trials/mnist-pytorch/mnist.py b/examples/trials/mnist-pytorch/mnist.py index 94f371c81f..a7ca27816a 100644 --- a/examples/trials/mnist-pytorch/mnist.py +++ b/examples/trials/mnist-pytorch/mnist.py @@ -88,7 +88,7 @@ def main(args): kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {} - data_dir = os.path.join(args['data_dir'], nni.get_trial_id()) + data_dir = args['data_dir'] train_loader = torch.utils.data.DataLoader( datasets.MNIST(data_dir, train=True, download=True, @@ -129,7 +129,7 @@ def get_params(): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument("--data_dir", type=str, - default='/tmp/pytorch/mnist/input_data', help="data directory") + default='./data', help="data directory") parser.add_argument('--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument("--batch_num", type=int, default=None) diff --git a/test/config/integration_tests.yml b/test/config/integration_tests.yml index 3fb713cbe3..399435d13e 100644 --- a/test/config/integration_tests.yml +++ b/test/config/integration_tests.yml @@ -51,6 +51,8 @@ testCases: - name: mnist-pytorch configFile: test/config/examples/mnist-pytorch.yml + # download data first, to prevent concurrent issue. + launchCommand: python3 ../examples/trials/mnist-pytorch/mnist.py --epochs 1 --batch_num 0 --data_dir ../examples/trials/mnist-pytorch/data && nnictl create --config $configFile --debug - name: mnist-annotation configFile: test/config/examples/mnist-annotation.yml @@ -84,7 +86,7 @@ testCases: configFile: test/config/examples/classic-nas-pytorch.yml # remove search space file stopCommand: nnictl stop - onExitCommand: python3 -c 'import os; os.remove("config/examples/nni-nas-search-space.json")' + onExitCommand: python3 -c "import os; os.remove('config/examples/nni-nas-search-space.json')" trainingService: local ######################################################################### diff --git a/test/nni_test/nnitest/run_tests.py b/test/nni_test/nnitest/run_tests.py index 1c31ffa89b..2dc9bfe8a6 100644 --- a/test/nni_test/nnitest/run_tests.py +++ b/test/nni_test/nnitest/run_tests.py @@ -1,25 +1,28 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. -import sys -import os import argparse -import subprocess -import time import datetime -import shlex -import traceback import json +import os +import subprocess +import sys +import time + import ruamel.yaml as yaml -from utils import get_experiment_status, get_yml_content, dump_yml_content, get_experiment_id, \ - parse_max_duration_time, get_trial_stats, deep_update, print_trial_job_log, get_failed_trial_jobs, \ - get_experiment_dir, print_experiment_log -from utils import GREEN, RED, CLEAR, STATUS_URL, TRIAL_JOBS_URL, EXPERIMENT_URL, REST_ENDPOINT, wait_for_port_available import validators +from utils import (CLEAR, EXPERIMENT_URL, GREEN, RED, REST_ENDPOINT, + STATUS_URL, TRIAL_JOBS_URL, deep_update, dump_yml_content, + get_experiment_dir, get_experiment_id, + get_experiment_status, get_failed_trial_jobs, + get_trial_stats, get_yml_content, parse_max_duration_time, + print_experiment_log, print_trial_job_log, + wait_for_port_available) it_variables = {} + def update_training_service_config(config, training_service): it_ts_config = get_yml_content(os.path.join('config', 'training_service.yml')) @@ -39,6 +42,7 @@ def update_training_service_config(config, training_service): deep_update(config, it_ts_config['all']) deep_update(config, it_ts_config[training_service]) + def prepare_config_file(test_case_config, it_config, args): config_path = args.nni_source_dir + test_case_config['configFile'] test_yml_config = get_yml_content(config_path) @@ -63,6 +67,7 @@ def prepare_config_file(test_case_config, it_config, args): return new_config_file + def run_test_case(test_case_config, it_config, args): new_config_file = prepare_config_file(test_case_config, it_config, args) # set configFile variable @@ -75,15 +80,16 @@ def run_test_case(test_case_config, it_config, args): stop_command = get_command(test_case_config, 'stopCommand') print('Stop command:', stop_command, flush=True) if stop_command: - subprocess.run(shlex.split(stop_command)) + subprocess.run(stop_command, shell=True) exit_command = get_command(test_case_config, 'onExitCommand') print('Exit command:', exit_command, flush=True) if exit_command: - subprocess.run(shlex.split(exit_command), check=True) + subprocess.run(exit_command, shell=True, check=True) # remove tmp config file if os.path.exists(new_config_file): os.remove(new_config_file) + def invoke_validator(test_case_config, nni_source_dir, training_service): validator_config = test_case_config.get('validator') if validator_config is None or validator_config.get('class') is None: @@ -100,10 +106,12 @@ def invoke_validator(test_case_config, nni_source_dir, training_service): print_trial_job_log(training_service, TRIAL_JOBS_URL) raise + def get_max_values(config_file): experiment_config = get_yml_content(config_file) return parse_max_duration_time(experiment_config['maxExecDuration']), experiment_config['maxTrialNum'] + def get_command(test_case_config, commandKey): command = test_case_config.get(commandKey) if commandKey == 'launchCommand': @@ -121,11 +129,12 @@ def get_command(test_case_config, commandKey): return command + def launch_test(config_file, training_service, test_case_config): launch_command = get_command(test_case_config, 'launchCommand') print('launch command: ', launch_command, flush=True) - proc = subprocess.run(shlex.split(launch_command)) + proc = subprocess.run(launch_command, shell=True) assert proc.returncode == 0, 'launch command failed with code %d' % proc.returncode @@ -150,7 +159,7 @@ def launch_test(config_file, training_service, test_case_config): experiment_id = get_experiment_id(EXPERIMENT_URL) while True: waited_time = time.time() - bg_time - if waited_time > max_duration + 10: + if waited_time > max_duration + 10: print('waited: {}, max_duration: {}'.format(waited_time, max_duration)) break status = get_experiment_status(STATUS_URL) @@ -176,6 +185,7 @@ def launch_test(config_file, training_service, test_case_config): print_trial_job_log(training_service, TRIAL_JOBS_URL) raise AssertionError('Failed to finish in maxExecDuration') + def case_excluded(name, excludes): if name is None: return False @@ -186,6 +196,7 @@ def case_excluded(name, excludes): return True return False + def case_included(name, cases): assert cases is not None for case in cases.split(','): @@ -193,9 +204,11 @@ def case_included(name, cases): return True return False + def match_platform(test_case_config): return sys.platform in test_case_config['platform'].split(' ') + def match_training_service(test_case_config, cur_training_service): case_ts = test_case_config['trainingService'] assert case_ts is not None @@ -205,6 +218,7 @@ def match_training_service(test_case_config, cur_training_service): return True return False + def run(args): it_config = get_yml_content(args.config) @@ -227,7 +241,8 @@ def run(args): continue if not match_training_service(test_case_config, args.ts): - print('skipped {}, training service {} not match [{}]'.format(name, args.ts, test_case_config['trainingService'])) + print('skipped {}, training service {} not match [{}]'.format( + name, args.ts, test_case_config['trainingService'])) continue wait_for_port_available(8080, 30) @@ -244,7 +259,8 @@ def run(args): parser.add_argument("--nni_source_dir", type=str, default='../') parser.add_argument("--cases", type=str, default=None) parser.add_argument("--exclude", type=str, default=None) - parser.add_argument("--ts", type=str, choices=['local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller'], default='local') + parser.add_argument("--ts", type=str, choices=['local', 'remote', 'pai', + 'kubeflow', 'frameworkcontroller'], default='local') args = parser.parse_args() run(args) diff --git a/test/pipelines/pipelines-it-local-tf2.yml b/test/pipelines/pipelines-it-local-tf2.yml index e208b33f11..26f3d4c87d 100644 --- a/test/pipelines/pipelines-it-local-tf2.yml +++ b/test/pipelines/pipelines-it-local-tf2.yml @@ -11,7 +11,7 @@ jobs: - script: | set -e python3 -m pip install scikit-learn==0.20.0 --user - python3 -m pip install torch==1.3.1 torchvision==0.4.1 -f https://download.pytorch.org/whl/torch_stable.html --user + python3 -m pip install torch==1.3.1 torchvision==0.4.2 -f https://download.pytorch.org/whl/torch_stable.html --user python3 -m pip install tensorflow-gpu==2.2.0 tensorflow-estimator==2.2.0 --force --user python3 -m pip install keras==2.4.2 --user sudo apt-get install swig -y diff --git a/test/pipelines/pipelines-it-local.yml b/test/pipelines/pipelines-it-local.yml index fd87e95d65..02ae3f0825 100644 --- a/test/pipelines/pipelines-it-local.yml +++ b/test/pipelines/pipelines-it-local.yml @@ -11,7 +11,7 @@ jobs: - script: | set -e python3 -m pip install scikit-learn==0.20.0 --user - python3 -m pip install torchvision==0.4.1 --user + python3 -m pip install torchvision==0.4.2 --user python3 -m pip install torch==1.3.1 --user python3 -m pip install keras==2.1.6 --user python3 -m pip install tensorflow-gpu==1.15.2 tensorflow-estimator==1.15.1 --force --user From 3d57dd73703c4f9978b45ad82bab7f9a71b01cc9 Mon Sep 17 00:00:00 2001 From: QuanluZhang Date: Mon, 29 Jun 2020 17:40:33 +0800 Subject: [PATCH 12/17] Refactor doc of NNI model compression (#2595) --- docs/en_US/Compressor/Framework.md | 149 +++++++- .../Compressor/LotteryTicketHypothesis.md | 23 -- docs/en_US/Compressor/ModelSpeedup.md | 2 +- docs/en_US/Compressor/Overview.md | 350 ++---------------- docs/en_US/Compressor/Pruner.md | 52 ++- docs/en_US/Compressor/Quantizer.md | 26 +- docs/en_US/Compressor/QuickStart.md | 139 ++++++- docs/en_US/Compressor/SlimPruner.md | 39 -- docs/en_US/Compressor/l1filterpruner.md | 38 -- docs/en_US/model_compression.rst | 8 +- docs/en_US/pruners.rst | 16 - docs/en_US/quantizers.rst | 11 - 12 files changed, 384 insertions(+), 469 deletions(-) delete mode 100644 docs/en_US/Compressor/LotteryTicketHypothesis.md delete mode 100644 docs/en_US/Compressor/SlimPruner.md delete mode 100644 docs/en_US/Compressor/l1filterpruner.md delete mode 100644 docs/en_US/pruners.rst delete mode 100644 docs/en_US/quantizers.rst diff --git a/docs/en_US/Compressor/Framework.md b/docs/en_US/Compressor/Framework.md index 87c23329d3..66083197be 100644 --- a/docs/en_US/Compressor/Framework.md +++ b/docs/en_US/Compressor/Framework.md @@ -1,6 +1,16 @@ -# Design Doc +# Customize A New Compression Algorithm -## Overview +```eval_rst +.. contents:: +``` + +To simplify writing a new compression algorithm, we design programming interfaces which are simple but flexible enough. There are interfaces for pruning and quantization respectively. Below, we first demonstrate how to customize a new pruning algorithm and then demonstrate how to customize a new quantization algorithm. + +## Customize a new pruning algorithm + +To better demonstrate how to customize a new pruning algorithm, it is necessary for users to first understand the framework for supporting various pruning algorithms in NNI. + +### Framework overview for pruning algorithms Following example shows how to use a pruner: @@ -26,11 +36,11 @@ A pruner receives `model`, `config_list` and `optimizer` as arguments. It prunes From implementation perspective, a pruner consists of a `weight masker` instance and multiple `module wrapper` instances. -### Weight masker +#### Weight masker A `weight masker` is the implementation of pruning algorithms, it can prune a specified layer wrapped by `module wrapper` with specified sparsity. -### Module wrapper +#### Module wrapper A `module wrapper` is a module containing: @@ -43,7 +53,7 @@ the reasons to use `module wrapper`: 1. some buffers are needed by `calc_mask` to calculate masks and these buffers should be registered in `module wrapper` so that the original modules are not contaminated. 2. a new `forward` method is needed to apply masks to weight before calling the real `forward` method. -### Pruner +#### Pruner A `pruner` is responsible for: @@ -52,9 +62,9 @@ A `pruner` is responsible for: 3. Use `weight masker` to calculate masks of layers while pruning. 4. Export pruned model weights and masks. -## Implement a new pruning algorithm +### Implement a new pruning algorithm -Implementing a new pruning algorithm requires implementing a `weight masker` class which shoud be a subclass of `WeightMasker`, and a `pruner` class, which should a subclass `Pruner`. +Implementing a new pruning algorithm requires implementing a `weight masker` class which shoud be a subclass of `WeightMasker`, and a `pruner` class, which should be a subclass `Pruner`. An implementation of `weight masker` may look like this: @@ -74,7 +84,7 @@ class MyMasker(WeightMasker): You can reference nni provided [weight masker](https://github.com/microsoft/nni/blob/master/src/sdk/pynni/nni/compression/torch/pruning/structured_pruning.py) implementations to implement your own weight masker. -A basic pruner looks likes this: +A basic `pruner` looks likes this: ```python class MyPruner(Pruner): @@ -142,3 +152,126 @@ self.pruner.remove_activation_collector(collector_id) On multi-GPU training, buffers and parameters are copied to multiple GPU every time the `forward` method runs on multiple GPU. If buffers and parameters are updated in the `forward` method, an `in-place` update is needed to ensure the update is effective. Since `calc_mask` is called in the `optimizer.step` method, which happens after the `forward` method and happens only on one GPU, it supports multi-GPU naturally. + +*** + +## Customize a new quantization algorithm + +To write a new quantization algorithm, you can write a class that inherits `nni.compression.torch.Quantizer`. Then, override the member functions with the logic of your algorithm. The member function to override is `quantize_weight`. `quantize_weight` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask. + +```python +from nni.compression.torch import Quantizer + +class YourQuantizer(Quantizer): + def __init__(self, model, config_list): + """ + Suggest you to use the NNI defined spec for config + """ + super().__init__(model, config_list) + + def quantize_weight(self, weight, config, **kwargs): + """ + quantize should overload this method to quantize weight tensors. + This method is effectively hooked to :meth:`forward` of the model. + + Parameters + ---------- + weight : Tensor + weight that needs to be quantized + config : dict + the configuration for weight quantization + """ + + # Put your code to generate `new_weight` here + + return new_weight + + def quantize_output(self, output, config, **kwargs): + """ + quantize should overload this method to quantize output. + This method is effectively hooked to `:meth:`forward` of the model. + + Parameters + ---------- + output : Tensor + output that needs to be quantized + config : dict + the configuration for output quantization + """ + + # Put your code to generate `new_output` here + + return new_output + + def quantize_input(self, *inputs, config, **kwargs): + """ + quantize should overload this method to quantize input. + This method is effectively hooked to :meth:`forward` of the model. + + Parameters + ---------- + inputs : Tensor + inputs that needs to be quantized + config : dict + the configuration for inputs quantization + """ + + # Put your code to generate `new_input` here + + return new_input + + def update_epoch(self, epoch_num): + pass + + def step(self): + """ + Can do some processing based on the model or weights binded + in the func bind_model + """ + pass +``` + +### Customize backward function + +Sometimes it's necessary for a quantization operation to have a customized backward function, such as [Straight-Through Estimator](https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste), user can customize a backward function as follow: + +```python +from nni.compression.torch.compressor import Quantizer, QuantGrad, QuantType + +class ClipGrad(QuantGrad): + @staticmethod + def quant_backward(tensor, grad_output, quant_type): + """ + This method should be overrided by subclass to provide customized backward function, + default implementation is Straight-Through Estimator + Parameters + ---------- + tensor : Tensor + input of quantization operation + grad_output : Tensor + gradient of the output of quantization operation + quant_type : QuantType + the type of quantization, it can be `QuantType.QUANT_INPUT`, `QuantType.QUANT_WEIGHT`, `QuantType.QUANT_OUTPUT`, + you can define different behavior for different types. + Returns + ------- + tensor + gradient of the input of quantization operation + """ + + # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1 + if quant_type == QuantType.QUANT_OUTPUT: + grad_output[torch.abs(tensor) > 1] = 0 + return grad_output + + +class YourQuantizer(Quantizer): + def __init__(self, model, config_list): + super().__init__(model, config_list) + # set your customized backward function to overwrite default backward function + self.quant_grad = ClipGrad + +``` + +If you do not customize `QuantGrad`, the default backward is Straight-Through Estimator. +_Coming Soon_ ... \ No newline at end of file diff --git a/docs/en_US/Compressor/LotteryTicketHypothesis.md b/docs/en_US/Compressor/LotteryTicketHypothesis.md deleted file mode 100644 index 5ac64155fa..0000000000 --- a/docs/en_US/Compressor/LotteryTicketHypothesis.md +++ /dev/null @@ -1,23 +0,0 @@ -Lottery Ticket Hypothesis on NNI -=== - -## Introduction - -The paper [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://arxiv.org/abs/1803.03635) is mainly a measurement and analysis paper, it delivers very interesting insights. To support it on NNI, we mainly implement the training approach for finding *winning tickets*. - -In this paper, the authors use the following process to prune a model, called *iterative prunning*: ->1. Randomly initialize a neural network f(x;theta_0) (where theta_0 follows D_{theta}). ->2. Train the network for j iterations, arriving at parameters theta_j. ->3. Prune p% of the parameters in theta_j, creating a mask m. ->4. Reset the remaining parameters to their values in theta_0, creating the winning ticket f(x;m*theta_0). ->5. Repeat step 2, 3, and 4. - -If the configured final sparsity is P (e.g., 0.8) and there are n times iterative pruning, each iterative pruning prunes 1-(1-P)^(1/n) of the weights that survive the previous round. - -## Reproduce Results - -We try to reproduce the experiment result of the fully connected network on MNIST using the same configuration as in the paper. The code can be referred [here](https://github.com/microsoft/nni/tree/master/examples/model_compress/lottery_torch_mnist_fc.py). In this experiment, we prune 10 times, for each pruning we train the pruned model for 50 epochs. - -![](../../img/lottery_ticket_mnist_fc.png) - -The above figure shows the result of the fully connected network. `round0-sparsity-0.0` is the performance without pruning. Consistent with the paper, pruning around 80% also obtain similar performance compared to non-pruning, and converges a little faster. If pruning too much, e.g., larger than 94%, the accuracy becomes lower and convergence becomes a little slower. A little different from the paper, the trend of the data in the paper is relatively more clear. diff --git a/docs/en_US/Compressor/ModelSpeedup.md b/docs/en_US/Compressor/ModelSpeedup.md index c3b9c76614..4158532634 100644 --- a/docs/en_US/Compressor/ModelSpeedup.md +++ b/docs/en_US/Compressor/ModelSpeedup.md @@ -1,6 +1,6 @@ # Speed up Masked Model -*This feature is still in Alpha version.* +*This feature is in Beta version.* ## Introduction diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md index 757f13a8ce..a81095ed79 100644 --- a/docs/en_US/Compressor/Overview.md +++ b/docs/en_US/Compressor/Overview.md @@ -1,340 +1,70 @@ # Model Compression with NNI -As larger neural networks with more layers and nodes are considered, reducing their storage and computational cost becomes critical, especially for some real-time applications. Model compression can be used to address this problem. -We are glad to introduce model compression toolkit on top of NNI, it's still in the experiment phase which might evolve based on usage feedback. We'd like to invite you to use, feedback and even contribute. +```eval_rst +.. contents:: +``` + +As larger neural networks with more layers and nodes are considered, reducing their storage and computational cost becomes critical, especially for some real-time applications. Model compression can be used to address this problem. + +NNI provides a model compression toolkit to help user compress and speed up their model with state-of-the-art compression algorithms and strategies. There are several core features supported by NNI model compression: -NNI provides an easy-to-use toolkit to help user design and use compression algorithms. It currently supports PyTorch with unified interface. For users to compress their models, they only need to add several lines in their code. There are some popular model compression algorithms built-in in NNI. Users could further use NNI's auto tuning power to find the best compressed model, which is detailed in [Auto Model Compression](./AutoCompression.md). On the other hand, users could easily customize their new compression algorithms using NNI's interface, refer to the tutorial [here](#customize-new-compression-algorithms). Details about how model compression framework works can be found in [here](./Framework.md). +* Support many popular pruning and quantization algorithms. +* Automate model pruning and quantization process with state-of-the-art strategies and NNI's auto tuning power. +* Speed up a compressed model to make it have lower inference latency and also make it become smaller. +* Provide friendly and easy-to-use compression utilities for users to dive into the compression process and results. +* Concise interface for users to customize their own compression algorithms. -For a survey of model compression, you can refer to this paper: [Recent Advances in Efficient Computation of Deep Convolutional Neural Networks](https://arxiv.org/pdf/1802.00939.pdf). +*Note that the interface and APIs are unified for both PyTorch and TensorFlow, currently only PyTorch version has been supported, TensorFlow version will be supported in future.* -## Supported algorithms -We have provided several compression algorithms, including several pruning and quantization algorithms: +## Supported Algorithms -**Pruning** +The algorithms include pruning algorithms and quantization algorithms. + +### Pruning Algorithms Pruning algorithms compress the original network by removing redundant weights or channels of layers, which can reduce model complexity and address the over-fitting issue. |Name|Brief Introduction of Algorithm| |---|---| -| [Level Pruner](./Pruner.md#level-pruner) | Pruning the specified ratio on each weight based on absolute values of weights | -| [AGP Pruner](./Pruner.md#agp-pruner) | Automated gradual pruning (To prune, or not to prune: exploring the efficacy of pruning for model compression) [Reference Paper](https://arxiv.org/abs/1710.01878)| -| [Lottery Ticket Pruner](./Pruner.md#agp-pruner) | The pruning process used by "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks". It prunes a model iteratively. [Reference Paper](https://arxiv.org/abs/1803.03635)| -| [FPGM Pruner](./Pruner.md#fpgm-pruner) | Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration [Reference Paper](https://arxiv.org/pdf/1811.00250.pdf)| -| [L1Filter Pruner](./Pruner.md#l1filter-pruner) | Pruning filters with the smallest L1 norm of weights in convolution layers (Pruning Filters for Efficient Convnets) [Reference Paper](https://arxiv.org/abs/1608.08710) | -| [L2Filter Pruner](./Pruner.md#l2filter-pruner) | Pruning filters with the smallest L2 norm of weights in convolution layers | -| [ActivationAPoZRankFilterPruner](./Pruner.md#ActivationAPoZRankFilterPruner) | Pruning filters based on the metric APoZ (average percentage of zeros) which measures the percentage of zeros in activations of (convolutional) layers. [Reference Paper](https://arxiv.org/abs/1607.03250) | -| [ActivationMeanRankFilterPruner](./Pruner.md#ActivationMeanRankFilterPruner) | Pruning filters based on the metric that calculates the smallest mean value of output activations | -| [Slim Pruner](./Pruner.md#slim-pruner) | Pruning channels in convolution layers by pruning scaling factors in BN layers(Learning Efficient Convolutional Networks through Network Slimming) [Reference Paper](https://arxiv.org/abs/1708.06519) | -| [TaylorFO Pruner](./Pruner.md#taylorfoweightfilterpruner) | Pruning filters based on the first order taylor expansion on weights(Importance Estimation for Neural Network Pruning) [Reference Paper](http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf) | +| [Level Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#level-pruner) | Pruning the specified ratio on each weight based on absolute values of weights | +| [AGP Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#agp-pruner) | Automated gradual pruning (To prune, or not to prune: exploring the efficacy of pruning for model compression) [Reference Paper](https://arxiv.org/abs/1710.01878)| +| [Lottery Ticket Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#lottery-ticket-hypothesis) | The pruning process used by "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks". It prunes a model iteratively. [Reference Paper](https://arxiv.org/abs/1803.03635)| +| [FPGM Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#fpgm-pruner) | Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration [Reference Paper](https://arxiv.org/pdf/1811.00250.pdf)| +| [L1Filter Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#l1filter-pruner) | Pruning filters with the smallest L1 norm of weights in convolution layers (Pruning Filters for Efficient Convnets) [Reference Paper](https://arxiv.org/abs/1608.08710) | +| [L2Filter Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#l2filter-pruner) | Pruning filters with the smallest L2 norm of weights in convolution layers | +| [ActivationAPoZRankFilterPruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#activationapozrankfilterpruner) | Pruning filters based on the metric APoZ (average percentage of zeros) which measures the percentage of zeros in activations of (convolutional) layers. [Reference Paper](https://arxiv.org/abs/1607.03250) | +| [ActivationMeanRankFilterPruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#activationmeanrankfilterpruner) | Pruning filters based on the metric that calculates the smallest mean value of output activations | +| [Slim Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#slim-pruner) | Pruning channels in convolution layers by pruning scaling factors in BN layers(Learning Efficient Convolutional Networks through Network Slimming) [Reference Paper](https://arxiv.org/abs/1708.06519) | +| [TaylorFO Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#taylorfoweightfilterpruner) | Pruning filters based on the first order taylor expansion on weights(Importance Estimation for Neural Network Pruning) [Reference Paper](http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf) | -**Quantization** +### Quantization Algorithms Quantization algorithms compress the original network by reducing the number of bits required to represent weights or activations, which can reduce the computations and the inference time. |Name|Brief Introduction of Algorithm| |---|---| -| [Naive Quantizer](./Quantizer.md#naive-quantizer) | Quantize weights to default 8 bits | -| [QAT Quantizer](./Quantizer.md#qat-quantizer) | Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. [Reference Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)| -| [DoReFa Quantizer](./Quantizer.md#dorefa-quantizer) | DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [Reference Paper](https://arxiv.org/abs/1606.06160)| -| [BNN Quantizer](./Quantizer.md#BNN-Quantizer) | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. [Reference Paper](https://arxiv.org/abs/1602.02830)| - -## Usage of built-in compression algorithms - -We use a simple example to show how to modify your trial code in order to apply the compression algorithms. Let's say you want to prune all weight to 80% sparsity with Level Pruner, you can add the following three lines into your code before training your model ([here](https://github.com/microsoft/nni/tree/master/examples/model_compress) is complete code). +| [Naive Quantizer](https://nni.readthedocs.io/en/latest/Compressor/Quantizer.html#naive-quantizer) | Quantize weights to default 8 bits | +| [QAT Quantizer](https://nni.readthedocs.io/en/latest/Compressor/Quantizer.html#qat-quantizer) | Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. [Reference Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)| +| [DoReFa Quantizer](https://nni.readthedocs.io/en/latest/Compressor/Quantizer.html#dorefa-quantizer) | DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [Reference Paper](https://arxiv.org/abs/1606.06160)| +| [BNN Quantizer](https://nni.readthedocs.io/en/latest/Compressor/Quantizer.html#bnn-quantizer) | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. [Reference Paper](https://arxiv.org/abs/1602.02830)| -PyTorch code - -```python -from nni.compression.torch import LevelPruner -config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }] -pruner = LevelPruner(model, config_list) -pruner.compress() -``` - -Tensorflow code - -```python -from nni.compression.tensorflow import LevelPruner -config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }] -pruner = LevelPruner(tf.get_default_graph(), config_list) -pruner.compress() -``` +## Automatic Model Compression +Given targeted compression ratio, it is pretty hard to obtain the best compressed ratio in a one shot manner. An automatic model compression algorithm usually need to explore the compression space by compressing different layers with different sparsities. NNI provides such algorithms to free users from specifying sparsity of each layer in a model. Moreover, users could leverage NNI's auto tuning power to automatically compress a model. Detailed document can be found [here](./AutoCompression.md). -You can use other compression algorithms in the package of `nni.compression`. The algorithms are implemented in both PyTorch and Tensorflow, under `nni.compression.torch` and `nni.compression.tensorflow` respectively. You can refer to [Pruner](./Pruner.md) and [Quantizer](./Quantizer.md) for detail description of supported algorithms. Also if you want to use knowledge distillation, you can refer to [KDExample](../TrialExample/KDExample.md) +## Model Speedup -The function call `pruner.compress()` modifies user defined model (in Tensorflow the model can be obtained with `tf.get_default_graph()`, while in PyTorch the model is the defined model class), and the model is modified with masks inserted. Then when you run the model, the masks take effect. The masks can be adjusted at runtime by the algorithms. +The final goal of model compression is to reduce inference latency and model size. However, existing model compression algorithms mainly use simulation to check the performance (e.g., accuracy) of compressed model, for example, using masks for pruning algorithms, and storing quantized values still in float32 for quantization algorithms. Given the output masks and quantization bits produced by those algorithms, NNI can really speed up the model. The detailed tutorial of Model Speedup can be found [here](./ModelSpeedup.md). -When instantiate a compression algorithm, there is `config_list` passed in. We describe how to write this config below. +## Compression Utilities -### User configuration for a compression algorithm -When compressing a model, users may want to specify the ratio for sparsity, to specify different ratios for different types of operations, to exclude certain types of operations, or to compress only a certain types of operations. For users to express these kinds of requirements, we define a configuration specification. It can be seen as a python `list` object, where each element is a `dict` object. - -The `dict`s in the `list` are applied one by one, that is, the configurations in latter `dict` will overwrite the configurations in former ones on the operations that are within the scope of both of them. - -#### Common keys -In each `dict`, there are some keys commonly supported by NNI compression: - -* __op_types__: This is to specify what types of operations to be compressed. 'default' means following the algorithm's default setting. -* __op_names__: This is to specify by name what operations to be compressed. If this field is omitted, operations will not be filtered by it. -* __exclude__: Default is False. If this field is True, it means the operations with specified types and names will be excluded from the compression. - -#### Keys for quantization algorithms -**If you use quantization algorithms, you need to specify more keys. If you use pruning algorithms, you can safely skip these keys** - -* __quant_types__ : list of string. - -Type of quantization you want to apply, currently support 'weight', 'input', 'output'. 'weight' means applying quantization operation -to the weight parameter of modules. 'input' means applying quantization operation to the input of module forward method. 'output' means applying quantization operation to the output of module forward method, which is often called as 'activation' in some papers. - -* __quant_bits__ : int or dict of {str : int} - -bits length of quantization, key is the quantization type, value is the quantization bits length, eg. -``` -{ - quant_bits: { - 'weight': 8, - 'output': 4, - }, -} -``` -when the value is int type, all quantization types share same bits length. eg. -``` -{ - quant_bits: 8, # weight or output quantization are all 8 bits -} -``` -#### Other keys specified for every compression algorithm -There are also other keys in the `dict`, but they are specific for every compression algorithm. For example, [Level Pruner](./Pruner.md#level-pruner) requires `sparsity` key to specify how much a model should be pruned. - - -#### example -A simple example of configuration is shown below: - -```python -[ - { - 'sparsity': 0.8, - 'op_types': ['default'] - }, - { - 'sparsity': 0.6, - 'op_names': ['op_name1', 'op_name2'] - }, - { - 'exclude': True, - 'op_names': ['op_name3'] - } -] -``` - -It means following the algorithm's default setting for compressed operations with sparsity 0.8, but for `op_name1` and `op_name2` use sparsity 0.6, and please do not compress `op_name3`. - -### Other APIs - -Some compression algorithms use epochs to control the progress of compression (e.g. [AGP](./Pruner.md#agp-pruner)), and some algorithms need to do something after every minibatch. Therefore, we provide another two APIs for users to invoke. One is `update_epoch`, you can use it as follows: - -Tensorflow code - -```python -pruner.update_epoch(epoch, sess) -``` - -PyTorch code - -```python -pruner.update_epoch(epoch) -``` - -The other is `step`, it can be called with `pruner.step()` after each minibatch. Note that not all algorithms need these two APIs, for those that do not need them, calling them is allowed but has no effect. - -You can easily export the compressed model using the following API if you are pruning your model, ```state_dict``` of the sparse model weights will be stored in ```model.pth```, which can be loaded by ```torch.load('model.pth')``` - -``` -pruner.export_model(model_path='model.pth') -``` +Compression utilities include some useful tools for users to understand and analyze the model they want to compress. For example, users could check sensitivity of each layer to pruning. Users could easily calculate the FLOPs and parameter size of a model. Please refer to [here](./CompressionUtils.md) for a complete list of compression utilities. -```mask_dict ``` and pruned model in ```onnx``` format(```input_shape``` need to be specified) can also be exported like this: - -```python -pruner.export_model(model_path='model.pth', mask_path='mask.pth', onnx_path='model.onnx', input_shape=[1, 1, 28, 28]) -``` - -## Customize new compression algorithms - -To simplify writing a new compression algorithm, we design programming interfaces which are simple but flexible enough. There are interfaces for pruner and quantizer respectively. - -### Pruning algorithm - -If you want to write a new pruning algorithm, you can write a class that inherits `nni.compression.tensorflow.Pruner` or `nni.compression.torch.Pruner` depending on which framework you use. Then, override the member functions with the logic of your algorithm. - -```python -# This is writing a pruner in tensorflow. -# For writing a pruner in PyTorch, you can simply replace -# nni.compression.tensorflow.Pruner with -# nni.compression.torch.Pruner -class YourPruner(nni.compression.tensorflow.Pruner): - def __init__(self, model, config_list): - """ - Suggest you to use the NNI defined spec for config - """ - super().__init__(model, config_list) - - def calc_mask(self, layer, config): - """ - Pruners should overload this method to provide mask for weight tensors. - The mask must have the same shape and type comparing to the weight. - It will be applied with ``mul()`` operation on the weight. - This method is effectively hooked to ``forward()`` method of the model. - - Parameters - ---------- - layer: LayerInfo - calculate mask for ``layer``'s weight - config: dict - the configuration for generating the mask - """ - return your_mask - - # note for pytorch version, there is no sess in input arguments - def update_epoch(self, epoch_num, sess): - pass - - # note for pytorch version, there is no sess in input arguments - def step(self, sess): - """ - Can do some processing based on the model or weights binded - in the func bind_model - """ - pass -``` - -For the simplest algorithm, you only need to override ``calc_mask``. It receives the to-be-compressed layers one by one along with their compression configuration. You generate the mask for this weight in this function and return. Then NNI applies the mask for you. - -Some algorithms generate mask based on training progress, i.e., epoch number. We provide `update_epoch` for the pruner to be aware of the training progress. It should be called at the beginning of each epoch. - -Some algorithms may want global information for generating masks, for example, all weights of the model (for statistic information). Your can use `self.bound_model` in the Pruner class for accessing weights. If you also need optimizer's information (for example in Pytorch), you could override `__init__` to receive more arguments such as model's optimizer. Then `step` can process or update the information according to the algorithm. You can refer to [source code of built-in algorithms](https://github.com/microsoft/nni/tree/master/src/sdk/pynni/nni/compressors) for example implementations. - -### Quantization algorithm - -The interface for customizing quantization algorithm is similar to that of pruning algorithms. The only difference is that `calc_mask` is replaced with `quantize_weight`. `quantize_weight` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask. - -```python -from nni.compression.torch.compressor import Quantizer - -class YourQuantizer(Quantizer): - def __init__(self, model, config_list): - """ - Suggest you to use the NNI defined spec for config - """ - super().__init__(model, config_list) - - def quantize_weight(self, weight, config, **kwargs): - """ - quantize should overload this method to quantize weight tensors. - This method is effectively hooked to :meth:`forward` of the model. - - Parameters - ---------- - weight : Tensor - weight that needs to be quantized - config : dict - the configuration for weight quantization - """ - - # Put your code to generate `new_weight` here - - return new_weight - - def quantize_output(self, output, config, **kwargs): - """ - quantize should overload this method to quantize output. - This method is effectively hooked to `:meth:`forward` of the model. - - Parameters - ---------- - output : Tensor - output that needs to be quantized - config : dict - the configuration for output quantization - """ - - # Put your code to generate `new_output` here - - return new_output - - def quantize_input(self, *inputs, config, **kwargs): - """ - quantize should overload this method to quantize input. - This method is effectively hooked to :meth:`forward` of the model. - - Parameters - ---------- - inputs : Tensor - inputs that needs to be quantized - config : dict - the configuration for inputs quantization - """ - - # Put your code to generate `new_input` here - - return new_input - - def update_epoch(self, epoch_num): - pass - - def step(self): - """ - Can do some processing based on the model or weights binded - in the func bind_model - """ - pass -``` -#### Customize backward function -Sometimes it's necessary for a quantization operation to have a customized backward function, such as [Straight-Through Estimator](https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste), user can customize a backward function as follow: - -```python -from nni.compression.torch.compressor import Quantizer, QuantGrad, QuantType - -class ClipGrad(QuantGrad): - @staticmethod - def quant_backward(tensor, grad_output, quant_type): - """ - This method should be overrided by subclass to provide customized backward function, - default implementation is Straight-Through Estimator - Parameters - ---------- - tensor : Tensor - input of quantization operation - grad_output : Tensor - gradient of the output of quantization operation - quant_type : QuantType - the type of quantization, it can be `QuantType.QUANT_INPUT`, `QuantType.QUANT_WEIGHT`, `QuantType.QUANT_OUTPUT`, - you can define different behavior for different types. - Returns - ------- - tensor - gradient of the input of quantization operation - """ - - # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1 - if quant_type == QuantType.QUANT_OUTPUT: - grad_output[torch.abs(tensor) > 1] = 0 - return grad_output - - -class YourQuantizer(Quantizer): - def __init__(self, model, config_list): - super().__init__(model, config_list) - # set your customized backward function to overwrite default backward function - self.quant_grad = ClipGrad - -``` +## Customize Your Own Compression Algorithms -If you do not customize `QuantGrad`, the default backward is Straight-Through Estimator. -_Coming Soon_ ... +NNI model compression leaves simple interface for users to customize a new compression algorithm. The design philosophy of the interface is making users focus on the compression logic while hiding framework specific implementation details from users. The detailed tutorial for customizing a new compression algorithm (pruning algorithm or quantization algorithm) can be found [here](./Framework.md). ## Reference and Feedback * To [report a bug](https://github.com/microsoft/nni/issues/new?template=bug-report.md) for this feature in GitHub; diff --git a/docs/en_US/Compressor/Pruner.md b/docs/en_US/Compressor/Pruner.md index 102d6471b0..fcc17fe2b0 100644 --- a/docs/en_US/Compressor/Pruner.md +++ b/docs/en_US/Compressor/Pruner.md @@ -1,10 +1,13 @@ -Pruner on NNI Compressor -=== +# Supported Pruning Algorithms on NNI -Index of supported pruning algorithms +We provide several pruning algorithms that support fine-grained weight pruning and structural filter pruning. **Weight pruning** generally results in unstructured models, which need specialized haredware or software to speed up the sparse network. **Filter Pruning** achieves acceleratation by removing the entire filter. We also provide an algorithm to control the **pruning schedule**. + + +**Weight Pruning** * [Level Pruner](#level-pruner) -* [AGP Pruner](#agp-pruner) * [Lottery Ticket Hypothesis](#lottery-ticket-hypothesis) + +**Filter Pruning** * [Slim Pruner](#slim-pruner) * [Filter Pruners with Weight Rank](#weightrankfilterpruner) * [FPGM Pruner](#fpgm-pruner) @@ -15,6 +18,9 @@ Index of supported pruning algorithms * [Activation Mean Rank Pruner](#activationmeanrankfilterpruner) * [Filter Pruners with Gradient Rank](#gradientrankfilterpruner) * [Taylor FO On Weight Pruner](#taylorfoweightfilterpruner) + +**Pruning Schedule** +* [AGP Pruner](#agp-pruner) ## Level Pruner @@ -145,7 +151,7 @@ for _ in pruner.get_prune_iterations(): ... ``` -The above configuration means that there are 5 times of iterative pruning. As the 5 times iterative pruning are executed in the same run, LotteryTicketPruner needs `model` and `optimizer` (**Note that should add `lr_scheduler` if used**) to reset their states every time a new prune iteration starts. Please use `get_prune_iterations` to get the pruning iterations, and invoke `prune_iteration_start` at the beginning of each iteration. `epoch_num` is better to be large enough for model convergence, because the hypothesis is that the performance (accuracy) got in latter rounds with high sparsity could be comparable with that got in the first round. Simple reproducing results can be found [here](./LotteryTicketHypothesis.md). +The above configuration means that there are 5 times of iterative pruning. As the 5 times iterative pruning are executed in the same run, LotteryTicketPruner needs `model` and `optimizer` (**Note that should add `lr_scheduler` if used**) to reset their states every time a new prune iteration starts. Please use `get_prune_iterations` to get the pruning iterations, and invoke `prune_iteration_start` at the beginning of each iteration. `epoch_num` is better to be large enough for model convergence, because the hypothesis is that the performance (accuracy) got in latter rounds with high sparsity could be comparable with that got in the first round. *Tensorflow version will be supported later.* @@ -155,6 +161,14 @@ The above configuration means that there are 5 times of iterative pruning. As th * **prune_iterations:** The number of rounds for the iterative pruning, i.e., the number of iterative pruning. * **sparsity:** The final sparsity when the compression is done. +### Reproduced Experiment + +We try to reproduce the experiment result of the fully connected network on MNIST using the same configuration as in the paper. The code can be referred [here](https://github.com/microsoft/nni/tree/master/examples/model_compress/lottery_torch_mnist_fc.py). In this experiment, we prune 10 times, for each pruning we train the pruned model for 50 epochs. + +![](../../img/lottery_ticket_mnist_fc.png) + +The above figure shows the result of the fully connected network. `round0-sparsity-0.0` is the performance without pruning. Consistent with the paper, pruning around 80% also obtain similar performance compared to non-pruning, and converges a little faster. If pruning too much, e.g., larger than 94%, the accuracy becomes lower and convergence becomes a little slower. A little different from the paper, the trend of the data in the paper is relatively more clear. + *** ## Slim Pruner @@ -181,6 +195,18 @@ pruner.compress() - **sparsity:** This is to specify the sparsity operations to be compressed to - **op_types:** Only BatchNorm2d is supported in Slim Pruner +### Reproduced Experiment + +We implemented one of the experiments in ['Learning Efficient Convolutional Networks through Network Slimming'](https://arxiv.org/pdf/1708.06519.pdf), we pruned $70\%$ channels in the **VGGNet** for CIFAR-10 in the paper, in which $88.5\%$ parameters are pruned. Our experiments results are as follows: + +| Model | Error(paper/ours) | Parameters | Pruned | +| ------------- | ----------------- | ---------- | --------- | +| VGGNet | 6.34/6.40 | 20.04M | | +| Pruned-VGGNet | 6.20/6.26 | 2.03M | 88.5% | + +The experiments code can be found at [examples/model_compress]( https://github.com/microsoft/nni/tree/master/examples/model_compress/) + +*** ## WeightRankFilterPruner WeightRankFilterPruner is a series of pruners which prune the filters with the smallest importance criterion calculated from the weights in convolution layers to achieve a preset level of network sparsity @@ -238,7 +264,7 @@ You can view example for more information ### L1Filter Pruner -This is an one-shot pruner, In ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https://arxiv.org/abs/1608.08710), authors Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet and Hans Peter Graf. The reproduced experiment results can be found [here](l1filterpruner.md) +This is an one-shot pruner, In ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https://arxiv.org/abs/1608.08710), authors Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet and Hans Peter Graf. ![](../../img/l1filter_pruner.png) @@ -270,6 +296,17 @@ pruner.compress() - **sparsity:** This is to specify the sparsity operations to be compressed to - **op_types:** Only Conv1d and Conv2d is supported in L1Filter Pruner +#### Reproduced Experiment + +We implemented one of the experiments in ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https://arxiv.org/abs/1608.08710) with **L1FilterPruner**, we pruned **VGG-16** for CIFAR-10 to **VGG-16-pruned-A** in the paper, in which $64\%$ parameters are pruned. Our experiments results are as follows: + +| Model | Error(paper/ours) | Parameters | Pruned | +| --------------- | ----------------- | --------------- | -------- | +| VGG-16 | 6.75/6.49 | 1.5x10^7 | | +| VGG-16-pruned-A | 6.60/6.47 | 5.4x10^6 | 64.0% | + +The experiments code can be found at [examples/model_compress]( https://github.com/microsoft/nni/tree/master/examples/model_compress/) + *** ### L2Filter Pruner @@ -292,6 +329,8 @@ pruner.compress() - **sparsity:** This is to specify the sparsity operations to be compressed to - **op_types:** Only Conv1d and Conv2d is supported in L2Filter Pruner +*** + ## ActivationRankFilterPruner ActivationRankFilterPruner is a series of pruners which prune the filters with the smallest importance criterion calculated from the output activations of convolution layers to achieve a preset level of network sparsity. @@ -355,6 +394,7 @@ You can view example for more information - **sparsity:** How much percentage of convolutional filters are to be pruned. - **op_types:** Only Conv2d is supported in ActivationMeanRankFilterPruner. +*** ## GradientRankFilterPruner diff --git a/docs/en_US/Compressor/Quantizer.md b/docs/en_US/Compressor/Quantizer.md index 574926c7ad..55fde6db66 100644 --- a/docs/en_US/Compressor/Quantizer.md +++ b/docs/en_US/Compressor/Quantizer.md @@ -1,5 +1,11 @@ -Quantizer on NNI Compressor -=== +# Supported Quantization Algorithms on NNI + +Index of supported quantization algorithms +* [Naive Quantizer](#naive-quantizer) +* [QAT Quantizer](#qat-quantizer) +* [DoReFa Quantizer](#dorefa-quantizer) +* [BNN Quantizer](#bnn-quantizer) + ## Naive Quantizer We provide Naive Quantizer to quantizer weight to default 8 bits, you can use it to test quantize algorithm without any configure. @@ -47,7 +53,8 @@ quantizer.compress() You can view example for more information #### User configuration for QAT Quantizer -common configuration needed by compression algorithms can be found at : [Common configuration](./Overview.md#User-configuration-for-a-compression-algorithm) + +common configuration needed by compression algorithms can be found at [Specification of `config_list`](./QuickStart.md). configuration needed by this algorithm : @@ -57,13 +64,17 @@ disable quantization until model are run by certain number of steps, this allows state where activation quantization ranges do not exclude a significant fraction of values, default value is 0 ### note + batch normalization folding is currently not supported. + *** ## DoReFa Quantizer + In [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://arxiv.org/abs/1606.06160), authors Shuchang Zhou and Yuxin Wu provide an algorithm named DoReFa to quantize the weight, activation and gradients with training. ### Usage + To implement DoReFa Quantizer, you can add code below before your training code PyTorch code @@ -81,12 +92,15 @@ quantizer.compress() You can view example for more information #### User configuration for DoReFa Quantizer -common configuration needed by compression algorithms can be found at : [Common configuration](./Overview.md#User-configuration-for-a-compression-algorithm) + +common configuration needed by compression algorithms can be found at [Specification of `config_list`](./QuickStart.md). configuration needed by this algorithm : +*** ## BNN Quantizer + In [Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1](https://arxiv.org/abs/1602.02830), >We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. @@ -118,11 +132,13 @@ model = quantizer.compress() You can view example [examples/model_compress/BNN_quantizer_cifar10.py]( https://github.com/microsoft/nni/tree/master/examples/model_compress/BNN_quantizer_cifar10.py) for more information. #### User configuration for BNN Quantizer -common configuration needed by compression algorithms can be found at : [Common configuration](./Overview.md#User-configuration-for-a-compression-algorithm) + +common configuration needed by compression algorithms can be found at [Specification of `config_list`](./QuickStart.md). configuration needed by this algorithm : ### Experiment + We implemented one of the experiments in [Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1](https://arxiv.org/abs/1602.02830), we quantized the **VGGNet** for CIFAR-10 in the paper. Our experiments results are as follows: | Model | Accuracy | diff --git a/docs/en_US/Compressor/QuickStart.md b/docs/en_US/Compressor/QuickStart.md index 656d78c5d9..b47175e832 100644 --- a/docs/en_US/Compressor/QuickStart.md +++ b/docs/en_US/Compressor/QuickStart.md @@ -1,8 +1,16 @@ -# Quick Start to Compress a Model +# Tutorial for Model Compression -NNI provides very simple APIs for compressing a model. The compression includes pruning algorithms and quantization algorithms. The usage of them are the same, thus, here we use slim pruner as an example to show the usage. +```eval_rst +.. contents:: +``` + +In this tutorial, we use the [first section](#quick-start-to-compress-a-model) to quickly go through the usage of model compression on NNI. Then use the [second section](#detailed-usage-guide) to explain more details of the usage. + +## Quick Start to Compress a Model + +NNI provides very simple APIs for compressing a model. The compression includes pruning algorithms and quantization algorithms. The usage of them are the same, thus, here we use [slim pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#slim-pruner) as an example to show the usage. -## Write configuration +### Write configuration Write a configuration to specify the layers that you want to prune. The following configuration means pruning all the `BatchNorm2d`s to sparsity 0.7 while keeping other layers unpruned. @@ -13,9 +21,9 @@ configure_list = [{ }] ``` -The specification of configuration can be found [here](Overview.md#user-configuration-for-a-compression-algorithm). Note that different pruners may have their own defined fields in configuration, for exmaple `start_epoch` in AGP pruner. Please refer to each pruner's [usage](Overview.md#supported-algorithms) for details, and adjust the configuration accordingly. +The specification of configuration can be found [here](#specification-of-config-list). Note that different pruners may have their own defined fields in configuration, for exmaple `start_epoch` in AGP pruner. Please refer to each pruner's [usage](./Pruner.md) for details, and adjust the configuration accordingly. -## Choose a compression algorithm +### Choose a compression algorithm Choose a pruner to prune your model. First instantiate the chosen pruner with your model and configuration as arguments, then invoke `compress()` to compress your model. @@ -26,7 +34,7 @@ model = pruner.compress() Then, you can train your model using traditional training approach (e.g., SGD), pruning is applied transparently during the training. Some pruners prune once at the beginning, the following training can be seen as fine-tune. Some pruners prune your model iteratively, the masks are adjusted epoch by epoch during training. -## Export compression result +### Export compression result After training, you get accuracy of the pruned model. You can export model weights to a file, and the generated masks to a file as well. Exporting onnx model is also supported. @@ -36,7 +44,7 @@ pruner.export_model(model_path='pruned_vgg19_cifar10.pth', mask_path='mask_vgg19 The complete code of model compression examples can be found [here](https://github.com/microsoft/nni/blob/master/examples/model_compress/model_prune_torch.py). -## Speed up the model +### Speed up the model Masks do not provide real speedup of your model. The model should be speeded up based on the exported masks, thus, we provide an API to speed up your model as shown below. After invoking `apply_compression_results` on your model, your model becomes a smaller one with shorter inference latency. @@ -45,4 +53,119 @@ from nni.compression.torch import apply_compression_results apply_compression_results(model, 'mask_vgg19_cifar10.pth') ``` -Please refer to [here](ModelSpeedup.md) for detailed description. \ No newline at end of file +Please refer to [here](ModelSpeedup.md) for detailed description. + +## Detailed Usage Guide + +The example code for users to apply model compression on a user model can be found below: + +PyTorch code + +```python +from nni.compression.torch import LevelPruner +config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }] +pruner = LevelPruner(model, config_list) +pruner.compress() +``` + +Tensorflow code + +```python +from nni.compression.tensorflow import LevelPruner +config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }] +pruner = LevelPruner(tf.get_default_graph(), config_list) +pruner.compress() +``` + + +You can use other compression algorithms in the package of `nni.compression`. The algorithms are implemented in both PyTorch and TensorFlow (partial support on TensorFlow), under `nni.compression.torch` and `nni.compression.tensorflow` respectively. You can refer to [Pruner](./Pruner.md) and [Quantizer](./Quantizer.md) for detail description of supported algorithms. Also if you want to use knowledge distillation, you can refer to [KDExample](../TrialExample/KDExample.md) + +A compression algorithm is first instantiated with a `config_list` passed in. The specification of this `config_list` will be described later. + +The function call `pruner.compress()` modifies user defined model (in Tensorflow the model can be obtained with `tf.get_default_graph()`, while in PyTorch the model is the defined model class), and the model is modified with masks inserted. Then when you run the model, the masks take effect. The masks can be adjusted at runtime by the algorithms. + +*Note that, `pruner.compress` simply adds masks on model weights, it does not include fine tuning logic. If users want to fine tune the compressed model, they need to write the fine tune logic by themselves after `pruner.compress`.* + +### Specification of `config_list` + +Users can specify the configuration (i.e., `config_list`) for a compression algorithm. For example,when compressing a model, users may want to specify the sparsity ratio, to specify different ratios for different types of operations, to exclude certain types of operations, or to compress only a certain types of operations. For users to express these kinds of requirements, we define a configuration specification. It can be seen as a python `list` object, where each element is a `dict` object. + +The `dict`s in the `list` are applied one by one, that is, the configurations in latter `dict` will overwrite the configurations in former ones on the operations that are within the scope of both of them. + +There are different keys in a `dict`. Some of them are common keys supported by all the compression algorithms: + +* __op_types__: This is to specify what types of operations to be compressed. 'default' means following the algorithm's default setting. +* __op_names__: This is to specify by name what operations to be compressed. If this field is omitted, operations will not be filtered by it. +* __exclude__: Default is False. If this field is True, it means the operations with specified types and names will be excluded from the compression. + +Some other keys are often specific to a certain algorithms, users can refer to [pruning algorithms](./Pruner.md) and [quantization algorithms](./Quantizer.md) for the keys allowed by each algorithm. + +A simple example of configuration is shown below: + +```python +[ + { + 'sparsity': 0.8, + 'op_types': ['default'] + }, + { + 'sparsity': 0.6, + 'op_names': ['op_name1', 'op_name2'] + }, + { + 'exclude': True, + 'op_names': ['op_name3'] + } +] +``` + +It means following the algorithm's default setting for compressed operations with sparsity 0.8, but for `op_name1` and `op_name2` use sparsity 0.6, and do not compress `op_name3`. + +#### Quantization specific keys + +**If you use quantization algorithms, you need to specify more keys. If you use pruning algorithms, you can safely skip these keys** + +* __quant_types__ : list of string. + +Type of quantization you want to apply, currently support 'weight', 'input', 'output'. 'weight' means applying quantization operation +to the weight parameter of modules. 'input' means applying quantization operation to the input of module forward method. 'output' means applying quantization operation to the output of module forward method, which is often called as 'activation' in some papers. + +* __quant_bits__ : int or dict of {str : int} + +bits length of quantization, key is the quantization type, value is the quantization bits length, eg. +``` +{ + quant_bits: { + 'weight': 8, + 'output': 4, + }, +} +``` +when the value is int type, all quantization types share same bits length. eg. +``` +{ + quant_bits: 8, # weight or output quantization are all 8 bits +} +``` + +### APIs for Updating Fine Tuning Status + +Some compression algorithms use epochs to control the progress of compression (e.g. [AGP](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#agp-pruner)), and some algorithms need to do something after every minibatch. Therefore, we provide another two APIs for users to invoke: `pruner.update_epoch(epoch)` and `pruner.step()`. + +`update_epoch` should be invoked in every epoch, while `step` should be invoked after each minibatch. Note that most algorithms do not require calling the two APIs. Please refer to each algorithm's document for details. For the algorithms that do not need them, calling them is allowed but has no effect. + +### Export Compressed Model + +You can easily export the compressed model using the following API if you are pruning your model, ```state_dict``` of the sparse model weights will be stored in ```model.pth```, which can be loaded by ```torch.load('model.pth')```. In this exported ```model.pth```, the masked weights are zero. + +``` +pruner.export_model(model_path='model.pth') +``` + +```mask_dict ``` and pruned model in ```onnx``` format(```input_shape``` need to be specified) can also be exported like this: + +```python +pruner.export_model(model_path='model.pth', mask_path='mask.pth', onnx_path='model.onnx', input_shape=[1, 1, 28, 28]) +``` + +If you want to really speed up the compressed model, please refer to [NNI model speedup](./ModelSpeedup.md) for details. \ No newline at end of file diff --git a/docs/en_US/Compressor/SlimPruner.md b/docs/en_US/Compressor/SlimPruner.md deleted file mode 100644 index 18fe589155..0000000000 --- a/docs/en_US/Compressor/SlimPruner.md +++ /dev/null @@ -1,39 +0,0 @@ -SlimPruner on NNI Compressor -=== - -## 1. Slim Pruner - -SlimPruner is a structured pruning algorithm for pruning channels in the convolutional layers by pruning corresponding scaling factors in the later BN layers. - -In ['Learning Efficient Convolutional Networks through Network Slimming'](https://arxiv.org/pdf/1708.06519.pdf), authors Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan and Changshui Zhang. - -![](../../img/slim_pruner.png) - -> Slim Pruner **prunes channels in the convolution layers by masking corresponding scaling factors in the later BN layers**, L1 regularization on the scaling factors should be applied in batch normalization (BN) layers while training, scaling factors of BN layers are **globally ranked** while pruning, so the sparse model can be automatically found given sparsity. - -## 2. Usage - -PyTorch code - -``` -from nni.compression.torch import SlimPruner -config_list = [{ 'sparsity': 0.8, 'op_types': ['BatchNorm2d'] }] -pruner = SlimPruner(model, config_list) -pruner.compress() -``` - -#### User configuration for Filter Pruner - -- **sparsity:** This is to specify the sparsity operations to be compressed to -- **op_types:** Only BatchNorm2d is supported in Slim Pruner - -## 3. Experiment - -We implemented one of the experiments in ['Learning Efficient Convolutional Networks through Network Slimming'](https://arxiv.org/pdf/1708.06519.pdf), we pruned $70\%$ channels in the **VGGNet** for CIFAR-10 in the paper, in which $88.5\%$ parameters are pruned. Our experiments results are as follows: - -| Model | Error(paper/ours) | Parameters | Pruned | -| ------------- | ----------------- | ---------- | --------- | -| VGGNet | 6.34/6.40 | 20.04M | | -| Pruned-VGGNet | 6.20/6.26 | 2.03M | 88.5% | - -The experiments code can be found at [examples/model_compress]( https://github.com/microsoft/nni/tree/master/examples/model_compress/) diff --git a/docs/en_US/Compressor/l1filterpruner.md b/docs/en_US/Compressor/l1filterpruner.md deleted file mode 100644 index dc42d6478d..0000000000 --- a/docs/en_US/Compressor/l1filterpruner.md +++ /dev/null @@ -1,38 +0,0 @@ -L1FilterPruner on NNI -=== - -## Introduction - -L1FilterPruner is a general structured pruning algorithm for pruning filters in the convolutional layers. - -In ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https://arxiv.org/abs/1608.08710), authors Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet and Hans Peter Graf. - -![](../../img/l1filter_pruner.png) - -> L1Filter Pruner prunes filters in the **convolution layers** -> -> The procedure of pruning m filters from the ith convolutional layer is as follows: -> -> 1. For each filter ![](http://latex.codecogs.com/gif.latex?F_{i,j}), calculate the sum of its absolute kernel weights![](http://latex.codecogs.com/gif.latex?s_j=\sum_{l=1}^{n_i}\sum|K_l|) -> 2. Sort the filters by ![](http://latex.codecogs.com/gif.latex?s_j). -> 3. Prune ![](http://latex.codecogs.com/gif.latex?m) filters with the smallest sum values and their corresponding feature maps. The -> kernels in the next convolutional layer corresponding to the pruned feature maps are also -> removed. -> 4. A new kernel matrix is created for both the ![](http://latex.codecogs.com/gif.latex?i)th and ![](http://latex.codecogs.com/gif.latex?i+1)th layers, and the remaining kernel -> weights are copied to the new model. - -## Experiment - -We implemented one of the experiments in ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https://arxiv.org/abs/1608.08710) with **L1FilterPruner**, we pruned **VGG-16** for CIFAR-10 to **VGG-16-pruned-A** in the paper, in which $64\%$ parameters are pruned. Our experiments results are as follows: - -| Model | Error(paper/ours) | Parameters | Pruned | -| --------------- | ----------------- | --------------- | -------- | -| VGG-16 | 6.75/6.49 | 1.5x10^7 | | -| VGG-16-pruned-A | 6.60/6.47 | 5.4x10^6 | 64.0% | - -The experiments code can be found at [examples/model_compress]( https://github.com/microsoft/nni/tree/master/examples/model_compress/) - - - - - diff --git a/docs/en_US/model_compression.rst b/docs/en_US/model_compression.rst index f6821e3045..a1d4f01b8c 100644 --- a/docs/en_US/model_compression.rst +++ b/docs/en_US/model_compression.rst @@ -17,9 +17,9 @@ For details, please refer to the following tutorials: Overview Quick Start - Pruners - Quantizers - Model Speedup + Pruners + Quantizers Automatic Model Compression - Implementation + Model Speedup Compression Utilities + Customize Compression Algorithms diff --git a/docs/en_US/pruners.rst b/docs/en_US/pruners.rst deleted file mode 100644 index bf3771df16..0000000000 --- a/docs/en_US/pruners.rst +++ /dev/null @@ -1,16 +0,0 @@ -############################ -Supported Pruning Algorithms -############################ - -.. toctree:: - :maxdepth: 1 - - Level Pruner - AGP Pruner - Lottery Ticket Pruner - FPGM Pruner - L1Filter Pruner - L2Filter Pruner - ActivationAPoZRankFilterPruner - ActivationMeanRankFilterPruner - Slim Pruner diff --git a/docs/en_US/quantizers.rst b/docs/en_US/quantizers.rst deleted file mode 100644 index 8b082c2789..0000000000 --- a/docs/en_US/quantizers.rst +++ /dev/null @@ -1,11 +0,0 @@ -################################# -Supported Quantization Algorithms -################################# - -.. toctree:: - :maxdepth: 1 - - Naive Quantizer - QAT Quantizer - DoReFa Quantizer - BNN Quantizer \ No newline at end of file From 487d71aa2cda04082aad03ad1cbcff93a58e0cdd Mon Sep 17 00:00:00 2001 From: QuanluZhang Date: Mon, 29 Jun 2020 17:46:09 +0800 Subject: [PATCH 13/17] Refactor doc of NNI model compression (#2595) From 25c4c3b5fa472a07cab61a964a1ae632b4ee5925 Mon Sep 17 00:00:00 2001 From: Yuge Zhang Date: Mon, 29 Jun 2020 19:09:10 +0800 Subject: [PATCH 14/17] Add docs for nniignore and improve docs of training service (#2561) * Improve docs * All using # to start * uploaded -> excluded * Set versionCheck default to true * Add .nniignore example * Rename training service docs * Update training service docs * Fix step numbering * Resolve comments * Fix typo * Resolve comments * Fix broken link --- README.md | 2 +- docs/en_US/NAS/Benchmarks.md | 4 +- .../HowToImplementTrainingService.md | 5 +- docs/en_US/TrainingService/Overview.md | 50 ++++++++++++ .../TrainingService/SupportTrainingService.md | 39 ---------- docs/en_US/Tuner/PBTTuner.md | 2 +- docs/en_US/Tutorial/ExperimentConfig.md | 2 +- docs/en_US/Tutorial/QuickStart.md | 76 +++++++++++++------ docs/en_US/_templates/index.html | 2 +- docs/en_US/conf.py | 1 + docs/en_US/training_services.rst | 2 +- docs/static/css/custom.css | 6 +- examples/trials/mnist-tfv1/.nniignore | 7 ++ 13 files changed, 124 insertions(+), 74 deletions(-) create mode 100644 docs/en_US/TrainingService/Overview.md delete mode 100644 docs/en_US/TrainingService/SupportTrainingService.md create mode 100644 examples/trials/mnist-tfv1/.nniignore diff --git a/README.md b/README.md index dbce191cac..9a72dae882 100644 --- a/README.md +++ b/README.md @@ -197,7 +197,7 @@ Within the following table, we summarized the current NNI capabilities, we are g diff --git a/docs/en_US/NAS/Benchmarks.md b/docs/en_US/NAS/Benchmarks.md index a5de456001..ffe9460edc 100644 --- a/docs/en_US/NAS/Benchmarks.md +++ b/docs/en_US/NAS/Benchmarks.md @@ -16,13 +16,13 @@ To avoid storage and legal issues, we do not provide any prepared databases. We strongly recommend users to use docker to run the generation scripts, to ease the burden of installing multiple dependencies. Please follow the following steps. -1. Clone NNI repo. Replace `${NNI_VERSION}` with a released version name or branch name, e.g., `v1.6`. +**Step 1.** Clone NNI repo. Replace `${NNI_VERSION}` with a released version name or branch name, e.g., `v1.6`. ```bash git clone -b ${NNI_VERSION} https://github.com/microsoft/nni ``` -2. Run docker. +**Step 2.** Run docker. For NAS-Bench-101, diff --git a/docs/en_US/TrainingService/HowToImplementTrainingService.md b/docs/en_US/TrainingService/HowToImplementTrainingService.md index 123401b6bf..923b59684a 100644 --- a/docs/en_US/TrainingService/HowToImplementTrainingService.md +++ b/docs/en_US/TrainingService/HowToImplementTrainingService.md @@ -1,10 +1,11 @@ -**How to Implement TrainingService in NNI** -=== +# How to Implement Training Service in NNI ## Overview + TrainingService is a module related to platform management and job schedule in NNI. TrainingService is designed to be easily implemented, we define an abstract class TrainingService as the parent class of all kinds of TrainingService, users just need to inherit the parent class and complete their own child class if they want to implement customized TrainingService. ## System architecture + ![](../../img/NNIDesign.jpg) The brief system architecture of NNI is shown in the picture. NNIManager is the core management module of system, in charge of calling TrainingService to manage trial jobs and the communication between different modules. Dispatcher is a message processing center responsible for message dispatch. TrainingService is a module to manage trial jobs, it communicates with nniManager module, and has different instance according to different training platform. For the time being, NNI supports [local platfrom](LocalMode.md), [remote platfrom](RemoteMachineMode.md), [PAI platfrom](PaiMode.md), [kubeflow platform](KubeflowMode.md) and [FrameworkController platfrom](FrameworkControllerMode.md). diff --git a/docs/en_US/TrainingService/Overview.md b/docs/en_US/TrainingService/Overview.md new file mode 100644 index 0000000000..77e46fafdf --- /dev/null +++ b/docs/en_US/TrainingService/Overview.md @@ -0,0 +1,50 @@ +# Training Service + +## What is Training Service? + +NNI training service is designed to allow users to focus on AutoML itself, agnostic to the underlying computing infrastructure where the trials are actually run. When migrating from one cluster to another (e.g., local machine to Kubeflow), users only need to tweak several configurations, and the experiment can be easily scaled. + +Users can use training service provided by NNI, to run trial jobs on [local machine](./LocalMode.md), [remote machines](./RemoteMachineMode.md), and on clusters like [PAI](./PaiMode.md), [Kubeflow](./KubeflowMode.md) and [FrameworkController](./FrameworkControllerMode.md). These are called *built-in training services*. + +If the computing resource customers try to use is not listed above, NNI provides interface that allows users to build their own training service easily. Please refer to "[how to implement training service](./HowToImplementTrainingService)" for details. + +## How to use Training Service? + +Training service needs to be chosen and configured properly in experiment configuration YAML file. Users could refer to the document of each training service for how to write the configuration. Also, [reference](../Tutorial/ExperimentConfig) provides more details on the specification of the experiment configuration file. + +Next, users should prepare code directory, which is specified as `codeDir` in config file. Please note that in non-local mode, the code directory will be uploaded to remote or cluster before the experiment. Therefore, we limit the number of files to 2000 and total size to 300MB. If the code directory contains too many files, users can choose which files and subfolders should be excluded by adding a `.nniignore` file that works like a `.gitignore` file. For more details on how to write this file, see the [git documentation](https://git-scm.com/docs/gitignore#_pattern_format). + +In case users intend to use large files in their experiment (like large-scaled datasets) and they are not using local mode, they can either: 1) download the data before each trial launches by putting it into trial command; or 2) use a shared storage that is accessible to worker nodes. Usually, training platforms are equipped with shared storage, and NNI allows users to easily use them. Refer to docs of each built-in training service for details. + +## Built-in Training Services + +|TrainingService|Brief Introduction| +|---|---| +|[__Local__](./LocalMode.html)|NNI supports running an experiment on local machine, called local mode. Local mode means that NNI will run the trial jobs and nniManager process in same machine, and support gpu schedule function for trial jobs.| +|[__Remote__](./RemoteMachineMode.html)|NNI supports running an experiment on multiple machines through SSH channel, called remote mode. NNI assumes that you have access to those machines, and already setup the environment for running deep learning training code. NNI will submit the trial jobs in remote machine, and schedule suitable machine with enough gpu resource if specified.| +|[__PAI__](./PaiMode.html)|NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai) (aka PAI), called PAI mode. Before starting to use NNI PAI mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In PAI mode, your trial program will run in PAI's container created by Docker.| +|[__Kubeflow__](./KubeflowMode.html)|NNI supports running experiment on [Kubeflow](https://github.com/kubeflow/kubeflow), called kubeflow mode. Before starting to use NNI kubeflow mode, you should have a Kubernetes cluster, either on-premises or [Azure Kubernetes Service(AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/), a Ubuntu machine on which [kubeconfig](https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/) is setup to connect to your Kubernetes cluster. If you are not familiar with Kubernetes, [here](https://kubernetes.io/docs/tutorials/kubernetes-basics/) is a good start. In kubeflow mode, your trial program will run as Kubeflow job in Kubernetes cluster.| +|[__FrameworkController__](./FrameworkControllerMode.html)|NNI supports running experiment using [FrameworkController](https://github.com/Microsoft/frameworkcontroller), called frameworkcontroller mode. FrameworkController is built to orchestrate all kinds of applications on Kubernetes, you don't need to install Kubeflow for specific deep learning framework like tf-operator or pytorch-operator. Now you can use FrameworkController as the training service to run NNI experiment.| +|[__DLTS__](./DLTSMode.html)|NNI supports running experiment using [DLTS](https://github.com/microsoft/DLWorkspace.git), which is an open source toolkit, developed by Microsoft, that allows AI scientists to spin up an AI cluster in turn-key fashion.| + +## What does Training Service do? + +

+drawing +

+ +According to the architecture shown in [Overview](../Overview), training service (platform) is actually responsible for two events: 1) initiating a new trial; 2) collecting metrics and communicating with NNI core (NNI manager); 3) monitoring trial job status. To demonstrated in detail how training service works, we show the workflow of training service from the very beginning to the moment when first trial succeeds. + +Step 1. **Validate config and prepare the training platform.** Training service will first check whether the training platform user specifies is valid (e.g., is there anything wrong with authentication). After that, training service will start to prepare for the experiment by making the code directory (`codeDir`) accessible to training platform. + +```eval_rst +.. Note:: Different training services have different ways to handle ``codeDir``. For example, local training service directly runs trials in ``codeDir``. Remote training service packs ``codeDir`` into a zip and uploads it to each machine. K8S-based training services copy ``codeDir`` onto a shared storage, which is either provided by training platform itself, or configured by users in config file. +``` + +Step 2. **Submit the first trial.** To initiate a trial, usually (in non-reuse mode), NNI copies another few files (including parameters, launch script and etc.) onto training platform. After that, NNI launches the trial through subprocess, SSH, RESTful API, and etc. + +```eval_rst +.. Warning:: The working directory of trial command has exactly the same content as ``codeDir``, but can have a differen path (even on differen machines) Local mode is the only training service that shares one ``codeDir`` across all trials. Other training services copies a ``codeDir`` from the shared copy prepared in step 1 and each trial has an independent working directory. We strongly advise users not to rely on the shared behavior in local mode, as it will make your experiments difficult to scale to other training services. +``` + +Step 3. **Collect metrics.** NNI then monitors the status of trial, updates the status (e.g., from `WAITING` to `RUNNING`, `RUNNING` to `SUCCEEDED`) recorded, and also collects the metrics. Currently, most training services are implemented in an "active" way, i.e., training service will call the RESTful API on NNI manager to update the metrics. Note that this usually requires the machine that runs NNI manager to be at least accessible to the worker node. diff --git a/docs/en_US/TrainingService/SupportTrainingService.md b/docs/en_US/TrainingService/SupportTrainingService.md deleted file mode 100644 index ca2b9283fc..0000000000 --- a/docs/en_US/TrainingService/SupportTrainingService.md +++ /dev/null @@ -1,39 +0,0 @@ -# TrainingService - -NNI TrainingService provides the training platform for running NNI trial jobs. NNI supports [local](./LocalMode.md), [remote](./RemoteMachineMode.md), [pai](./PaiMode.md), [kubeflow](./KubeflowMode.md) and [frameworkcontroller](./FrameworkControllerMode.md) built-in training services. -NNI not only provides few built-in training service options, but also provides a method for customers to build their own training service easily. - -## Built-in TrainingService - -|TrainingService|Brief Introduction| -|---|---| -|[__Local__](./LocalMode.md)|NNI supports running an experiment on local machine, called local mode. Local mode means that NNI will run the trial jobs and nniManager process in same machine, and support gpu schedule function for trial jobs.| -|[__Remote__](./RemoteMachineMode.md)|NNI supports running an experiment on multiple machines through SSH channel, called remote mode. NNI assumes that you have access to those machines, and already setup the environment for running deep learning training code. NNI will submit the trial jobs in remote machine, and schedule suitable machine with enough gpu resource if specified.| -|[__Pai__](./PaiMode.md)|NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai) (aka pai), called pai mode. Before starting to use NNI pai mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In pai mode, your trial program will run in pai's container created by Docker.| -|[__Kubeflow__](./KubeflowMode.md)|NNI supports running experiment on [Kubeflow](https://github.com/kubeflow/kubeflow), called kubeflow mode. Before starting to use NNI kubeflow mode, you should have a Kubernetes cluster, either on-premises or [Azure Kubernetes Service(AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/), a Ubuntu machine on which [kubeconfig](https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/) is setup to connect to your Kubernetes cluster. If you are not familiar with Kubernetes, [here](https://kubernetes.io/docs/tutorials/kubernetes-basics/) is a good start. In kubeflow mode, your trial program will run as Kubeflow job in Kubernetes cluster.| -|[__FrameworkController__](./FrameworkControllerMode.md)|NNI supports running experiment using [FrameworkController](https://github.com/Microsoft/frameworkcontroller), called frameworkcontroller mode. FrameworkController is built to orchestrate all kinds of applications on Kubernetes, you don't need to install Kubeflow for specific deep learning framework like tf-operator or pytorch-operator. Now you can use FrameworkController as the training service to run NNI experiment.| - -## TrainingService Implementation - -TrainingService is designed to be easily implemented, we define an abstract class TrainingService as the parent class of all kinds of TrainingService, users just need to inherit the parent class and complete their own child class if they want to implement customized TrainingService. -The abstract function in TrainingService is shown below: - -```javascript -abstract class TrainingService { - public abstract listTrialJobs(): Promise; - public abstract getTrialJob(trialJobId: string): Promise; - public abstract addTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void; - public abstract removeTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void; - public abstract submitTrialJob(form: JobApplicationForm): Promise; - public abstract updateTrialJob(trialJobId: string, form: JobApplicationForm): Promise; - public abstract get isMultiPhaseJobSupported(): boolean; - public abstract cancelTrialJob(trialJobId: string, isEarlyStopped?: boolean): Promise; - public abstract setClusterMetadata(key: string, value: string): Promise; - public abstract getClusterMetadata(key: string): Promise; - public abstract cleanUp(): Promise; - public abstract run(): Promise; -} -``` - -The parent class of TrainingService has a few abstract functions, users need to inherit the parent class and implement all of these abstract functions. -For more information about how to write your own TrainingService, please [refer](https://github.com/microsoft/nni/blob/master/docs/en_US/TrainingService/HowToImplementTrainingService.md). diff --git a/docs/en_US/Tuner/PBTTuner.md b/docs/en_US/Tuner/PBTTuner.md index 6d321ff2ab..1554039a33 100644 --- a/docs/en_US/Tuner/PBTTuner.md +++ b/docs/en_US/Tuner/PBTTuner.md @@ -11,7 +11,7 @@ PBTTuner initializes a population with several trials (i.e., `population_size`). ### Provide checkpoint directory -Since some trials need to load other trial's checkpoint, users should provide a directory (i.e., `all_checkpoint_dir`) which is accessible by every trial. It is easy for local mode, users could directly use the default directory or specify any directory on the local machine. For other training services, users should follow [the document of those training services](../TrainingService/SupportTrainingService.md) to provide a directory in a shared storage, such as NFS, Azure storage. +Since some trials need to load other trial's checkpoint, users should provide a directory (i.e., `all_checkpoint_dir`) which is accessible by every trial. It is easy for local mode, users could directly use the default directory or specify any directory on the local machine. For other training services, users should follow [the document of those training services](../TrainingService/Overview.md) to provide a directory in a shared storage, such as NFS, Azure storage. ### Modify your trial code diff --git a/docs/en_US/Tutorial/ExperimentConfig.md b/docs/en_US/Tutorial/ExperimentConfig.md index bfe7baf3f0..c3b1cb247d 100644 --- a/docs/en_US/Tutorial/ExperimentConfig.md +++ b/docs/en_US/Tutorial/ExperimentConfig.md @@ -228,7 +228,7 @@ Note: The maxExecDuration spec set the time of an experiment, not a trial job. I ### versionCheck -Optional. Bool. Default: false. +Optional. Bool. Default: true. NNI will check the version of nniManager process and the version of trialKeeper in remote, pai and kubernetes platform. If you want to disable version check, you could set versionCheck be false. diff --git a/docs/en_US/Tutorial/QuickStart.md b/docs/en_US/Tutorial/QuickStart.md index 81c5b9e05d..5b8e2f50be 100644 --- a/docs/en_US/Tutorial/QuickStart.md +++ b/docs/en_US/Tutorial/QuickStart.md @@ -4,23 +4,29 @@ We currently support Linux, macOS, and Windows. Ubuntu 16.04 or higher, macOS 10.14.1, and Windows 10.1809 are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`. -**Linux and macOS** +### Linux and macOS ```bash - python3 -m pip install --upgrade nni +python3 -m pip install --upgrade nni ``` -**Windows** +### Windows ```bash - python -m pip install --upgrade nni +python -m pip install --upgrade nni ``` -Note: +```eval_rst +.. Note:: For Linux and macOS, ``--user`` can be added if you want to install NNI in your home directory; this does not require any special privileges. +``` -* For Linux and macOS, `--user` can be added if you want to install NNI in your home directory; this does not require any special privileges. -* If there is an error like `Segmentation fault`, please refer to the [FAQ](FAQ.md). -* For the `system requirements` of NNI, please refer to [Install NNI on Linux&Mac](InstallationLinux.md) or [Windows](InstallationWin.md). +```eval_rst +.. Note:: If there is an error like ``Segmentation fault``, please refer to the :doc:`FAQ `. +``` + +```eval_rst +.. Note:: For the system requirements of NNI, please refer to :doc:`Install NNI on Linux & Mac ` or :doc:`Windows `. +``` ## "Hello World" example on MNIST @@ -33,7 +39,12 @@ def run_trial(params): # Input data mnist = input_data.read_data_sets(params['data_dir'], one_hot=True) # Build network - mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'], channel_2_num=params['channel_2_num'], conv_size=params['conv_size'], hidden_size=params['hidden_size'], pool_size=params['pool_size'], learning_rate=params['learning_rate']) + mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'], + channel_2_num=params['channel_2_num'], + conv_size=params['conv_size'], + hidden_size=params['hidden_size'], + pool_size=params['pool_size'], + learning_rate=params['learning_rate']) mnist_network.build_network() test_acc = 0.0 @@ -44,11 +55,20 @@ def run_trial(params): test_acc = mnist_network.evaluate(mnist) if __name__ == '__main__': - params = {'data_dir': '/tmp/tensorflow/mnist/input_data', 'dropout_rate': 0.5, 'channel_1_num': 32, 'channel_2_num': 64, 'conv_size': 5, 'pool_size': 2, 'hidden_size': 1024, 'learning_rate': 1e-4, 'batch_num': 2000, 'batch_size': 32} + params = {'data_dir': '/tmp/tensorflow/mnist/input_data', + 'dropout_rate': 0.5, + 'channel_1_num': 32, + 'channel_2_num': 64, + 'conv_size': 5, + 'pool_size': 2, + 'hidden_size': 1024, + 'learning_rate': 1e-4, + 'batch_num': 2000, + 'batch_size': 32} run_trial(params) ``` -Note: If you want to see the full implementation, please refer to [examples/trials/mnist-tfv1/mnist_before.py](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/mnist_before.py). +If you want to see the full implementation, please refer to [examples/trials/mnist-tfv1/mnist_before.py](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/mnist_before.py). The above code can only try one set of parameters at a time; if we want to tune learning rate, we need to manually modify the hyperparameter and start the trial again and again. @@ -69,9 +89,9 @@ output: one optimal hyperparameter configuration If you want to use NNI to automatically train your model and find the optimal hyper-parameters, you need to do three changes based on your code: -**Three steps to start an experiment** +### Three steps to start an experiment -**Step 1**: Give a `Search Space` file in JSON, including the `name` and the `distribution` (discrete-valued or continuous-valued) of all the hyperparameters you need to search. +**Step 1**: Write a `Search Space` file in JSON, including the `name` and the `distribution` (discrete-valued or continuous-valued) of all the hyperparameters you need to search. ```diff - params = {'data_dir': '/tmp/tensorflow/mnist/input_data', 'dropout_rate': 0.5, 'channel_1_num': 32, 'channel_2_num': 64, @@ -85,7 +105,7 @@ If you want to use NNI to automatically train your model and find the optimal hy + } ``` -*Implemented code directory: [search_space.json](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/search_space.json)* +*Example: [search_space.json](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/search_space.json)* **Step 2**: Modify your `Trial` file to get the hyperparameter set from NNI and report the final result to NNI. @@ -110,7 +130,7 @@ If you want to use NNI to automatically train your model and find the optimal hy run_trial(params) ``` -*Implemented code directory: [mnist.py](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/mnist.py)* +*Example: [mnist.py](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/mnist.py)* **Step 3**: Define a `config` file in YAML which declares the `path` to the search space and trial files. It also gives other information such as the tuning algorithm, max trial number, and max duration arguments. @@ -133,31 +153,37 @@ trial: gpuNum: 0 ``` -Note, **for Windows, you need to change the trial command from `python3` to `python`**. +```eval_rst +.. Note:: If you are planning to use remote machines or clusters as your :doc:`training service <../TrainingService/Overview>`, to avoid too much pressure on network, we limit the number of files to 2000 and total size to 300MB. If your codeDir contains too many files, you can choose which files and subfolders should be excluded by adding a ``.nniignore`` file that works like a ``.gitignore`` file. For more details on how to write this file, see the `git documentation `_. +``` -*Implemented code directory: [config.yml](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/config.yml)* +*Example: [config.yml](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/config.yml) [.nniignore](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/.nniignore)* -All the cod above is already prepared and stored in [examples/trials/mnist-tfv1/](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1). +All the code above is already prepared and stored in [examples/trials/mnist-tfv1/](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1). -**Linux and macOS** +#### Linux and macOS Run the **config.yml** file from your command line to start an MNIST experiment. ```bash - nnictl create --config nni/examples/trials/mnist-tfv1/config.yml +nnictl create --config nni/examples/trials/mnist-tfv1/config.yml ``` -**Windows** +#### Windows Run the **config_windows.yml** file from your command line to start an MNIST experiment. -Note: if you're using NNI on Windows, you need to change `python3` to `python` in the config.yml file or use the config_windows.yml file to start the experiment. - ```bash - nnictl create --config nni\examples\trials\mnist-tfv1\config_windows.yml +nnictl create --config nni\examples\trials\mnist-tfv1\config_windows.yml +``` + +```eval_rst +.. Note:: If you're using NNI on Windows, you probably need to change ``python3`` to ``python`` in the config.yml file or use the config_windows.yml file to start the experiment. ``` -Note: `nnictl` is a command line tool that can be used to control experiments, such as start/stop/resume an experiment, start/stop NNIBoard, etc. Click [here](Nnictl.md) for more usage of `nnictl` +```eval_rst +.. Note:: ``nnictl`` is a command line tool that can be used to control experiments, such as start/stop/resume an experiment, start/stop NNIBoard, etc. Click :doc:`here ` for more usage of ``nnictl``. +``` Wait for the message `INFO: Successfully started experiment!` in the command line. This message indicates that your experiment has been successfully started. And this is what we expect to get: diff --git a/docs/en_US/_templates/index.html b/docs/en_US/_templates/index.html index 9ca37b459c..5cc8257298 100644 --- a/docs/en_US/_templates/index.html +++ b/docs/en_US/_templates/index.html @@ -219,7 +219,7 @@

NNI capabilities in a glance

diff --git a/docs/en_US/conf.py b/docs/en_US/conf.py index 16808e2143..41ac9c939c 100644 --- a/docs/en_US/conf.py +++ b/docs/en_US/conf.py @@ -46,6 +46,7 @@ 'sphinxarg.ext', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode', + 'sphinx.ext.intersphinx', 'nbsphinx', ] diff --git a/docs/en_US/training_services.rst b/docs/en_US/training_services.rst index fd63f9bcaf..435abc0c26 100644 --- a/docs/en_US/training_services.rst +++ b/docs/en_US/training_services.rst @@ -2,7 +2,7 @@ Introduction to NNI Training Services ===================================== .. toctree:: - Overview <./TrainingService/SupportTrainingService> + Overview <./TrainingService/Overview> Local<./TrainingService/LocalMode> Remote<./TrainingService/RemoteMachineMode> OpenPAI<./TrainingService/PaiMode> diff --git a/docs/static/css/custom.css b/docs/static/css/custom.css index ba10407098..e1a667bf0a 100644 --- a/docs/static/css/custom.css +++ b/docs/static/css/custom.css @@ -113,4 +113,8 @@ td.framework{ .or{ vertical-align: middle; -} \ No newline at end of file +} + +.wy-plain-list-disc li, .rst-content .section ul li, .rst-content .toctree-wrapper ul li, article ul li { + margin-bottom: 0px; +} diff --git a/examples/trials/mnist-tfv1/.nniignore b/examples/trials/mnist-tfv1/.nniignore new file mode 100644 index 0000000000..04886e1d80 --- /dev/null +++ b/examples/trials/mnist-tfv1/.nniignore @@ -0,0 +1,7 @@ +# Exclude the following directories when uploading codeDir. +data +logs +checkpoints + +# They can also be files +outputs.log From 6de15707c1f9ec7409432aea231e149c91b79626 Mon Sep 17 00:00:00 2001 From: gxiaotian Date: Tue, 30 Jun 2020 10:55:19 +0800 Subject: [PATCH 15/17] Add OpEvo example (#2549) --- README.md | 3 +- docs/en_US/TrialExample/OpEvoExamples.md | 85 ++++ docs/en_US/examples.rst | 1 + examples/trials/systems/opevo/Dockerfile | 42 ++ examples/trials/systems/opevo/Makefile | 14 + .../systems/opevo/src/algorithms/gbfs.py | 278 +++++++++++ .../systems/opevo/src/algorithms/na2c.py | 401 +++++++++++++++ .../systems/opevo/src/algorithms/opevo.py | 455 ++++++++++++++++++ .../opevo/src/compiler_auto_tune_stable.py | 399 +++++++++++++++ .../bmm/B960N128K128M64PNN/config_opevo.yml | 25 + .../bmm/B960N128K128M64PNN/search_space.json | 1 + .../bmm/B960N128K128M64PTN/config_opevo.yml | 25 + .../bmm/B960N128K128M64PTN/search_space.json | 1 + .../bmm/B960N128K64M128PNT/config_opevo.yml | 25 + .../bmm/B960N128K64M128PNT/search_space.json | 1 + .../N512C3HW227F64K11ST4PD0/config_opevo.yml | 25 + .../N512C3HW227F64K11ST4PD0/search_space.json | 1 + .../N512C64HW27F192K5ST1PD2/config_opevo.yml | 25 + .../N512C64HW27F192K5ST1PD2/search_space.json | 1 + .../mm/N512K1024M1024/config_gbfs.yml | 23 + .../mm/N512K1024M1024/config_na2c.yml | 22 + .../mm/N512K1024M1024/config_opevo.yml | 25 + .../mm/N512K1024M1024/search_space.json | 1 + .../mm/N512K1024M4096/config_gbfs.yml | 23 + .../mm/N512K1024M4096/config_na2c.yml | 22 + .../mm/N512K1024M4096/config_opevo.yml | 25 + .../mm/N512K1024M4096/search_space.json | 1 + .../mm/N512K4096M1024/config_gbfs.yml | 23 + .../mm/N512K4096M1024/config_na2c.yml | 22 + .../mm/N512K4096M1024/config_opevo.yml | 25 + .../mm/N512K4096M1024/search_space.json | 1 + examples/trials/systems/opevo/src/run.sh | 25 + .../opevo/src/templates/batch_matmul.py | 119 +++++ .../opevo/src/templates/convfwd_direct.py | 130 +++++ .../systems/opevo/src/templates/matmul.py | 111 +++++ .../systems/opevo/tvm_patches/libcuda.so.1 | Bin 0 -> 46448 bytes .../systems/opevo/tvm_patches/tvm_v0.6.patch | 84 ++++ 37 files changed, 2489 insertions(+), 1 deletion(-) create mode 100644 docs/en_US/TrialExample/OpEvoExamples.md create mode 100644 examples/trials/systems/opevo/Dockerfile create mode 100644 examples/trials/systems/opevo/Makefile create mode 100644 examples/trials/systems/opevo/src/algorithms/gbfs.py create mode 100644 examples/trials/systems/opevo/src/algorithms/na2c.py create mode 100644 examples/trials/systems/opevo/src/algorithms/opevo.py create mode 100644 examples/trials/systems/opevo/src/compiler_auto_tune_stable.py create mode 100644 examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PNN/config_opevo.yml create mode 100644 examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PNN/search_space.json create mode 100644 examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PTN/config_opevo.yml create mode 100644 examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PTN/search_space.json create mode 100644 examples/trials/systems/opevo/src/experiments/bmm/B960N128K64M128PNT/config_opevo.yml create mode 100644 examples/trials/systems/opevo/src/experiments/bmm/B960N128K64M128PNT/search_space.json create mode 100644 examples/trials/systems/opevo/src/experiments/conv/N512C3HW227F64K11ST4PD0/config_opevo.yml create mode 100644 examples/trials/systems/opevo/src/experiments/conv/N512C3HW227F64K11ST4PD0/search_space.json create mode 100644 examples/trials/systems/opevo/src/experiments/conv/N512C64HW27F192K5ST1PD2/config_opevo.yml create mode 100644 examples/trials/systems/opevo/src/experiments/conv/N512C64HW27F192K5ST1PD2/search_space.json create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_gbfs.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_na2c.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_opevo.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/search_space.json create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_gbfs.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_na2c.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_opevo.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/search_space.json create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_gbfs.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_na2c.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_opevo.yml create mode 100644 examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/search_space.json create mode 100644 examples/trials/systems/opevo/src/run.sh create mode 100644 examples/trials/systems/opevo/src/templates/batch_matmul.py create mode 100644 examples/trials/systems/opevo/src/templates/convfwd_direct.py create mode 100644 examples/trials/systems/opevo/src/templates/matmul.py create mode 100644 examples/trials/systems/opevo/tvm_patches/libcuda.so.1 create mode 100644 examples/trials/systems/opevo/tvm_patches/tvm_v0.6.patch diff --git a/README.md b/README.md index 9a72dae882..3ea89ebdee 100644 --- a/README.md +++ b/README.md @@ -90,6 +90,7 @@ Within the following table, we summarized the current NNI capabilities, we are g
  • Cifar10-pytorch
  • Scikit-learn
  • EfficientNet
    • +
  • Kernel Tunning
    • More...
    @@ -170,7 +171,7 @@ Within the following table, we summarized the current NNI capabilities, we are g
  • Kubeflow
  • FrameworkController on K8S (AKS etc.)
    • -
    • DLWorkspace (aka. DLTS)
      • + diff --git a/docs/en_US/TrialExample/OpEvoExamples.md b/docs/en_US/TrialExample/OpEvoExamples.md new file mode 100644 index 0000000000..5db331f1bf --- /dev/null +++ b/docs/en_US/TrialExample/OpEvoExamples.md @@ -0,0 +1,85 @@ +# Tuning Tensor Operators on NNI + +## Overview + +Abundant applications raise the demands of training and inference deep neural networks (DNNs) efficiently on diverse hardware platforms ranging from cloud servers to embedded devices. Moreover, computational graph-level optimization of deep neural network, like tensor operator fusion, may introduce new tensor operators. Thus, manually optimized tensor operators provided by hardware-specific libraries have limitations in terms of supporting new hardware platforms or supporting new operators, so automatically optimizing tensor operators on diverse hardware platforms is essential for large-scale deployment and application of deep learning technologies in the real-world problems. + +Tensor operator optimization is substantially a combinatorial optimization problem. The objective function is the performance of a tensor operator on specific hardware platform, which should be maximized with respect to the hyper-parameters of corresponding device code, such as how to tile a matrix or whether to unroll a loop. This example illustrates how to automatically tune tensor operators with NNI. Three tuning algorithms, OpEvo, G-BFS and N-A2C are provided. Please refer to [OpEvo: An Evolutionary Method for Tensor Operator Optimization](https://arxiv.org/abs/2006.05664) for detailed explanation about these algorithms. + + +## Environment Setup + +We prepared a dockerfile for setting up experiment environments. Before starting, please make sure the Docker daemon is running and the driver of your GPU accelerator is properly installed. Enter into the example folder `examples/trials/systems/opevo` and run below command to build and instantiate a Docker image from the dockerfile. +```bash +# if you are using Nvidia GPU +make cuda-env +# if you are using AMD GPU +make rocm-env +``` + +## Run Experiments: + +Three representative kinds of tensor operators, **matrix multiplication**, **batched matrix multiplication** and **2D convolution**, are chosen from BERT and AlexNet, and tuned with NNI. The `Trial` code for all tensor operators is `/root/compiler_auto_tune_stable.py`, and `Search Space` files and `config` files for each tuning algorithm locate in `/root/experiments/`, which are categorized by tensor operators. Here `/root` refers to the root of the container. + +For tuning the operators of matrix multiplication, please run below commands from `/root`: +```bash +# (N, K) x (K, M) represents a matrix of shape (N, K) multiplies a matrix of shape (K, M) + +# (512, 1024) x (1024, 1024) +# tuning with opevo +nnictl create --config experiments/mm/N512K1024M1024/config_opevo.yml +# tuning with g-bfs +nnictl create --config experiments/mm/N512K1024M1024/config_gbfs.yml +# tuning with n-a2c +nnictl create --config experiments/mm/N512K1024M1024/config_na2c.yml + +# (512, 1024) x (1024, 4096) +# tuning with opevo +nnictl create --config experiments/mm/N512K1024M4096/config_opevo.yml +# tuning with g-bfs +nnictl create --config experiments/mm/N512K1024M4096/config_gbfs.yml +# tuning with n-a2c +nnictl create --config experiments/mm/N512K1024M4096/config_na2c.yml + +# (512, 4096) x (4096, 1024) +# tuning with opevo +nnictl create --config experiments/mm/N512K1024M4096/config_opevo.yml +# tuning with g-bfs +nnictl create --config experiments/mm/N512K1024M4096/config_gbfs.yml +# tuning with n-a2c +nnictl create --config experiments/mm/N512K1024M4096/config_na2c.yml +``` + +For tuning the operators of batched matrix multiplication, please run below commands from `/root`: +```bash +# batched matrix with batch size 960 and shape of matrix (128, 128) multiplies batched matrix with batch size 960 and shape of matrix (128, 64) +nnictl create --config experiments/bmm/B960N128K128M64PNN/config_opevo.yml +# batched matrix with batch size 960 and shape of matrix (128, 128) is transposed first and then multiplies batched matrix with batch size 960 and shape of matrix (128, 64) +nnictl create --config experiments/bmm/B960N128K128M64PTN/config_opevo.yml +# batched matrix with batch size 960 and shape of matrix (128, 64) is transposed first and then right multiplies batched matrix with batch size 960 and shape of matrix (128, 64). +nnictl create --config experiments/bmm/B960N128K64M128PNT/config_opevo.yml +``` + +For tuning the operators of 2D convolution, please run below commands from `/root`: +```bash +# image tensor of shape $(512, 3, 227, 227)$ convolves with kernel tensor of shape $(64, 3, 11, 11)$ with stride 4 and padding 0 +nnictl create --config experiments/conv/N512C3HW227F64K11ST4PD0/config_opevo.yml +# image tensor of shape $(512, 64, 27, 27)$ convolves with kernel tensor of shape $(192, 64, 5, 5)$ with stride 1 and padding 2 +nnictl create --config experiments/conv/N512C64HW27F192K5ST1PD2/config_opevo.yml +``` + +Please note that G-BFS and N-A2C are not eligible to tune the operators of batched matrix multiplication and 2D convolution, since there are unsupportable parameters in the search spaces of these operators. + +## Citing OpEvo + +If you use OpEvo in your research, please consider citing the paper as follows: +``` +@misc{gao2020opevo, + title={OpEvo: An Evolutionary Method for Tensor Operator Optimization}, + author={Xiaotian Gao and Cui Wei and Lintao Zhang and Mao Yang}, + year={2020}, + eprint={2006.05664}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` diff --git a/docs/en_US/examples.rst b/docs/en_US/examples.rst index 77ea9733b4..c44f081997 100644 --- a/docs/en_US/examples.rst +++ b/docs/en_US/examples.rst @@ -11,5 +11,6 @@ Examples EvolutionSQuAD<./TrialExample/SquadEvolutionExamples> GBDT<./TrialExample/GbdtExample> RocksDB <./TrialExample/RocksdbExamples> + OpEvo <./TrialExample/OpEvoExamples> KDExample <./TrialExample/KDExample> EfficientNet <./TrialExample/EfficientNet> diff --git a/examples/trials/systems/opevo/Dockerfile b/examples/trials/systems/opevo/Dockerfile new file mode 100644 index 0000000000..a1c589c368 --- /dev/null +++ b/examples/trials/systems/opevo/Dockerfile @@ -0,0 +1,42 @@ +FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04 + +ENV PYTHONDONTWRITEBYTECODE 1 +ENV HIP_PLATFORM hcc +ENV PATH $PATH:/opt/rocm/bin:/usr/local/nvidia/lib64/bin +ENV TVM_HOME=/opt/tvm +ENV PYTHONPATH=/usr/local/rocm/src:$TVM_HOME/python:$TVM_HOME/topi/python:$TVM_HOME/nnvm/python +ENV HSA_USERPTR_FOR_PAGED_MEM=0 + +RUN env > /etc/environment + +RUN apt-get update && apt install -y --no-install-recommends git ca-certificates \ + python3-pip python3-wheel python3-setuptools python3-dev python3-pytest \ + vim less netcat-openbsd inetutils-ping curl patch iproute2 \ + g++ libpci3 libnuma-dev make cmake file openssh-server kmod gdb libopenmpi-dev openmpi-bin \ + autoconf automake autotools-dev libtool multiarch-support \ + && rm -rf /var/lib/apt/lists/* + +RUN curl -sL http://repo.radeon.com/rocm/apt/debian/rocm.gpg.key | apt-key add - && \ + printf "deb [arch=amd64] http://repo.radeon.com/rocm/apt/3.3/ xenial main" | tee /etc/apt/sources.list.d/rocm_hip.list && \ + apt update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ + rocm-dev zlib1g-dev unzip librdmacm-dev rocblas hipsparse rccl rocfft rocrand miopen-hip && apt-get clean && rm -rf /var/lib/apt/lists/* +RUN ln -sf libcudart.so /usr/local/cuda-10.0/targets/x86_64-linux/lib/libcudart_static.a + +RUN pip3 install tornado psutil xgboost==0.80 numpy decorator attrs && rm -rf ~/.cache +RUN git clone https://github.com/dmlc/tvm $TVM_HOME + +RUN cd $TVM_HOME && git checkout v0.6 && git submodule init && git submodule update && \ + mkdir -p build && cd build && cp ../cmake/config.cmake . && \ + sed -i 's/LLVM ON/LLVM OFF/g' config.cmake && sed -i 's/CUDA OFF/CUDA ON/g' config.cmake && \ + cmake .. && make -j16 + +RUN pip3 install nni==1.5 && rm -rf ~/.cache +RUN pip3 install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html && rm -rf ~/.cache + +ADD tvm_patches/tvm_v0.6.patch $TVM_HOME/tvm_v0.6.patch +ADD tvm_patches/libcuda.so.1 $TVM_HOME/build +RUN ln -sf libcuda.so.1 $TVM_HOME/build/libcudart.so.10.0 +RUN cd $TVM_HOME && git apply tvm_v0.6.patch && cd build && make -j16 + +ADD src /root/ + diff --git a/examples/trials/systems/opevo/Makefile b/examples/trials/systems/opevo/Makefile new file mode 100644 index 0000000000..ba7ea78c18 --- /dev/null +++ b/examples/trials/systems/opevo/Makefile @@ -0,0 +1,14 @@ +rocm-env: build + docker run -it --rm --privileged -v /:/host -w /root \ + -e BACKEND=c-rocm -p 8080:8080 \ + tvm4nni bash || true + +cuda-env: build + docker run -it --rm --privileged -v /:/host -w /root \ + -e BACKEND=c-cuda -p 8080:8080 \ + -v /usr/lib/x86_64-linux-gnu/libcuda.so.1:/usr/lib/x86_64-linux-gnu/libcuda.so.1 \ + -v $(shell dirname `ldd /usr/lib/x86_64-linux-gnu/libcuda.so.1 | grep nvidia-fatbinaryloader | awk '{print $$3}'`):/usr/local/nvidia/lib64 \ + tvm4nni bash || true + +build: + docker build -t tvm4nni --network=host . diff --git a/examples/trials/systems/opevo/src/algorithms/gbfs.py b/examples/trials/systems/opevo/src/algorithms/gbfs.py new file mode 100644 index 0000000000..5a4a91e21b --- /dev/null +++ b/examples/trials/systems/opevo/src/algorithms/gbfs.py @@ -0,0 +1,278 @@ +import math +import random +import logging +import copy + +import nni +from nni.tuner import Tuner + + +class Factor(object): + """factor type parameter + """ + def __init__(self, value): + self.product, self.num = value + self.partition = [1] * self.num + self.partition[0] = self.product + + def pick_out(self): + return self.partition + + def step(self, action): + tmp = copy.deepcopy(self) + tmp.partition[action[0]] = int(tmp.partition[action[0]] / action[2]) + tmp.partition[action[1]] = int(tmp.partition[action[1]] * action[2]) + + return tmp + + def get_actions(self): + actions = [] + prime_factors = self._get_prime_factors(self.product, False) + for i in range(self.num): + for j in range(self.num): + if i != j: + for k in range(len(prime_factors)): + action = [i] + action.append(j) + action.append(prime_factors[k]) + if self.partition[action[0]] % action[2] == 0: + actions.append(action) + return actions + + def __repr__(self): + string = "[" + for factor in self.partition: + string += factor.__repr__() + " " + string = string[:-1] + "]" + + return string + + def _get_prime_factors(self, n, repeat=True): + prime_factors = [] + + while n % 2 == 0: + if 2 not in prime_factors: + prime_factors.append(2) + elif repeat: + prime_factors.append(2) + n = n / 2 + + for i in range(3, int(math.sqrt(n)) + 1, 2): + while n % i == 0: + if i not in prime_factors: + prime_factors.append(i) + elif repeat: + prime_factors.append(i) + n = n / i + + if n > 2: + prime_factors.append(int(n)) + + return prime_factors + + +class Configuration(object): + """Configuration class + """ + def __init__(self, search_space): + self.params = {} + for key in search_space.keys(): + if search_space[key]['_type'] == 'factor': + self.params[key] = \ + Factor(search_space[key]['_value']) + else: + raise RuntimeError( + "G_BFS Tuner doesn't support this kind of parameter: " + + str(search_space[key]['_type']) + ) + + def __eq__(self, other): + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + else: + return False + + def __repr__(self): + string = "" + for param in self.params: + string += param.__repr__() + '\n' + + return string + + def pick_out(self): + output = {} + for key in self.params.keys(): + output[key] = self.params[key].pick_out() + + return output + + def step(self, action): + config = copy.deepcopy(self) + config.params[action[0]] = config.params[action[0]].step(action[1]) + + return config + + def get_actions(self): + actions = [] + for key, value in self.params.items(): + subactions = value.get_actions() + for subaction in subactions: + action = [key] + action.append(subaction) + actions.append(action) + + return actions + + +class Population(object): + """Population class + """ + + def __init__(self, opt_mode, search_space, num_samples): + self.opt_mode = opt_mode + self.search_space = search_space + self.num_samples = num_samples + + self.queue = [] + self.population = [] + self.fitness = [] + + def append(self, individual, fitness): + if self.opt_mode == "minimize": + fitness = -1 * fitness + + self.population.append(individual) + self.queue.insert(0, individual) + self.fitness.insert(0, fitness) + + i = 0 + while (i < len(self.fitness) - 1 + and self.fitness[i] < self.fitness[i + 1]): + self.fitness[i], self.fitness[i + 1] = \ + self.fitness[i + 1], self.fitness[i] + self.queue[i], self.queue[i + 1] = \ + self.queue[i + 1], self.queue[i] + i += 1 + + def generate(self): + if not self.fitness and not self.population: + return [Configuration(self.search_space)] + elif not self.fitness and self.population: + return [] + else: + self.fitness.pop(0) + config = self.queue.pop(0) + + action_space = config.get_actions() + num = len(action_space) + if num > self.num_samples: + indices = random.sample(range(num), self.num_samples) + else: + indices = range(num) + + res = [] + for idx in indices: + tmp = config.step(action_space[idx]) + if tmp not in self.population: + res.append(tmp) + + return res + + +class G_BFS(Tuner): + """G-BFS Tuner + Based on paper Compiler-Level Matrix Multiplication Optimization for Deep Learning + + Parameters + ---------- + optimize_mode: str, 'maximize' or 'minimize' + num_samples: int, + The random selection parameter rho + """ + def __init__(self, optimize_mode="maximize", num_samples=5): + self.logger = logging.getLogger( + self.__module__ + "." + self.__class__.__name__) + self.logger.setLevel('DEBUG') + + self.opt_mode = optimize_mode + self.num_samples = num_samples + + self.request_list = [] + self.serve_list = [] + self.wait_dict = {} + + def update_search_space(self, search_space): + """Update the self.bounds and self.types by the search_space.json file. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if not isinstance(search_space, dict): + self.logger.info("The format of search space is not a dict.") + raise RuntimeError("The format of search space is not a dict.") + + self.population = \ + Population(self.opt_mode, search_space, self.num_samples) + + if not self.serve_list: + self.serve_list = self.population.generate() + + def generate_multiple_parameters(self, parameter_id_list, **kwargs): + """Returns multiple sets of trial (hyper-)parameters, + as iterable of serializable objects. + """ + result = [] + self.send_trial_callback = kwargs['st_callback'] + for parameter_id in parameter_id_list: + had_exception = False + try: + self.logger.debug("generating param for %s", parameter_id) + res = self.generate_parameters(parameter_id, **kwargs) + except nni.NoMoreTrialError: + had_exception = True + if not had_exception: + result.append(res) + return result + + def generate_parameters(self, parameter_id, **kwargs): + """Method which provides one set of hyper-parameters. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if self.serve_list: + self.wait_dict[parameter_id] = self.serve_list.pop() + return self.wait_dict[parameter_id].pick_out() + else: + self.request_list.append(parameter_id) + raise nni.NoMoreTrialError('no more parameters now.') + + def receive_trial_result(self, parameter_id, parameters, value, **kwargs): + """Method invoked when a trial reports its final result. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if isinstance(value, dict): + value = value['default'] + + self.population.append(self.wait_dict[parameter_id], value) + del self.wait_dict[parameter_id] + + if not self.serve_list and not self.wait_dict: + self.serve_list = self.population.generate() + if not self.serve_list: + raise RuntimeError("Tuner stopped since no candidates") + + while self.request_list and self.serve_list: + param_id = self.request_list[0] + self.wait_dict[param_id] = self.serve_list.pop() + self.send_trial_callback( + param_id, self.wait_dict[param_id].pick_out()) + self.request_list.pop(0) + + def trial_end(self, parameter_id, success, **kwargs): + """Method invoked when a trial is completed or terminated. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if not success: + self.population.append(self.wait_dict[parameter_id], 0.0) + del self.wait_dict[parameter_id] diff --git a/examples/trials/systems/opevo/src/algorithms/na2c.py b/examples/trials/systems/opevo/src/algorithms/na2c.py new file mode 100644 index 0000000000..7c51a36e40 --- /dev/null +++ b/examples/trials/systems/opevo/src/algorithms/na2c.py @@ -0,0 +1,401 @@ +import math +import random +import logging +import copy + +import torch +from torch import optim +from torch import nn +import torch.nn.functional as F +import numpy as np + +import nni +from nni.tuner import Tuner + + +class Factor(object): + """factor type parameter + """ + def __init__(self, value): + self.product, self.num = value + self.partition = [1] * self.num + self.partition[0] = self.product + + def pick_out(self): + return self.partition + + def step(self, action): + if self.partition[action[0]] % action[2] == 0: + self.partition[action[0]] /= action[2] + self.partition[action[1]] *= action[2] + status = True + else: + status = False + + return status + + def get_actions(self): + actions = [] + prime_factors = self._get_prime_factors(self.product, False) + for i in range(self.num): + for j in range(self.num): + if i != j: + for k in range(len(prime_factors)): + action = [i] + action.append(j) + action.append(prime_factors[k]) + actions.append(action) + + return actions + + def __repr__(self): + return self.partition.__repr__() + + def __eq__(self, other): + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + else: + return False + + def _get_prime_factors(self, n, repeat=True): + prime_factors = [] + + while n % 2 == 0: + if 2 not in prime_factors: + prime_factors.append(2) + elif repeat: + prime_factors.append(2) + n = n / 2 + + for i in range(3, int(math.sqrt(n)) + 1, 2): + while n % i == 0: + if i not in prime_factors: + prime_factors.append(i) + elif repeat: + prime_factors.append(i) + n = n / i + + if n > 2: + prime_factors.append(int(n)) + + return prime_factors + + +class Configuration(object): + """Configuration class + """ + def __init__(self, search_space): + self.params = {} + self.key_order = [] + for key in search_space.keys(): + if search_space[key]['_type'] == 'factor': + self.key_order.append(key) + self.params[key] = \ + Factor(search_space[key]['_value']) + else: + raise RuntimeError( + "N_A2C Tuner doesn't support this kind of parameter: " + + str(search_space[key]['_type']) + ) + + def __eq__(self, other): + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + else: + return False + + def __repr__(self): + string = "" + for key, value in self.params.items(): + string += key + ': ' + value.__repr__() + ' ' + + return string + + def pick_out(self): + output = {} + for key in self.params.keys(): + output[key] = self.params[key].pick_out() + + return output + + def step(self, action): + config = copy.deepcopy(self) + status = config.params[action[0]].step(action[1]) + + return status, config + + def get_actions(self): + actions = [] + for key, value in self.params.items(): + subactions = value.get_actions() + for subaction in subactions: + action = [key] + action.append(subaction) + actions.append(action) + + return actions + + def to_torch(self): + states = [] + for key in self.key_order: + state = torch.tensor(self.params[key].partition).float() / \ + self.params[key].product - 0.5 + states.append(state) + + return torch.cat(states).float() + + +class ActorCritic(nn.Module): + def __init__(self, num_states, num_actions, hidden_size): + super(ActorCritic, self).__init__() + + self.num_actions = num_actions + self.fc = nn.Linear(num_states, hidden_size) + self.critic_linear2 = nn.Linear(hidden_size, 1) + self.actor_linear2 = nn.Linear(hidden_size, num_actions) + + def forward(self, state): + x = F.relu(self.fc(state)) + value = self.critic_linear2(x) + policy_dist = F.softmax(self.actor_linear2(x)) + + return value, policy_dist + + +class Population(object): + """Population class + """ + def __init__(self, search_space, opt_mode, n_states, n_steps, + hidden_size, lr): + self.search_space = search_space + self.opt_mode = opt_mode + self.n_states = n_states + self.n_steps = n_steps + self.hidden_size = hidden_size + self.lr = lr + + self.config = Configuration(search_space) + self.max_reward = 0.0 + + self.action_space = self.config.get_actions() + self.dim_actions = len(self.action_space) + self.dim_states = len(self.config.to_torch()) + self.log_probs = [] + self.values = [] + self.rewards = [] + + self.population = [] + + self.actor_critic = ActorCritic( + self.dim_states, self.dim_actions, self.hidden_size + ) + self.ac_optimizer = optim.Adam( + self.actor_critic.parameters(), lr=self.lr + ) + + def append(self, individual, fitness): + if self.opt_mode == "minimize": + fitness = -1 * fitness + + self.population.append(individual) + + if self.max_reward < fitness: + self.max_reward = fitness + self.config = individual + + if self.collect: + idx = self.collect.index(individual) + self.waiting_rewards[idx] = fitness + del self.collect[idx] + else: + raise RuntimeError("Received unexpected trials.") + + if not self.collect: + self.rewards.extend(self.waiting_rewards) + + self.ac_optimizer.zero_grad() + gradient_loss = 0 + value_loss = 0 + for i in range(len(self.values)): + advantage = self.rewards[i] - self.values[i] + gradient_loss += self.log_probs[i] * advantage + value_loss += torch.pow(advantage, 2) + loss = gradient_loss + value_loss + loss.backward() + self.ac_optimizer.step() + + self.rewards = [] + self.values = [] + self.log_probs = [] + self.collect = [] + + def generate(self): + self.collect = [] + while len(self.collect) < self.n_states: + config = self.config + for i in range(self.n_steps): + value, policy_dist = self.actor_critic(config.to_torch()) + dist = policy_dist.detach().numpy() + + if random.uniform(0, 1) < 0.1: + action = random.choice(range(self.dim_actions)) + else: + action = np.random.choice( + self.dim_actions, p=np.squeeze(dist)) + + log_prob = torch.log(policy_dist.squeeze(0)[action]) + # entropy = -np.sum(np.mean(dist) * np.log(dist)) + flag, new_config = config.step(self.action_space[action]) + + if (flag and new_config not in self.population + and new_config not in self.collect): + self.collect.append(new_config) + self.log_probs.append(log_prob) + self.values.append(value) + + config = new_config + # print([math.exp(float(i)) for i in self.log_probs]) + + self.waiting_rewards = [0.0] * len(self.collect) + return copy.deepcopy(self.collect) + + +class N_A2C(Tuner): + """N-A2C Tuner + Based on paper Compiler-Level Matrix Multiplication Optimization for Deep Learning + + Parameters + ---------- + optimize_mode: str, 'maximize' or 'minimize' + n_states: int, + The maximum search steps Tau + n_steps: int + number of steps to train the policy and critic networks each iteration + hidden_size: int, + number of hidden size of the policy and critic networks + lr: float, + learning rate of the policy and critic networks + """ + + def __init__(self, + optimize_mode="maximize", + n_states=6, + n_steps=3, + hidden_size=128, + lr=1e-3): + self.logger = logging.getLogger( + self.__module__ + "." + self.__class__.__name__) + self.logger.setLevel('DEBUG') + + self.opt_mode = optimize_mode + self.n_states = n_states + self.n_steps = n_steps + self.hidden_size = 128 + self.lr = lr + + self.request_list = [] + self.serve_list = [] + self.wait_dict = {} + + def update_search_space(self, search_space): + """Update the self.bounds and self.types by the search_space.json file. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if not isinstance(search_space, dict): + self.logger.info("The format of search space is not a dict.") + raise RuntimeError("The format of search space is not a dict.") + + self.population = \ + Population( + search_space, + self.opt_mode, + self.n_states, + self.n_steps, + self.hidden_size, + self.lr + ) + + if not self.serve_list: + self.serve_list = self.population.generate() + + def generate_multiple_parameters(self, parameter_id_list, **kwargs): + """Returns multiple sets of trial (hyper-)parameters, + as iterable of serializable objects. + """ + result = [] + self.send_trial_callback = kwargs['st_callback'] + for parameter_id in parameter_id_list: + had_exception = False + try: + self.logger.debug("generating param for %s", parameter_id) + res = self.generate_parameters(parameter_id, **kwargs) + except nni.NoMoreTrialError: + had_exception = True + if not had_exception: + result.append(res) + return result + + def generate_parameters(self, parameter_id, **kwargs): + """Method which provides one set of hyper-parameters. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if self.serve_list: + self.wait_dict[parameter_id] = self.serve_list.pop() + return self.wait_dict[parameter_id].pick_out() + else: + self.request_list.append(parameter_id) + raise nni.NoMoreTrialError('no more parameters now.') + + def receive_trial_result(self, parameter_id, parameters, value, **kwargs): + """Method invoked when a trial reports its final result. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if isinstance(value, dict): + value = value['default'] + + self.population.append(self.wait_dict[parameter_id], value) + del self.wait_dict[parameter_id] + + if not self.serve_list and not self.wait_dict: + self.serve_list = self.population.generate() + if not self.serve_list: + raise RuntimeError("Tuner stopped since no candidates") + + while self.request_list and self.serve_list: + param_id = self.request_list[0] + self.wait_dict[param_id] = self.serve_list.pop() + self.send_trial_callback( + param_id, self.wait_dict[param_id].pick_out()) + self.request_list.pop(0) + + # print('request_list: ' + str(len(self.request_list))) + # print('serve_list: ' + str(len(self.serve_list))) + # print('wait_dict: ' + str(len(self.wait_dict.keys()))) + + def trial_end(self, parameter_id, success, **kwargs): + """Method invoked when a trial is completed or terminated. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if not success: + self.population.append(self.wait_dict[parameter_id], 0.0) + del self.wait_dict[parameter_id] + + if not self.serve_list and not self.wait_dict: + self.serve_list = self.population.generate() + if not self.serve_list: + raise RuntimeError("Tuner stopped since no candidates") + + while self.request_list and self.serve_list: + param_id = self.request_list[0] + self.wait_dict[param_id] = self.serve_list.pop() + self.send_trial_callback( + param_id, self.wait_dict[param_id].pick_out()) + self.request_list.pop(0) + + # print('trial_end request_list: ' + str(len(self.request_list))) + # print('trial_end serve_list: ' + str(len(self.serve_list))) + # print('trial_end wait_dict: ' + str(len(self.wait_dict.keys()))) diff --git a/examples/trials/systems/opevo/src/algorithms/opevo.py b/examples/trials/systems/opevo/src/algorithms/opevo.py new file mode 100644 index 0000000000..31346ab873 --- /dev/null +++ b/examples/trials/systems/opevo/src/algorithms/opevo.py @@ -0,0 +1,455 @@ +import math +import logging +import copy +import random +import numpy as np +from itertools import permutations, combinations + +import nni +from nni.tuner import Tuner + + +class Parameter(object): + """Base class for all types of parameters + """ + def mutate(self): + raise NotImplementedError + + def reset(self): + raise NotImplementedError + + def pick_out(self): + raise NotImplementedError + + def get_cardinality(self): + raise NotImplementedError + + def __eq__(self, other): + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + else: + return False + + +class Choice(Parameter): + """choice type parameter + """ + def __init__(self, choices, mutate_rate): + self.choices = choices + self.value = random.choice(self.choices) + self.mutate_rate = mutate_rate + + def get_cardinality(self): + return len(self.choices) + + def reset(self): + self.value = random.choice(self.choices) + + def mutate(self): + child = copy.deepcopy(self) + while random.uniform(0, 1) < child.mutate_rate: + choices = copy.deepcopy(child.choices) + choices.remove(child.value) + if choices: + child.value = random.choice(choices) + else: + break + + return child + + def pick_out(self): + return self.value + + +class Discrete(Parameter): + """choice type parameter + """ + def __init__(self, numbers, mutate_rate): + numbers.sort() + self.numbers = numbers + self.value = random.choice(self.numbers) + self.mutate_rate = mutate_rate + + def get_cardinality(self): + return len(self.numbers) + + def reset(self): + self.value = random.choice(self.numbers) + + def mutate(self): + child = copy.deepcopy(self) + while random.uniform(0, 1) < child.mutate_rate: + idx = child.numbers.index(child.value) + if idx == 0 and idx + 1 < len(child.numbers): + child.value = child.numbers[idx + 1] + elif idx + 1 == len(child.numbers) and idx - 1 >= 0: + child.value = child.numbers[idx - 1] + elif idx == 0 and idx + 1 == len(child.numbers): + break + else: + shift = random.choice([-1, 1]) + child.value = child.numbers[idx + shift] + + return child + + def pick_out(self): + return self.value + + +class Factor(Parameter): + """factor type parameter + """ + def __init__(self, value, mutate_rate): + self.product, self.num = value + self.mutate_rate = mutate_rate + self.all_partitions = self._get_all_partitions(self.product, self.num) + self.partition = random.choice(self.all_partitions) + + def reset(self): + self.partition = random.choice(self.all_partitions) + + def get_cardinality(self): + return len(self.all_partitions) + + def mutate(self): + child = copy.deepcopy(self) + while random.uniform(0, 1) < self.mutate_rate: + action = random.choice(child._get_actions()) + child._step(action) + + return child + + def pick_out(self): + return self.partition + + def _step(self, action): + self.partition[action[0]] = int(self.partition[action[0]] / action[2]) + self.partition[action[1]] = int(self.partition[action[1]] * action[2]) + + def _get_actions(self): + actions = [] + prime_factors = self._get_prime_factors(self.product, False) + for i in range(self.num): + for j in range(self.num): + if i != j: + for k in range(len(prime_factors)): + action = [i] + action.append(j) + action.append(prime_factors[k]) + if self.partition[action[0]] % action[2] == 0: + actions.append(action) + return actions + + def __repr__(self): + string = "[" + for factor in self.partition: + string += factor.__repr__() + " " + string = string[:-1] + "]" + + return string + + def _get_all_partitions(self, product, num): + # get all prime factors with repetition + prime_factors = self._get_prime_factors(product) + + # group all prime factors + groups = {} + for prime_factor in prime_factors: + if prime_factor in groups.keys(): + groups[prime_factor] += 1 + else: + groups[prime_factor] = 1 + + # partition each group + for key, value in groups.items(): + partitions = [] + for comb in combinations(range(value + num - 1), num - 1): + # print(comb) + partition = [] + start_idx = -1 + for idx in comb: + partition.append(key**(idx - start_idx - 1)) + start_idx = idx + partition.append(key**(value + num - 2 - start_idx)) + partitions.append(partition) + groups[key] = partitions + + # generate partitions + partitions = [] + + def part(groups, mul=[]): + if not groups: + partition = [1] * num + for i in range(num): + for m in mul: + partition[i] *= m[i] + partitions.append(partition) + + for key, group in groups.items(): + for partition in group: + mul.append(partition) + tmp = copy.deepcopy(groups) + del tmp[key] + part(tmp, mul) + mul.pop() + break + + part(groups) + return partitions + + def _get_prime_factors(self, n, repeat=True): + prime_factors = [] + + while n % 2 == 0: + if 2 not in prime_factors: + prime_factors.append(2) + elif repeat: + prime_factors.append(2) + n = n / 2 + + for i in range(3, int(math.sqrt(n)) + 1, 2): + while n % i == 0: + if i not in prime_factors: + prime_factors.append(i) + elif repeat: + prime_factors.append(i) + n = n / i + + if n > 2: + prime_factors.append(int(n)) + + return prime_factors + + +class Individual(object): + """Individual class + """ + def __init__(self, search_space, mutate_rate): + self.params = {} + for key in search_space.keys(): + if search_space[key]['_type'] == 'choice': + self.params[key] = \ + Choice(search_space[key]['_value'], mutate_rate) + elif search_space[key]['_type'] == 'discrete': + self.params[key] = \ + Discrete(search_space[key]['_value'], mutate_rate) + elif search_space[key]['_type'] == 'factor': + self.params[key] = \ + Factor(search_space[key]['_value'], mutate_rate) + else: + raise RuntimeError( + "OpEvo Tuner doesn't support this kind of parameter: " + + str(search_space[key]['_type']) + ) + + def __eq__(self, other): + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + else: + return False + + def __repr__(self): + string = "" + for param in self.params: + string += param.__repr__() + '\n' + + return string + + def mutate(self): + child = copy.deepcopy(self) + for key in child.params.keys(): + child.params[key] = child.params[key].mutate() + + return child + + def reset(self): + for key in self.params.keys(): + self.params[key].reset() + + return self + + def pick_out(self): + output = {} + for key in self.params.keys(): + output[key] = self.params[key].pick_out() + + return output + + +class Population(object): + """Population class + """ + + def __init__(self, search_space, mutate_rate, opt_mode='maximize'): + self.search_space = search_space + self.mutate_rate = mutate_rate + self.opt_mode = opt_mode + self.population = [] + self.fitness = [] + + self.individual = Individual(self.search_space, self.mutate_rate) + self.volume = 1 + for key, value in self.individual.params.items(): + self.volume *= self.individual.params[key].get_cardinality() + + def append(self, individual, fitness): + if self.opt_mode == "minimize": + fitness = -1 * fitness + + self.population.insert(0, individual) + self.fitness.insert(0, fitness) + + i = 0 + while (i < len(self.fitness) - 1 + and self.fitness[i] < self.fitness[i + 1]): + self.fitness[i], self.fitness[i + 1] = \ + self.fitness[i + 1], self.fitness[i] + self.population[i], self.population[i + 1] = \ + self.population[i + 1], self.population[i] + i += 1 + + def get_offspring(self, parents_size, offspring_size): + children = [] + if len(self.fitness) < parents_size: + for _ in range(offspring_size): + child = copy.deepcopy(self.individual.reset()) + while child in self.population or child in children: + child = child.mutate() + children.append(child) + elif self.fitness[0] < 1e-3: + for _ in range(offspring_size): + child = copy.deepcopy(self.individual.reset()) + while child in self.population or child in children: + child = child.mutate() + children.append(child) + else: + prob = np.array(self.fitness[:parents_size]) / \ + np.sum(self.fitness[:parents_size]) + + for _ in range(offspring_size): + child = copy.deepcopy(self.population[0]) + for key in child.params.keys(): + idx = np.random.choice(range(parents_size), p=prob) + child.params[key] = self.population[idx].params[key] + child = child.mutate() + while child in self.population or child in children: + child = child.mutate() + children.append(child) + + return children + + +class OpEvo(Tuner): + """OpEvo Tuner + + Parameters + ---------- + optimize_mode: str, 'maximize' or 'minimize' + parents_size: int + offspring_size: int + parents_size and offspring_size govern the diversity in evolutionary + process. OpEvo with large parents_size and offspring_size tends to get + rid of suboptimum but sacrifice data efficiency, while one with smaller + parants_size and offspring_size is easier to converge but suffers suboptimum. + mutate_rate: float, (0, 1) + Mutation rate ranging from 0 to 1. It trade-offs the exploration and + exploitation. OpEvo tends to exploration as q approaches 0, while tends + to exploitation as q approaches 1. + """ + + def __init__(self, + optimize_mode="maximize", + parents_size=20, + offspring_size=20, + mutate_rate=0.5): + self.logger = logging.getLogger( + self.__module__ + "." + self.__class__.__name__) + self.logger.setLevel('DEBUG') + + self.optimize_mode = optimize_mode + self.parents_size = parents_size + self.offspring_size = offspring_size + self.mutate_rate = mutate_rate + + self.request_list = [] + self.serve_list = [] + self.wait_dict = {} + + def update_search_space(self, search_space): + """Update the self.bounds and self.types by the search_space.json file. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if not isinstance(search_space, dict): + self.logger.info("The format of search space is not a dict.") + raise RuntimeError("The format of search space is not a dict.") + + self.population = Population(search_space, + self.mutate_rate, + self.optimize_mode) + self.logger.debug('Total search space volume: ' + + str(self.population.volume)) + + if not self.serve_list: + self.serve_list = self.population.get_offspring( + self.parents_size, self.offspring_size) + + def generate_multiple_parameters(self, parameter_id_list, **kwargs): + """Returns multiple sets of trial (hyper-)parameters, + as iterable of serializable objects. + """ + result = [] + self.send_trial_callback = kwargs['st_callback'] + for parameter_id in parameter_id_list: + had_exception = False + try: + self.logger.debug("generating param for %s", parameter_id) + res = self.generate_parameters(parameter_id, **kwargs) + except nni.NoMoreTrialError: + had_exception = True + if not had_exception: + result.append(res) + return result + + def generate_parameters(self, parameter_id, **kwargs): + """Method which provides one set of hyper-parameters. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if self.serve_list: + self.wait_dict[parameter_id] = self.serve_list.pop() + return self.wait_dict[parameter_id].pick_out() + else: + self.request_list.append(parameter_id) + raise nni.NoMoreTrialError('no more parameters now.') + + def receive_trial_result(self, parameter_id, parameters, value, **kwargs): + """Method invoked when a trial reports its final result. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if isinstance(value, dict): + value = value['default'] + + self.population.append(self.wait_dict[parameter_id], value) + del self.wait_dict[parameter_id] + + if not self.serve_list: + self.serve_list = self.population.get_offspring( + self.parents_size, self.offspring_size) + + while self.request_list and self.serve_list: + param_id = self.request_list[0] + self.wait_dict[param_id] = self.serve_list.pop() + self.send_trial_callback( + param_id, self.wait_dict[param_id].pick_out()) + self.request_list.pop(0) + + def trial_end(self, parameter_id, success, **kwargs): + """Method invoked when a trial is completed or terminated. + + Override of the abstract method in :class:`~nni.tuner.Tuner`. + """ + if not success: + self.population.append(self.wait_dict[parameter_id], 0.0) + del self.wait_dict[parameter_id] diff --git a/examples/trials/systems/opevo/src/compiler_auto_tune_stable.py b/examples/trials/systems/opevo/src/compiler_auto_tune_stable.py new file mode 100644 index 0000000000..68a235c60c --- /dev/null +++ b/examples/trials/systems/opevo/src/compiler_auto_tune_stable.py @@ -0,0 +1,399 @@ +#!/usr/bin/env python3 + +## TODO: optimize c-mcpu metric; early-stop handler; fp16/int8; Kill pyRPC; + +import numpy as np +import tvm +import logging +import math +import re +import sys, time, subprocess, os, random, hashlib +from tvm import autotvm +import topi +import json +from topi.util import get_const_tuple +import importlib +from tvm.autotvm.task.dispatcher import ApplyConfig +from tvm.autotvm.task import ConfigEntity +from threading import Timer + +backend = os.environ['BACKEND'] if 'BACKEND' in os.environ else 'c-cuda' + +def system_lock(key_ids): + import socket, time + occupied_sock = None + while not occupied_sock: + for key_id in key_ids: + try: + sock = socket.socket(socket.AF_INET,socket.SOCK_STREAM) + sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + sock.bind(('127.0.0.1', 9050 + key_id)) + sock.listen(1) + occupied_sock = (sock, key_id) + break + except: + try: + sock.shutdown(socket.SHUT_RDWR) + sock.close() + except: + sock.close() + if occupied_sock: + break + # print('still waiting ..') + time.sleep(0.2) + + # print('Using key_id = %d' % occupied_sock[1]) + sock = occupied_sock[0] + + def unlock_fd(): + try: + sock.shutdown(socket.SHUT_RDWR) + sock.close() + except: + sock.close() + return unlock_fd, occupied_sock[1] + +def show_search_space(config_space, printable): + search_space = {} + for _, name in enumerate(config_space.space_map): + curr = config_space.space_map[name] + if (curr.__class__ == tvm.autotvm.task.space.SplitSpace): + search_space[name] = {"_type": "factor", "_value": [curr.product, curr.num_output]} + elif (curr.__class__ == tvm.autotvm.task.space.OtherOptionSpace): + search_space[name] = {"_type": "choice", "_value": [x.val for x in curr.entities]} + else: + raise Exception("Cannot recognize search space type: %s" % (config_space.space_map[name].__class__)) + json_space = json.dumps(search_space) + print("\n>> Search Space = %s" % json_space) + if printable: + print("\n>> Writing Search Space to './search_space.json'..") + with open("search_space.json", "w") as fp: + fp.write(json_space) + print("\n>> Done") + sys.exit(0) + +def get_tuning_parallism(): + if 'DEV_NUM' in os.environ: + dev_num = int(os.environ['DEV_NUM']) + else: + if backend in ['c-rocm', '#rocm']: + devices = subprocess.getoutput('/opt/rocm/bin/rocm_agent_enumerator | grep -v gfx000').split() + if not devices: + raise Exception("Not valid rocm device found.") + dev_num = len(devices) + elif backend in ['c-cuda', '#cuda']: + devices = subprocess.getoutput('ls /dev/nvidia[0-9]* 2>/dev/null').split() + if not devices: + raise Exception("Not valid rocm device found.") + dev_num = len(devices) + else: + raise Exception("Unrecognized backend: %s" % backend) + print(' >> Tuning parallism = %d' % dev_num) + return dev_num + +def local_get_dir_file(rel_file, dir_sid=None): + if not dir_sid: + dir_sid = os.environ['DIR_SID'] if 'DIR_SID' in os.environ else '_' + dir_space = '/tmp/tvm_autotvm_engine' + os.system('mkdir -p "%s/%s"' % (dir_space, dir_sid)) + return "%s/%s/%s" % (dir_space, dir_sid, rel_file) + +def run_process_with_timeout(args, timeout=None, envs=None): + try: + proc = subprocess.Popen(args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, env=envs) + retcode = proc.wait(timeout=timeout) + return retcode == 0 + except subprocess.TimeoutExpired: + print('Timed out - killing', proc.pid) + proc.kill() + return False + +def parse_launch_bounds(code): + func_arr = code.split('extern "C" __global__ ') + for i in range(1, len(func_arr)): + axis_map = dict() + lines = func_arr[i].split('\n') + for it in lines: + if it.startswith(' // [thread_extent] '): + words = it.split(' ') + nthread = int(words[-1]) + axis = words[-3] + if axis in axis_map: + if axis_map[axis] != nthread: + assert(False) + else: + axis_map[axis] = nthread + block_bound = axis_map.get('threadIdx.x', 1) * axis_map.get('threadIdx.y', 1) * axis_map.get('threadIdx.z', 1) + func_arr[i] = 'extern "C" __global__ __launch_bounds__(%d) %s' % (block_bound, func_arr[i]) + + code = ''.join(func_arr) + return code + +def translate_code(code): + if backend == 'c-rocm': + code = parse_launch_bounds(code) + code = '#include \n#include \n\n'+ code.replace('(__shared__ float4*)', '(float4*)').replace('#include ', '').replace('typedef unsigned long long uint64_t;', '') + elif backend in ['#cuda', 'c-cuda']: + code = parse_launch_bounds(code) + code = '#include \n#include \n\n' + code + else: + raise Exception("Unrecognized backend: %s" % backend) + return code + +@tvm.register_func +def tvm_callback_backend_proc(code): + native_code = translate_code(code) + # Compile code + module_data = None + if backend == 'c-rocm': + gcn_arch = subprocess.getoutput('/opt/rocm/bin/rocm_agent_enumerator | sort | uniq | grep -v gfx000 | tail -n 1').strip() + if not gcn_arch: + raise RuntimeError("Compilation error: no valid gcn_arch gpu detected!") + temp_code = local_get_dir_file("my_kernel.cc") + temp_cobj = local_get_dir_file("my_kernel.hsaco") + args = ['/opt/rocm/bin/lpl', temp_code, '-t=' + gcn_arch, '-f="-Wno-ignored-attributes -D__HIP_PLATFORM_HCC__=1"', '-o', temp_cobj] + elif backend in ['#cuda', 'c-cuda']: + temp_code = local_get_dir_file("my_kernel.cu") + temp_cobj = local_get_dir_file("my_kernel.ptx") + args = ['/usr/local/cuda/bin/nvcc', temp_code, '--ptx', '-O3', '-o', temp_cobj] + else: + raise Exception("Unrecognized backend: %s" % backend) + with open(temp_code, 'w') as fp: + fp.write(native_code) + print('[Build @%x]' % os.getpid(), ' '.join(args)) + if not run_process_with_timeout(args, 10): + raise Exception("Compilation failed or time limit exceeded") + if module_data is None: + module_data = bytearray(open(temp_cobj, "rb").read()) + return module_data + +def run_config_entity(params_given, dir_sid, expected_timecost='inf', tune_slot_id=0): + dir_sid = str(dir_sid) + result_file = local_get_dir_file('result.txt', dir_sid) + try: + os.remove(result_file) + except: + pass + config_str = json.dumps(params_given) + envs = os.environ.copy() + envs['CONFIG'] = config_str + envs['DIR_SID'] = dir_sid + envs['CUDA_VISIBLE_DEVICES'] = str(tune_slot_id) + print(" >> Try param_entity on sid = %s: config = %s, slot_id = %d" % (dir_sid, config_str, tune_slot_id)) + try: + assert(True == run_process_with_timeout(["python%d" % sys.version_info.major] + sys.argv, envs=envs)) + result = float(open(result_file, 'r').read().strip()) + except: + result = float('inf') + print(" >> Try param_entity on sid = %s: result = `%.6f`" % (dir_sid, result)) + return result + +def compute_gflops(flop, t): + return flop / (t * 1e3) / 1e6 + +def search_op_config(code_only=False): + tvm_target = 'cuda' + logging.getLogger('autotvm').setLevel(logging.DEBUG) + logging.getLogger('autotvm').addHandler(logging.StreamHandler(sys.stdout)) + + default_tune_op = importlib.import_module('templates.' + (os.environ['OP'])) + print(' >> Backend = %s, Python PID = %s, Task = %s;' % (backend, os.getpid(), default_tune_op.__name__)) + + task = autotvm.task.create(default_tune_op.get_template_op, args=(), target=tvm_target) + op_attributes = default_tune_op.op_attributes + op_summary = '_'.join([k + str(op_attributes[k]) for k in op_attributes]) + + def json_to_config(json_dict): + config = ConfigEntity.from_json_dict({"i": -1, "t": "", "c": None, "e": json_dict}) + return config + + def config_to_json(config): + jobj = config.to_json_dict()['e'] + json_dict = dict() + for i in range(len(jobj)): + assert(jobj[i][1] in ['sp', 'ot']) + json_dict[jobj[i][0]] = jobj[i][2] + return json_dict + + num_trials = int(os.environ['STEP']) if 'STEP' in os.environ else 0 + + if 'CONFIG' in os.environ: + params_given = json.loads(os.environ['CONFIG']) + print("====>> [Current Config Option]", os.environ['CONFIG']) + + trial_config = [] + for key in params_given: + trial_config.append([key, "sp" if type(params_given[key]) is list else "ot", params_given[key]]) + best_config = json_to_config(trial_config) + + elif 'NNI_TRIAL_JOB_ID' in os.environ: + show_search_space(task.config_space, os.environ['NNI_TRIAL_JOB_ID'] == '@') + import nni + params_given = nni.get_next_parameter() + if params_given is None: + raise + local_dir_id = os.environ['NNI_TRIAL_JOB_ID'] + t = run_config_entity(params_given, local_dir_id) + gflops = compute_gflops(task.flop, t) + print('[TVM-engine] Final entity result is: %g' % gflops) + try: + nni.report_final_result(gflops) + except: + print('[TVM-engine] (not reporting final result to NNI.)') + exit(0) + + elif num_trials > 0: + n_parallel = 16 if 'BATCH' not in os.environ else int(os.environ['BATCH']) + measure_option = autotvm.measure_option( + builder=autotvm.LocalBuilder(n_parallel=n_parallel), + runner=autotvm.LocalRunner(repeat=3, min_repeat_ms=100, timeout=4) + ) + # if DO_TUNING: + tuner = autotvm.tuner.XGBTuner(task, num_threads=8) + + from concurrent.futures import ThreadPoolExecutor + thread_pool = ThreadPoolExecutor(max_workers=n_parallel) + + dev_num = get_tuning_parallism() + + def parse_configs(task, configs): + results = [] + futures = [] + expected_timecost = 'inf' + for i in range(len(configs)): + futures.append(thread_pool.submit(run_config_entity, config_to_json(configs[i]), i, expected_timecost, i % dev_num)) + for i in range(len(configs)): + t = futures[i].result() + if t < tuner.task.best_config[0]: + tuner.task.best_config = (t, configs[i]) + results.append(autotvm.measure.MeasureResult(costs=(t,), error_no=0, all_cost=i, timestamp=time.time())) + return results + + tuner.task.best_config = (float('inf'), None) + tuner.parse_configs = parse_configs + + tuner.tune(n_trial=num_trials, measure_option=measure_option, callbacks=[]) + assert(not math.isinf(tuner.task.best_config[0])) + best_config = tuner.task.best_config[1] + print('\n[Best Config]', json.dumps(config_to_json(best_config))) + else: + best_config = task.config_space + + with ApplyConfig(best_config): + with tvm.target.create(tvm_target): + s, arg_bufs = default_tune_op.get_template_op() + lower_source = str(tvm.lower(s, arg_bufs, simple_mode=True)) + + # Verify Source Code + assert(len(('\n' + lower_source).split('\nproduce ')) == 2) + lower_file = local_get_dir_file('my_kernel.lower') + with open(lower_file, 'w') as fp: + fp.write(lower_source) + + max_threads_per_block = tvm.ndarray.gpu(0).max_threads_per_block + max_shared_memory_per_block = tvm.ndarray.gpu(0).max_shared_memory_per_block + + thread_extents = subprocess.getoutput("cat '%s' | grep '^ *// attr.*iter_var.*thread_extent'" % (lower_file)).split('\n') + reserved_axes = dict({'threadIdx.x': None, 'threadIdx.y': None, 'threadIdx.z': None, 'blockIdx.x': None, 'blockIdx.y': None, 'blockIdx.z': None}) + for line in thread_extents: + thread_name = line.split('[iter_var(')[-1].split(',')[0] + if thread_name in reserved_axes: + thread_val = int(line.split('thread_extent = ')[-1]) + if reserved_axes[thread_name] is not None: + if reserved_axes[thread_name] != thread_val: + assert(False) + else: + reserved_axes[thread_name] = thread_val + else: + raise Exception("Invalid thread_axis name: %s" % thread_name) + + num_threads = 1 + for thread_name in ['threadIdx.x', 'threadIdx.y', 'threadIdx.z']: + if reserved_axes[thread_name] is not None: + num_threads *= reserved_axes[thread_name] + if num_threads > max_threads_per_block: + raise Exception("Invalid kernel code: using num_threads %d > max_threads_per_block %d" % (num_threads, max_threads_per_block)) + + allocate_shared = subprocess.getoutput("cat '%s' | grep 'allocate .*shared\[.*\]'" % (lower_file)).split('\n') + shared_memory_in_bytes = 0 + for line in allocate_shared: + if not line: + continue + parts = line.split('[') + assert(len(parts) == 2) + parts = parts[1].split(' * ') + assert(len(parts) == 2) + assert(parts[1][-1] == ']') + allocate_type = parts[0] + allocate_val = int(parts[1][:-1]) + if allocate_type in ['float32']: + shared_memory_in_bytes += allocate_val * 4 + else: + raise Exception("Unrecognized shared memory data type: %s" % allocate_type) + if shared_memory_in_bytes > max_shared_memory_per_block: + raise Exception("Invalid kernel code: using shared_memory_in_bytes %d > max_shared_memory_per_block %d" % (shared_memory_in_bytes, max_shared_memory_per_block)) + + func = tvm.build(s, arg_bufs, tvm_target, name='template_op') + + assert(len(func.imported_modules) == 1) + device_source = translate_code(func.imported_modules[0].get_source()) + + if code_only: + return device_source + + if lower_source and device_source: + tune_slot_id = 0 if 'CUDA_VISIBLE_DEVICES' not in os.environ else int(os.environ['CUDA_VISIBLE_DEVICES']) + exec_fd, _ = system_lock([tune_slot_id]) + gpu_id = 0 + ctx = tvm.context(tvm_target, gpu_id) + tensors, outs = [], [] + for arg in arg_bufs: + shape = [int(x) for x in arg.shape] + is_output = arg.op.__class__ != tvm.tensor.PlaceholderOp + from tvm._ffi.ndarray import empty + td = empty(shape, arg.dtype, ctx) + if is_output: + outs.append(td) + tensors.append(td) + + def timeout_handler(): + print("Error: Timeout during Kernel warmup") + os._exit(1) + + my_timer = Timer(10, timeout_handler, []) + my_timer.start() + # Warmup + func(*tensors) + tvm.ndarray.gpu(gpu_id).sync() + # Estimate + t_start = time.time() + func(*tensors) + tvm.ndarray.gpu(gpu_id).sync() + t_diff = time.time() - t_start + my_timer.cancel() + del my_timer + + num_runs = max(3, min(100, math.floor(1.0 / t_diff))) + timeout_seconds = math.ceil((num_runs + 5) * t_diff) + my_timer = Timer(timeout_seconds, timeout_handler, []) + my_timer.start() + timer_f = func.time_evaluator(func.entry_name, ctx, number=num_runs) + t = timer_f(*tensors).mean + my_timer.cancel() + exec_fd() + + gflops = compute_gflops(task.flop, t) + print("[TVM-engine] Average time cost of %d runs = %g ms, %g gflops." % (num_runs, t * 1e3, gflops)) + + with open(local_get_dir_file('result.txt'), 'w') as fp: + fp.write(str(t)) + + +if __name__ == '__main__': + try: + search_op_config() + except SystemExit: + sys.exit(0) + except: + import traceback + traceback.print_exc() diff --git a/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PNN/config_opevo.yml b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PNN/config_opevo.yml new file mode 100644 index 0000000000..5e2854e6d2 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PNN/config_opevo.yml @@ -0,0 +1,25 @@ +authorName: default +experimentName: BatchMatMul_B960N128K128M64PNN_OPEVO +trialConcurrency: 8 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: opevo.py + className: OpEvo + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + parents_size: 8 + offspring_size: 8 + mutate_rate: 0.5 +trial: + command: OP=batch_matmul B=960 N=128 K=128 M=64 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PNN/search_space.json b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PNN/search_space.json new file mode 100644 index 0000000000..ef533e6ece --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PNN/search_space.json @@ -0,0 +1 @@ +{"B": {"_type": "factor", "_value": [960, 2]}, "K": {"_type": "factor", "_value": [128, 3]}, "X": {"_type": "factor", "_value": [128, 4]}, "Y": {"_type": "factor", "_value": [64, 4]}} \ No newline at end of file diff --git a/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PTN/config_opevo.yml b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PTN/config_opevo.yml new file mode 100644 index 0000000000..a84b80a70d --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PTN/config_opevo.yml @@ -0,0 +1,25 @@ +authorName: default +experimentName: BatchMatMul_B960N128K128M64PTN_OPEVO +trialConcurrency: 8 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: opevo.py + className: OpEvo + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + parents_size: 8 + offspring_size: 8 + mutate_rate: 0.5 +trial: + command: OP=batch_matmul B=960 N=128 K=128 M=64 P=TN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PTN/search_space.json b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PTN/search_space.json new file mode 100644 index 0000000000..ef533e6ece --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K128M64PTN/search_space.json @@ -0,0 +1 @@ +{"B": {"_type": "factor", "_value": [960, 2]}, "K": {"_type": "factor", "_value": [128, 3]}, "X": {"_type": "factor", "_value": [128, 4]}, "Y": {"_type": "factor", "_value": [64, 4]}} \ No newline at end of file diff --git a/examples/trials/systems/opevo/src/experiments/bmm/B960N128K64M128PNT/config_opevo.yml b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K64M128PNT/config_opevo.yml new file mode 100644 index 0000000000..36e04b4da3 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K64M128PNT/config_opevo.yml @@ -0,0 +1,25 @@ +authorName: default +experimentName: BatchMatMul_B960N128K64M128PNT_OPEVO +trialConcurrency: 8 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: opevo.py + className: OpEvo + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + parents_size: 8 + offspring_size: 8 + mutate_rate: 0.5 +trial: + command: OP=batch_matmul B=960 N=128 K=64 M=128 P=NT ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/bmm/B960N128K64M128PNT/search_space.json b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K64M128PNT/search_space.json new file mode 100644 index 0000000000..a34020c349 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/bmm/B960N128K64M128PNT/search_space.json @@ -0,0 +1 @@ +{"B": {"_type": "factor", "_value": [960, 2]}, "K": {"_type": "factor", "_value": [64, 3]}, "X": {"_type": "factor", "_value": [128, 4]}, "Y": {"_type": "factor", "_value": [128, 4]}} \ No newline at end of file diff --git a/examples/trials/systems/opevo/src/experiments/conv/N512C3HW227F64K11ST4PD0/config_opevo.yml b/examples/trials/systems/opevo/src/experiments/conv/N512C3HW227F64K11ST4PD0/config_opevo.yml new file mode 100644 index 0000000000..d1b88e883c --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/conv/N512C3HW227F64K11ST4PD0/config_opevo.yml @@ -0,0 +1,25 @@ +authorName: default +experimentName: Conv_N512C3HW227F64K11ST4PD0_OPEVO +trialConcurrency: 8 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: opevo.py + className: OpEvo + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + parents_size: 8 + offspring_size: 8 + mutate_rate: 0.5 +trial: + command: OP=convfwd_direct N=512 C=3 H=227 W=227 F=64 K=11 ST=4 PD=0 ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/conv/N512C3HW227F64K11ST4PD0/search_space.json b/examples/trials/systems/opevo/src/experiments/conv/N512C3HW227F64K11ST4PD0/search_space.json new file mode 100644 index 0000000000..7f818119b9 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/conv/N512C3HW227F64K11ST4PD0/search_space.json @@ -0,0 +1 @@ +{"tile_f": {"_type": "factor", "_value": [64, 4]}, "tile_y": {"_type": "factor", "_value": [55, 4]}, "tile_x": {"_type": "factor", "_value": [55, 4]}, "tile_rc": {"_type": "factor", "_value": [3, 2]}, "tile_ry": {"_type": "factor", "_value": [11, 2]}, "tile_rx": {"_type": "factor", "_value": [11, 2]}, "auto_unroll_max_step": {"_type": "discrete", "_value": [0, 125, 256]}, "unroll_explicit": {"_type": "choice", "_value": [0, 1]}} diff --git a/examples/trials/systems/opevo/src/experiments/conv/N512C64HW27F192K5ST1PD2/config_opevo.yml b/examples/trials/systems/opevo/src/experiments/conv/N512C64HW27F192K5ST1PD2/config_opevo.yml new file mode 100644 index 0000000000..1f84af8d67 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/conv/N512C64HW27F192K5ST1PD2/config_opevo.yml @@ -0,0 +1,25 @@ +authorName: default +experimentName: Conv_N512C64HW27F192K5ST1PD2_OPEVO +trialConcurrency: 8 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: opevo.py + className: OpEvo + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + parents_size: 8 + offspring_size: 8 + mutate_rate: 0.5 +trial: + command: OP=convfwd_direct N=512 C=64 H=27 W=27 F=192 K=5 ST=1 PD=2 ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/conv/N512C64HW27F192K5ST1PD2/search_space.json b/examples/trials/systems/opevo/src/experiments/conv/N512C64HW27F192K5ST1PD2/search_space.json new file mode 100644 index 0000000000..ee2e1fc2ba --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/conv/N512C64HW27F192K5ST1PD2/search_space.json @@ -0,0 +1 @@ +{"tile_f": {"_type": "factor", "_value": [192, 4]}, "tile_y": {"_type": "factor", "_value": [27, 4]}, "tile_x": {"_type": "factor", "_value": [27, 4]}, "tile_rc": {"_type": "factor", "_value": [64, 2]}, "tile_ry": {"_type": "factor", "_value": [5, 2]}, "tile_rx": {"_type": "factor", "_value": [5, 2]}, "auto_unroll_max_step": {"_type": "discrete", "_value": [0, 125, 256]}, "unroll_explicit": {"_type": "choice", "_value": [0, 1]}} diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_gbfs.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_gbfs.yml new file mode 100644 index 0000000000..b24388fc59 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_gbfs.yml @@ -0,0 +1,23 @@ +authorName: default +experimentName: MatMul_N512K1024M1024_GBFS +trialConcurrency: 5 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: gbfs.py + className: G_BFS + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + num_samples: 5 +trial: + command: OP=matmul N=512 K=1024 M=1024 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_na2c.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_na2c.yml new file mode 100644 index 0000000000..e7eb2df09e --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_na2c.yml @@ -0,0 +1,22 @@ +authorName: default +experimentName: MatMul_N512K1024M1024_NA2C +trialConcurrency: 6 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: na2c.py + className: N_A2C + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize +trial: + command: OP=matmul N=512 K=1024 M=1024 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_opevo.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_opevo.yml new file mode 100644 index 0000000000..bf04dbbf6b --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/config_opevo.yml @@ -0,0 +1,25 @@ +authorName: default +experimentName: MatMul_N512K1024M1024_OPEVO +trialConcurrency: 8 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: opevo.py + className: OpEvo + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + parents_size: 8 + offspring_size: 8 + mutate_rate: 0.5 +trial: + command: OP=matmul N=512 K=1024 M=1024 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/search_space.json b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/search_space.json new file mode 100644 index 0000000000..af331ca6cb --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M1024/search_space.json @@ -0,0 +1 @@ +{"K": {"_type": "factor", "_value": [1024, 3]}, "X": {"_type": "factor", "_value": [512, 4]}, "Y": {"_type": "factor", "_value": [1024, 4]}} diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_gbfs.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_gbfs.yml new file mode 100644 index 0000000000..1a26abf2ec --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_gbfs.yml @@ -0,0 +1,23 @@ +authorName: default +experimentName: MatMul_N512K1024M4096_GBFS +trialConcurrency: 5 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: gbfs.py + className: G_BFS + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + num_samples: 5 +trial: + command: OP=matmul N=512 K=1024 M=4096 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_na2c.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_na2c.yml new file mode 100644 index 0000000000..6defbf2072 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_na2c.yml @@ -0,0 +1,22 @@ +authorName: default +experimentName: MatMul_N512K1024M4096_NA2C +trialConcurrency: 6 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: na2c.py + className: N_A2C + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize +trial: + command: OP=matmul N=512 K=1024 M=4096 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_opevo.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_opevo.yml new file mode 100644 index 0000000000..6bb7b83065 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/config_opevo.yml @@ -0,0 +1,25 @@ +authorName: default +experimentName: MatMul_N512K1024M4096_OPEVO +trialConcurrency: 8 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: opevo.py + className: OpEvo + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + parents_size: 8 + offspring_size: 8 + mutate_rate: 0.5 +trial: + command: OP=matmul N=512 K=1024 M=4096 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/search_space.json b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/search_space.json new file mode 100644 index 0000000000..fd7d449107 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K1024M4096/search_space.json @@ -0,0 +1 @@ +{"K": {"_type": "factor", "_value": [1024, 3]}, "X": {"_type": "factor", "_value": [512, 4]}, "Y": {"_type": "factor", "_value": [4096, 4]}} \ No newline at end of file diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_gbfs.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_gbfs.yml new file mode 100644 index 0000000000..1de78a51f9 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_gbfs.yml @@ -0,0 +1,23 @@ +authorName: default +experimentName: MatMul_N512K4096M1024_GBFS +trialConcurrency: 5 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: gbfs.py + className: G_BFS + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + num_samples: 5 +trial: + command: OP=matmul N=512 K=4096 M=1024 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_na2c.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_na2c.yml new file mode 100644 index 0000000000..2ba096ce60 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_na2c.yml @@ -0,0 +1,22 @@ +authorName: default +experimentName: MatMul_N512K4096M1024_NA2C +trialConcurrency: 6 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: na2c.py + className: N_A2C + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize +trial: + command: OP=matmul N=512 K=4096 M=1024 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_opevo.yml b/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_opevo.yml new file mode 100644 index 0000000000..eb07e78b48 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/config_opevo.yml @@ -0,0 +1,25 @@ +authorName: default +experimentName: MatMul_N512K4096M1024_OPEVO +trialConcurrency: 8 +maxExecDuration: 24h +maxTrialNum: 512 +#choice: local, remote, pai +trainingServicePlatform: local +searchSpacePath: search_space.json +#choice: true, false +useAnnotation: false +tuner: + codeDir: /root/algorithms/ + classFileName: opevo.py + className: OpEvo + # Any parameter need to pass to your tuner class __init__ constructor + # can be specified in this optional classArgs field, for example + classArgs: + optimize_mode: maximize + parents_size: 8 + offspring_size: 8 + mutate_rate: 0.5 +trial: + command: OP=matmul N=512 K=4096 M=1024 P=NN ./run.sh + codeDir: /root + # gpuNum: 0 diff --git a/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/search_space.json b/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/search_space.json new file mode 100644 index 0000000000..3dce726458 --- /dev/null +++ b/examples/trials/systems/opevo/src/experiments/mm/N512K4096M1024/search_space.json @@ -0,0 +1 @@ +{"K": {"_type": "factor", "_value": [4096, 3]}, "X": {"_type": "factor", "_value": [512, 4]}, "Y": {"_type": "factor", "_value": [1024, 4]}} \ No newline at end of file diff --git a/examples/trials/systems/opevo/src/run.sh b/examples/trials/systems/opevo/src/run.sh new file mode 100644 index 0000000000..3b4c484ed0 --- /dev/null +++ b/examples/trials/systems/opevo/src/run.sh @@ -0,0 +1,25 @@ +#!/bin/bash -e + +cd $(dirname $0) + +export BACKEND=${BACKEND:-c-cuda} + +if [[ "${BACKEND}" == "c-cuda" ]]; then + export BACKEND="#cuda" +fi + +if [[ "${BACKEND}" != "#cuda" ]]; then + export LD_LIBRARY_PATH=/opt/tvm/build +else + export LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 +fi + +export HIP_PLATFORM=hcc +export HSA_USERPTR_FOR_PAGED_MEM=0 +export PYTHONDONTWRITEBYTECODE=1 +export PYTHONPATH=/opt/tvm/python:/opt/tvm/topi/python:/opt/tvm/nnvm/python:/usr/local/rocm/src + +ldconfig + +time OP=${OP:-matmul} S=${S:-0} python3 ./compiler_auto_tune_stable.py "$@" + diff --git a/examples/trials/systems/opevo/src/templates/batch_matmul.py b/examples/trials/systems/opevo/src/templates/batch_matmul.py new file mode 100644 index 0000000000..4c84fbf56a --- /dev/null +++ b/examples/trials/systems/opevo/src/templates/batch_matmul.py @@ -0,0 +1,119 @@ +import numpy as np +import tvm +import logging +import sys, time, subprocess +from tvm import autotvm +import topi +import json +from topi.util import get_const_tuple +import os + + +op_attributes = { + "B": int(os.environ['B']) if 'B' in os.environ else 6, + "N": int(os.environ['N']) if 'N' in os.environ else 1024, + "K": int(os.environ['K']) if 'K' in os.environ else 64, + "M": int(os.environ['M']) if 'M' in os.environ else 4096, + "P": os.environ['P'] if 'P' in os.environ else "NN", +} + +@autotvm.template +def get_template_op(**kargs): + batch = op_attributes["B"] + M = op_attributes["N"] + K = op_attributes["K"] + N = op_attributes["M"] + pose = op_attributes["P"] + + if pose == 'NN': + A = tvm.placeholder((batch, M, K), name='A', dtype="float32") + B = tvm.placeholder((batch, K, N), name='B', dtype="float32") + k = tvm.reduce_axis((0, K), name='k') + C = tvm.compute((batch, M, N), lambda b, i, j: tvm.sum( + A[b, i, k] * B[b, k, j], axis=k), name='C') + elif pose == 'NT': + A = tvm.placeholder((batch, M, K), name='A', dtype="float32") + B = tvm.placeholder((batch, N, K), name='B', dtype="float32") + k = tvm.reduce_axis((0, K), name='k') + C = tvm.compute((batch, M, N), lambda b, i, j: tvm.sum( + A[b, i, k] * B[b, j, k], axis=k), name='C') + elif pose == 'TN': + A = tvm.placeholder((batch, K, M), name='A', dtype="float32") + B = tvm.placeholder((batch, K, N), name='B', dtype="float32") + k = tvm.reduce_axis((0, K), name='k') + C = tvm.compute((batch, M, N), lambda b, i, j: tvm.sum( + A[b, k, i] * B[b, k, j], axis=k), name='C') + elif pose == 'TT': + A = tvm.placeholder((batch, K, M), name='A', dtype="float32") + B = tvm.placeholder((batch, N, K), name='B', dtype="float32") + k = tvm.reduce_axis((0, K), name='k') + C = tvm.compute((batch, M, N), lambda b, i, j: tvm.sum( + A[b, k, i] * B[b, j, k], axis=k), name='C') + else: + raise + + cfg = autotvm.get_config() + s = tvm.create_schedule(C.op) + AA = s.cache_read(A, "shared", [C]) + AL = s.cache_read(AA, "local", [C]) + BB = s.cache_read(B, "shared", [C]) + BL = s.cache_read(BB, "local", [C]) + CC = s.cache_write(C, "local") + + b, y, x = C.op.axis + k = CC.op.reduce_axis[0] + + cfg.define_split('B', cfg.axis(b), num_outputs=2) + bo, bi = cfg['B'].apply(s, C, b) + + cfg.define_split('K', cfg.axis(k), num_outputs=3) + ko, kt, ki = cfg['K'].apply(s, CC, k) + + block_x = tvm.thread_axis('blockIdx.x') + block_y = tvm.thread_axis('blockIdx.y') + block_z = tvm.thread_axis('blockIdx.z') + thread_x = tvm.thread_axis('threadIdx.x') + thread_y = tvm.thread_axis('threadIdx.y') + thread_z = tvm.thread_axis('threadIdx.z') + + cfg.define_split('X', cfg.axis(y), num_outputs=4) + cfg.define_split('Y', cfg.axis(x), num_outputs=4) + + by, tyz, ty, yi = cfg['X'].apply(s, C, y) + bx, txz, tx, xi = cfg['Y'].apply(s, C, x) + + s[C].bind(bo, block_z) + s[C].bind(by, block_y) + s[C].bind(bx, block_x) + s[C].bind(tyz, tvm.thread_axis('vthread')) + s[C].bind(txz, tvm.thread_axis('vthread')) + s[C].bind(bi, thread_z) + s[C].bind(ty, thread_y) + s[C].bind(tx, thread_x) + s[C].reorder(by, bx, tyz, txz, ty, tx, yi, xi) + + s[CC].compute_at(s[C], tx) + + bo, yo, xo = CC.op.axis + s[CC].reorder(ko, kt, yo, xo, ki) + s[CC].unroll(kt) + + for stage in [AL, BL]: + s[stage].compute_at(s[CC], kt) + s[stage].double_buffer() + + for stage in [AA, BB]: + s[stage].compute_at(s[CC], ko) + + fused = s[stage].fuse(*s[stage].op.axis) + ty, tx = s[stage].split(fused, nparts=cfg['X'].size[2]) + tx, xi = s[stage].split(tx, nparts=cfg['Y'].size[2]) + _, xi = s[stage].split(xi, factor=4) + + s[stage].bind(ty, thread_y) + s[stage].bind(tx, thread_x) + s[stage].vectorize(xi) + s[stage].double_buffer() + + cfg.add_flop(batch * M * K * N * 2.0) + return s, [A, B, C] diff --git a/examples/trials/systems/opevo/src/templates/convfwd_direct.py b/examples/trials/systems/opevo/src/templates/convfwd_direct.py new file mode 100644 index 0000000000..5f40abff80 --- /dev/null +++ b/examples/trials/systems/opevo/src/templates/convfwd_direct.py @@ -0,0 +1,130 @@ +import numpy as np +import tvm +import logging +import sys, time, subprocess +from tvm import autotvm +import topi +import json +from topi.util import get_const_tuple +import os + + +op_attributes = { + "N": int(os.environ['N']) if 'N' in os.environ else 64, + "C": int(os.environ['C']) if 'C' in os.environ else 3, + "H": int(os.environ['H']) if 'H' in os.environ else 229, + "W": int(os.environ['W']) if 'W' in os.environ else 229, + "F": int(os.environ['F']) if 'F' in os.environ else 32, + "K": int(os.environ['K']) if 'K' in os.environ else 5, + "ST": int(os.environ['ST']) if 'ST' in os.environ else 1, + "PD": int(os.environ['PD']) if 'PD' in os.environ else 2, +} + + +@autotvm.template +def get_template_op(**kargs): + N = op_attributes["N"] + CI = op_attributes["C"] + H = op_attributes["H"] + W = op_attributes["W"] + H = op_attributes["H"] + CO = op_attributes["F"] + KH = KW = op_attributes["K"] + stride = op_attributes["ST"] + padding = op_attributes["PD"] + dilation = 1 + + data = tvm.placeholder((N, CI, H, W), name='data') + kernel = tvm.placeholder((CO, CI, KH, KW), name='kernel') + conv = topi.nn.conv2d_nchw( + data, kernel, (stride, stride), (padding, padding), dilation=1, out_dtype='float32') + s = tvm.create_schedule([conv.op]) + cfg = autotvm.get_config() + + ##### space definition begin ##### + n, f, y, x = s[conv].op.axis + rc, ry, rx = s[conv].op.reduce_axis + cfg.define_split("tile_f", f, num_outputs=4) + cfg.define_split("tile_y", y, num_outputs=4) + cfg.define_split("tile_x", x, num_outputs=4) + cfg.define_split("tile_rc", rc, num_outputs=2) + cfg.define_split("tile_ry", ry, num_outputs=2) + cfg.define_split("tile_rx", rx, num_outputs=2) + cfg.define_knob("auto_unroll_max_step", [0, 125, 256]) + + target = tvm.target.current_target() + if target.target_name in ['nvptx', 'rocm']: + cfg.define_knob("unroll_explicit", [1]) + else: + cfg.define_knob("unroll_explicit", [0, 1]) + + pad_data, kernel = s[conv].op.input_tensors + + s[pad_data].compute_inline() + if isinstance(kernel.op, tvm.tensor.ComputeOp) and 'dilate' in kernel.op.tag: + s[kernel].compute_inline() + + if conv.op in s.outputs: + output = conv + OL = s.cache_write(conv, 'local') + else: + output = s.outputs[0].output(0) + s[conv].set_scope('local') + OL = conv + + # create cache stage + AA = s.cache_read(pad_data, 'shared', [OL]) + WW = s.cache_read(kernel, 'shared', [OL]) + + # tile and bind spatial axes + n, f, y, x = s[output].op.axis + kernel_scope, n = s[output].split(n, nparts=1) + + bf, vf, tf, fi = cfg["tile_f"].apply(s, output, f) + by, vy, ty, yi = cfg["tile_y"].apply(s, output, y) + bx, vx, tx, xi = cfg["tile_x"].apply(s, output, x) + + bf = s[output].fuse(n, bf) + s[output].bind(bf, tvm.thread_axis("blockIdx.z")) + s[output].bind(by, tvm.thread_axis("blockIdx.y")) + s[output].bind(bx, tvm.thread_axis("blockIdx.x")) + s[output].bind(vf, tvm.thread_axis("vthread")) + s[output].bind(vy, tvm.thread_axis("vthread")) + s[output].bind(vx, tvm.thread_axis("vthread")) + s[output].bind(tf, tvm.thread_axis("threadIdx.z")) + s[output].bind(ty, tvm.thread_axis("threadIdx.y")) + s[output].bind(tx, tvm.thread_axis("threadIdx.x")) + s[output].reorder(bf, by, bx, vf, vy, vx, tf, ty, tx, fi, yi, xi) + s[OL].compute_at(s[output], tx) + + # tile reduction axes + n, f, y, x = s[OL].op.axis + rc, ry, rx = s[OL].op.reduce_axis + rco, rci = cfg['tile_rc'].apply(s, OL, rc) + ryo, ryi = cfg['tile_rx'].apply(s, OL, ry) + rxo, rxi = cfg['tile_ry'].apply(s, OL, rx) + s[OL].reorder(rco, ryo, rxo, rci, ryi, rxi, n, f, y, x) + + s[AA].compute_at(s[OL], rxo) + s[WW].compute_at(s[OL], rxo) + + # cooperative fetching + for load in [AA, WW]: + n, f, y, x = s[load].op.axis + fused = s[load].fuse(n, f, y, x) + tz, fused = s[load].split(fused, nparts=cfg["tile_f"].size[2]) + ty, fused = s[load].split(fused, nparts=cfg["tile_y"].size[2]) + tx, fused = s[load].split(fused, nparts=cfg["tile_x"].size[2]) + s[load].bind(tz, tvm.thread_axis("threadIdx.z")) + s[load].bind(ty, tvm.thread_axis("threadIdx.y")) + s[load].bind(tx, tvm.thread_axis("threadIdx.x")) + + # unroll + s[output].pragma(kernel_scope, 'auto_unroll_max_step', cfg['auto_unroll_max_step'].val) + s[output].pragma(kernel_scope, 'unroll_explicit', cfg['unroll_explicit'].val) + + N, CO, OH, OW = get_const_tuple(output.shape) + _, KH, KW, CI = get_const_tuple(kernel.shape) + + cfg.add_flop(2 * N * OH * OW * CO * CI * KH * KW) + return s, [data, kernel, conv] diff --git a/examples/trials/systems/opevo/src/templates/matmul.py b/examples/trials/systems/opevo/src/templates/matmul.py new file mode 100644 index 0000000000..7cbd75c458 --- /dev/null +++ b/examples/trials/systems/opevo/src/templates/matmul.py @@ -0,0 +1,111 @@ +import numpy as np +import tvm +import logging +import sys, time, subprocess +from tvm import autotvm +import topi +import json +from topi.util import get_const_tuple +import os + + +op_attributes = { + "N": int(os.environ['N']) if 'N' in os.environ else 1024, + "K": int(os.environ['K']) if 'K' in os.environ else 64, + "M": int(os.environ['M']) if 'M' in os.environ else 4096, + "P": os.environ['P'] if 'P' in os.environ else "NN", +} + +@autotvm.template +def get_template_op(**kargs): + batch = op_attributes["N"] + in_dim = op_attributes["K"] + out_dim = op_attributes["M"] + pose = op_attributes["P"] + + if pose == 'NN': + A = tvm.placeholder((batch, in_dim), name='A', dtype="float32") + B = tvm.placeholder((in_dim, out_dim), name='B', dtype="float32") + k = tvm.reduce_axis((0, in_dim), name='k') + C = tvm.compute((batch, out_dim), lambda i, j: tvm.sum( + A[i, k] * B[k, j], axis=k), name='C') + elif pose == 'NT': + A = tvm.placeholder((batch, in_dim), name='A', dtype="float32") + B = tvm.placeholder((out_dim, in_dim), name='B', dtype="float32") + k = tvm.reduce_axis((0, in_dim), name='k') + C = tvm.compute((batch, out_dim), lambda i, j: tvm.sum( + A[i, k] * B[j, k], axis=k), name='C') + elif pose == 'TN': + A = tvm.placeholder((in_dim, batch), name='A', dtype="float32") + B = tvm.placeholder((in_dim, out_dim), name='B', dtype="float32") + k = tvm.reduce_axis((0, in_dim), name='k') + C = tvm.compute((batch, out_dim), lambda i, j: tvm.sum( + A[k, i] * B[k, j], axis=k), name='C') + elif pose == 'TT': + A = tvm.placeholder((in_dim, batch), name='A', dtype="float32") + B = tvm.placeholder((out_dim, in_dim), name='B', dtype="float32") + k = tvm.reduce_axis((0, in_dim), name='k') + C = tvm.compute((batch, out_dim), lambda i, j: tvm.sum( + A[k, i] * B[j, k], axis=k), name='C') + else: + raise + + cfg = autotvm.get_config() + s = tvm.create_schedule(C.op) + + cfg.add_flop(batch * in_dim * out_dim * 2.0) + + AA = s.cache_read(A, "shared", [C]) + AL = s.cache_read(AA, "local", [C]) + BB = s.cache_read(B, "shared", [C]) + BL = s.cache_read(BB, "local", [C]) + CC = s.cache_write(C, "local") + + y, x = C.op.axis + k = CC.op.reduce_axis[0] + + cfg.define_split('K', cfg.axis(k), num_outputs=3) + cfg.define_split('X', cfg.axis(y), num_outputs=4) + cfg.define_split('Y', cfg.axis(x), num_outputs=4) + + ko, kt, ki = cfg['K'].apply(s, CC, k) + + block_x = tvm.thread_axis('blockIdx.x') + block_y = tvm.thread_axis('blockIdx.y') + thread_x = tvm.thread_axis('threadIdx.x') + thread_y = tvm.thread_axis('threadIdx.y') + + by, tyz, ty, yi = cfg['X'].apply(s, C, y) + bx, txz, tx, xi = cfg['Y'].apply(s, C, x) + + s[C].bind(by, block_y) + s[C].bind(bx, block_x) + s[C].bind(tyz, tvm.thread_axis('vthread')) + s[C].bind(txz, tvm.thread_axis('vthread')) + s[C].bind(ty, thread_y) + s[C].bind(tx, thread_x) + s[C].reorder(by, bx, tyz, txz, ty, tx, yi, xi) + + s[CC].compute_at(s[C], tx) + + yo, xo = CC.op.axis + s[CC].reorder(ko, kt, yo, xo, ki) + s[CC].unroll(kt) + + for stage in [AL, BL]: + s[stage].compute_at(s[CC], kt) + + for stage in [AA, BB]: + s[stage].compute_at(s[CC], ko) + + fused = s[stage].fuse(*s[stage].op.axis) + ty, tx = s[stage].split(fused, nparts=cfg['X'].size[2]) + tx, xi = s[stage].split(tx, nparts=cfg['Y'].size[2]) + _, xi = s[stage].split(xi, factor=4) + + s[stage].bind(ty, thread_y) + s[stage].bind(tx, thread_x) + s[stage].vectorize(xi) + s[stage].double_buffer() + + return s, [A, B, C] diff --git a/examples/trials/systems/opevo/tvm_patches/libcuda.so.1 b/examples/trials/systems/opevo/tvm_patches/libcuda.so.1 new file mode 100644 index 0000000000000000000000000000000000000000..8417946346afe0b7629f59f4a96ba5b6dac257ab GIT binary patch literal 46448 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zDZQ)u%8#x?U2FU5Is8r6iUwDTB>JuDtMr3}`qAh0yG0c`U(b4}dMf=4g>mio=uEz; zhm&ZW6i`h63Qk95;-&gO*rRjNR#jgWFjmthw9Kpsq#HW z$gcxEB6IX4k)DC19Us+i6~AbFCWy!t_Wg(xEh8a-sdh!F_{~09{rz_c3VM#;dL@9` F{|3tT);9nE literal 0 HcmV?d00001 diff --git a/examples/trials/systems/opevo/tvm_patches/tvm_v0.6.patch b/examples/trials/systems/opevo/tvm_patches/tvm_v0.6.patch new file mode 100644 index 0000000000..e290b230e1 --- /dev/null +++ b/examples/trials/systems/opevo/tvm_patches/tvm_v0.6.patch @@ -0,0 +1,84 @@ +diff --git a/python/tvm/autotvm/tuner/tuner.py b/python/tvm/autotvm/tuner/tuner.py +index 76d088f4c..7ed4ff02a 100644 +--- a/python/tvm/autotvm/tuner/tuner.py ++++ b/python/tvm/autotvm/tuner/tuner.py +@@ -122,7 +122,7 @@ class Tuner(object): + configs = self.next_batch(min(n_parallel, n_trial - i)) + + inputs = [MeasureInput(self.task.target, self.task, config) for config in configs] +- results = measure_batch(inputs) ++ results = self.parse_configs(self.task, configs) if hasattr(self, 'parse_configs') else measure_batch(inputs) + + # keep best config + for k, (inp, res) in enumerate(zip(inputs, results)): +diff --git a/src/codegen/codegen_c.cc b/src/codegen/codegen_c.cc +index eab542dd3..2f1a11303 100644 +--- a/src/codegen/codegen_c.cc ++++ b/src/codegen/codegen_c.cc +@@ -808,6 +808,7 @@ void CodeGenC::VisitStmt_(const AttrStmt* op) { + IterVar iv = Downcast(op->node); + if (iv->thread_tag.length() != 0) { + if (!var_idmap_.count(iv->var.get())) { ++ this->currentOp = op; + BindThreadIndex(iv); + } + } +diff --git a/src/codegen/codegen_c.h b/src/codegen/codegen_c.h +index 8701cda1e..7d3d56ddc 100644 +--- a/src/codegen/codegen_c.h ++++ b/src/codegen/codegen_c.h +@@ -174,6 +174,8 @@ class CodeGenC : + // Get a cast type from to + virtual std::string CastFromTo(std::string value, Type from, Type target); + ++ const AttrStmt* currentOp; ++ + protected: + // Print reference to struct location + std::string GetStructRef( +diff --git a/src/codegen/codegen_cuda.cc b/src/codegen/codegen_cuda.cc +index 6656fa077..a4f0f962d 100644 +--- a/src/codegen/codegen_cuda.cc ++++ b/src/codegen/codegen_cuda.cc +@@ -106,6 +106,9 @@ void CodeGenCUDA::BindThreadIndex(const IterVar& iv) { + CHECK(!var_idmap_.count(iv->var.get())); + var_idmap_[iv->var.get()] = + CastFromTo(iv->thread_tag, UInt(32), iv->var.type()); ++ int nthread = static_cast(this->currentOp->value.as()->value); ++ if (iv->thread_tag.find("threadIdx.") == 0 || iv->thread_tag.find("blockIdx.") == 0) ++ this->stream << " // [thread_extent] " << iv->thread_tag << " = " << nthread << "\n"; + } + + void CodeGenCUDA::PrintType(Type t, std::ostream& os) { // NOLINT(*) +diff --git a/src/codegen/opt/build_cuda_on.cc b/src/codegen/opt/build_cuda_on.cc +index 1992ac5d9..9b0ff4cd9 100644 +--- a/src/codegen/opt/build_cuda_on.cc ++++ b/src/codegen/opt/build_cuda_on.cc +@@ -137,6 +137,9 @@ runtime::Module BuildCUDA(Array funcs) { + cg.AddFunction(f); + } + std::string code = cg.Finish(); ++ const auto* backendproc = Registry::Get("tvm_callback_backend_proc"); ++ if (backendproc) ++ return CUDAModuleCreate((*backendproc)(code).operator std::string(), "cubin", ExtractFuncInfo(funcs), code); + + if (const auto* f = Registry::Get("tvm_callback_cuda_postproc")) { + code = (*f)(code).operator std::string(); +diff --git a/src/lang/expr_operator.cc b/src/lang/expr_operator.cc +index 220d4378c..cc435d138 100644 +--- a/src/lang/expr_operator.cc ++++ b/src/lang/expr_operator.cc +@@ -208,11 +208,11 @@ Expr operator%(Expr a, Expr b) { + + // TODO(tqchen): switch to floordiv + Expr indexdiv(Expr a, Expr b) { +- return floordiv(a, b); ++ return truncdiv(a, b); + } + + Expr indexmod(Expr a, Expr b) { +- return floormod(a, b); ++ return truncmod(a, b); + } + + Expr floordiv(Expr a, Expr b) { From 0b9d6ce6d1cf6515c5b553a61f78cd333bf4700f Mon Sep 17 00:00:00 2001 From: Chi Song <27178119+squirrelsc@users.noreply.github.com> Date: Tue, 30 Jun 2020 12:54:35 +0800 Subject: [PATCH 16/17] Reuse OpenPAI jobs to run multiple trials (#2521) Designed new interface to support reusable training service, currently only applies to OpenPAI, and default disabled. Replace trial_keeper.py to trial_runner.py, trial_runner holds an environment, and receives commands from nni manager to run or stop an trial, and return events to nni manager. Add trial dispatcher, which inherits from original trianing service interface. It uses to share as many as possible code of all training service, and isolate with training services. Add EnvironmentService interface to manage environment, including start/stop an environment, refresh status of environments. Add command channel on both nni manager and trial runner parts, it supports different ways to pass messages between them. Current supported channels are file, web sockets. and supported commands from nni manager are start, kill trial, send new parameters; from runner are initialized(support some channel doesn't know which runner connected), trial end, stdout ((new type), including metric like before), version check (new type), gpu info (new type). Add storage service to wrapper a storage to standard file operations, like NFS, azure storage and so on. Partial support run multiple trials in parallel on runner side, but not supported by trial dispatcher side. Other minor changes, Add log_level to TS UT, so that UT can show debug level log. Expose platform to start info. Add RouterTrainingService to keep origianl OpenPAI training service, and support dynamic IOC binding. Add more GPU info for future usage, including GPU mem total/free/used, gpu type. Make some license information consistence. Fix async/await problems on Array.forEach, this method doesn't support async actually. Fix IT errors on download data, which causes by my #2484 . Accelerate some run loop pattern by reducing sleep seconds. --- deployment/pypi/setup.py | 3 +- docs/en_US/TrainingService/PaiMode.md | 123 +++- docs/en_US/Tutorial/ExperimentConfig.md | 7 + setup.py | 3 +- .../common/experimentStartupInfo.ts | 24 +- src/nni_manager/common/utils.ts | 8 +- src/nni_manager/core/commands.ts | 22 + src/nni_manager/core/ipcInterface.ts | 2 +- src/nni_manager/main.ts | 44 +- src/nni_manager/package.json | 4 +- .../rest_server/restValidationSchemas.ts | 1 + .../training_service/common/gpuData.ts | 13 +- .../training_service/pai/paiConfig.ts | 5 +- .../pai/paiK8S/paiK8SConfig.ts | 20 +- .../training_service/pai/paiK8S/paiK8SData.ts | 20 +- .../pai/paiK8S/paiK8STrainingService.ts | 20 +- .../remoteMachineTrainingService.ts | 16 +- .../reusable/channels/fileCommandChannel.ts | 136 ++++ .../reusable/channels/webCommandChannel.ts | 126 ++++ .../reusable/commandChannel.ts | 129 ++++ .../training_service/reusable/environment.ts | 109 +++ .../environments/openPaiEnvironmentService.ts | 417 ++++++++++++ .../reusable/routerTrainingService.ts | 157 +++++ .../reusable/storageService.ts | 185 +++++ .../storages/mountedStorageService.ts | 148 ++++ .../training_service/reusable/trial.ts | 42 ++ .../reusable/trialDispatcher.ts | 643 ++++++++++++++++++ src/nni_manager/yarn.lock | 119 +--- src/sdk/pynni/requirements.txt | 1 + test/config/integration_tests.yml | 9 +- tools/nni_cmd/config_schema.py | 6 +- tools/nni_cmd/rest_utils.py | 4 +- tools/nni_trial_tool/base_channel.py | 154 +++++ tools/nni_trial_tool/commands.py | 25 + tools/nni_trial_tool/file_channel.py | 75 ++ tools/nni_trial_tool/gpu.py | 69 ++ tools/nni_trial_tool/log_utils.py | 42 +- tools/nni_trial_tool/test/__init__.py | 11 + .../nni_trial_tool/test/test_file_channel.py | 123 ++++ tools/nni_trial_tool/trial.py | 148 ++++ tools/nni_trial_tool/trial_keeper.py | 2 +- tools/nni_trial_tool/trial_runner.py | 247 +++++++ tools/nni_trial_tool/url_utils.py | 3 + tools/nni_trial_tool/web_channel.py | 53 ++ tools/setup.py | 3 +- 45 files changed, 3264 insertions(+), 257 deletions(-) create mode 100644 src/nni_manager/training_service/reusable/channels/fileCommandChannel.ts create mode 100644 src/nni_manager/training_service/reusable/channels/webCommandChannel.ts create mode 100644 src/nni_manager/training_service/reusable/commandChannel.ts create mode 100644 src/nni_manager/training_service/reusable/environment.ts create mode 100644 src/nni_manager/training_service/reusable/environments/openPaiEnvironmentService.ts create mode 100644 src/nni_manager/training_service/reusable/routerTrainingService.ts create mode 100644 src/nni_manager/training_service/reusable/storageService.ts create mode 100644 src/nni_manager/training_service/reusable/storages/mountedStorageService.ts create mode 100644 src/nni_manager/training_service/reusable/trial.ts create mode 100644 src/nni_manager/training_service/reusable/trialDispatcher.ts create mode 100644 tools/nni_trial_tool/base_channel.py create mode 100644 tools/nni_trial_tool/commands.py create mode 100644 tools/nni_trial_tool/file_channel.py create mode 100644 tools/nni_trial_tool/gpu.py create mode 100644 tools/nni_trial_tool/test/__init__.py create mode 100644 tools/nni_trial_tool/test/test_file_channel.py create mode 100644 tools/nni_trial_tool/trial.py create mode 100644 tools/nni_trial_tool/trial_runner.py create mode 100644 tools/nni_trial_tool/web_channel.py diff --git a/deployment/pypi/setup.py b/deployment/pypi/setup.py index 3c8bd4429f..61f7ff0178 100644 --- a/deployment/pypi/setup.py +++ b/deployment/pypi/setup.py @@ -64,7 +64,8 @@ 'coverage', 'colorama', 'scikit-learn>=0.20,<0.22', - 'pkginfo' + 'pkginfo', + 'websockets' ], classifiers = [ 'Programming Language :: Python :: 3', diff --git a/docs/en_US/TrainingService/PaiMode.md b/docs/en_US/TrainingService/PaiMode.md index 1b430cd4ad..a14ae25798 100644 --- a/docs/en_US/TrainingService/PaiMode.md +++ b/docs/en_US/TrainingService/PaiMode.md @@ -1,36 +1,46 @@ **Run an Experiment on OpenPAI** === -NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai) (aka pai), called pai mode. Before starting to use NNI pai mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In pai mode, your trial program will run in pai's container created by Docker. +NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai), called pai mode. Before starting to use NNI pai mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In pai mode, your trial program will run in pai's container created by Docker. ## Setup environment + Step 1. Install NNI, follow the install guide [here](../Tutorial/QuickStart.md). -Step 2. Get PAI token. -Click `My profile` button in the top-right side of PAI's webprotal. -![](../../img/pai_profile.jpg) +Step 2. Get token. + +Open web portal of OpenPAI, and click `My profile` button in the top-right side. +![](../../img/pai_profile.jpg) + Click `copy` button in the page to copy a jwt token. -![](../../img/pai_token.jpg) +![](../../img/pai_token.jpg) Step 3. Mount NFS storage to local machine. - Click `Submit job` button in PAI's webportal. -![](../../img/pai_job_submission_page.jpg) - Find the data management region in job submission page. + + Click `Submit job` button in web portal. +![](../../img/pai_job_submission_page.jpg) + + Find the data management region in job submission page. ![](../../img/pai_data_management_page.jpg) -The `Preview container paths` is the NFS host and path that PAI provided, you need to mount the corresponding host and path to your local machine first, then NNI could use the PAI's NFS storage. + +The `Preview container paths` is the NFS host and path that OpenPAI provided, you need to mount the corresponding host and path to your local machine first, then NNI could use the OpenPAI's NFS storage. For example, use the following command: -``` + +```bash sudo mount -t nfs4 gcr-openpai-infra02:/pai/data /local/mnt ``` + Then the `/data` folder in container will be mounted to `/local/mnt` folder in your local machine. You could use the following configuration in your NNI's config file: -``` + +```yaml nniManagerNFSMountPath: /local/mnt ``` -Step 4. Get PAI's storage config name and nniManagerMountPath -The `Team share storage` field is storage configuration used to specify storage value in PAI. You can get `paiStorageConfigName` and `containerNFSMountPath` field in `Team share storage`, for example: +Step 4. Get OpenPAI's storage config name and nniManagerMountPath -``` +The `Team share storage` field is storage configuration used to specify storage value in OpenPAI. You can get `paiStorageConfigName` and `containerNFSMountPath` field in `Team share storage`, for example: + +```yaml paiStorageConfigName: confignfs-data containerNFSMountPath: /mnt/confignfs-data ``` @@ -73,44 +83,86 @@ paiConfig: userName: your_pai_nni_user token: your_pai_token host: 10.1.1.1 + # optional, experimental feature. + reuse: true ``` -Note: You should set `trainingServicePlatform: pai` in NNI config YAML file if you want to start experiment in pai mode. The host field in configuration file is PAI's job submission page uri, like `10.10.5.1`, the default http protocol in NNI is `http`, if your PAI's cluster enabled https, please use the uri in `https://10.10.5.1` format. +Note: You should set `trainingServicePlatform: pai` in NNI config YAML file if you want to start experiment in pai mode. + +### Trial configurations + +Compared with [LocalMode](LocalMode.md) and [RemoteMachineMode](RemoteMachineMode.md), `trial` configuration in pai mode have these additional keys: -Compared with [LocalMode](LocalMode.md) and [RemoteMachineMode](RemoteMachineMode.md), trial configuration in pai mode have these additional keys: * cpuNum - * Optional key. Should be positive number based on your trial program's CPU requirement. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field. + + Optional key. Should be positive number based on your trial program's CPU requirement. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field. + * memoryMB - * Optional key. Should be positive number based on your trial program's memory requirement. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field. + + Optional key. Should be positive number based on your trial program's memory requirement. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field. + * image - * Optional key. In pai mode, your trial program will be scheduled by OpenPAI to run in [Docker container](https://www.docker.com/). This key is used to specify the Docker image used to create the container in which your trial will run. - * We already build a docker image [nnimsra/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](https://github.com/Microsoft/nni/tree/master/deployment/docker/Dockerfile). You can either use this image directly in your config file, or build your own image based on it. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field. + + Optional key. In pai mode, your trial program will be scheduled by OpenPAI to run in [Docker container](https://www.docker.com/). This key is used to specify the Docker image used to create the container in which your trial will run. + + We already build a docker image [nnimsra/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](https://github.com/Microsoft/nni/tree/master/deployment/docker/Dockerfile). You can either use this image directly in your config file, or build your own image based on it. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field. + * virtualCluster - * Optional key. Set the virtualCluster of OpenPAI. If omitted, the job will run on default virtual cluster. + + Optional key. Set the virtualCluster of OpenPAI. If omitted, the job will run on default virtual cluster. + * nniManagerNFSMountPath - * Required key. Set the mount path in your nniManager machine. + + Required key. Set the mount path in your nniManager machine. + * containerNFSMountPath - * Required key. Set the mount path in your container used in PAI. + + Required key. Set the mount path in your container used in OpenPAI. + * paiStorageConfigName: - * Optional key. Set the storage name used in PAI. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field. -* command - * Optional key. Set the commands used in PAI container. + + Optional key. Set the storage name used in OpenPAI. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field. + +* command + + Optional key. Set the commands used in OpenPAI container. + * paiConfigPath - * Optional key. Set the file path of pai job configuration, the file is in yaml format. - If users set `paiConfigPath` in NNI's configuration file, no need to specify the fields `command`, `paiStorageConfigName`, `virtualCluster`, `image`, `memoryMB`, `cpuNum`, `gpuNum` in `trial` configuration. These fields will use the values from the config file specified by `paiConfigPath`. - ``` - Note: - 1. The job name in PAI's configuration file will be replaced by a new job name, the new job name is created by NNI, the name format is nni_exp_${this.experimentId}_trial_${trialJobId}. + Optional key. Set the file path of OpenPAI job configuration, the file is in yaml format. + + If users set `paiConfigPath` in NNI's configuration file, no need to specify the fields `command`, `paiStorageConfigName`, `virtualCluster`, `image`, `memoryMB`, `cpuNum`, `gpuNum` in `trial` configuration. These fields will use the values from the config file specified by `paiConfigPath`. + + Note: + 1. The job name in OpenPAI's configuration file will be replaced by a new job name, the new job name is created by NNI, the name format is nni_exp_${this.experimentId}_trial_${trialJobId}. - 2. If users set multiple taskRoles in PAI's configuration file, NNI will wrap all of these taksRoles and start multiple tasks in one trial job, users should ensure that only one taskRole report metric to NNI, otherwise there might be some conflict error. + 2. If users set multiple taskRoles in OpenPAI's configuration file, NNI will wrap all of these taksRoles and start multiple tasks in one trial job, users should ensure that only one taskRole report metric to NNI, otherwise there might be some conflict error. - ``` +### OpenPAI configurations +`paiConfig` includes OpenPAI specific configurations, + +* userName + + Required key. User name of OpenPAI platform. + +* token + + Required key. Authentication key of OpenPAI platform. + +* host + + Required key. The host of OpenPAI platform. It's OpenPAI's job submission page uri, like `10.10.5.1`, the default http protocol in NNI is `http`, if your OpenPAI cluster enabled https, please use the uri in `https://10.10.5.1` format. + +* reuse (experimental feature) + + Optional key, default is false. If it's true, NNI will reuse OpenPAI jobs to run as many as possible trials. It can save time of creating new jobs. User needs to make sure each trial can run independent in same job, for example, avoid loading checkpoint from previous trials. Once complete to fill NNI experiment config file and save (for example, save as exp_pai.yml), then run the following command -``` + +```bash nnictl create --config exp_pai.yml ``` + to start the experiment in pai mode. NNI will create OpenPAI job for each trial, and the job name format is something like `nni_exp_{experiment_id}_trial_{trial_id}`. You can see jobs created by NNI in the OpenPAI cluster's web portal, like: ![](../../img/nni_pai_joblist.jpg) @@ -128,11 +180,12 @@ And you will be redirected to HDFS web portal to browse the output files of that You can see there're three fils in output folder: stderr, stdout, and trial.log ## data management -Before using NNI to start your experiment, users should set the corresponding mount data path in your nniManager machine. PAI has their own storage(NFS, AzureBlob ...), and the storage will used in PAI will be mounted to the container when it start a job. Users should set the PAI storage type by `paiStorageConfigName` field to choose a storage in PAI. Then users should mount the storage to their nniManager machine, and set the `nniManagerNFSMountPath` field in configuration file, NNI will generate bash files and copy data in `codeDir` to the `nniManagerNFSMountPath` folder, then NNI will start a trial job. The data in `nniManagerNFSMountPath` will be sync to PAI storage, and will be mounted to PAI's container. The data path in container is set in `containerNFSMountPath`, NNI will enter this folder first, and then run scripts to start a trial job. +Before using NNI to start your experiment, users should set the corresponding mount data path in your nniManager machine. OpenPAI has their own storage(NFS, AzureBlob ...), and the storage will used in OpenPAI will be mounted to the container when it start a job. Users should set the OpenPAI storage type by `paiStorageConfigName` field to choose a storage in OpenPAI. Then users should mount the storage to their nniManager machine, and set the `nniManagerNFSMountPath` field in configuration file, NNI will generate bash files and copy data in `codeDir` to the `nniManagerNFSMountPath` folder, then NNI will start a trial job. The data in `nniManagerNFSMountPath` will be sync to OpenPAI storage, and will be mounted to OpenPAI's container. The data path in container is set in `containerNFSMountPath`, NNI will enter this folder first, and then run scripts to start a trial job. ## version check NNI support version check feature in since version 0.6. It is a policy to insure the version of NNIManager is consistent with trialKeeper, and avoid errors caused by version incompatibility. Check policy: + 1. NNIManager before v0.6 could run any version of trialKeeper, trialKeeper support backward compatibility. 2. Since version 0.6, NNIManager version should keep same with triakKeeper version. For example, if NNIManager version is 0.6, trialKeeper version should be 0.6 too. 3. Note that the version check feature only check first two digits of version.For example, NNIManager v0.6.1 could use trialKeeper v0.6 or trialKeeper v0.6.2, but could not use trialKeeper v0.5.1 or trialKeeper v0.7. diff --git a/docs/en_US/Tutorial/ExperimentConfig.md b/docs/en_US/Tutorial/ExperimentConfig.md index c3b1cb247d..a75cb3cc48 100644 --- a/docs/en_US/Tutorial/ExperimentConfig.md +++ b/docs/en_US/Tutorial/ExperimentConfig.md @@ -70,6 +70,7 @@ This document describes the rules to write the config file, and provides some ex - [password](#password) - [token](#token) - [host](#host) + - [reuse](#reuse) * [Examples](#examples) + [Local mode](#local-mode) + [Remote mode](#remote-mode) @@ -656,6 +657,12 @@ Required. String. The hostname of IP address of PAI. +#### reuse + +Optional. Bool. default: `false`. It's an experimental feature. + +If it's true, NNI will reuse OpenPAI jobs to run as many as possible trials. It can save time of creating new jobs. User needs to make sure each trial can run independent in same job, for example, avoid loading checkpoint from previous trials. + ## Examples ### Local mode diff --git a/setup.py b/setup.py index 6f5bdda244..30d4f448c6 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,8 @@ def read(fname): 'PythonWebHDFS', 'colorama', 'scikit-learn>=0.20,<0.22', - 'pkginfo' + 'pkginfo', + 'websockets' ], entry_points = { diff --git a/src/nni_manager/common/experimentStartupInfo.ts b/src/nni_manager/common/experimentStartupInfo.ts index 887460d77a..5a398bdaef 100644 --- a/src/nni_manager/common/experimentStartupInfo.ts +++ b/src/nni_manager/common/experimentStartupInfo.ts @@ -17,14 +17,16 @@ class ExperimentStartupInfo { private logDir: string = ''; private logLevel: string = ''; private readonly: boolean = false; + private platform: string = ''; - public setStartupInfo(newExperiment: boolean, experimentId: string, basePort: number, logDir?: string, logLevel?: string, readonly?: boolean): void { + public setStartupInfo(newExperiment: boolean, experimentId: string, basePort: number, platform: string, logDir?: string, logLevel?: string, readonly?: boolean): void { assert(!this.initialized); assert(experimentId.trim().length > 0); this.newExperiment = newExperiment; this.experimentId = experimentId; this.basePort = basePort; this.initialized = true; + this.platform = platform; if (logDir !== undefined && logDir.length > 0) { this.logDir = path.join(path.normalize(logDir), this.getExperimentId()); @@ -59,6 +61,12 @@ class ExperimentStartupInfo { return this.newExperiment; } + public getPlatform(): string { + assert(this.initialized); + + return this.platform; + } + public getLogDir(): string { assert(this.initialized); @@ -90,19 +98,25 @@ function isNewExperiment(): boolean { return component.get(ExperimentStartupInfo).isNewExperiment(); } +function getPlatform(): string { + return component.get(ExperimentStartupInfo).getPlatform(); +} + function getExperimentStartupInfo(): ExperimentStartupInfo { return component.get(ExperimentStartupInfo); } function setExperimentStartupInfo( - newExperiment: boolean, experimentId: string, basePort: number, logDir?: string, logLevel?: string, readonly?: boolean): void { + newExperiment: boolean, experimentId: string, basePort: number, platform: string, logDir?: string, logLevel?: string, readonly?: boolean): void { component.get(ExperimentStartupInfo) - .setStartupInfo(newExperiment, experimentId, basePort, logDir, logLevel, readonly); + .setStartupInfo(newExperiment, experimentId, basePort, platform, logDir, logLevel, readonly); } function isReadonly(): boolean { return component.get(ExperimentStartupInfo).isReadonly(); } -export { ExperimentStartupInfo, getBasePort, getExperimentId, isNewExperiment, getExperimentStartupInfo, - setExperimentStartupInfo, isReadonly }; +export { + ExperimentStartupInfo, getBasePort, getExperimentId, isNewExperiment, getPlatform, getExperimentStartupInfo, + setExperimentStartupInfo, isReadonly +}; diff --git a/src/nni_manager/common/utils.ts b/src/nni_manager/common/utils.ts index 734c601dfa..7150ca19f9 100644 --- a/src/nni_manager/common/utils.ts +++ b/src/nni_manager/common/utils.ts @@ -19,6 +19,7 @@ import { Database, DataStore } from './datastore'; import { ExperimentStartupInfo, getExperimentStartupInfo, setExperimentStartupInfo } from './experimentStartupInfo'; import { ExperimentParams, Manager } from './manager'; import { HyperParameters, TrainingService, TrialJobStatus } from './trainingService'; +import { logLevelNameMap } from './log'; function getExperimentRootDir(): string { return getExperimentStartupInfo() @@ -184,7 +185,12 @@ function prepareUnitTest(): void { Container.snapshot(TrainingService); Container.snapshot(Manager); - setExperimentStartupInfo(true, 'unittest', 8080); + const logLevel: string = parseArg(['--log_level', '-ll']); + if (logLevel.length > 0 && !logLevelNameMap.has(logLevel)) { + console.log(`FATAL: invalid log_level: ${logLevel}`); + } + + setExperimentStartupInfo(true, 'unittest', 8080, 'unittest', undefined, logLevel); mkDirPSync(getLogDir()); const sqliteFile: string = path.join(getDefaultDatabaseDir(), 'nni.sqlite'); diff --git a/src/nni_manager/core/commands.ts b/src/nni_manager/core/commands.ts index 7e626a6859..ba1e9b3925 100644 --- a/src/nni_manager/core/commands.ts +++ b/src/nni_manager/core/commands.ts @@ -12,12 +12,30 @@ const TRIAL_END = 'EN'; const TERMINATE = 'TE'; const PING = 'PI'; +const GPU_INFO = 'GI'; +const STDOUT = 'SO'; +const VERSION_CHECK = 'VC'; + const INITIALIZED = 'ID'; const NEW_TRIAL_JOB = 'TR'; const SEND_TRIAL_JOB_PARAMETER = 'SP'; const NO_MORE_TRIAL_JOBS = 'NO'; const KILL_TRIAL_JOB = 'KI'; +const TRIAL_COMMANDS: Set = new Set([ + // from ctl to node + NEW_TRIAL_JOB, + SEND_TRIAL_JOB_PARAMETER, + KILL_TRIAL_JOB, + + // from node to ctl + INITIALIZED, + TRIAL_END, + GPU_INFO, + STDOUT, + VERSION_CHECK, +]); + const TUNER_COMMANDS: Set = new Set([ INITIALIZE, REQUEST_TRIAL_JOBS, @@ -53,11 +71,15 @@ export { TRIAL_END, TERMINATE, PING, + GPU_INFO, + STDOUT, + VERSION_CHECK, INITIALIZED, NEW_TRIAL_JOB, NO_MORE_TRIAL_JOBS, KILL_TRIAL_JOB, TUNER_COMMANDS, ASSESSOR_COMMANDS, + TRIAL_COMMANDS, SEND_TRIAL_JOB_PARAMETER }; diff --git a/src/nni_manager/core/ipcInterface.ts b/src/nni_manager/core/ipcInterface.ts index e7c45beec6..8ef78069a4 100644 --- a/src/nni_manager/core/ipcInterface.ts +++ b/src/nni_manager/core/ipcInterface.ts @@ -135,4 +135,4 @@ function createDispatcherInterface(process: ChildProcess): IpcInterface { return new IpcInterface(process, new Set([...CommandType.TUNER_COMMANDS, ...CommandType.ASSESSOR_COMMANDS])); } -export { IpcInterface, createDispatcherInterface }; +export { IpcInterface, createDispatcherInterface, encodeCommand, decodeCommand }; diff --git a/src/nni_manager/main.ts b/src/nni_manager/main.ts index 66dd3fce0b..a999c04d5f 100644 --- a/src/nni_manager/main.ts +++ b/src/nni_manager/main.ts @@ -21,7 +21,7 @@ import { NNIRestServer } from './rest_server/nniRestServer'; import { FrameworkControllerTrainingService } from './training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService'; import { KubeflowTrainingService } from './training_service/kubernetes/kubeflow/kubeflowTrainingService'; import { LocalTrainingService } from './training_service/local/localTrainingService'; -import { PAIK8STrainingService } from './training_service/pai/paiK8S/paiK8STrainingService'; +import { RouterTrainingService } from './training_service/reusable/routerTrainingService'; import { PAIYarnTrainingService } from './training_service/pai/paiYarn/paiYarnTrainingService'; import { RemoteMachineTrainingService @@ -29,11 +29,11 @@ import { import { DLTSTrainingService } from './training_service/dlts/dltsTrainingService'; function initStartupInfo( - startExpMode: string, resumeExperimentId: string, basePort: number, + startExpMode: string, resumeExperimentId: string, basePort: number, platform: string, logDirectory: string, experimentLogLevel: string, readonly: boolean): void { const createNew: boolean = (startExpMode === ExperimentStartUpMode.NEW); const expId: string = createNew ? uniqueString(8) : resumeExperimentId; - setExperimentStartupInfo(createNew, expId, basePort, logDirectory, experimentLogLevel, readonly); + setExperimentStartupInfo(createNew, expId, basePort, platform, logDirectory, experimentLogLevel, readonly); } async function initContainer(foreground: boolean, platformMode: string, logFileName?: string): Promise { @@ -47,10 +47,10 @@ async function initContainer(foreground: boolean, platformMode: string, logFileN .scope(Scope.Singleton); } else if (platformMode === 'pai') { Container.bind(TrainingService) - .to(PAIK8STrainingService) + .to(RouterTrainingService) .scope(Scope.Singleton); } else if (platformMode === 'paiYarn') { - Container.bind(TrainingService) + Container.bind(TrainingService) .to(PAIYarnTrainingService) .scope(Scope.Singleton); } else if (platformMode === 'kubeflow') { @@ -153,31 +153,31 @@ if (!('true' || 'false').includes(readonlyArg.toLowerCase())) { } const readonly = readonlyArg.toLowerCase() == 'true' ? true : false; -initStartupInfo(startMode, experimentId, port, logDir, logLevel, readonly); +initStartupInfo(startMode, experimentId, port, mode, logDir, logLevel, readonly); mkDirP(getLogDir()) .then(async () => { - try { - await initContainer(foreground, mode); - const restServer: NNIRestServer = component.get(NNIRestServer); - await restServer.start(); - const log: Logger = getLogger(); - log.info(`Rest server listening on: ${restServer.endPoint}`); - } catch (err) { - const log: Logger = getLogger(); - log.error(`${err.stack}`); - throw err; - } -}) -.catch((err: Error) => { - console.error(`Failed to create log dir: ${err.stack}`); -}); + try { + await initContainer(foreground, mode); + const restServer: NNIRestServer = component.get(NNIRestServer); + await restServer.start(); + const log: Logger = getLogger(); + log.info(`Rest server listening on: ${restServer.endPoint}`); + } catch (err) { + const log: Logger = getLogger(); + log.error(`${err.stack}`); + throw err; + } + }) + .catch((err: Error) => { + console.error(`Failed to create log dir: ${err.stack}`); + }); function getStopSignal(): any { if (process.platform === "win32") { return 'SIGBREAK'; } - else{ + else { return 'SIGTERM'; } } diff --git a/src/nni_manager/package.json b/src/nni_manager/package.json index 9b32372fd4..b2817eeb7c 100644 --- a/src/nni_manager/package.json +++ b/src/nni_manager/package.json @@ -29,7 +29,8 @@ "ts-deferred": "^1.0.4", "typescript-ioc": "^1.2.4", "typescript-string-operations": "^1.3.1", - "webhdfs": "^1.2.0" + "webhdfs": "^1.2.0", + "ws": "^7.3.0" }, "devDependencies": { "@types/chai": "^4.1.4", @@ -46,6 +47,7 @@ "@types/stream-buffers": "^3.0.2", "@types/tar": "^4.0.3", "@types/tmp": "^0.0.33", + "@types/ws": "^7.2.5", "@typescript-eslint/eslint-plugin": "^2.10.0", "@typescript-eslint/parser": "^2.10.0", "chai": "^4.1.2", diff --git a/src/nni_manager/rest_server/restValidationSchemas.ts b/src/nni_manager/rest_server/restValidationSchemas.ts index 7b620c9537..07e9dade16 100644 --- a/src/nni_manager/rest_server/restValidationSchemas.ts +++ b/src/nni_manager/rest_server/restValidationSchemas.ts @@ -104,6 +104,7 @@ export namespace ValidationSchemas { passWord: joi.string().min(1), token: joi.string().min(1), host: joi.string().min(1).required(), + reuse: joi.boolean(), }), kubeflow_config: joi.object({ // eslint-disable-line @typescript-eslint/camelcase operator: joi.string().min(1).required(), diff --git a/src/nni_manager/training_service/common/gpuData.ts b/src/nni_manager/training_service/common/gpuData.ts index caad31eaa9..655c885d59 100644 --- a/src/nni_manager/training_service/common/gpuData.ts +++ b/src/nni_manager/training_service/common/gpuData.ts @@ -16,12 +16,21 @@ export class GPUInfo { public gpuUtil: number; // the index number of this GPU (starting from 0) public readonly index: number; + public gpuMemTotal: number; + public gpuMemFree: number; + public gpuMemUsed: number; + public gpuType: string; - constructor(activeProcessNum: number, gpuMemUtil: number, gpuUtil: number, index: number) { + constructor(activeProcessNum: number, gpuMemUtil: number, gpuUtil: number, index: number, + gpuMemTotal: number, gpuMemFree: number, gpuMemUsed: number, gpuType: string) { this.activeProcessNum = activeProcessNum; this.gpuMemUtil = gpuMemUtil; this.gpuUtil = gpuUtil; this.index = index; + this.gpuMemTotal = gpuMemTotal; + this.gpuMemFree = gpuMemFree; + this.gpuMemUsed = gpuMemUsed; + this.gpuType = gpuType; } } @@ -44,7 +53,7 @@ export class GPUSummary { } export const GPU_INFO_COLLECTOR_FORMAT_WINDOWS: string = -` + ` $env:METRIC_OUTPUT_DIR="{0}" $app = Start-Process "python" -ArgumentList "-m nni_gpu_tool.gpu_metrics_collector" -passthru -NoNewWindow Write $app.ID | Out-File {1} -NoNewline -encoding utf8 diff --git a/src/nni_manager/training_service/pai/paiConfig.ts b/src/nni_manager/training_service/pai/paiConfig.ts index 61742f378a..eceda619c7 100644 --- a/src/nni_manager/training_service/pai/paiConfig.ts +++ b/src/nni_manager/training_service/pai/paiConfig.ts @@ -10,6 +10,7 @@ export class PAIClusterConfig { public readonly passWord?: string; public host: string; public readonly token?: string; + public readonly reuse?: boolean; /** * Constructor @@ -17,12 +18,14 @@ export class PAIClusterConfig { * @param passWord password of PAI Cluster * @param host Host IP of PAI Cluster * @param token PAI token of PAI Cluster + * @param reuse If job is reusable for multiple trials */ - constructor(userName: string, host: string, passWord?: string, token?: string) { + constructor(userName: string, host: string, passWord?: string, token?: string, reuse?: boolean) { this.userName = userName; this.passWord = passWord; this.host = host; this.token = token; + this.reuse = reuse; } } diff --git a/src/nni_manager/training_service/pai/paiK8S/paiK8SConfig.ts b/src/nni_manager/training_service/pai/paiK8S/paiK8SConfig.ts index 3cb7aac64f..216832f71a 100644 --- a/src/nni_manager/training_service/pai/paiK8S/paiK8SConfig.ts +++ b/src/nni_manager/training_service/pai/paiK8S/paiK8SConfig.ts @@ -1,21 +1,5 @@ -/** - * Copyright (c) Microsoft Corporation - * All rights reserved. - * - * MIT License - * - * Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated - * documentation files (the "Software"), to deal in the Software without restriction, including without limitation - * the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and - * to permit persons to whom the Software is furnished to do so, subject to the following conditions: - * The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING - * BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND - * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, - * DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - */ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. 'use strict'; import {TrialConfig} from '../../common/trialConfig'; diff --git a/src/nni_manager/training_service/pai/paiK8S/paiK8SData.ts b/src/nni_manager/training_service/pai/paiK8S/paiK8SData.ts index 7851ad7c84..2c6b9f3d66 100644 --- a/src/nni_manager/training_service/pai/paiK8S/paiK8SData.ts +++ b/src/nni_manager/training_service/pai/paiK8S/paiK8SData.ts @@ -1,21 +1,5 @@ -/** - * Copyright (c) Microsoft Corporation - * All rights reserved. - * - * MIT License - * - * Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated - * documentation files (the "Software"), to deal in the Software without restriction, including without limitation - * the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and - * to permit persons to whom the Software is furnished to do so, subject to the following conditions: - * The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING - * BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND - * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, - * DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - */ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. 'use strict'; diff --git a/src/nni_manager/training_service/pai/paiK8S/paiK8STrainingService.ts b/src/nni_manager/training_service/pai/paiK8S/paiK8STrainingService.ts index 8d8560e9d2..e243387d39 100644 --- a/src/nni_manager/training_service/pai/paiK8S/paiK8STrainingService.ts +++ b/src/nni_manager/training_service/pai/paiK8S/paiK8STrainingService.ts @@ -1,21 +1,5 @@ -/** - * Copyright (c) Microsoft Corporation - * All rights reserved. - * - * MIT License - * - * Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated - * documentation files (the "Software"), to deal in the Software without restriction, including without limitation - * the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and - * to permit persons to whom the Software is furnished to do so, subject to the following conditions: - * The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING - * BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND - * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, - * DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - */ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. 'use strict'; diff --git a/src/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts b/src/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts index e85c4a5e2d..15cea1ab4a 100644 --- a/src/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts +++ b/src/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts @@ -406,12 +406,11 @@ class RemoteMachineTrainingService implements TrainingService { private async setupConnections(machineList: string): Promise { this.log.debug(`Connecting to remote machines: ${machineList}`); - const deferred: Deferred = new Deferred(); //TO DO: verify if value's format is wrong, and json parse failed, how to handle error const rmMetaList: RemoteMachineMeta[] = JSON.parse(machineList); - let connectedRMNum: number = 0; - rmMetaList.forEach(async (rmMeta: RemoteMachineMeta) => { + const connectionPromises = []; + for (const rmMeta of rmMetaList) { rmMeta.occupiedGpuIndexMap = new Map(); const executorManager: ExecutorManager = new ExecutorManager(rmMeta); this.log.info(`connecting to ${rmMeta.username}@${rmMeta.ip}:${rmMeta.port}`); @@ -419,14 +418,11 @@ class RemoteMachineTrainingService implements TrainingService { this.log.debug(`reached ${executor.name}`); this.machineExecutorManagerMap.set(rmMeta, executorManager); this.log.debug(`initializing ${executor.name}`); - await this.initRemoteMachineOnConnected(rmMeta, executor); + connectionPromises.push(this.initRemoteMachineOnConnected(rmMeta, executor)); this.log.info(`connected to ${executor.name}`); - if (++connectedRMNum === rmMetaList.length) { - deferred.resolve(); - } - }); + } - return deferred.promise; + await Promise.all(connectionPromises); } private async initRemoteMachineOnConnected(rmMeta: RemoteMachineMeta, executor: ShellExecutor): Promise { @@ -460,7 +456,7 @@ class RemoteMachineTrainingService implements TrainingService { this.timer.unsubscribe(disposable); } } - if (this.stopping){ + if (this.stopping) { this.timer.unsubscribe(disposable); this.log.debug(`Stopped GPU collector on ${rmMeta.ip}, since experiment is exiting.`); } diff --git a/src/nni_manager/training_service/reusable/channels/fileCommandChannel.ts b/src/nni_manager/training_service/reusable/channels/fileCommandChannel.ts new file mode 100644 index 0000000000..3c5149603a --- /dev/null +++ b/src/nni_manager/training_service/reusable/channels/fileCommandChannel.ts @@ -0,0 +1,136 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import * as component from "../../../common/component"; +import { delay } from "../../../common/utils"; +import { CommandChannel, RunnerConnection } from "../commandChannel"; +import { EnvironmentInformation, Channel } from "../environment"; +import { StorageService } from "../storageService"; + +class FileHandler { + public fileName: string; + public offset: number = 0; + + constructor(fileName: string) { + this.fileName = fileName; + } +} + + +class FileRunnerConnection extends RunnerConnection { + public handlers: Map = new Map(); +} + +export class FileCommandChannel extends CommandChannel { + private readonly commandPath = "commands"; + private stopping: boolean = false; + // make sure no concurrent issue when sending commands. + private sendQueues: [EnvironmentInformation, string][] = []; + + public get channelName(): Channel { + return "file"; + } + + public async config(_key: string, _value: any): Promise { + // do nothing + } + + public async start(): Promise { + // start command loops + this.receiveLoop(); + this.sendLoop(); + } + + public async stop(): Promise { + this.stopping = true; + } + + protected async sendCommandInternal(environment: EnvironmentInformation, message: string): Promise { + this.sendQueues.push([environment, message]); + } + + protected createRunnerConnection(environment: EnvironmentInformation): RunnerConnection { + return new FileRunnerConnection(environment); + } + + private async sendLoop(): Promise { + const intervalSeconds = 0.5; + while (!this.stopping) { + const start = new Date(); + + if (this.sendQueues.length > 0) { + const storageService = component.get(StorageService); + + while (this.sendQueues.length > 0) { + const item = this.sendQueues.shift(); + if (item === undefined) { + break; + } + const environment = item[0]; + const message = `${item[1]}\n`; + + const fileName = storageService.joinPath(environment.workingFolder, this.commandPath, `manager_commands.txt`); + await storageService.save(message, fileName, true); + } + } + + const end = new Date(); + const delayMs = intervalSeconds * 1000 - (end.valueOf() - start.valueOf()); + if (delayMs > 0) { + await delay(delayMs); + } + } + } + + private async receiveLoop(): Promise { + const intervalSeconds = 2; + const storageService = component.get(StorageService); + + while (!this.stopping) { + const start = new Date(); + + const runnerConnections = [...this.runnerConnections.values()] as FileRunnerConnection[]; + for (const runnerConnection of runnerConnections) { + const envCommandFolder = storageService.joinPath(runnerConnection.environment.workingFolder, this.commandPath); + // open new command files + if (runnerConnection.handlers.size < runnerConnection.environment.nodeCount) { + // to find all node commands file + const commandFileNames = await storageService.listDirectory(envCommandFolder); + const toAddedFileNames = []; + for (const commandFileName of commandFileNames) { + if (commandFileName.startsWith("runner_commands") && !runnerConnection.handlers.has(commandFileName)) { + toAddedFileNames.push(commandFileName); + } + } + + for (const toAddedFileName of toAddedFileNames) { + const fullPath = storageService.joinPath(envCommandFolder, toAddedFileName); + const fileHandler: FileHandler = new FileHandler(fullPath); + runnerConnection.handlers.set(toAddedFileName, fileHandler); + this.log.debug(`FileCommandChannel: added fileHandler env ${runnerConnection.environment.id} ${toAddedFileName}`); + } + } + + // to loop all commands + for (const fileHandler of runnerConnection.handlers.values()) { + const newContent = await storageService.readFileContent(fileHandler.fileName, fileHandler.offset, undefined); + if (newContent.length > 0) { + const commands = newContent.split('\n'); + for (const command of commands) { + this.handleCommand(runnerConnection.environment, command); + } + fileHandler.offset += newContent.length; + } + } + } + + const end = new Date(); + const delayMs = intervalSeconds * 1000 - (end.valueOf() - start.valueOf()); + if (delayMs > 0) { + await delay(delayMs); + } + } + } +} diff --git a/src/nni_manager/training_service/reusable/channels/webCommandChannel.ts b/src/nni_manager/training_service/reusable/channels/webCommandChannel.ts new file mode 100644 index 0000000000..774b1a8ffc --- /dev/null +++ b/src/nni_manager/training_service/reusable/channels/webCommandChannel.ts @@ -0,0 +1,126 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import { Server as SocketServer } from "ws"; +import { getBasePort, getExperimentId } from "../../../common/experimentStartupInfo"; +import { INITIALIZED } from '../../../core/commands'; +import { CommandChannel, RunnerConnection } from "../commandChannel"; +import { Channel, EnvironmentInformation } from "../environment"; + +class WebRunnerConnection extends RunnerConnection { + public readonly clients: WebSocket[] = []; + + public async close(): Promise { + await super.close(); + while (this.clients.length > 0) { + const client = this.clients.shift(); + if (client !== undefined) { + client.close(); + } + } + } + + public AddClient(client: WebSocket): void { + this.clients.push(client); + } +} + +export class WebCommandChannel extends CommandChannel { + private readonly expId: string = getExperimentId(); + + private webSocketServer: SocketServer | undefined; + private clients: Map = new Map(); + + public get channelName(): Channel { + return "web"; + } + + public async config(_key: string, _value: any): Promise { + // do nothing + } + + public async start(): Promise { + const port = getBasePort() + 1; + this.webSocketServer = new SocketServer({ port }); + + this.webSocketServer.on('connection', (client: WebSocket) => { + this.log.debug(`WebCommandChannel: received connection`); + client.onerror = (event): void => { + this.log.error(`error on client ${JSON.stringify(event)}`); + } + + this.clients.set(client, undefined); + client.onmessage = (message): void => { + this.receivedWebSocketMessage(client, message); + }; + }).on('error', (error) => { + this.log.error(`error on websocket server ${error}`); + }); + } + + public async stop(): Promise { + if (this.webSocketServer !== undefined) { + this.webSocketServer.close(); + } + } + + protected async sendCommandInternal(environment: EnvironmentInformation, message: string): Promise { + if (this.webSocketServer === undefined) { + throw new Error(`WebCommandChannel: uninitialized!`) + } + const runnerConnection = this.runnerConnections.get(environment.id) as WebRunnerConnection; + if (runnerConnection !== undefined) { + for (const client of runnerConnection.clients) { + client.send(message); + } + } else { + this.log.warning(`WebCommandChannel: cannot find client for env ${environment.id}, message is ignored.`); + } + } + + protected createRunnerConnection(environment: EnvironmentInformation): RunnerConnection { + return new WebRunnerConnection(environment); + } + + private receivedWebSocketMessage(client: WebSocket, message: MessageEvent): void { + let connection = this.clients.get(client) as WebRunnerConnection | undefined; + const rawCommands = message.data.toString(); + + if (connection === undefined) { + // undefined means it's expecting initializing message. + const commands = this.parseCommands(rawCommands); + let isValid = false; + this.log.debug(`WebCommandChannel: received initialize message: ${JSON.stringify(rawCommands)}`); + + if (commands.length > 0) { + const commandType = commands[0][0]; + const result = commands[0][1]; + if (commandType === INITIALIZED && + result.expId === this.expId && + this.runnerConnections.has(result.runnerId) + ) { + const runnerConnection = this.runnerConnections.get(result.runnerId) as WebRunnerConnection; + this.clients.set(client, runnerConnection); + runnerConnection.AddClient(client); + connection = runnerConnection; + isValid = true; + this.log.debug(`WebCommandChannel: client of env ${runnerConnection.environment.id} initialized`); + } else { + this.log.warning(`WebCommandChannel: client is not initialized, runnerId: ${result.runnerId}, command: ${commandType}, expId: ${this.expId}, exists: ${this.runnerConnections.has(result.runnerId)}`); + } + } + + if (!isValid) { + this.log.warning(`WebCommandChannel: rejected client with invalid init message ${rawCommands}`); + client.close(); + this.clients.delete(client); + } + } + + if (connection !== undefined) { + this.handleCommand(connection.environment, rawCommands); + } + } +} diff --git a/src/nni_manager/training_service/reusable/commandChannel.ts b/src/nni_manager/training_service/reusable/commandChannel.ts new file mode 100644 index 0000000000..80b1ddc5b0 --- /dev/null +++ b/src/nni_manager/training_service/reusable/commandChannel.ts @@ -0,0 +1,129 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import { EventEmitter } from "events"; +import { getLogger, Logger } from "../../common/log"; +import { TRIAL_COMMANDS } from "../../core/commands"; +import { encodeCommand } from "../../core/ipcInterface"; +import { Channel, EnvironmentInformation } from "./environment"; + +const acceptedCommands: Set = new Set(TRIAL_COMMANDS); + +export class Command { + public readonly environment: EnvironmentInformation; + public readonly command: string; + public readonly data: any; + + constructor(environment: EnvironmentInformation, command: string, data: any) { + if (!acceptedCommands.has(command)) { + throw new Error(`unaccepted command ${command}`); + } + this.environment = environment; + this.command = command; + this.data = data; + } +} + +export class RunnerConnection { + public readonly environment: EnvironmentInformation; + + constructor(environment: EnvironmentInformation) { + this.environment = environment; + } + + public async open(): Promise { + // do nothing + } + + public async close(): Promise { + // do nothing + } +} + +export abstract class CommandChannel { + protected readonly log: Logger; + protected runnerConnections: Map = new Map(); + protected readonly commandEmitter: EventEmitter; + + private readonly commandPattern: RegExp = /(?[\w]{2})(?[\d]{14})(?.*)\n?/gm; + + public constructor(commandEmitter: EventEmitter) { + this.log = getLogger(); + this.commandEmitter = commandEmitter; + } + + public abstract get channelName(): Channel; + public abstract config(key: string, value: any): Promise; + public abstract start(): Promise; + public abstract stop(): Promise; + + protected abstract sendCommandInternal(environment: EnvironmentInformation, message: string): Promise; + protected abstract createRunnerConnection(environment: EnvironmentInformation): RunnerConnection; + + public async sendCommand(environment: EnvironmentInformation, commantType: string, data: any): Promise { + const command = encodeCommand(commantType, JSON.stringify(data)); + this.log.debug(`CommandChannel: env ${environment.id} sending command: ${command}`); + await this.sendCommandInternal(environment, command.toString("utf8")); + } + + public async open(environment: EnvironmentInformation): Promise { + if (this.runnerConnections.has(environment.id)) { + throw new Error(`CommandChannel: env ${environment.id} is opened already, shouldn't be opened again.`); + } + const connection = this.createRunnerConnection(environment); + this.runnerConnections.set(environment.id, connection); + await connection.open(); + } + + public async close(environment: EnvironmentInformation): Promise { + if (this.runnerConnections.has(environment.id)) { + const connection = this.runnerConnections.get(environment.id); + this.runnerConnections.delete(environment.id); + if (connection !== undefined) { + await connection.close(); + } + } + } + + protected parseCommands(content: string): [string, any][] { + const commands: [string, any][] = []; + + let matches = this.commandPattern.exec(content); + + while (matches) { + if (undefined !== matches.groups) { + const commandType = matches.groups["type"]; + const dataLength = parseInt(matches.groups["length"]); + const data: any = matches.groups["data"]; + if (dataLength !== data.length) { + throw new Error(`dataLength ${dataLength} not equal to actual length ${data.length}: ${data}`); + } + try { + const finalData = JSON.parse(data); + // to handle encode('utf8') of Python + commands.push([commandType, finalData]); + } catch (error) { + this.log.error(`CommandChannel: error on parseCommands ${error}, original: ${matches.groups["data"]}`); + throw error; + } + } + matches = this.commandPattern.exec(content); + } + + return commands; + } + + protected handleCommand(environment: EnvironmentInformation, content: string): void { + const parsedResults = this.parseCommands(content); + + for (const parsedResult of parsedResults) { + const commandType = parsedResult[0]; + const data = parsedResult[1]; + const command = new Command(environment, commandType, data); + this.commandEmitter.emit("command", command); + this.log.trace(`CommandChannel: env ${environment.id} emit command: ${commandType}, ${data}.`); + } + } +} diff --git a/src/nni_manager/training_service/reusable/environment.ts b/src/nni_manager/training_service/reusable/environment.ts new file mode 100644 index 0000000000..14db7a661d --- /dev/null +++ b/src/nni_manager/training_service/reusable/environment.ts @@ -0,0 +1,109 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import { GPUSummary } from "training_service/common/gpuData"; +import { getLogger, Logger } from "../../common/log"; +import { TrialJobStatus } from "../../common/trainingService"; +import { EventEmitter } from "events"; +import { WebCommandChannel } from "./channels/webCommandChannel"; +import { CommandChannel } from "./commandChannel"; + + +export type EnvironmentStatus = 'UNKNOWN' | 'WAITING' | 'RUNNING' | 'SUCCEEDED' | 'FAILED' | 'USER_CANCELED'; +export type Channel = "web" | "file" | "aml" | "ut"; + + +export class EnvironmentInformation { + private log: Logger; + + // NNI environment ID + public id: string; + // training platform unique job ID. + public jobId: string; + // training platform job friendly name, in case it's different with job ID. + public jobName: string; + + // key states + // true: environment is ready to run trial. + public isIdle: boolean = false; + // true: environment is running, waiting, or unknown. + public isAlive: boolean = true; + // don't set status in environment directly, use setFinalState function to set a final state. + public status: EnvironmentStatus = "UNKNOWN"; + + public trackingUrl: string = ""; + public workingFolder: string = ""; + public runnerWorkingFolder: string = ""; + public command: string = ""; + public nodeCount: number = 1; + + // it's used to aggregate node status for multiple node trial + public nodes: Map; + public gpuSummary: Map = new Map(); + + constructor(id: string, jobName: string, jobId?: string) { + this.log = getLogger(); + this.id = id; + this.jobName = jobName; + this.jobId = jobId ? jobId : jobName; + this.nodes = new Map(); + } + + public setFinalStatus(status: EnvironmentStatus): void { + switch (status) { + case 'WAITING': + case 'SUCCEEDED': + case 'FAILED': + case 'USER_CANCELED': + this.status = status; + break; + default: + this.log.error(`Environment: job ${this.jobId} set an invalid final state ${status}.`); + break; + } + } +} +export abstract class EnvironmentService { + + public abstract get hasStorageService(): boolean; + + public abstract config(key: string, value: string): Promise; + public abstract refreshEnvironmentsStatus(environments: EnvironmentInformation[]): Promise; + public abstract startEnvironment(environment: EnvironmentInformation): Promise; + public abstract stopEnvironment(environment: EnvironmentInformation): Promise; + + public getCommandChannel(commandEmitter: EventEmitter): CommandChannel { + return new WebCommandChannel(commandEmitter); + } + + public createEnviornmentInfomation(envId: string, envName: string): EnvironmentInformation { + return new EnvironmentInformation(envId, envName); + } +} + +export class NodeInfomation { + public id: string; + public status: TrialJobStatus = "UNKNOWN"; + public endTime?: number; + + constructor(id: string) { + this.id = id; + } +} + +export class RunnerSettings { + public experimentId: string = ""; + public platform: string = ""; + public nniManagerIP: string = ""; + public nniManagerPort: number = 8081; + public nniManagerVersion: string = ""; + public logCollection: string = "none"; + public command: string = ""; + public enableGpuCollector: boolean = false; + + // specify which communication channel is used by runner. + // supported channel includes: rest, storage, aml + public commandChannel: Channel = "file"; +} diff --git a/src/nni_manager/training_service/reusable/environments/openPaiEnvironmentService.ts b/src/nni_manager/training_service/reusable/environments/openPaiEnvironmentService.ts new file mode 100644 index 0000000000..ba8a3f4473 --- /dev/null +++ b/src/nni_manager/training_service/reusable/environments/openPaiEnvironmentService.ts @@ -0,0 +1,417 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import * as fs from 'fs'; +import * as request from 'request'; +import { Deferred } from 'ts-deferred'; +import * as component from '../../../common/component'; +import { getExperimentId } from '../../../common/experimentStartupInfo'; +import { getLogger, Logger } from '../../../common/log'; +import { TrialConfigMetadataKey } from '../../common/trialConfigMetadataKey'; +import { PAIClusterConfig } from '../../pai/paiConfig'; +import { NNIPAIK8STrialConfig } from '../../pai/paiK8S/paiK8SConfig'; +import { EnvironmentInformation, EnvironmentService } from '../environment'; +import { StorageService } from '../storageService'; + +const yaml = require('js-yaml'); + +/** + * Collector PAI jobs info from PAI cluster, and update pai job status locally + */ +@component.Singleton +export class OpenPaiEnvironmentService extends EnvironmentService { + + private readonly log: Logger = getLogger(); + private paiClusterConfig: PAIClusterConfig | undefined; + private paiTrialConfig: NNIPAIK8STrialConfig | undefined; + private paiJobConfig: any; + private paiToken?: string; + private paiTokenUpdateTime?: number; + private readonly paiTokenUpdateInterval: number; + private protocol: string = 'http'; + + private experimentId: string; + + constructor() { + super(); + this.paiTokenUpdateInterval = 7200000; //2hours + this.experimentId = getExperimentId(); + } + + public get hasStorageService(): boolean { + return true; + } + + public async config(key: string, value: string): Promise { + switch (key) { + case TrialConfigMetadataKey.PAI_CLUSTER_CONFIG: + this.paiClusterConfig = JSON.parse(value); + this.paiClusterConfig.host = this.formatPAIHost(this.paiClusterConfig.host); + if (this.paiClusterConfig.passWord) { + // Get PAI authentication token + await this.updatePaiToken(); + } else if (this.paiClusterConfig.token) { + this.paiToken = this.paiClusterConfig.token; + } + break; + + case TrialConfigMetadataKey.TRIAL_CONFIG: { + if (this.paiClusterConfig === undefined) { + this.log.error('pai cluster config is not initialized'); + break; + } + this.paiTrialConfig = JSON.parse(value); + // Validate to make sure codeDir doesn't have too many files + + const storageService = component.get(StorageService); + const remoteRoot = storageService.joinPath(this.paiTrialConfig.nniManagerNFSMountPath, this.experimentId); + storageService.initialize(this.paiTrialConfig.nniManagerNFSMountPath, remoteRoot); + + if (this.paiTrialConfig.paiConfigPath) { + this.paiJobConfig = yaml.safeLoad(fs.readFileSync(this.paiTrialConfig.paiConfigPath, 'utf8')); + } + break; + } + default: + this.log.debug(`OpenPAI not proccessed metadata key: '${key}', value: '${value}'`); + } + } + + public async refreshEnvironmentsStatus(environments: EnvironmentInformation[]): Promise { + const deferred: Deferred = new Deferred(); + await this.refreshPlatform(); + + if (this.paiClusterConfig === undefined) { + throw new Error('PAI Cluster config is not initialized'); + } + if (this.paiToken === undefined) { + throw new Error('PAI token is not initialized'); + } + + const getJobInfoRequest: request.Options = { + uri: `${this.protocol}://${this.paiClusterConfig.host}/rest-server/api/v2/jobs?username=${this.paiClusterConfig.userName}`, + method: 'GET', + json: true, + headers: { + 'Content-Type': 'application/json', + Authorization: `Bearer ${this.paiToken}` + } + }; + + request(getJobInfoRequest, async (error: any, response: request.Response, body: any) => { + if ((error !== undefined && error !== null) || response.statusCode >= 400) { + this.log.error(`OpenPAI: get environment list from PAI Cluster failed!\nerror: ${error}`); + deferred.reject(error); + } else { + const jobInfos = new Map(); + body.forEach((jobInfo: any) => { + jobInfos.set(jobInfo.name, jobInfo); + }); + + environments.forEach((environment) => { + if (jobInfos.has(environment.jobId)) { + const jobResponse = jobInfos.get(environment.jobId); + if (jobResponse && jobResponse.state) { + const oldEnvironmentStatus = environment.status; + switch (jobResponse.state) { + case 'RUNNING': + case 'WAITING': + // RUNNING status is set by runner, and ignore waiting status + break; + case 'SUCCEEDED': + case 'FAILED': + environment.setFinalStatus(jobResponse.state); + break; + case 'STOPPED': + case 'STOPPING': + environment.setFinalStatus('USER_CANCELED'); + break; + default: + this.log.error(`OpenPAI: job ${environment.jobId} returns unknown state ${jobResponse.state}.`); + environment.setFinalStatus('UNKNOWN'); + } + if (oldEnvironmentStatus !== environment.status) { + this.log.debug(`OpenPAI: job ${environment.jobId} change status ${oldEnvironmentStatus} to ${environment.status} due to job is ${jobResponse.state}.`) + } + } else { + this.log.error(`OpenPAI: job ${environment.jobId} has no state returned. body:${JSON.stringify(jobResponse)}`); + // some error happens, and mark this environment + environment.status = 'FAILED'; + } + } else { + this.log.error(`OpenPAI job ${environment.jobId} is not found in job list.`); + environment.status = 'UNKNOWN'; + } + }); + deferred.resolve(); + } + }); + return deferred.promise; + } + + public async startEnvironment(environment: EnvironmentInformation): Promise { + const deferred: Deferred = new Deferred(); + + await this.refreshPlatform(); + + if (this.paiClusterConfig === undefined) { + throw new Error('PAI Cluster config is not initialized'); + } + if (this.paiToken === undefined) { + throw new Error('PAI token is not initialized'); + } + if (this.paiTrialConfig === undefined) { + throw new Error('PAI trial config is not initialized'); + } + + // Step 1. Prepare PAI job configuration + environment.runnerWorkingFolder = `${this.paiTrialConfig.containerNFSMountPath}/${this.experimentId}/envs/${environment.id}`; + environment.command = `cd ${environment.runnerWorkingFolder} && ${environment.command}` + environment.trackingUrl = `${this.protocol}://${this.paiClusterConfig.host}/job-detail.html?username=${this.paiClusterConfig.userName}&jobName=${environment.jobId}` + + // Step 2. Generate Job Configuration in yaml format + const paiJobConfig = this.generateJobConfigInYamlFormat(environment); + this.log.debug(`generated paiJobConfig: ${paiJobConfig}`); + + // Step 3. Submit PAI job via Rest call + const submitJobRequest: request.Options = { + uri: `${this.protocol}://${this.paiClusterConfig.host}/rest-server/api/v2/jobs`, + method: 'POST', + body: paiJobConfig, + headers: { + 'Content-Type': 'text/yaml', + Authorization: `Bearer ${this.paiToken}` + } + }; + request(submitJobRequest, (error, response, body) => { + if ((error !== undefined && error !== null) || response.statusCode >= 400) { + const errorMessage: string = (error !== undefined && error !== null) ? error.message : + `start environment ${environment.jobId} failed, http code:${response.statusCode}, http body: ${body}`; + + this.log.error(errorMessage); + environment.status = 'FAILED'; + } + deferred.resolve(); + }); + + return deferred.promise; + } + + public async stopEnvironment(environment: EnvironmentInformation): Promise { + const deferred: Deferred = new Deferred(); + + if (this.paiClusterConfig === undefined) { + return Promise.reject(new Error('PAI Cluster config is not initialized')); + } + if (this.paiToken === undefined) { + return Promise.reject(Error('PAI token is not initialized')); + } + + const stopJobRequest: request.Options = { + uri: `${this.protocol}://${this.paiClusterConfig.host}/rest-server/api/v2/jobs/${this.paiClusterConfig.userName}~${environment.jobId}/executionType`, + method: 'PUT', + json: true, + body: { value: 'STOP' }, + time: true, + headers: { + 'Content-Type': 'application/json', + Authorization: `Bearer ${this.paiToken}` + } + }; + + this.log.debug(`stopping OpenPAI environment ${environment.jobId}, ${stopJobRequest.uri}`); + + try { + request(stopJobRequest, (error, response, _body) => { + try { + if ((error !== undefined && error !== null) || (response && response.statusCode >= 400)) { + this.log.error(`OpenPAI: stop job ${environment.jobId} failed with ${response.statusCode}\n${error}`); + deferred.reject((error !== undefined && error !== null) ? error : + `Stop trial failed, http code: ${response.statusCode}`); + } else { + this.log.info(`OpenPAI job ${environment.jobId} stopped.`); + } + deferred.resolve(); + } catch (error) { + this.log.error(`OpenPAI error when inner stopping environment ${error}`); + deferred.reject(error); + } + }); + } catch (error) { + this.log.error(`OpenPAI error when stopping environment ${error}`); + return Promise.reject(error); + } + + return deferred.promise; + } + + private async refreshPlatform(): Promise { + if (this.paiClusterConfig && this.paiClusterConfig.passWord) { + try { + await this.updatePaiToken(); + } catch (error) { + this.log.error(`${error}`); + if (this.paiToken === undefined) { + throw new Error(error); + } + } + } + } + + private generateJobConfigInYamlFormat(environment: EnvironmentInformation): any { + if (this.paiTrialConfig === undefined) { + throw new Error('trial config is not initialized'); + } + const jobName = environment.jobId; + + let nniJobConfig: any = undefined; + if (this.paiTrialConfig.paiConfigPath) { + nniJobConfig = JSON.parse(JSON.stringify(this.paiJobConfig)); //Trick for deep clone in Typescript + nniJobConfig.name = jobName; + if (nniJobConfig.taskRoles) { + + environment.nodeCount = 0; + // count instance + for (const taskRoleName in nniJobConfig.taskRoles) { + const taskRole = nniJobConfig.taskRoles[taskRoleName]; + let instanceCount = 1; + if (taskRole.instances) { + instanceCount = taskRole.instances; + } + environment.nodeCount += instanceCount; + } + + + // Each taskRole will generate new command in NNI's command format + // Each command will be formatted to NNI style + for (const taskRoleName in nniJobConfig.taskRoles) { + const taskRole = nniJobConfig.taskRoles[taskRoleName]; + // replace ' to '\'' + const joinedCommand = taskRole.commands.join(" && ").replace("'", "'\\''").trim(); + const nniTrialCommand = `${environment.command} --node_count ${environment.nodeCount} --trial_command '${joinedCommand}'`; + this.log.debug(`replace command ${taskRole.commands} to ${[nniTrialCommand]}`); + taskRole.commands = [nniTrialCommand]; + } + } + + } else { + nniJobConfig = { + protocolVersion: 2, + name: jobName, + type: 'job', + jobRetryCount: 0, + prerequisites: [ + { + type: 'dockerimage', + uri: this.paiTrialConfig.image, + name: 'docker_image_0' + } + ], + taskRoles: { + taskrole: { + instances: 1, + completion: { + minFailedInstances: 1, + minSucceededInstances: -1 + }, + taskRetryCount: 0, + dockerImage: 'docker_image_0', + resourcePerInstance: { + gpu: this.paiTrialConfig.gpuNum, + cpu: this.paiTrialConfig.cpuNum, + memoryMB: this.paiTrialConfig.memoryMB + }, + commands: [ + environment.command + ] + } + }, + extras: { + 'storages': [ + { + name: this.paiTrialConfig.paiStorageConfigName + } + ], + submitFrom: 'submit-job-v2' + } + } + if (this.paiTrialConfig.virtualCluster) { + nniJobConfig.defaults = { + virtualCluster: this.paiTrialConfig.virtualCluster + } + } + } + return yaml.safeDump(nniJobConfig); + } + + protected formatPAIHost(host: string): string { + // If users' host start with 'http://' or 'https://', use the original host, + // or format to 'http//${host}' + if (host.startsWith('http://')) { + this.protocol = 'http'; + return host.replace('http://', ''); + } else if (host.startsWith('https://')) { + this.protocol = 'https'; + return host.replace('https://', ''); + } else { + return host; + } + } + /** + * Update pai token by the interval time or initialize the pai token + */ + protected async updatePaiToken(): Promise { + const deferred: Deferred = new Deferred(); + + const currentTime: number = new Date().getTime(); + //If pai token initialized and not reach the interval time, do not update + if (this.paiTokenUpdateTime !== undefined && (currentTime - this.paiTokenUpdateTime) < this.paiTokenUpdateInterval) { + return Promise.resolve(); + } + + if (this.paiClusterConfig === undefined) { + const paiClusterConfigError: string = `pai cluster config not initialized!`; + this.log.error(`${paiClusterConfigError}`); + throw Error(`${paiClusterConfigError}`); + } + + const authenticationReq: request.Options = { + uri: `${this.protocol}://${this.paiClusterConfig.host}/rest-server/api/v1/token`, + method: 'POST', + json: true, + body: { + username: this.paiClusterConfig.userName, + password: this.paiClusterConfig.passWord + } + }; + + request(authenticationReq, (error: any, response: request.Response, body: any) => { + if (error !== undefined && error !== null) { + this.log.error(`Get PAI token failed: ${error.message}, authenticationReq: ${authenticationReq}`); + deferred.reject(new Error(`Get PAI token failed: ${error.message}`)); + } else { + if (response.statusCode !== 200) { + this.log.error(`Get PAI token failed: get PAI Rest return code ${response.statusCode}, authenticationReq: ${authenticationReq}`); + deferred.reject(new Error(`Get PAI token failed code: ${response.statusCode}, body: ${response.body}, authenticationReq: ${authenticationReq}, please check paiConfig username or password`)); + } else { + this.paiToken = body.token; + this.paiTokenUpdateTime = new Date().getTime(); + deferred.resolve(); + } + } + }); + + let timeoutId: NodeJS.Timer; + const timeoutDelay: Promise = new Promise((_resolve: Function, reject: Function): void => { + // Set timeout and reject the promise once reach timeout (5 seconds) + timeoutId = setTimeout( + () => reject(new Error('Get PAI token timeout. Please check your PAI cluster.')), + 5000); + }); + + return Promise.race([timeoutDelay, deferred.promise]) + .finally(() => { clearTimeout(timeoutId); }); + } +} diff --git a/src/nni_manager/training_service/reusable/routerTrainingService.ts b/src/nni_manager/training_service/reusable/routerTrainingService.ts new file mode 100644 index 0000000000..06280a8124 --- /dev/null +++ b/src/nni_manager/training_service/reusable/routerTrainingService.ts @@ -0,0 +1,157 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import { Container, Scope } from 'typescript-ioc'; +import * as component from '../../common/component'; +import { getLogger, Logger } from '../../common/log'; +import { TrainingService, TrialJobApplicationForm, TrialJobDetail, TrialJobMetric } from '../../common/trainingService'; +import { delay } from '../../common/utils'; +import { TrialConfigMetadataKey } from '../common/trialConfigMetadataKey'; +import { PAIClusterConfig } from '../pai/paiConfig'; +import { PAIK8STrainingService } from '../pai/paiK8S/paiK8STrainingService'; +import { EnvironmentService } from './environment'; +import { OpenPaiEnvironmentService } from './environments/openPaiEnvironmentService'; +import { MountedStorageService } from './storages/mountedStorageService'; +import { StorageService } from './storageService'; +import { TrialDispatcher } from './trialDispatcher'; + + +/** + * It's a intermedia implementation to support reusable training service. + * The final goal is to support reusable training job in higher level than training service. + */ +@component.Singleton +class RouterTrainingService implements TrainingService { + protected readonly log!: Logger; + private internalTrainingService: TrainingService | undefined; + private metaDataCache: Map = new Map(); + + constructor() { + this.log = getLogger(); + } + + public async listTrialJobs(): Promise { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + return await this.internalTrainingService.listTrialJobs(); + } + + public async getTrialJob(trialJobId: string): Promise { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + return await this.internalTrainingService.getTrialJob(trialJobId); + } + + public addTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + this.internalTrainingService.addTrialJobMetricListener(listener); + } + + public removeTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + this.internalTrainingService.removeTrialJobMetricListener(listener); + } + + public async submitTrialJob(form: TrialJobApplicationForm): Promise { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + return await this.internalTrainingService.submitTrialJob(form); + } + + public async updateTrialJob(trialJobId: string, form: TrialJobApplicationForm): Promise { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + return await this.internalTrainingService.updateTrialJob(trialJobId, form); + } + + public get isMultiPhaseJobSupported(): boolean { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + return this.internalTrainingService.isMultiPhaseJobSupported; + } + + public async cancelTrialJob(trialJobId: string, isEarlyStopped?: boolean | undefined): Promise { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + await this.internalTrainingService.cancelTrialJob(trialJobId, isEarlyStopped); + } + + public async setClusterMetadata(key: string, value: string): Promise { + if (this.internalTrainingService === undefined) { + if (key === TrialConfigMetadataKey.PAI_CLUSTER_CONFIG) { + const config = JSON.parse(value); + if (config.reuse === true) { + this.log.info(`reuse flag enabled, use EnvironmentManager.`); + this.internalTrainingService = component.get(TrialDispatcher); + + // TODO to support other serivces later. + Container.bind(EnvironmentService) + .to(OpenPaiEnvironmentService) + .scope(Scope.Singleton); + // TODO to support other storages later. + Container.bind(StorageService) + .to(MountedStorageService) + .scope(Scope.Singleton); + } else { + this.log.debug(`caching metadata key:{} value:{}, as training service is not determined.`); + this.internalTrainingService = component.get(PAIK8STrainingService); + } + for (const [key, value] of this.metaDataCache) { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + await this.internalTrainingService.setClusterMetadata(key, value); + } + + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + await this.internalTrainingService.setClusterMetadata(key, value); + + this.metaDataCache.clear(); + } else { + this.log.debug(`caching metadata key:{} value:{}, as training service is not determined.`); + this.metaDataCache.set(key, value); + } + } else { + await this.internalTrainingService.setClusterMetadata(key, value); + } + } + + public async getClusterMetadata(key: string): Promise { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + return await this.internalTrainingService.getClusterMetadata(key); + } + + public async cleanUp(): Promise { + if (this.internalTrainingService === undefined) { + throw new Error("TrainingService is not assigned!"); + } + await this.internalTrainingService.cleanUp(); + } + + public async run(): Promise { + // wait internal training service is assigned. + // It will be assigned after set metadata of paiConfig + while (this.internalTrainingService === undefined) { + await delay(100); + } + return await this.internalTrainingService.run(); + } +} + +export { RouterTrainingService }; diff --git a/src/nni_manager/training_service/reusable/storageService.ts b/src/nni_manager/training_service/reusable/storageService.ts new file mode 100644 index 0000000000..75b64c2606 --- /dev/null +++ b/src/nni_manager/training_service/reusable/storageService.ts @@ -0,0 +1,185 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import * as fs from 'fs'; +import * as os from 'os'; +import * as path from 'path'; +import { getLogger, Logger } from '../../common/log'; +import { uniqueString } from '../../common/utils'; +import { tarAdd } from '../common/util'; + +export abstract class StorageService { + protected localRoot: string = ""; + protected remoteRoot: string = ""; + protected logger: Logger; + + protected abstract internalConfig(key: string, value: string): void; + protected abstract async internalRemove(remotePath: string, isDirectory: boolean, isRecursive: boolean): Promise; + protected abstract async internalRename(remotePath: string, newName: string): Promise; + protected abstract async internalMkdir(remotePath: string): Promise; + protected abstract async internalCopy(sourcePath: string, targetPath: string, isDirectory: boolean, isFromRemote: boolean, isToRemote: boolean): Promise; + protected abstract async internalExists(remotePath: string): Promise; + protected abstract async internalRead(remotePath: string, offset: number, length: number): Promise; + protected abstract async internalList(remotePath: string): Promise; + protected abstract async internalAttach(remotePath: string, content: string): Promise; + protected abstract internalIsRelativePath(path: string): boolean; + protected abstract internalJoin(...paths: string[]): string; + protected abstract internalDirname(...paths: string[]): string; + protected abstract internalBasename(...paths: string[]): string; + + constructor() { + this.logger = getLogger(); + } + + public initialize(localRoot: string, remoteRoot: string): void { + this.logger.debug(`Initializing storage to local: ${localRoot} remote: ${remoteRoot}`); + this.localRoot = localRoot; + this.remoteRoot = remoteRoot; + } + + public async rename(remotePath: string, newName: string): Promise { + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`rename remotePath: ${remotePath} to: ${newName}`); + await this.internalRename(remotePath, newName); + } + + public async createDirectory(remotePath: string): Promise { + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`create remotePath: ${remotePath}`); + await this.internalMkdir(remotePath); + } + + public async copyDirectory(localPath: string, remotePath: string, asGzip: boolean = false): Promise { + localPath = this.expandPath(false, localPath); + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`copy localPath: ${localPath} to remotePath: ${remotePath}, asGzip ${asGzip}`); + if (!await this.internalExists(remotePath)) { + await this.internalMkdir(remotePath); + } + + if (asGzip) { + const localPathBaseName = path.basename(localPath); + const tempTarFileName = `nni_tmp_${localPathBaseName}_${uniqueString(5)}.tar.gz`; + const tarFileName = `${localPathBaseName}.tar.gz`; + const localTarPath: string = path.join(os.tmpdir(), tempTarFileName); + await tarAdd(localTarPath, localPath); + await this.internalCopy(localTarPath, remotePath, false, false, true); + const remoteFileName = this.internalJoin(remotePath, tempTarFileName); + await this.internalRename(remoteFileName, tarFileName); + await fs.promises.unlink(localTarPath); + + remotePath = this.internalJoin(remotePath, tarFileName); + } else { + await this.internalCopy(localPath, remotePath, true, false, true); + remotePath = this.internalJoin(remotePath, path.basename(localPath)); + } + + return remotePath; + } + + public async copyDirectoryBack(remotePath: string, localPath: string): Promise { + localPath = this.expandPath(false, localPath); + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`copy remotePath: ${remotePath} to localPath: ${localPath}`); + return await this.internalCopy(localPath, remotePath, true, true, false); + } + + public async removeDirectory(remotePath: string, isRecursive: boolean): Promise { + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`remove remotePath: ${remotePath}`); + await this.internalRemove(remotePath, true, isRecursive); + } + + public async readFileContent(remotePath: string, offset: number = -1, length: number = -1): Promise { + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`read remote file: ${remotePath}, offset: ${offset}, length: ${length}`); + return this.internalRead(remotePath, offset, length); + } + + public async listDirectory(remotePath: string): Promise { + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`list remotePath: ${remotePath}`); + return await this.internalList(remotePath); + } + + public async exists(remotePath: string): Promise { + remotePath = this.expandPath(true, remotePath); + const exists = await this.internalExists(remotePath); + this.logger.debug(`exists remotePath: ${remotePath} is ${exists}`); + return exists + } + + public async save(content: string, remotePath: string, isAttach: boolean = false): Promise { + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`saving content to remotePath: ${remotePath}, length: ${content.length}, isAttach: ${isAttach}`); + const remoteDir = this.internalDirname(remotePath); + + if (isAttach) { + if (await this.internalExists(remoteDir) === false) { + await this.internalMkdir(remoteDir); + } + const result = await this.internalAttach(remotePath, content); + if (false === result) { + throw new Error("this.internalAttach doesn't support"); + } + } else { + const fileName = this.internalBasename(remotePath); + const tempFileName = `temp_${uniqueString(4)}_${fileName}`; + const localTempFileName = path.join(os.tmpdir(), tempFileName); + const remoteTempFile = this.internalJoin(remoteDir, tempFileName); + + if (await this.internalExists(remotePath) === true) { + await this.internalRemove(remotePath, false, false); + } + await fs.promises.writeFile(localTempFileName, content); + await this.internalCopy(localTempFileName, remoteDir, false, false, true); + await this.rename(remoteTempFile, fileName); + await fs.promises.unlink(localTempFileName); + } + } + + public async copyFile(localPath: string, remotePath: string): Promise { + localPath = this.expandPath(false, localPath); + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`copying file localPath: ${localPath} to remotePath: ${remotePath}`); + await this.internalCopy(localPath, remotePath, false, false, true); + } + + public async copyFileBack(remotePath: string, localPath: string): Promise { + localPath = this.expandPath(false, localPath); + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`copy file remotePath: ${remotePath} to localPath: ${localPath}`); + await this.internalCopy(localPath, remotePath, false, true, false); + } + + public async removeFile(remotePath: string): Promise { + remotePath = this.expandPath(true, remotePath); + this.logger.debug(`remove file remotePath: ${remotePath}`); + await this.internalRemove(remotePath, false, false); + } + + public joinPath(...paths: string[]): string { + let fullPath = this.internalJoin(...paths); + if (this.internalIsRelativePath(fullPath) === true && this.remoteRoot !== "") { + fullPath = this.internalJoin(this.remoteRoot, fullPath); + } + return fullPath; + } + + private expandPath(isRemote: boolean, ...paths: string[]): string { + let normalizedPath: string; + + if (isRemote) { + normalizedPath = this.joinPath(...paths); + } else { + normalizedPath = path.join(...paths); + if (!path.isAbsolute(normalizedPath) && this.localRoot !== "") { + normalizedPath = path.join(this.localRoot, normalizedPath); + } + } + + return normalizedPath; + } +} diff --git a/src/nni_manager/training_service/reusable/storages/mountedStorageService.ts b/src/nni_manager/training_service/reusable/storages/mountedStorageService.ts new file mode 100644 index 0000000000..a3e592e74c --- /dev/null +++ b/src/nni_manager/training_service/reusable/storages/mountedStorageService.ts @@ -0,0 +1,148 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +import * as fs from 'fs'; +import * as path from 'path'; +import { Deferred } from "ts-deferred"; +import { StorageService } from "../storageService"; + +export class MountedStorageService extends StorageService { + + protected internalConfig(_key: string, _value: string): void { + // nothing to config + } + + protected async internalRemove(path: string, isDirectory: boolean, isRecursive: boolean): Promise { + if (isDirectory) { + if (isRecursive) { + const children = await fs.promises.readdir(path); + for (const file of children) { + const stat = await fs.promises.lstat(file); + this.internalRemove(file, stat.isDirectory(), isRecursive); + } + } else { + await fs.promises.rmdir(path); + } + } else { + await fs.promises.unlink(path); + } + } + + protected async internalRename(remotePath: string, newName: string): Promise { + const dirName = path.dirname(remotePath); + newName = this.internalJoin(dirName, newName); + + await fs.promises.rename(remotePath, newName); + } + + protected async internalMkdir(remotePath: string): Promise { + if (!fs.existsSync(remotePath)) { + await fs.promises.mkdir(remotePath, { recursive: true }); + } + } + + protected async internalCopy(sourcePath: string, targetPath: string, isDirectory: boolean, isFromRemote: boolean = false, isToRemote: boolean = true): Promise { + if (sourcePath === targetPath) { + return targetPath; + } + + this.logger.debug(`copying ${sourcePath} to ${targetPath}, dir ${isDirectory}, isFromRemote ${isFromRemote}, isToRemote: ${isToRemote}`); + if (isDirectory) { + const basename = isFromRemote ? this.internalBasename(sourcePath) : path.basename(sourcePath); + if (isToRemote) { + targetPath = this.internalJoin(targetPath, basename); + await this.internalMkdir(targetPath); + } else { + targetPath = path.join(targetPath, basename); + await fs.promises.mkdir(targetPath); + } + const children = await fs.promises.readdir(sourcePath); + for (const child of children) { + const childSourcePath = this.internalJoin(sourcePath, child); + const stat = await fs.promises.lstat(childSourcePath); + await this.internalCopy(childSourcePath, targetPath, stat.isDirectory(), isFromRemote, isToRemote); + } + return targetPath; + } else { + // This behavior may not be consistent for each platform, but it needs to correct to same + await this.internalMkdir(targetPath); + const targetFileName = path.join(targetPath, path.basename(sourcePath)); + await fs.promises.copyFile(sourcePath, targetFileName); + return targetFileName; + } + } + + protected async internalExists(remotePath: string): Promise { + const deferred = new Deferred(); + fs.exists(remotePath, (exists) => { + deferred.resolve(exists); + }); + return deferred.promise; + } + + protected async internalRead(remotePath: string, offset?: number, length?: number): Promise { + const deferred = new Deferred(); + // set a max length to 1MB for performance concern. + const maxLength = 1024 * 1024; + if (offset === undefined) { + offset = -1; + } + const current: number = offset < 0 ? 0 : offset; + if (length === undefined) { + length = -1; + } + const readLength: number = length < 0 ? maxLength : length; + let result: string = ""; + + const stream = fs.createReadStream(remotePath, + { + encoding: "utf8", + start: current, + end: readLength + current, + }).on("data", (data) => { + result += data; + }).on("end", () => { + stream.close(); + deferred.resolve(result); + }).on("error", (err) => { + deferred.reject(err); + }); + + return deferred.promise; + + } + + protected async internalList(remotePath: string): Promise { + let results: string[] = []; + + if (await this.internalExists(remotePath) === true) { + results = await fs.promises.readdir(remotePath); + } + + return results; + } + + protected async internalAttach(remotePath: string, content: string): Promise { + await fs.promises.appendFile(remotePath, content, { + encoding: "utf8", + flag: "a", + }); + return true; + } + + protected internalIsRelativePath(remotePath: string): boolean { + return !path.isAbsolute(remotePath); + } + + protected internalJoin(...paths: string[]): string { + return path.join(...paths); + } + + protected internalDirname(remotePath: string): string { + return path.dirname(remotePath); + } + + protected internalBasename(remotePath: string): string { + return path.basename(remotePath); + } +} diff --git a/src/nni_manager/training_service/reusable/trial.ts b/src/nni_manager/training_service/reusable/trial.ts new file mode 100644 index 0000000000..7f1c6323a5 --- /dev/null +++ b/src/nni_manager/training_service/reusable/trial.ts @@ -0,0 +1,42 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import { TrialJobApplicationForm, TrialJobDetail, TrialJobStatus } from "../../common/trainingService"; +import { GPUInfo } from "../../training_service/common/gpuData"; +import { EnvironmentInformation, NodeInfomation } from "./environment"; + +export class TrialDetail implements TrialJobDetail { + public id: string; + public status: TrialJobStatus; + public submitTime: number; + public startTime?: number; + public endTime?: number; + public tags?: string[]; + public url?: string; + public workingDirectory: string; + public form: TrialJobApplicationForm; + public isEarlyStopped?: boolean; + public environment?: EnvironmentInformation; + + // init settings of trial + public settings = {}; + // it's used to aggregate node status for multiple node trial + public nodes: Map; + // assigned GPUs for multi-trial scheduled. + public assignedGpus: GPUInfo[] = []; + + public readonly TRIAL_METADATA_DIR = ".nni"; + + constructor(id: string, status: TrialJobStatus, submitTime: number, + workingDirectory: string, form: TrialJobApplicationForm) { + this.id = id; + this.status = status; + this.submitTime = submitTime; + this.workingDirectory = workingDirectory; + this.form = form; + this.tags = []; + this.nodes = new Map(); + } +} diff --git a/src/nni_manager/training_service/reusable/trialDispatcher.ts b/src/nni_manager/training_service/reusable/trialDispatcher.ts new file mode 100644 index 0000000000..c9bb29bd62 --- /dev/null +++ b/src/nni_manager/training_service/reusable/trialDispatcher.ts @@ -0,0 +1,643 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +'use strict'; + +import { EventEmitter } from 'events'; +import * as fs from 'fs'; +import * as path from 'path'; +import { Writable } from 'stream'; +import { String } from 'typescript-string-operations'; +import * as component from '../../common/component'; +import { getExperimentId, getPlatform, getBasePort } from '../../common/experimentStartupInfo'; +import { getLogger, Logger } from '../../common/log'; +import { NNIManagerIpConfig, TrainingService, TrialJobApplicationForm, TrialJobMetric, TrialJobStatus } from '../../common/trainingService'; +import { delay, getExperimentRootDir, getLogLevel, getVersion, mkDirPSync, uniqueString } from '../../common/utils'; +import { GPU_INFO, INITIALIZED, KILL_TRIAL_JOB, NEW_TRIAL_JOB, REPORT_METRIC_DATA, SEND_TRIAL_JOB_PARAMETER, STDOUT, TRIAL_END, VERSION_CHECK } from '../../core/commands'; +import { GPUSummary } from '../../training_service/common/gpuData'; +import { CONTAINER_INSTALL_NNI_SHELL_FORMAT } from '../common/containerJobData'; +import { TrialConfig } from '../common/trialConfig'; +import { TrialConfigMetadataKey } from '../common/trialConfigMetadataKey'; +import { validateCodeDir } from '../common/util'; +import { WebCommandChannel } from './channels/webCommandChannel'; +import { Command, CommandChannel } from './commandChannel'; +import { EnvironmentInformation, EnvironmentService, NodeInfomation, RunnerSettings } from './environment'; +import { StorageService } from './storageService'; +import { TrialDetail } from './trial'; + + +/** + * It uses to manage jobs on training platforms + * and expose trial as trial job to upper level. +**/ +@component.Singleton +class TrialDispatcher implements TrainingService { + + private readonly NNI_METRICS_PATTERN: string = `NNISDK_MEb'(?.*?)'`; + private readonly log: Logger; + private readonly isDeveloping: boolean = false; + private stopping: boolean = false; + + private readonly metricsEmitter: EventEmitter; + private readonly experimentId: string; + + private enableVersionCheck: boolean = true; + + private trialConfig: TrialConfig | undefined; + private runnerSettings: RunnerSettings; + + private commandEmitter: EventEmitter | undefined; + private commandChannel: CommandChannel | undefined; + + private readonly trials: Map; + private readonly environments: Map; + + constructor() { + this.log = getLogger(); + this.trials = new Map(); + this.environments = new Map(); + this.metricsEmitter = new EventEmitter(); + this.experimentId = getExperimentId(); + this.runnerSettings = new RunnerSettings(); + this.runnerSettings.experimentId = this.experimentId; + this.runnerSettings.platform = getPlatform(); + + const logLevel = getLogLevel(); + this.log.debug(`current folder ${__dirname}`); + // different source folder in Linux and Windows + if (logLevel == "debug" && (fs.existsSync("../../../src/nni_manager") || __dirname.endsWith("src\\nni_manager\\dist\\training_service\\reusable"))) { + this.log.debug("log level is debug, and exist code folder, so set to developing mode."); + this.isDeveloping = true; + this.runnerSettings.enableGpuCollector = true; + } + } + + public async listTrialJobs(): Promise { + const trials: TrialDetail[] = []; + + for (const key of this.trials.keys()) { + trials.push(await this.getTrialJob(key)); + } + + return trials; + } + + public async getTrialJob(trialJobId: string): Promise { + const trial: TrialDetail | undefined = this.trials.get(trialJobId); + if (trial === undefined) { + throw new Error(`trial job ${trialJobId} not found`); + } + + return trial; + } + + public async submitTrialJob(form: TrialJobApplicationForm): Promise { + if (this.trialConfig === undefined) { + throw new Error(`trialConfig not initialized!`); + } + + const trialId: string = uniqueString(5); + + const environmentService = component.get(EnvironmentService); + let trialWorkingFolder: string = ""; + if (environmentService.hasStorageService) { + const storageService = component.get(StorageService); + trialWorkingFolder = storageService.joinPath('trials', trialId); + } + const trialJobDetail: TrialDetail = new TrialDetail(trialId, "WAITING", Date.now(), trialWorkingFolder, form); + + this.trials.set(trialId, trialJobDetail); + + return trialJobDetail; + } + + // to support multi phase + public async updateTrialJob(trialJobId: string, form: TrialJobApplicationForm): Promise { + const trialDetail = await this.getTrialJob(trialJobId); + const environment = trialDetail.environment; + if (environment === undefined) { + throw new Error(`TrialDispatcher: trial ${trialJobId}'s env shouldn't be undefined in updateTrialJob.`); + } + if (this.commandChannel === undefined) { + throw new Error(`TrialDispatcher: commandChannel shouldn't be undefined in updateTrialJob.`); + } + + const message = { + "trialId": trialJobId, + "parameters": form.hyperParameters, + } + await this.commandChannel.sendCommand(environment, SEND_TRIAL_JOB_PARAMETER, message); + + return trialDetail; + } + + public async cancelTrialJob(trialJobId: string, isEarlyStopped?: boolean | undefined): Promise { + if (this.commandChannel === undefined) { + throw new Error(`TrialDispatcher: commandChannel shouldn't be undefined in cancelTrialJob.`); + } + const trial = await this.getTrialJob(trialJobId); + switch (trial.status) { + case "RUNNING": + case "WAITING": + case "UNKNOWN": + { + const environment = trial.environment; + if (environment) { + await this.commandChannel.sendCommand(environment, KILL_TRIAL_JOB, trial.id); + trial.isEarlyStopped = isEarlyStopped; + trial.status = trial.isEarlyStopped === true ? + 'EARLY_STOPPED' : 'USER_CANCELED'; + this.releaseEnvironment(trial); + } + } + break; + } + } + + public async run(): Promise { + const environmentService = component.get(EnvironmentService); + + this.commandEmitter = new EventEmitter(); + this.commandChannel = new WebCommandChannel(this.commandEmitter); + + // TODO it's a hard code of web channel, it needs to be improved. + this.runnerSettings.nniManagerPort = getBasePort() + 1; + this.runnerSettings.commandChannel = this.commandChannel.channelName; + + // for AML channel, other channels can ignore this. + this.commandChannel.config("MetricEmitter", this.metricsEmitter); + + // start channel + this.commandEmitter.on("command", (command: Command): void => { + this.handleCommand(command).catch((err: Error) => { + this.log.error(`TrialDispatcher: error on handle env ${command.environment.id} command: ${command.command}, data: ${command.data}, error: ${err}`); + }) + }); + this.commandChannel.start(); + this.log.info(`TrialDispatcher: started channel: ${this.commandChannel.constructor.name}`); + + if (this.trialConfig === undefined) { + throw new Error(`trial config shouldn't be undefined in run()`); + } + + if (environmentService.hasStorageService) { + this.log.info(`TrialDispatcher: copying code and settings.`); + const storageService = component.get(StorageService); + // Copy the compressed file to remoteDirectory and delete it + const codeDir = path.resolve(this.trialConfig.codeDir); + const envDir = storageService.joinPath("envs"); + const codeFileName = await storageService.copyDirectory(codeDir, envDir, true); + storageService.rename(codeFileName, "nni-code.tar.gz"); + + const installFileName = storageService.joinPath(envDir, 'install_nni.sh'); + await storageService.save(CONTAINER_INSTALL_NNI_SHELL_FORMAT, installFileName); + + const runnerSettings = storageService.joinPath(envDir, "settings.json"); + await storageService.save(JSON.stringify(this.runnerSettings), runnerSettings); + + if (this.isDeveloping) { + let trialToolsPath = path.join(__dirname, "../../../../../tools/nni_trial_tool"); + if (false === fs.existsSync(trialToolsPath)) { + trialToolsPath = path.join(__dirname, "..\\..\\..\\..\\..\\tools\\nni_trial_tool"); + } + await storageService.copyDirectory(trialToolsPath, envDir, true); + } + } + + this.log.info(`TrialDispatcher: run loop started.`); + await Promise.all([ + this.environmentMaintenanceLoop(), + this.trialManagementLoop(), + ]); + } + + public addTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void { + this.metricsEmitter.on('metric', listener); + } + + public removeTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void { + this.metricsEmitter.off('metric', listener); + } + + public get isMultiPhaseJobSupported(): boolean { + return true; + } + + public async setClusterMetadata(key: string, value: string): Promise { + switch (key) { + case TrialConfigMetadataKey.NNI_MANAGER_IP: + this.runnerSettings.nniManagerIP = (JSON.parse(value)).nniManagerIp; + break; + case TrialConfigMetadataKey.VERSION_CHECK: + this.enableVersionCheck = (value === 'true' || value === 'True'); + this.runnerSettings.nniManagerVersion = this.enableVersionCheck ? await getVersion() : ''; + break; + case TrialConfigMetadataKey.LOG_COLLECTION: + this.runnerSettings.logCollection = value; + break; + case TrialConfigMetadataKey.TRIAL_CONFIG: + // TODO to support more storage types by better parameters. + this.trialConfig = JSON.parse(value); + + this.runnerSettings.command = this.trialConfig.command; + // Validate to make sure codeDir doesn't have too many files + await validateCodeDir(this.trialConfig.codeDir); + break; + } + const environmentService = component.get(EnvironmentService); + await environmentService.config(key, value); + } + + public getClusterMetadata(_key: string): Promise { + throw new Error('Not implemented!'); + } + + public async cleanUp(): Promise { + if (this.commandChannel === undefined) { + throw new Error(`TrialDispatcher: commandChannel shouldn't be undefined in cleanUp.`); + } + if (this.commandEmitter === undefined) { + throw new Error(`TrialDispatcher: commandEmitter shouldn't be undefined in cleanUp.`); + } + this.stopping = true; + const environmentService = component.get(EnvironmentService); + const environments = [...this.environments.values()]; + + for (let index = 0; index < environments.length; index++) { + const environment = environments[index]; + if (environment.isAlive === true) { + this.log.info(`stopping environment ${environment.id}...`); + await environmentService.stopEnvironment(environment); + await this.commandChannel.close(environment); + this.log.info(`stopped environment ${environment.id}.`); + } + } + + this.commandEmitter.off("command", this.handleCommand); + this.commandChannel.stop(); + } + + private async environmentMaintenanceLoop(): Promise { + if (this.commandChannel === undefined) { + throw new Error(`TrialDispatcher: commandChannel shouldn't be undefined in environmentMaintenanceLoop.`); + } + const environmentService = component.get(EnvironmentService); + while (!this.stopping) { + const environments: EnvironmentInformation[] = []; + for (const environment of this.environments.values()) { + if (environment.isAlive === true) { + environments.push(environment); + } else { + await this.commandChannel.close(environment); + } + } + await environmentService.refreshEnvironmentsStatus(environments); + + environments.forEach((environment) => { + const oldIsAlive = environment.isAlive; + switch (environment.status) { + case 'WAITING': + case 'RUNNING': + case 'UNKNOWN': + environment.isAlive = true; + break; + default: + environment.isAlive = false; + break; + } + if (oldIsAlive !== environment.isAlive) { + this.log.debug(`set environment ${environment.id} isAlive from ${oldIsAlive} to ${environment.isAlive} due to status is ${environment.status}.`); + } + }); + await delay(5000); + } + } + + private async trialManagementLoop(): Promise { + if (this.commandChannel === undefined) { + throw new Error(`TrialDispatcher: commandChannel shouldn't be undefined in trialManagementLoop.`); + } + + while (!this.stopping) { + await delay(2000); + + const toRefreshedTrials: TrialDetail[] = []; + for (const trial of this.trials.values()) { + if (trial.status === "RUNNING" || trial.status === "WAITING" || trial.status === "UNKNOWN") { + toRefreshedTrials.push(trial); + } + } + + if (toRefreshedTrials.length == 0) { + continue; + } + + const waitingTrials: TrialDetail[] = []; + let liveTrialsCount = 0; + for (const trial of toRefreshedTrials) { + const currentStatus = trial.status; + switch (currentStatus) { + case "RUNNING": + { + const environment = trial.environment; + + if (environment === undefined) { + this.log.error(`found running trial ${trial.id} has no environment, set trial to UNKNOWN.`); + trial.status = "UNKNOWN"; + liveTrialsCount++; + continue; + } + const environmentStatus = environment.status; + + // any node exit, then make sure the whole trial stopped. + if (trial.nodes.size > 0) { + const completedCount = trial.nodes.size; + let finalStatus: TrialJobStatus = "SUCCEEDED"; + let lastTimestamp: number | undefined; + this.log.debug(`found ${completedCount} completed trial node(s), nodeCount: ${environment.nodeCount}`); + + // if some trial processes doesn't exit, kill it for next one. + // for example, in horovod, it's just sleep command, has no impact on trial result. + if (environment.nodeCount > completedCount) { + this.log.info(`stop partial completed trial ${trial.id}`); + await this.commandChannel.sendCommand(environment, KILL_TRIAL_JOB, trial.id); + } + for (const node of trial.nodes.values()) { + if (node.status === "FAILED") { + finalStatus = "FAILED"; + } + if (node.endTime !== undefined) { + if (lastTimestamp === undefined) { + lastTimestamp = node.endTime + } else { + lastTimestamp = Math.max(node.endTime, lastTimestamp); + } + } + } + trial.status = finalStatus; + if (lastTimestamp === undefined) { + trial.endTime = lastTimestamp; + } + this.releaseEnvironment(trial); + } else if (environmentStatus !== "RUNNING") { + this.log.error(`found running trial ${trial.id} on '${environment.jobId}' with '${environmentStatus}', set trial to environment status.`); + this.releaseEnvironment(trial); + trial.status = environmentStatus; + } else { + liveTrialsCount++; + } + } + break; + case "WAITING": + case "UNKNOWN": + // deal it later, if there is free environment. + waitingTrials.push(trial); + liveTrialsCount++; + break; + } + } + + let liveEnvironmentsCount = 0; + const idleEnvironments: EnvironmentInformation[] = []; + this.environments.forEach((environment) => { + if (environment.isAlive === true) { + liveEnvironmentsCount++; + if (environment.status === "RUNNING" && environment.isIdle) { + idleEnvironments.push(environment); + } + } + }); + + while (idleEnvironments.length > 0 && waitingTrials.length > 0) { + const trial = waitingTrials.shift(); + const idleEnvironment = idleEnvironments.shift(); + if (trial !== undefined && idleEnvironment != undefined) { + await this.assignEnvironment(trial, idleEnvironment); + } + } + + if (liveEnvironmentsCount < liveTrialsCount) { + this.log.info(`request new environment, since live trials ${liveTrialsCount} ` + + `is more than live environments ${liveEnvironmentsCount}`); + for (let index = 0; index < liveTrialsCount - liveEnvironmentsCount; index++) { + await this.requestEnvironment(); + } + } + } + } + + private async requestEnvironment(): Promise { + if (this.commandChannel === undefined) { + throw new Error(`TrialDispatcher: commandChannel shouldn't be undefined in requestEnvironment.`); + } + + const environmentService = component.get(EnvironmentService); + const envId = uniqueString(5); + const envName = `nni_exp_${this.experimentId}_env_${envId}`; + const environment = environmentService.createEnviornmentInfomation(envId, envName); + + environment.command = `sh ../install_nni.sh && python3 -m nni_trial_tool.trial_runner`; + + if (this.isDeveloping) { + environment.command = "[ -d \"nni_trial_tool\" ] && echo \"nni_trial_tool exists already\" || (mkdir ./nni_trial_tool && tar -xof ../nni_trial_tool.tar.gz -C ./nni_trial_tool) && pip3 install websockets && " + environment.command; + } + + if (environmentService.hasStorageService) { + const storageService = component.get(StorageService); + environment.workingFolder = storageService.joinPath("envs", envId); + await storageService.createDirectory(environment.workingFolder); + } + + this.environments.set(environment.id, environment); + await environmentService.startEnvironment(environment); + + if (environment.status === "FAILED") { + environment.isIdle = false; + environment.isAlive = false; + throw new Error(`error on request environment ${environment.jobId}, please check log for more details.`); + } else { + environment.isIdle = true; + environment.isAlive = true; + } + + await this.commandChannel.open(environment); + this.log.info(`requested environment ${environment.id} and job id is ${environment.jobId}.`); + } + + private async assignEnvironment(trial: TrialDetail, environment: EnvironmentInformation): Promise { + if (this.commandChannel === undefined) { + throw new Error(`TrialDispatcher: commandChannel shouldn't be undefined in assignEnvironment.`); + } + + if (trial.environment) { + throw new Error(`trial ${trial.id} has assigned environment ${trial.environment.id} already, not assign to ${environment.id}!`); + } + if (environment.isIdle == false) { + throw new Error(`environment ${environment.id} is not idle, and cannot be assigned again!`); + } + this.log.info(`assigning environment ${environment.id} to trial ${trial.id}.`); + + environment.isIdle = false; + trial.environment = environment; + trial.settings = { + trialId: trial.id, + sequenceId: trial.form.sequenceId, + parameter: trial.form.hyperParameters, + } + trial.startTime = Date.now(); + trial.status = "RUNNING"; + await this.commandChannel.sendCommand(trial.environment, NEW_TRIAL_JOB, trial.settings); + } + + private releaseEnvironment(trial: TrialDetail): void { + if (!trial.environment) { + throw new Error(`environment is not assigned to trial ${trial.id}, and cannot be released!`); + } + if (trial.environment.isIdle) { + throw new Error(`environment ${trial.environment.id} is idle already!`); + } + trial.environment.isIdle = true; + trial.environment = undefined; + } + + private async handleMetricData(trialId: string, data: any): Promise { + if (Array.isArray(data)) { + for (const subItem of data) { + this.metricsEmitter.emit('metric', { + id: trialId, + data: subItem + }); + } + } else { + this.metricsEmitter.emit('metric', { + id: trialId, + data: data + }); + } + } + + private async handleStdout(commandData: any): Promise { + const trialLogDir: string = path.join(getExperimentRootDir(), 'trials', commandData["trial"]); + mkDirPSync(trialLogDir); + const trialLogPath: string = path.join(trialLogDir, 'stdout_log_collection.log'); + try { + let skipLogging: boolean = false; + if (commandData["tag"] === 'trial' && commandData["msg"] !== undefined) { + const message = commandData["msg"]; + const metricsContent: any = message.match(this.NNI_METRICS_PATTERN); + if (metricsContent && metricsContent.groups) { + const key: string = 'metrics'; + const data = metricsContent.groups[key]; + const metricData = JSON.parse('"' + data.split('"').join('\\"') + '"'); + await this.handleMetricData(commandData["trial"], metricData); + skipLogging = true; + } + } + + if (!skipLogging) { + // Construct write stream to write remote trial's log into local file + const writeStream: Writable = fs.createWriteStream(trialLogPath, { + flags: 'a+', + encoding: 'utf8', + autoClose: true + }); + + writeStream.write(String.Format('{0}\n', commandData["msg"])); + writeStream.end(); + } + } catch (err) { + this.log.error(`TrialDispatcher: handleStdout error: ${err}`); + } + } + + private async handleCommand(command: Command): Promise { + this.log.debug(`TrialDispatcher: env ${command.environment.id} received command ${command.command}, data: ${command.data}`); + const environment = command.environment; + const data = command.data; + const nodeId = data["node"]; + switch (command.command) { + case REPORT_METRIC_DATA: + this.log.error(`TrialDispatcher: TODO: not implement to handle direct REPORT_METRIC_DATA command yet.`); + break; + case STDOUT: + await this.handleStdout(data); + break; + case INITIALIZED: + { + const oldStatus = environment.status; + let isAllReady = true; + + if (environment.nodeCount > 1) { + let node = environment.nodes.get(nodeId); + if (node === undefined) { + node = new NodeInfomation(nodeId); + environment.nodes.set(nodeId, node); + } + const oldNodeStatus = node.status; + if (oldNodeStatus === "UNKNOWN" || oldNodeStatus === "WAITING") { + node.status = "RUNNING"; + } + + if (environment.nodes.size === environment.nodeCount) { + for (const node of environment.nodes.values()) { + if (node.status !== "RUNNING") { + isAllReady = false; + break; + } + } + } else { + isAllReady = false; + } + } + + // single node is always ready to set env status + if (isAllReady && oldStatus === "UNKNOWN") { + environment.status = "RUNNING"; + this.log.info(`TrialDispatcher: env ${environment.id} received initialized message, old status: ${oldStatus}, new status: ${environment.status}.`); + } + } + break; + case VERSION_CHECK: + { + if (this.enableVersionCheck) { + const checkResultSuccess: boolean = data["tag"] === 'VCSuccess' ? true : false; + if (checkResultSuccess) { + this.log.info(`TrialDispatcher: Version check in trialKeeper success!`); + } else { + const errorMessage = `TrialDispatcher: Version check error, ${data["msg"]}!`; + this.log.error(errorMessage); + } + } + } + break; + case GPU_INFO: + environment.gpuSummary.set(nodeId, (data)); + break; + case TRIAL_END: + { + const trialId = data["trial"]; + const trial = await this.getTrialJob(trialId); + const code = parseInt(data["code"]); + const timestamp = parseInt(data["time"]); + let exitStatus: TrialJobStatus = "SUCCEEDED"; + if (code !== 0) { + exitStatus = "FAILED"; + } + + let node = environment.nodes.get(nodeId); + if (node === undefined) { + node = new NodeInfomation(nodeId); + trial.nodes.set(nodeId, node); + } + if (undefined === node) { + throw new Error("node is impossible to be undefined (see above code), but make eslint happy!"); + } + node.status = exitStatus; + node.endTime = timestamp; + } + break; + } + } +} + +export { TrialDispatcher }; diff --git a/src/nni_manager/yarn.lock b/src/nni_manager/yarn.lock index afe17342f2..15b5b7340a 100644 --- a/src/nni_manager/yarn.lock +++ b/src/nni_manager/yarn.lock @@ -11,14 +11,12 @@ "@babel/code-frame@^7.8.3": version "7.8.3" resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.8.3.tgz#33e25903d7481181534e12ec0a25f16b6fcf419e" - integrity sha512-a9gxpmdXtZEInkCSHUJDLHZVBgb1QS0jhss4cPP93EW7s+uC5bikET2twEF3KV+7rDblJcmNvTR7VJejqd2C2g== dependencies: "@babel/highlight" "^7.8.3" "@babel/core@^7.7.5": version "7.9.0" resolved "https://registry.yarnpkg.com/@babel/core/-/core-7.9.0.tgz#ac977b538b77e132ff706f3b8a4dbad09c03c56e" - integrity sha512-kWc7L0fw1xwvI0zi8OKVBuxRVefwGOrKSQMvrQ3dW+bIIavBY3/NpXmpjMy7bQnLgwgzWQZ8TlM57YHpHNHz4w== dependencies: "@babel/code-frame" "^7.8.3" "@babel/generator" "^7.9.0" @@ -40,7 +38,6 @@ "@babel/generator@^7.9.0": version "7.9.4" resolved "https://registry.yarnpkg.com/@babel/generator/-/generator-7.9.4.tgz#12441e90c3b3c4159cdecf312075bf1a8ce2dbce" - integrity sha512-rjP8ahaDy/ouhrvCoU1E5mqaitWrxwuNGU+dy1EpaoK48jZay4MdkskKGIMHLZNewg8sAsqpGSREJwP0zH3YQA== dependencies: "@babel/types" "^7.9.0" jsesc "^2.5.1" @@ -50,7 +47,6 @@ "@babel/helper-function-name@^7.8.3": version "7.8.3" resolved "https://registry.yarnpkg.com/@babel/helper-function-name/-/helper-function-name-7.8.3.tgz#eeeb665a01b1f11068e9fb86ad56a1cb1a824cca" - integrity sha512-BCxgX1BC2hD/oBlIFUgOCQDOPV8nSINxCwM3o93xP4P9Fq6aV5sgv2cOOITDMtCfQ+3PvHp3l689XZvAM9QyOA== dependencies: "@babel/helper-get-function-arity" "^7.8.3" "@babel/template" "^7.8.3" @@ -59,28 +55,24 @@ "@babel/helper-get-function-arity@^7.8.3": version "7.8.3" resolved "https://registry.yarnpkg.com/@babel/helper-get-function-arity/-/helper-get-function-arity-7.8.3.tgz#b894b947bd004381ce63ea1db9f08547e920abd5" - integrity sha512-FVDR+Gd9iLjUMY1fzE2SR0IuaJToR4RkCDARVfsBBPSP53GEqSFjD8gNyxg246VUyc/ALRxFaAK8rVG7UT7xRA== dependencies: "@babel/types" "^7.8.3" "@babel/helper-member-expression-to-functions@^7.8.3": version "7.8.3" resolved "https://registry.yarnpkg.com/@babel/helper-member-expression-to-functions/-/helper-member-expression-to-functions-7.8.3.tgz#659b710498ea6c1d9907e0c73f206eee7dadc24c" - integrity sha512-fO4Egq88utkQFjbPrSHGmGLFqmrshs11d46WI+WZDESt7Wu7wN2G2Iu+NMMZJFDOVRHAMIkB5SNh30NtwCA7RA== dependencies: "@babel/types" "^7.8.3" "@babel/helper-module-imports@^7.8.3": version "7.8.3" resolved "https://registry.yarnpkg.com/@babel/helper-module-imports/-/helper-module-imports-7.8.3.tgz#7fe39589b39c016331b6b8c3f441e8f0b1419498" - integrity sha512-R0Bx3jippsbAEtzkpZ/6FIiuzOURPcMjHp+Z6xPe6DtApDJx+w7UYyOLanZqO8+wKR9G10s/FmHXvxaMd9s6Kg== dependencies: "@babel/types" "^7.8.3" "@babel/helper-module-transforms@^7.9.0": version "7.9.0" resolved "https://registry.yarnpkg.com/@babel/helper-module-transforms/-/helper-module-transforms-7.9.0.tgz#43b34dfe15961918707d247327431388e9fe96e5" - integrity sha512-0FvKyu0gpPfIQ8EkxlrAydOWROdHpBmiCiRwLkUiBGhCUPRRbVD2/tm3sFr/c/GWFrQ/ffutGUAnx7V0FzT2wA== dependencies: "@babel/helper-module-imports" "^7.8.3" "@babel/helper-replace-supers" "^7.8.6" @@ -93,14 +85,12 @@ "@babel/helper-optimise-call-expression@^7.8.3": version "7.8.3" resolved "https://registry.yarnpkg.com/@babel/helper-optimise-call-expression/-/helper-optimise-call-expression-7.8.3.tgz#7ed071813d09c75298ef4f208956006b6111ecb9" - integrity sha512-Kag20n86cbO2AvHca6EJsvqAd82gc6VMGule4HwebwMlwkpXuVqrNRj6CkCV2sKxgi9MyAUnZVnZ6lJ1/vKhHQ== dependencies: "@babel/types" "^7.8.3" "@babel/helper-replace-supers@^7.8.6": version "7.8.6" resolved "https://registry.yarnpkg.com/@babel/helper-replace-supers/-/helper-replace-supers-7.8.6.tgz#5ada744fd5ad73203bf1d67459a27dcba67effc8" - integrity sha512-PeMArdA4Sv/Wf4zXwBKPqVj7n9UF/xg6slNRtZW84FM7JpE1CbG8B612FyM4cxrf4fMAMGO0kR7voy1ForHHFA== dependencies: "@babel/helper-member-expression-to-functions" "^7.8.3" "@babel/helper-optimise-call-expression" "^7.8.3" @@ -110,7 +100,6 @@ "@babel/helper-simple-access@^7.8.3": version "7.8.3" resolved "https://registry.yarnpkg.com/@babel/helper-simple-access/-/helper-simple-access-7.8.3.tgz#7f8109928b4dab4654076986af575231deb639ae" - integrity sha512-VNGUDjx5cCWg4vvCTR8qQ7YJYZ+HBjxOgXEl7ounz+4Sn7+LMD3CFrCTEU6/qXKbA2nKg21CwhhBzO0RpRbdCw== dependencies: "@babel/template" "^7.8.3" "@babel/types" "^7.8.3" @@ -118,19 +107,16 @@ "@babel/helper-split-export-declaration@^7.8.3": version "7.8.3" resolved "https://registry.yarnpkg.com/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.8.3.tgz#31a9f30070f91368a7182cf05f831781065fc7a9" - integrity sha512-3x3yOeyBhW851hroze7ElzdkeRXQYQbFIb7gLK1WQYsw2GWDay5gAJNw1sWJ0VFP6z5J1whqeXH/WCdCjZv6dA== dependencies: "@babel/types" "^7.8.3" "@babel/helper-validator-identifier@^7.9.0": version "7.9.0" resolved "https://registry.yarnpkg.com/@babel/helper-validator-identifier/-/helper-validator-identifier-7.9.0.tgz#ad53562a7fc29b3b9a91bbf7d10397fd146346ed" - integrity sha512-6G8bQKjOh+of4PV/ThDm/rRqlU7+IGoJuofpagU5GlEl29Vv0RGqqt86ZGRV8ZuSOY3o+8yXl5y782SMcG7SHw== "@babel/helpers@^7.9.0": version "7.9.2" resolved "https://registry.yarnpkg.com/@babel/helpers/-/helpers-7.9.2.tgz#b42a81a811f1e7313b88cba8adc66b3d9ae6c09f" - integrity sha512-JwLvzlXVPjO8eU9c/wF9/zOIN7X6h8DYf7mG4CiFRZRvZNKEF5dQ3H3V+ASkHoIB3mWhatgl5ONhyqHRI6MppA== dependencies: "@babel/template" "^7.8.3" "@babel/traverse" "^7.9.0" @@ -147,7 +133,6 @@ "@babel/highlight@^7.8.3": version "7.9.0" resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.9.0.tgz#4e9b45ccb82b79607271b2979ad82c7b68163079" - integrity sha512-lJZPilxX7Op3Nv/2cvFdnlepPXDxi29wxteT57Q965oc5R9v86ztx0jfxVrTcBk8C2kcPkkDa2Z4T3ZsPPVWsQ== dependencies: "@babel/helper-validator-identifier" "^7.9.0" chalk "^2.0.0" @@ -156,12 +141,10 @@ "@babel/parser@^7.7.5", "@babel/parser@^7.8.6", "@babel/parser@^7.9.0": version "7.9.4" resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.9.4.tgz#68a35e6b0319bbc014465be43828300113f2f2e8" - integrity sha512-bC49otXX6N0/VYhgOMh4gnP26E9xnDZK3TmbNpxYzzz9BQLBosQwfyOe9/cXUU3txYhTzLCbcqd5c8y/OmCjHA== "@babel/template@^7.7.4", "@babel/template@^7.8.3", "@babel/template@^7.8.6": version "7.8.6" resolved "https://registry.yarnpkg.com/@babel/template/-/template-7.8.6.tgz#86b22af15f828dfb086474f964dcc3e39c43ce2b" - integrity sha512-zbMsPMy/v0PWFZEhQJ66bqjhH+z0JgMoBWuikXybgG3Gkd/3t5oQ1Rw2WQhnSrsOmsKXnZOx15tkC4qON/+JPg== dependencies: "@babel/code-frame" "^7.8.3" "@babel/parser" "^7.8.6" @@ -170,7 +153,6 @@ "@babel/traverse@^7.7.4", "@babel/traverse@^7.8.6", "@babel/traverse@^7.9.0": version "7.9.0" resolved "https://registry.yarnpkg.com/@babel/traverse/-/traverse-7.9.0.tgz#d3882c2830e513f4fe4cec9fe76ea1cc78747892" - integrity sha512-jAZQj0+kn4WTHO5dUZkZKhbFrqZE7K5LAQ5JysMnmvGij+wOdr+8lWqPeW0BcF4wFwrEXXtdGO7wcV6YPJcf3w== dependencies: "@babel/code-frame" "^7.8.3" "@babel/generator" "^7.9.0" @@ -185,7 +167,6 @@ "@babel/types@^7.8.3", "@babel/types@^7.8.6", "@babel/types@^7.9.0": version "7.9.0" resolved "https://registry.yarnpkg.com/@babel/types/-/types-7.9.0.tgz#00b064c3df83ad32b2dbf5ff07312b15c7f1efb5" - integrity sha512-BS9JKfXkzzJl8RluW4JGknzpiUV7ZrvTayM6yfqLTVBEnFtyowVIOu6rqxRd5cVO6yGoWf4T8u8dgK9oB+GCng== dependencies: "@babel/helper-validator-identifier" "^7.9.0" lodash "^4.17.13" @@ -194,7 +175,6 @@ "@istanbuljs/load-nyc-config@^1.0.0": version "1.0.0" resolved "https://registry.yarnpkg.com/@istanbuljs/load-nyc-config/-/load-nyc-config-1.0.0.tgz#10602de5570baea82f8afbfa2630b24e7a8cfe5b" - integrity sha512-ZR0rq/f/E4f4XcgnDvtMWXCUJpi8eO0rssVhmztsZqLIEFA9UUP9zmpE0VxlM+kv/E1ul2I876Fwil2ayptDVg== dependencies: camelcase "^5.3.1" find-up "^4.1.0" @@ -204,7 +184,6 @@ "@istanbuljs/schema@^0.1.2": version "0.1.2" resolved "https://registry.yarnpkg.com/@istanbuljs/schema/-/schema-0.1.2.tgz#26520bf09abe4a5644cd5414e37125a8954241dd" - integrity sha512-tsAQNx32a8CoFhjhijUIhI4kccIAgmGhy8LZMZgGfmXcpMbPRUqn5LWmgRttILi6yeGmBJd2xsPkFMs0PzgPCw== "@sindresorhus/is@^0.7.0": version "0.7.0" @@ -234,7 +213,6 @@ "@types/color-name@^1.1.1": version "1.1.1" resolved "https://registry.yarnpkg.com/@types/color-name/-/color-name-1.1.1.tgz#1c1261bbeaa10a8055bbc5d8ab84b7b2afc846a0" - integrity sha512-rr+OQyAjxze7GgWrSaJwydHStIhHq2lvY3BOC2Mj7KnzI7XK0Uw1TOOdI9lDoajEbSWLiYgoo4f1R51erQfhPQ== "@types/connect@*": version "3.4.32" @@ -465,6 +443,12 @@ version "2.3.3" resolved "https://registry.yarnpkg.com/@types/tough-cookie/-/tough-cookie-2.3.3.tgz#7f226d67d654ec9070e755f46daebf014628e9d9" +"@types/ws@^7.2.5": + version "7.2.5" + resolved "https://registry.yarnpkg.com/@types/ws/-/ws-7.2.5.tgz#513f28b04a1ea1aa9dc2cad3f26e8e37c88aae49" + dependencies: + "@types/node" "*" + "@typescript-eslint/eslint-plugin@^2.10.0": version "2.10.0" resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-2.10.0.tgz#c4cb103275e555e8a7e9b3d14c5951eb6d431e70" @@ -529,7 +513,6 @@ acorn-jsx@^5.1.0: acorn@>=7.1.1, acorn@^7.1.0: version "7.1.1" resolved "https://registry.yarnpkg.com/acorn/-/acorn-7.1.1.tgz#e35668de0b402f359de515c5482a1ab9f89a69bf" - integrity sha512-add7dgA5ppRPxCFJoAGfMDi7PIBXq1RtGo7BhbLaxwrXPOmw8gq48Y9ozT01hUKy9byMjlR20EJhu5zlkErEkg== agent-base@4, agent-base@^4.3.0: version "4.3.0" @@ -559,7 +542,6 @@ aggregate-error@^1.0.0: aggregate-error@^3.0.0: version "3.0.1" resolved "https://registry.yarnpkg.com/aggregate-error/-/aggregate-error-3.0.1.tgz#db2fe7246e536f40d9b5442a39e117d7dd6a24e0" - integrity sha512-quoaXsZ9/BLNae5yiNoUz+Nhkwz83GhWwtYFglcjEQB2NDHCIpApbqXxIFnm4Pq/Nvhrsq5sYJFyohrrxnTGAA== dependencies: clean-stack "^2.0.0" indent-string "^4.0.0" @@ -591,7 +573,6 @@ ansi-align@^2.0.0: ansi-colors@3.2.3: version "3.2.3" resolved "https://registry.yarnpkg.com/ansi-colors/-/ansi-colors-3.2.3.tgz#57d35b8686e851e2cc04c403f1c00203976a1813" - integrity sha512-LEHHyuhlPY3TmuUYMh2oz89lTShfvgbmzaBcxve9t/9Wuy7Dwf4yoAKcND7KFT1HAQfqZ12qtc+DUrBMeKF9nw== ansi-escapes@^4.2.1: version "4.3.0" @@ -624,7 +605,6 @@ ansi-styles@^3.2.0, ansi-styles@^3.2.1: ansi-styles@^4.0.0: version "4.2.1" resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-4.2.1.tgz#90ae75c424d008d2624c5bf29ead3177ebfcf359" - integrity sha512-9VGjrMsG1vePxcSweQsN20KY/c4zN0h9fLjqAbwbPfahM3t+NL+M9HC8xeXG2I8pX5NoamTGNuomEUFI7fcUjA== dependencies: "@types/color-name" "^1.1.1" color-convert "^2.0.1" @@ -640,7 +620,6 @@ ansistyles@~0.1.3: anymatch@~3.1.1: version "3.1.1" resolved "https://registry.yarnpkg.com/anymatch/-/anymatch-3.1.1.tgz#c55ecf02185e2469259399310c173ce31233b142" - integrity sha512-mM8522psRCqzV+6LhomX5wgp25YVibjh8Wj23I5RPkPppSVSjyKD2A2mBJmWGa+KN7f2D6LNh9jkBCeyLktzjg== dependencies: normalize-path "^3.0.0" picomatch "^2.0.4" @@ -648,7 +627,6 @@ anymatch@~3.1.1: append-transform@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/append-transform/-/append-transform-2.0.0.tgz#99d9d29c7b38391e6f428d28ce136551f0b77e12" - integrity sha512-7yeyCEurROLQJFv5Xj4lEGTy0borxepjFv1g22oAdqFu//SrAlDl1O1Nxx15SH1RoliUml6p8dwJW9jvZughhg== dependencies: default-require-extensions "^3.0.0" @@ -779,7 +757,6 @@ bin-links@^1.1.2, bin-links@^1.1.6: binary-extensions@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.0.0.tgz#23c0df14f6a88077f5f986c0d167ec03c3d5537c" - integrity sha512-Phlt0plgpIIBOGTT/ehfFnbNlfsDEiqmzE2KRXoX1bLIlir4X/MR+zSyBEkL05ffWgnRSf/DXv+WrUAVr93/ow== bluebird@^3.5.1, bluebird@^3.5.3, bluebird@^3.5.5: version "3.7.2" @@ -828,7 +805,6 @@ brace-expansion@^1.1.7: braces@~3.0.2: version "3.0.2" resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.2.tgz#3454e1a462ee8d599e236df336cd9ea4f8afe107" - integrity sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A== dependencies: fill-range "^7.0.1" @@ -899,7 +875,6 @@ cacheable-request@^2.1.1: caching-transform@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/caching-transform/-/caching-transform-4.0.0.tgz#00d297a4206d71e2163c39eaffa8157ac0651f0f" - integrity sha512-kpqOvwXnjjN44D89K5ccQC+RUrsy7jB/XLlRrx0D7/2HNcTPqzsb6XgYoErwko6QsV184CA2YgS1fxDiiDZMWA== dependencies: hasha "^5.0.0" make-dir "^3.0.0" @@ -925,7 +900,6 @@ camelcase@^4.0.0, camelcase@^4.1.0: camelcase@^5.0.0, camelcase@^5.3.1: version "5.3.1" resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-5.3.1.tgz#e3c9b31569e106811df242f715725a1f4c494320" - integrity sha512-L28STB170nwWS63UjtlEOE3dldQApaJXZkOI1uMFfzf3rRuPegHaHesyee+YxQ+W6SvRDQV6UrdOdRiR153wJg== capture-stack-trace@^1.0.0: version "1.0.1" @@ -987,7 +961,6 @@ child-process-promise@^2.2.1: chokidar@3.3.0: version "3.3.0" resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.3.0.tgz#12c0714668c55800f659e262d4962a97faf554a6" - integrity sha512-dGmKLDdT3Gdl7fBUe8XK+gAtGmzy5Fn0XkkWQuYxGIgWVPPse2CxFA5mtrlD0TOHaHjEUqkWNyP1XdHoJES/4A== dependencies: anymatch "~3.1.1" braces "~3.0.2" @@ -1033,7 +1006,6 @@ clean-stack@^1.0.0: clean-stack@^2.0.0: version "2.2.0" resolved "https://registry.yarnpkg.com/clean-stack/-/clean-stack-2.2.0.tgz#ee8472dbb129e727b31e8a10a427dee9dfe4008b" - integrity sha512-4diC9HaTE+KRAMWhDhrGOECgWZxoevMc5TlkObMqNSsVU62PYzXZ/SMTjzyGAFF1YusgxGcSWTEXBhp0CPwQ1A== cli-boxes@^1.0.0: version "1.0.0" @@ -1076,7 +1048,6 @@ cliui@^4.0.0: cliui@^5.0.0: version "5.0.0" resolved "https://registry.yarnpkg.com/cliui/-/cliui-5.0.0.tgz#deefcfdb2e800784aa34f46fa08e06851c7bbbc5" - integrity sha512-PYeGSEmmHM6zvoef2w8TPzlrnNpXIjTipYK780YswmIP9vjxmd6Y2a3CB2Ks6/AU8NHjZugXvo8w3oWM2qnwXA== dependencies: string-width "^3.1.0" strip-ansi "^5.2.0" @@ -1085,7 +1056,6 @@ cliui@^5.0.0: cliui@^6.0.0: version "6.0.0" resolved "https://registry.yarnpkg.com/cliui/-/cliui-6.0.0.tgz#511d702c0c4e41ca156d7d0e96021f23e13225b1" - integrity sha512-t6wbgtoCXvAzst7QgXxJYqPt0usEfbgQdftEPbLL/cvv6HPE5VgvqCuAIDR0NgU52ds6rFwqrgakNLrHEjCbrQ== dependencies: string-width "^4.2.0" strip-ansi "^6.0.0" @@ -1125,7 +1095,6 @@ color-convert@^1.9.0: color-convert@^2.0.1: version "2.0.1" resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3" - integrity sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ== dependencies: color-name "~1.1.4" @@ -1136,7 +1105,6 @@ color-name@1.1.1: color-name@~1.1.4: version "1.1.4" resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2" - integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA== colors@^1.1.2: version "1.4.0" @@ -1211,7 +1179,6 @@ content-type@~1.0.4: convert-source-map@^1.7.0: version "1.7.0" resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-1.7.0.tgz#17a2cb882d7f77d3490585e2ce6c524424a3a442" - integrity sha512-4FJkXzKXEDB1snCFZlLP4gpC3JILicCpGbzG9f9G7tGqGCzETQ2hWPrcinA9oU4wtf2biUaEH5065UnMeR33oA== dependencies: safe-buffer "~5.1.1" @@ -1272,7 +1239,6 @@ cross-spawn@^6.0.0, cross-spawn@^6.0.5: cross-spawn@^7.0.0: version "7.0.1" resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.1.tgz#0ab56286e0f7c24e153d04cc2aa027e43a9a5d14" - integrity sha512-u7v4o84SwFpD32Z8IIcPZ6z1/ie24O6RU3RbtL5Y316l3KuHVPx9ItBgWQ6VlfAFnRnTtMUrsQ9MUUTuEZjogg== dependencies: path-key "^3.1.0" shebang-command "^2.0.0" @@ -1355,7 +1321,6 @@ deepmerge@^2.1.1: default-require-extensions@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/default-require-extensions/-/default-require-extensions-3.0.0.tgz#e03f93aac9b2b6443fc52e5e4a37b3ad9ad8df96" - integrity sha512-ek6DpXq/SCpvjhpFsLFRVtIxJCRw6fUR42lYMVZuUMK7n8eMz4Uh5clckdBjEpLhn/gEBZo7hDJnJcwdKLKQjg== dependencies: strip-bom "^4.0.0" @@ -1766,7 +1731,6 @@ file-entry-cache@^5.0.1: fill-range@^7.0.1: version "7.0.1" resolved "https://registry.yarnpkg.com/fill-range/-/fill-range-7.0.1.tgz#1919a6a7c75fe38b2c7c77e5198535da9acdda40" - integrity sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ== dependencies: to-regex-range "^5.0.1" @@ -1785,7 +1749,6 @@ finalhandler@1.1.1: find-cache-dir@^3.2.0: version "3.3.1" resolved "https://registry.yarnpkg.com/find-cache-dir/-/find-cache-dir-3.3.1.tgz#89b33fad4a4670daa94f855f7fbe31d6d84fe880" - integrity sha512-t2GDMt3oGC/v+BMwzmllWDuJF/xcDtE5j/fCGbqDD7OLuJkj0cfh1YSA5VKPvwMeLFLNDBkwOKZ2X85jGLVftQ== dependencies: commondir "^1.0.1" make-dir "^3.0.2" @@ -1810,7 +1773,6 @@ find-up@^2.1.0: find-up@^4.0.0, find-up@^4.1.0: version "4.1.0" resolved "https://registry.yarnpkg.com/find-up/-/find-up-4.1.0.tgz#97afe7d6cdc0bc5928584b7c8d7b16e8a9aa5d19" - integrity sha512-PpOwAdQ/YlXQ2vj8a3h8IipDuYRi3wceVQQGYWxNINccq40Anw7BlsEXCMbt1Zt+OLA6Fq9suIpIWD0OsnISlw== dependencies: locate-path "^5.0.0" path-exists "^4.0.0" @@ -1826,7 +1788,6 @@ flat-cache@^2.0.1: flat@^4.1.0: version "4.1.0" resolved "https://registry.yarnpkg.com/flat/-/flat-4.1.0.tgz#090bec8b05e39cba309747f1d588f04dbaf98db2" - integrity sha512-Px/TiLIznH7gEDlPXcUD4KnBusa6kR6ayRUVcnEAbreRIuhkqow/mun59BuRXwoYk7ZQOLW1ZM05ilIvK38hFw== dependencies: is-buffer "~2.0.3" @@ -1850,7 +1811,6 @@ flush-write-stream@^1.0.0: foreground-child@^2.0.0: version "2.0.0" resolved 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gensync@^1.0.0-beta.1: version "1.0.0-beta.1" resolved "https://registry.yarnpkg.com/gensync/-/gensync-1.0.0-beta.1.tgz#58f4361ff987e5ff6e1e7a210827aa371eaac269" - integrity sha512-r8EC6NO1sngH/zdD9fiRDLdcgnbayXah+mLgManTaIZJqEC1MZstmnox8KpnI2/fxQwrp5OpCOYWLp4rBl4Jcg== gentle-fs@^2.3.0: version "2.3.0" @@ -1986,7 +1943,6 @@ get-caller-file@^1.0.1: get-caller-file@^2.0.1: version "2.0.5" resolved "https://registry.yarnpkg.com/get-caller-file/-/get-caller-file-2.0.5.tgz#4f94412a82db32f36e3b0b9741f8a97feb031f7e" - integrity sha512-DyFP3BM/3YHTQOCUL/w0OZHR0lpKeGrxotcHWcqNEdnltqFwXVfhEBQ94eIo34AfQpo0rGki4cyIiftY06h2Fg== get-func-name@^2.0.0: version "2.0.0" @@ -2024,7 +1980,6 @@ glob-parent@^5.0.0: glob-parent@~5.1.0: version "5.1.1" resolved "https://registry.yarnpkg.com/glob-parent/-/glob-parent-5.1.1.tgz#b6c1ef417c4e5663ea498f1c45afac6916bbc229" - integrity sha512-FnI+VGOpnlGHWZxthPGR+QhR78fuiK0sNLkHQv+bL9fQi57lNNdquIbna/WrfROrolq8GK5Ek6BiMwqL/voRYQ== dependencies: is-glob "^4.0.1" @@ -2162,7 +2117,6 @@ has-flag@^3.0.0: has-flag@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b" - integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ== has-symbol-support-x@^1.4.1: version "1.4.2" @@ -2198,7 +2152,6 @@ hash-base@^3.0.0: hasha@^5.0.0: version "5.2.0" resolved "https://registry.yarnpkg.com/hasha/-/hasha-5.2.0.tgz#33094d1f69c40a4a6ac7be53d5fe3ff95a269e0c" - integrity sha512-2W+jKdQbAdSIrggA8Q35Br8qKadTrqCTC8+XZvBWepKDK6m9XkX6Iz1a2yh2KP01kzAR/dpuMeUnocoLYDcskw== dependencies: is-stream "^2.0.0" type-fest "^0.8.0" @@ -2206,7 +2159,6 @@ hasha@^5.0.0: he@1.2.0: version "1.2.0" resolved "https://registry.yarnpkg.com/he/-/he-1.2.0.tgz#84ae65fa7eafb165fddb61566ae14baf05664f0f" - integrity sha512-F/1DnUGPopORZi0ni+CvrCgHQ5FyEAHRLSApuYWMmrbSwoN2Mn/7k+Gl38gJnR7yyDZk6WLXwiGod1JOWNDKGw== hoek@2.x.x, hoek@^4.2.1: version "4.2.1" @@ -2223,7 +2175,6 @@ hosted-git-info@^2.7.1, hosted-git-info@^2.8.5: html-escaper@^2.0.0: version "2.0.2" resolved "https://registry.yarnpkg.com/html-escaper/-/html-escaper-2.0.2.tgz#dfd60027da36a36dfcbe236262c00a5822681453" - integrity sha512-H2iMtd0I4Mt5eYiapRdIDjp+XzelXQ0tFE4JS7YFwFevXXMmOp9myNrUvCg0D6ws8iqkRPBfKHgbwig1SmlLfg== http-cache-semantics@3.8.1, http-cache-semantics@^3.8.1: version "3.8.1" @@ -2336,7 +2287,6 @@ indent-string@^3.0.0: indent-string@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/indent-string/-/indent-string-4.0.0.tgz#624f8f4497d619b2d9768531d58f4122854d7251" - integrity sha512-EdDDZu4A2OyIK7Lr/2zG+w5jmbuk1DVBnEwREQvBzspBJkCEbRa8GxU1lghYcaGJCnRWibjDXlq779X1/y5xwg== infer-owner@^1.0.3, infer-owner@^1.0.4: version "1.0.4" @@ -2418,14 +2368,12 @@ ipaddr.js@1.6.0: is-binary-path@~2.1.0: version "2.1.0" resolved "https://registry.yarnpkg.com/is-binary-path/-/is-binary-path-2.1.0.tgz#ea1f7f3b80f064236e83470f86c09c254fb45b09" - integrity sha512-ZMERYes6pDydyuGidse7OsHxtbI7WVeUEozgR/g7rd0xUimYNlvZRE/K2MgZTjWy725IfelLeVcEM97mmtRGXw== dependencies: binary-extensions "^2.0.0" is-buffer@~2.0.3: version "2.0.4" resolved "https://registry.yarnpkg.com/is-buffer/-/is-buffer-2.0.4.tgz#3e572f23c8411a5cfd9557c849e3665e0b290623" - integrity sha512-Kq1rokWXOPXWuaMAqZiJW4XxsmD9zGx9q4aePabbn3qCRGedtH7Cm+zV8WETitMfu1wdh+Rvd6w5egwSngUX2A== is-callable@^1.1.4: version "1.1.4" @@ -2491,7 +2439,6 @@ is-npm@^1.0.0: is-number@^7.0.0: version "7.0.0" resolved "https://registry.yarnpkg.com/is-number/-/is-number-7.0.0.tgz#7535345b896734d5f80c4d06c50955527a14f12b" - integrity sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng== is-obj@^1.0.0: version "1.0.1" @@ -2540,7 +2487,6 @@ is-stream@^1.0.0, is-stream@^1.1.0: is-stream@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.0.tgz#bde9c32680d6fae04129d6ac9d921ce7815f78e3" - integrity 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"^1.0.0" cross-spawn "^7.0.0" @@ -2622,7 +2563,6 @@ istanbul-lib-processinfo@^2.0.2: istanbul-lib-report@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/istanbul-lib-report/-/istanbul-lib-report-3.0.0.tgz#7518fe52ea44de372f460a76b5ecda9ffb73d8a6" - integrity sha512-wcdi+uAKzfiGT2abPpKZ0hSU1rGQjUQnLvtY5MpQ7QCTahD3VODhcu4wcfY1YtkGaDD5yuydOLINXsfbus9ROw== dependencies: istanbul-lib-coverage "^3.0.0" make-dir "^3.0.0" @@ -2631,7 +2571,6 @@ istanbul-lib-report@^3.0.0: istanbul-lib-source-maps@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/istanbul-lib-source-maps/-/istanbul-lib-source-maps-4.0.0.tgz#75743ce6d96bb86dc7ee4352cf6366a23f0b1ad9" - integrity sha512-c16LpFRkR8vQXyHZ5nLpY35JZtzj1PQY1iZmesUbf1FZHbIupcWfjgOXBY9YHkLEQ6puz1u4Dgj6qmU/DisrZg== dependencies: debug "^4.1.1" istanbul-lib-coverage "^3.0.0" @@ -2721,7 +2660,6 @@ json-stringify-safe@~5.0.1: json5@^2.1.2: version "2.1.2" resolved "https://registry.yarnpkg.com/json5/-/json5-2.1.2.tgz#43ef1f0af9835dd624751a6b7fa48874fb2d608e" - integrity sha512-MoUOQ4WdiN3yxhm7NEVJSJrieAo5hNSLQ5sj05OTRHPL9HOBy8u4Bu88jsC1jvqAdN+E1bJmsUcZH+1HQxliqQ== dependencies: minimist "^1.2.5" @@ -2924,7 +2862,6 @@ locate-path@^3.0.0: locate-path@^5.0.0: version "5.0.0" resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-5.0.0.tgz#1afba396afd676a6d42504d0a67a3a7eb9f62aa0" - integrity sha512-t7hw9pI+WvuwNJXwk5zVHpyhIqzg2qTlklJOf0mVxGSbe3Fp2VieZcduNYjaLDoy6p9uGpQEGWG87WpMKlNq8g== dependencies: p-locate "^4.1.0" @@ -3023,7 +2960,6 @@ lodash@^4.17.11, lodash@^4.17.13, lodash@^4.17.14, lodash@^4.17.15: log-symbols@3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/log-symbols/-/log-symbols-3.0.0.tgz#f3a08516a5dea893336a7dee14d18a1cfdab77c4" - integrity sha512-dSkNGuI7iG3mfvDzUuYZyvk5dD9ocYCYzNU6CYDE6+Xqd+gwme6Z00NS3dUh8mq/73HaEtT7m6W+yUPtU6BZnQ== dependencies: chalk "^2.4.2" @@ -3068,7 +3004,6 @@ make-dir@^1.0.0: make-dir@^3.0.0, make-dir@^3.0.2: version "3.0.2" resolved "https://registry.yarnpkg.com/make-dir/-/make-dir-3.0.2.tgz#04a1acbf22221e1d6ef43559f43e05a90dbb4392" - integrity sha512-rYKABKutXa6vXTXhoV18cBE7PaewPXHe/Bdq4v+ZLMhxbWApkFFplT0LcbMW+6BbjnQXzZ/sAvSE/JdguApG5w== dependencies: semver "^6.0.0" @@ -3170,7 +3105,6 @@ minimatch@3.0.4, minimatch@^3.0.4: minimist@^1.2.0, minimist@^1.2.5: version "1.2.5" resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.5.tgz#67d66014b66a6a8aaa0c083c5fd58df4e4e97602" - integrity sha1-Z9ZgFLZqaoqqDAg8X9WN9OTpdgI= minipass@^2.2.1, minipass@^2.3.3: version "2.3.3" @@ -3231,7 +3165,6 @@ mississippi@^3.0.0: mkdirp@0.5.3, mkdirp@^0.5.0, mkdirp@^0.5.1, mkdirp@~0.5.0, mkdirp@~0.5.1: version "0.5.3" resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.3.tgz#5a514b7179259287952881e94410ec5465659f8c" - integrity sha512-P+2gwrFqx8lhew375MQHHeTlY8AuOJSrGf0R5ddkEndUkmwpgUob/vQuBD1V22/Cw1/lJr4x+EjllSezBThzBg== dependencies: minimist "^1.2.5" @@ -3243,7 +3176,6 @@ mkdirp@^1.0.3: mocha@^7.1.1: version "7.1.1" resolved "https://registry.yarnpkg.com/mocha/-/mocha-7.1.1.tgz#89fbb30d09429845b1bb893a830bf5771049a441" - integrity sha512-3qQsu3ijNS3GkWcccT5Zw0hf/rWvu1fTN9sPvEd81hlwsr30GX2GcDSSoBxo24IR8FelmrAydGC6/1J5QQP4WA== dependencies: ansi-colors "3.2.3" browser-stdout "1.3.1" @@ -3328,7 +3260,6 @@ nice-try@^1.0.4: node-environment-flags@1.0.6: version "1.0.6" resolved "https://registry.yarnpkg.com/node-environment-flags/-/node-environment-flags-1.0.6.tgz#a30ac13621f6f7d674260a54dede048c3982c088" - integrity sha512-5Evy2epuL+6TM0lCQGpFIj6KwiEsGh1SrHUhTbNX+sLbBtjidPZFAnVK9y5yU1+h//RitLbRHTIMyxQPtxMdHw== dependencies: object.getownpropertydescriptors "^2.0.3" semver "^5.7.0" @@ -3399,7 +3330,6 @@ node-pre-gyp@^0.10.3: node-preload@^0.2.1: version "0.2.1" resolved "https://registry.yarnpkg.com/node-preload/-/node-preload-0.2.1.tgz#c03043bb327f417a18fee7ab7ee57b408a144301" - integrity 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"https://registry.yarnpkg.com/p-locate/-/p-locate-4.1.0.tgz#a3428bb7088b3a60292f66919278b7c297ad4f07" - integrity sha512-R79ZZ/0wAxKGu3oYMlz8jy/kbhsNrS7SKZ7PxEHBgJ5+F2mtFW2fK2cOtBh1cHYkQsbzFV7I+EoRKe6Yt0oK7A== dependencies: p-limit "^2.2.0" p-map@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/p-map/-/p-map-3.0.0.tgz#d704d9af8a2ba684e2600d9a215983d4141a979d" - integrity sha512-d3qXVTF/s+W+CdJ5A29wywV2n8CQQYahlgz2bFiA+4eVNJbHJodPZ+/gXwPGh0bOqA+j8S+6+ckmvLGPk1QpxQ== dependencies: aggregate-error "^3.0.0" @@ -3928,7 +3854,6 @@ p-try@^2.0.0: package-hash@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/package-hash/-/package-hash-4.0.0.tgz#3537f654665ec3cc38827387fc904c163c54f506" - integrity sha512-whdkPIooSu/bASggZ96BWVvZTRMOFxnyUG5PnTSGKoJE2gd5mbVNmR2Nj20QFzxYYgAXpoqC+AiXzl+UMRh7zQ== dependencies: graceful-fs "^4.1.15" hasha "^5.0.0" @@ -4014,7 +3939,6 @@ path-exists@^3.0.0: path-exists@^4.0.0: version "4.0.0" resolved 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resolved "https://registry.yarnpkg.com/pkg-dir/-/pkg-dir-4.2.0.tgz#f099133df7ede422e81d1d8448270eeb3e4261f3" - integrity sha512-HRDzbaKjC+AOWVXxAU/x54COGeIv9eb+6CkDSQoNTt4XyWoIJvuPsXizxu/Fr23EiekbtZwmh1IcIG/l/a10GQ== dependencies: find-up "^4.0.0" @@ -4102,7 +4023,6 @@ process-nextick-args@~2.0.0: process-on-spawn@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/process-on-spawn/-/process-on-spawn-1.0.0.tgz#95b05a23073d30a17acfdc92a440efd2baefdc93" - integrity sha512-1WsPDsUSMmZH5LeMLegqkPDrsGgsWwk1Exipy2hvB0o/F0ASzbpIctSCcZIK1ykJvtTJULEH+20WOFjMvGnCTg== dependencies: fromentries "^1.2.0" @@ -4355,7 +4275,6 @@ readdir-scoped-modules@^1.0.0, readdir-scoped-modules@^1.1.0: readdirp@~3.2.0: version "3.2.0" resolved "https://registry.yarnpkg.com/readdirp/-/readdirp-3.2.0.tgz#c30c33352b12c96dfb4b895421a49fd5a9593839" - integrity sha512-crk4Qu3pmXwgxdSgGhgA/eXiJAPQiX4GMOZZMXnqKxHX7TaoL+3gQVo/WeuAiogr07DpnfjIMpXXa+PAIvwPGQ== dependencies: picomatch "^2.0.4" @@ -4459,7 +4378,6 @@ require-main-filename@^1.0.1: require-main-filename@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/require-main-filename/-/require-main-filename-2.0.0.tgz#d0b329ecc7cc0f61649f62215be69af54aa8989b" - integrity sha512-NKN5kMDylKuldxYLSUfrbo5Tuzh4hd+2E8NPPX02mZtn1VuREQToYe/ZdlJy+J3uCpfaiGF05e7B8W0iXbQHmg== resolve-from@^4.0.0: version "4.0.0" @@ -4468,7 +4386,6 @@ resolve-from@^4.0.0: resolve-from@^5.0.0: version "5.0.0" resolved "https://registry.yarnpkg.com/resolve-from/-/resolve-from-5.0.0.tgz#c35225843df8f776df21c57557bc087e9dfdfc69" - integrity sha512-qYg9KP24dD5qka9J47d0aVky0N+b4fTU89LN9iDnjB5waksiC49rvMB0PrUJQGoTmH50XPiqOvAjDfaijGxYZw== resolve@^1.10.0: version "1.13.1" @@ -4479,7 +4396,6 @@ resolve@^1.10.0: resolve@^1.3.2: version "1.15.1" resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.15.1.tgz#27bdcdeffeaf2d6244b95bb0f9f4b4653451f3e8" - integrity sha512-84oo6ZTtoTUpjgNEr5SJyzQhzL72gaRodsSfyxC/AXRvwu0Yse9H8eF9IpGo7b8YetZhlI6v7ZQ6bKBFV/6S7w== dependencies: path-parse "^1.0.6" @@ -4525,7 +4441,6 @@ rimraf@^2.6.1, rimraf@^2.6.2: rimraf@^3.0.0: version "3.0.2" resolved "https://registry.yarnpkg.com/rimraf/-/rimraf-3.0.2.tgz#f1a5402ba6220ad52cc1282bac1ae3aa49fd061a" - integrity sha512-JZkJMZkAGFFPP2YqXZXPbMlMBgsxzE8ILs4lMIX/2o0L9UBw9O/Y3o6wFw/i9YLapcUJWwqbi3kdxIPdC62TIA== dependencies: glob "^7.1.3" @@ -4657,7 +4572,6 @@ shebang-command@^1.2.0: shebang-command@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/shebang-command/-/shebang-command-2.0.0.tgz#ccd0af4f8835fbdc265b82461aaf0c36663f34ea" - integrity sha512-kHxr2zZpYtdmrN1qDjrrX/Z1rR1kG8Dx+gkpK1G4eXmvXswmcE1hTWBWYUzlraYw1/yZp6YuDY77YtvbN0dmDA== dependencies: shebang-regex "^3.0.0" @@ -4668,7 +4582,6 @@ shebang-regex@^1.0.0: shebang-regex@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/shebang-regex/-/shebang-regex-3.0.0.tgz#ae16f1644d873ecad843b0307b143362d4c42172" - integrity sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A== signal-exit@^3.0.0, signal-exit@^3.0.2: version "3.0.2" @@ -4743,7 +4656,6 @@ source-map@^0.6.0, source-map@^0.6.1: spawn-wrap@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/spawn-wrap/-/spawn-wrap-2.0.0.tgz#103685b8b8f9b79771318827aa78650a610d457e" - integrity sha512-EeajNjfN9zMnULLwhZZQU3GWBoFNkbngTUPfaawT4RkMiviTxcX0qfhVbGey39mfctfDHkWtuecgQ8NJcyQWHg== dependencies: foreground-child "^2.0.0" is-windows "^1.0.2" @@ -4959,7 +4871,6 @@ strip-ansi@^6.0.0: strip-bom@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/strip-bom/-/strip-bom-4.0.0.tgz#9c3505c1db45bcedca3d9cf7a16f5c5aa3901878" - integrity sha512-3xurFv5tEgii33Zi8Jtp55wEIILR9eh34FAW00PZf+JnSsTmV/ioewSgQl97JHvgjoRGwPShsWm+IdrxB35d0w== strip-eof@^1.0.0: version "1.0.0" @@ -4976,7 +4887,6 @@ strip-json-comments@^3.0.1: supports-color@6.0.0: version "6.0.0" resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-6.0.0.tgz#76cfe742cf1f41bb9b1c29ad03068c05b4c0e40a" - integrity sha512-on9Kwidc1IUQo+bQdhi8+Tijpo0e1SS6RoGo2guUwn5vdaxw8RXOF9Vb2ws+ihWOmh4JnCJOvaziZWP1VABaLg== dependencies: has-flag "^3.0.0" @@ -4989,7 +4899,6 @@ supports-color@^5.3.0: supports-color@^7.1.0: version "7.1.0" resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.1.0.tgz#68e32591df73e25ad1c4b49108a2ec507962bfd1" - integrity sha512-oRSIpR8pxT1Wr2FquTNnGet79b3BWljqOuoW/h4oBhxJ/HUbX5nX6JSruTkvXDCFMwDPvsaTTbvMLKZWSy0R5g== dependencies: has-flag "^4.0.0" @@ -5051,7 +4960,6 @@ term-size@^1.2.0: test-exclude@^6.0.0: version "6.0.0" resolved "https://registry.yarnpkg.com/test-exclude/-/test-exclude-6.0.0.tgz#04a8698661d805ea6fa293b6cb9e63ac044ef15e" - integrity sha512-cAGWPIyOHU6zlmg88jwm7VRyXnMN7iV68OGAbYDk/Mh/xC/pzVPlQtY6ngoIH/5/tciuhGfvESU8GrHrcxD56w== dependencies: "@istanbuljs/schema" "^0.1.2" glob "^7.1.4" @@ -5093,7 +5001,6 @@ to-fast-properties@^2.0.0: to-regex-range@^5.0.1: version "5.0.1" resolved "https://registry.yarnpkg.com/to-regex-range/-/to-regex-range-5.0.1.tgz#1648c44aae7c8d988a326018ed72f5b4dd0392e4" - integrity sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ== dependencies: is-number "^7.0.0" @@ -5185,7 +5092,6 @@ type-is@~1.6.15, type-is@~1.6.16: typedarray-to-buffer@^3.1.5: version "3.1.5" resolved "https://registry.yarnpkg.com/typedarray-to-buffer/-/typedarray-to-buffer-3.1.5.tgz#a97ee7a9ff42691b9f783ff1bc5112fe3fca9080" - integrity sha512-zdu8XMNEDepKKR+XYOXAVPtWui0ly0NtohUscw+UmaHiAWT8hrV1rr//H6V+0DvJ3OQ19S979M0laLfX8rm82Q== dependencies: is-typedarray "^1.0.0" @@ -5369,7 +5275,6 @@ which@1.3.1, which@^1.2.9, which@^1.3.0, which@^1.3.1: which@^2.0.1: version "2.0.2" resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1" - integrity sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA== dependencies: isexe "^2.0.0" @@ -5405,7 +5310,6 @@ wrap-ansi@^2.0.0: wrap-ansi@^5.1.0: version "5.1.0" resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-5.1.0.tgz#1fd1f67235d5b6d0fee781056001bfb694c03b09" - integrity sha512-QC1/iN/2/RPVJ5jYK8BGttj5z83LmSKmvbvrXPNCLZSEb32KKVDJDl/MOt2N01qU2H/FkzEa9PKto1BqDjtd7Q== dependencies: ansi-styles "^3.2.0" string-width "^3.0.0" @@ -5414,7 +5318,6 @@ wrap-ansi@^5.1.0: wrap-ansi@^6.2.0: version "6.2.0" resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-6.2.0.tgz#e9393ba07102e6c91a3b221478f0257cd2856e53" - integrity sha512-r6lPcBGxZXlIcymEu7InxDMhdW0KDxpLgoFLcguasxCaJ/SOIZwINatK9KY/tf+ZrlywOKU0UDj3ATXUBfxJXA== dependencies: ansi-styles "^4.0.0" string-width "^4.1.0" @@ -5443,7 +5346,6 @@ write-file-atomic@^2.3.0, write-file-atomic@^2.4.3: write-file-atomic@^3.0.0: version "3.0.3" resolved "https://registry.yarnpkg.com/write-file-atomic/-/write-file-atomic-3.0.3.tgz#56bd5c5a5c70481cd19c571bd39ab965a5de56e8" - integrity sha512-AvHcyZ5JnSfq3ioSyjrBkH9yW4m7Ayk8/9My/DD9onKeu/94fwrMocemO2QAJFAlnnDN+ZDS+ZjAR5ua1/PV/Q== dependencies: imurmurhash "^0.1.4" is-typedarray "^1.0.0" @@ -5462,6 +5364,10 @@ ws@^6.0.0: dependencies: async-limiter "~1.0.0" +ws@^7.3.0: + version "7.3.0" + resolved "https://registry.yarnpkg.com/ws/-/ws-7.3.0.tgz#4b2f7f219b3d3737bc1a2fbf145d825b94d38ffd" + xdg-basedir@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/xdg-basedir/-/xdg-basedir-3.0.0.tgz#496b2cc109eca8dbacfe2dc72b603c17c5870ad4" @@ -5508,7 +5414,6 @@ yallist@^4.0.0: yargs-parser@13.1.2, yargs-parser@^13.1.2: version "13.1.2" resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-13.1.2.tgz#130f09702ebaeef2650d54ce6e3e5706f7a4fb38" - integrity sha512-3lbsNRf/j+A4QuSZfDRA7HRSfWrzO0YjqTJd5kjAq37Zep1CEgaYmrH9Q3GwPiB9cHyd1Y1UwggGhJGoxipbzg== dependencies: camelcase "^5.0.0" decamelize "^1.2.0" @@ -5516,7 +5421,6 @@ yargs-parser@13.1.2, yargs-parser@^13.1.2: yargs-parser@^18.1.1: version "18.1.2" resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-18.1.2.tgz#2f482bea2136dbde0861683abea7756d30b504f1" - integrity sha512-hlIPNR3IzC1YuL1c2UwwDKpXlNFBqD1Fswwh1khz5+d8Cq/8yc/Mn0i+rQXduu8hcrFKvO7Eryk+09NecTQAAQ== dependencies: camelcase "^5.0.0" decamelize "^1.2.0" @@ -5530,7 +5434,6 @@ yargs-parser@^9.0.2: yargs-unparser@1.6.0: version "1.6.0" resolved "https://registry.yarnpkg.com/yargs-unparser/-/yargs-unparser-1.6.0.tgz#ef25c2c769ff6bd09e4b0f9d7c605fb27846ea9f" - integrity sha512-W9tKgmSn0DpSatfri0nx52Joq5hVXgeLiqR/5G0sZNDoLZFOr/xjBUDcShCOGNsBnEMNo1KAMBkTej1Hm62HTw== dependencies: flat "^4.1.0" lodash "^4.17.15" @@ -5539,7 +5442,6 @@ yargs-unparser@1.6.0: yargs@13.3.2, yargs@^13.3.0: version "13.3.2" resolved "https://registry.yarnpkg.com/yargs/-/yargs-13.3.2.tgz#ad7ffefec1aa59565ac915f82dccb38a9c31a2dd" - integrity sha512-AX3Zw5iPruN5ie6xGRIDgqkT+ZhnRlZMLMHAs8tg7nRruy2Nb+i5o9bwghAogtM08q1dpr2LVoS8KSTMYpWXUw== dependencies: cliui "^5.0.0" find-up "^3.0.0" @@ -5572,7 +5474,6 @@ yargs@^11.0.0: yargs@^15.0.2: version "15.3.1" resolved "https://registry.yarnpkg.com/yargs/-/yargs-15.3.1.tgz#9505b472763963e54afe60148ad27a330818e98b" - integrity sha512-92O1HWEjw27sBfgmXiixJWT5hRBp2eobqXicLtPBIDBhYB+1HpwZlXmbW2luivBJHBzki+7VyCLRtAkScbTBQA== dependencies: cliui "^6.0.0" decamelize "^1.2.0" diff --git a/src/sdk/pynni/requirements.txt b/src/sdk/pynni/requirements.txt index 885c909ca8..282f572631 100644 --- a/src/sdk/pynni/requirements.txt +++ b/src/sdk/pynni/requirements.txt @@ -1,5 +1,6 @@ # core json_tricks +websockets # hyperopt tuner numpy diff --git a/test/config/integration_tests.yml b/test/config/integration_tests.yml index 399435d13e..86351ef7ea 100644 --- a/test/config/integration_tests.yml +++ b/test/config/integration_tests.yml @@ -49,10 +49,17 @@ testCases: maxTrialNum: 2 trialConcurrency: 1 -- name: mnist-pytorch +- name: mnist-pytorch-local configFile: test/config/examples/mnist-pytorch.yml # download data first, to prevent concurrent issue. launchCommand: python3 ../examples/trials/mnist-pytorch/mnist.py --epochs 1 --batch_num 0 --data_dir ../examples/trials/mnist-pytorch/data && nnictl create --config $configFile --debug + trainingService: local + +- name: mnist-pytorch-non-local + configFile: test/config/examples/mnist-pytorch.yml + # download data first, to prevent concurrent issue. + launchCommand: nnictl create --config $configFile --debug + trainingService: remote pai kubeflow frameworkcontroller dlts - name: mnist-annotation configFile: test/config/examples/mnist-annotation.yml diff --git a/tools/nni_cmd/config_schema.py b/tools/nni_cmd/config_schema.py index b1b903530d..c0a5483668 100644 --- a/tools/nni_cmd/config_schema.py +++ b/tools/nni_cmd/config_schema.py @@ -203,11 +203,13 @@ def validate(self, data): 'paiConfig': Or({ 'userName': setType('userName', str), 'passWord': setType('passWord', str), - 'host': setType('host', str) + 'host': setType('host', str), + Optional('reuse'): setType('reuse', bool) }, { 'userName': setType('userName', str), 'token': setType('token', str), - 'host': setType('host', str) + 'host': setType('host', str), + Optional('reuse'): setType('reuse', bool) }) } diff --git a/tools/nni_cmd/rest_utils.py b/tools/nni_cmd/rest_utils.py index 6fcc978b12..e98c9a8392 100644 --- a/tools/nni_cmd/rest_utils.py +++ b/tools/nni_cmd/rest_utils.py @@ -51,7 +51,7 @@ def rest_delete(url, timeout, show_error=False): def check_rest_server(rest_port): '''Check if restful server is ready''' - retry_count = 5 + retry_count = 20 for _ in range(retry_count): response = rest_get(check_status_url(rest_port), REST_TIME_OUT) if response: @@ -60,7 +60,7 @@ def check_rest_server(rest_port): else: return False, response else: - time.sleep(3) + time.sleep(1) return False, response def check_rest_server_quick(rest_port): diff --git a/tools/nni_trial_tool/base_channel.py b/tools/nni_trial_tool/base_channel.py new file mode 100644 index 0000000000..c1ce564ba8 --- /dev/null +++ b/tools/nni_trial_tool/base_channel.py @@ -0,0 +1,154 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import json +import threading +import time +from abc import ABC, abstractmethod +from queue import Empty, Queue + +from .log_utils import LogType, nni_log +from .commands import CommandType + +INTERVAL_SECONDS = 0.5 + + +class BaseChannel(ABC): + def __init__(self, args): + self.is_keep_parsed = args.node_count > 1 + self.args = args + self.node_id = self.args.node_id + + @abstractmethod + def _inner_send(self, message): + pass + + @abstractmethod + def _inner_receive(self): + return [] + + @abstractmethod + def _inner_open(self): + pass + + @abstractmethod + def _inner_close(self): + pass + + def open(self): + # initialize receive, send threads. + self.is_running = True + self.receive_queue = Queue() + self.receive_thread = threading.Thread(target=self._receive_loop) + self.receive_thread.start() + self.send_queue = Queue() + self.send_thread = threading.Thread(target=self._send_loop) + self.send_thread.start() + + self._inner_open() + + client_info = { + "isReady": True, + "runnerId": self.args.runner_id, + "expId": self.args.exp_id, + } + nni_log(LogType.Info, 'Channel: send ready information %s' % client_info) + self.send(CommandType.Initialized, client_info) + + def close(self): + self.is_running = False + self._inner_close() + + def send(self, command, data): + """Send command to Training Service. + command: CommandType object. + data: string payload. + the message is sent synchronized. + """ + data["node"] = self.node_id + data = json.dumps(data) + data = data.encode('utf8') + message = b'%b%014d%b' % (command.value, len(data), data) + self.send_queue.put(message) + + def sent(self): + return self.send_queue.qsize() == 0 + + def received(self): + return self.receive_queue.qsize() > 0 + + def receive(self): + """Receive a command from Training Service. + Returns a tuple of command (CommandType) and payload (str) + """ + command = None + data = None + + try: + command_content = self.receive_queue.get(False) + if command_content is not None: + if (len(command_content) < 16): + # invalid header + nni_log(LogType.Error, 'incorrect command is found, command must be greater than 16 bytes!') + return None, None + header = command_content[:16] + command = CommandType(header[:2]) + length = int(header[2:]) + if (len(command_content)-16 != length): + nni_log(LogType.Error, 'incorrect command length, length {}, actual data length is {}, header {}.' + .format(length, len(command_content)-16, header)) + return None, None + data = command_content[16:16+length] + data = json.loads(data.decode('utf8')) + if self.node_id is None: + nni_log(LogType.Info, 'Received command, header: [%s], data: [%s]' % (header, data)) + else: + nni_log(LogType.Info, 'Received command(%s), header: [%s], data: [%s]' % (self.node_id, header, data)) + except Empty: + # do nothing, if no command received. + pass + except Exception as identifier: + nni_log(LogType.Error, 'meet unhandled exception in base_channel: %s' % identifier) + return command, data + + def _fetch_message(self, buffer, has_new_line=False): + messages = [] + while(len(buffer)) >= 16: + header = buffer[:16] + length = int(header[2:]) + + message_length = length+16 + total_length = message_length + if has_new_line: + total_length += 1 + + # break, if buffer is too short. + if len(buffer) < total_length: + break + data = buffer[16:message_length] + if has_new_line and 10 != buffer[total_length-1]: + nni_log(LogType.Error, 'end of message should be \\n, but got {}'.format(self.in_cache[total_length-1])) + buffer = buffer[total_length:] + messages.append(header + data) + + return messages, buffer + + def _receive_loop(self): + while (self.is_running): + messages = self._inner_receive() + if messages is not None: + for message in messages: + self.receive_queue.put(message) + time.sleep(INTERVAL_SECONDS) + + def _send_loop(self): + while (self.is_running): + message = None + try: + # no sleep, since it's a block call with INTERVAL_SECONDS second timeout + message = self.send_queue.get(True, INTERVAL_SECONDS) + except Empty: + # do nothing, if no command received. + pass + if message is not None: + self._inner_send(message) diff --git a/tools/nni_trial_tool/commands.py b/tools/nni_trial_tool/commands.py new file mode 100644 index 0000000000..86b10a2fe9 --- /dev/null +++ b/tools/nni_trial_tool/commands.py @@ -0,0 +1,25 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +from enum import Enum + + +class CommandType(Enum): + Initialize = b'IN' + RequestTrialJobs = b'GE' + ReportMetricData = b'ME' + ReportGpuInfo = b'GI' + UpdateSearchSpace = b'SS' + ImportData = b'FD' + AddCustomizedTrialJob = b'AD' + TrialEnd = b'EN' + Terminate = b'TE' + Ping = b'PI' + + Initialized = b'ID' + NewTrialJob = b'TR' + SendTrialJobParameter = b'SP' + NoMoreTrialJobs = b'NO' + KillTrialJob = b'KI' + StdOut = b'SO' + VersionCheck = b'VC' diff --git a/tools/nni_trial_tool/file_channel.py b/tools/nni_trial_tool/file_channel.py new file mode 100644 index 0000000000..9a431d25f7 --- /dev/null +++ b/tools/nni_trial_tool/file_channel.py @@ -0,0 +1,75 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import os + +from .base_channel import BaseChannel + +command_path = "./commands" +runner_commands_file_name_prefix = "runner_commands" +manager_commands_file_name = "manager_commands.txt" + + +class FileChannel(BaseChannel): + + def __init__(self, args): + self.node_id = args.node_id + self.out_file = None + self.in_file = None + self.in_offset = 0 + self.in_cache = b"" + + super(FileChannel, self).__init__(args) + + def _inner_open(self): + pass + + def _inner_close(self): + if self.out_file is not None: + self.out_file.close() + self.out_file = None + if self.in_file is not None: + self.in_file.close() + self.in_file = None + + def _inner_send(self, message): + if self.out_file is None: + if not os.path.exists(command_path): + os.makedirs(command_path, exist_ok=True) + + if self.node_id is None: + file_name = os.path.join(command_path, "%s.txt" % runner_commands_file_name_prefix) + else: + file_name = os.path.join(command_path, "%s_%s.txt" % ( + runner_commands_file_name_prefix, self.node_id)) + self.out_file = open(file_name, "ab") + + self.out_file.write(message) + self.out_file.write(b'\n') + self.out_file.flush() + + def _open_manager_command(self): + full_name = os.path.join(command_path, manager_commands_file_name) + + if self.in_file is not None and self.in_file.closed: + self.in_file = None + + if self.in_file is None and os.path.exists(full_name): + self.in_file = open(full_name, "rb") + self.in_file.seek(self.in_offset) + + def _inner_receive(self): + messages = [] + + if self.in_file is None: + self._open_manager_command() + if self.in_file is not None: + self.in_file.seek(0, os.SEEK_END) + new_offset = self.in_file.tell() + self.in_file.seek(self.in_offset, os.SEEK_SET) + count = new_offset - self.in_offset + if count > 0: + self.in_cache += self.in_file.read(count) + self.in_offset = new_offset + messages, self.in_cache = self._fetch_message(self.in_cache, True) + return messages diff --git a/tools/nni_trial_tool/gpu.py b/tools/nni_trial_tool/gpu.py new file mode 100644 index 0000000000..48dab4b182 --- /dev/null +++ b/tools/nni_trial_tool/gpu.py @@ -0,0 +1,69 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import subprocess +import time +import traceback +from xml.dom import minidom + + +def collect_gpu_usage(node_id): + cmd = 'nvidia-smi -q -x'.split() + info = None + try: + smi_output = subprocess.check_output(cmd) + info = parse_nvidia_smi_result(smi_output) + except Exception: + traceback.print_exc() + info = gen_empty_gpu_metric() + return info + + +def parse_nvidia_smi_result(smi): + try: + output = {} + xmldoc = minidom.parseString(smi) + gpuList = xmldoc.getElementsByTagName('gpu') + output["Timestamp"] = time.asctime(time.localtime()) + output["gpuCount"] = len(gpuList) + output["gpuInfos"] = [] + for gpuIndex, gpu in enumerate(gpuList): + gpuInfo = {} + gpuInfo['index'] = gpuIndex + gpuInfo['gpuUtil'] = gpu.getElementsByTagName('utilization')[0]\ + .getElementsByTagName('gpu_util')[0]\ + .childNodes[0].data.replace("%", "").strip() + gpuInfo['gpuMemUtil'] = gpu.getElementsByTagName('utilization')[0]\ + .getElementsByTagName('memory_util')[0]\ + .childNodes[0].data.replace("%", "").strip() + processes = gpu.getElementsByTagName('processes') + runningProNumber = len(processes[0].getElementsByTagName('process_info')) + gpuInfo['activeProcessNum'] = runningProNumber + + gpuInfo['gpuType'] = gpu.getElementsByTagName('product_name')[0]\ + .childNodes[0].data + memUsage = gpu.getElementsByTagName('fb_memory_usage')[0] + gpuInfo['gpuMemTotal'] = memUsage.getElementsByTagName('total')[0]\ + .childNodes[0].data.replace("MiB", "").strip() + gpuInfo['gpuMemUsed'] = memUsage.getElementsByTagName('used')[0]\ + .childNodes[0].data.replace("MiB", "").strip() + gpuInfo['gpuMemFree'] = memUsage.getElementsByTagName('free')[0]\ + .childNodes[0].data.replace("MiB", "").strip() + + output["gpuInfos"].append(gpuInfo) + except Exception: + traceback.print_exc() + output = {} + return output + + +def gen_empty_gpu_metric(): + try: + output = {} + output["Timestamp"] = time.asctime(time.localtime()) + output["gpuCount"] = 0 + output["gpuInfos"] = [] + except Exception: + traceback.print_exc() + output = {} + return output diff --git a/tools/nni_trial_tool/log_utils.py b/tools/nni_trial_tool/log_utils.py index 20c8e74c09..20848d9804 100644 --- a/tools/nni_trial_tool/log_utils.py +++ b/tools/nni_trial_tool/log_utils.py @@ -18,6 +18,8 @@ from .rest_utils import rest_post from .url_utils import gen_send_stdout_url +from .commands import CommandType + @unique class LogType(Enum): @@ -28,23 +30,28 @@ class LogType(Enum): Error = 'ERROR' Fatal = 'FATAL' + @unique class StdOutputType(Enum): Stdout = 'stdout', Stderr = 'stderr' + def nni_log(log_type, log_message): '''Log message into stdout''' dt = datetime.now() print('[{0}] {1} {2}'.format(dt, log_type.value, log_message), flush=True) + class NNIRestLogHanlder(StreamHandler): - def __init__(self, host, port, tag, std_output_type=StdOutputType.Stdout): + def __init__(self, host, port, tag, trial_id, channel, std_output_type=StdOutputType.Stdout): StreamHandler.__init__(self) self.host = host self.port = port self.tag = tag self.std_output_type = std_output_type + self.trial_id = trial_id + self.channel = channel self.orig_stdout = sys.__stdout__ self.orig_stderr = sys.__stderr__ @@ -55,24 +62,32 @@ def emit(self, record): log_entry['msg'] = self.format(record) try: - rest_post(gen_send_stdout_url(self.host, self.port), json.dumps(log_entry), 10, True) + if self.channel is None: + rest_post(gen_send_stdout_url(self.host, self.port), json.dumps(log_entry), 10, True) + else: + if self.trial_id is not None: + log_entry["trial"] = self.trial_id + self.channel.send(CommandType.StdOut, log_entry) except Exception as e: self.orig_stderr.write(str(e) + '\n') self.orig_stderr.flush() + class RemoteLogger(object): """ NNI remote logger """ - def __init__(self, syslog_host, syslog_port, tag, std_output_type, log_collection, log_level=logging.INFO): + + def __init__(self, syslog_host, syslog_port, tag, std_output_type, log_collection, trial_id=None, channel=None, log_level=logging.INFO): ''' constructor ''' self.logger = logging.getLogger('nni_syslog_{}'.format(tag)) self.log_level = log_level self.logger.setLevel(self.log_level) - handler = NNIRestLogHanlder(syslog_host, syslog_port, tag) - self.logger.addHandler(handler) + self.pipeReader = None + self.handler = NNIRestLogHanlder(syslog_host, syslog_port, tag, trial_id, channel) + self.logger.addHandler(self.handler) if std_output_type == StdOutputType.Stdout: self.orig_stdout = sys.__stdout__ else: @@ -83,7 +98,8 @@ def get_pipelog_reader(self): ''' Get pipe for remote logger ''' - return PipeLogReader(self.logger, self.log_collection, logging.INFO) + self.pipeReader = PipeLogReader(self.logger, self.log_collection, logging.INFO) + return self.pipeReader def flush(self): ''' @@ -104,10 +120,22 @@ def write(self, buf): except Exception: pass + def close(self): + ''' + Close handlers and resources + ''' + if self.pipeReader is not None: + self.pipeReader.set_process_exit() + for handler in self.logger.handlers: + handler.close() + self.logger.removeHandler(handler) + + class PipeLogReader(threading.Thread): """ The reader thread reads log data from pipe """ + def __init__(self, logger, log_collection, log_level=logging.INFO): """Setup the object with a logger and a loglevel and start the thread @@ -129,7 +157,7 @@ def _populateQueue(stream, queue): ''' Collect lines from 'stream' and put them in 'quque'. ''' - time.sleep(5) + time.sleep(1) while True: cur_process_exit = self.process_exit try: diff --git a/tools/nni_trial_tool/test/__init__.py b/tools/nni_trial_tool/test/__init__.py new file mode 100644 index 0000000000..eb551d5088 --- /dev/null +++ b/tools/nni_trial_tool/test/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import os + +os.environ['NNI_PLATFORM'] = 'unittest' +os.environ['NNI_TRIAL_JOB_ID'] = 'test_trial_job_id' +os.environ["NNI_OUTPUT_DIR"] = "./unittest" +os.environ["NNI_SYS_DIR"] = "./unittest" +os.environ["NNI_EXP_ID"] = "test_exp_id" +os.environ["MULTI_PHASE"] = "true" diff --git a/tools/nni_trial_tool/test/test_file_channel.py b/tools/nni_trial_tool/test/test_file_channel.py new file mode 100644 index 0000000000..e08bd7fca9 --- /dev/null +++ b/tools/nni_trial_tool/test/test_file_channel.py @@ -0,0 +1,123 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import json +import os +import random +import shutil +import string +import sys +import time +import unittest +from argparse import Namespace +from datetime import datetime + +from tools.nni_trial_tool.base_channel import CommandType +from tools.nni_trial_tool.file_channel import (FileChannel, command_path, + manager_commands_file_name) + +sys.path.append("..") + +runner_file_name = "commands/runner_commands.txt" +manager_file_name = "commands/manager_commands.txt" + + +class FileChannelTest(unittest.TestCase): + + def setUp(self): + self.args = Namespace() + self.args.node_count = 1 + self.args.node_id = None + if os.path.exists(command_path): + shutil.rmtree(command_path) + + def test_send(self): + fc = None + try: + fc = FileChannel(self.args) + fc.send(CommandType.ReportGpuInfo, "command1") + fc.send(CommandType.ReportGpuInfo, "command2") + + self.check_timeout(2, lambda: os.path.exists(runner_file_name)) + + self.assertTrue(os.path.exists(runner_file_name)) + with open(runner_file_name, "rb") as runner: + lines = runner.readlines() + self.assertListEqual(lines, [b'GI00000000000010"command1"\n', b'GI00000000000010"command2"\n']) + finally: + if fc is not None: + fc.close() + + def test_send_multi_node(self): + fc1 = None + fc2 = None + try: + runner1_file_name = "commands/runner_commands_1.txt" + self.args.node_id = 1 + fc1 = FileChannel(self.args) + fc1.send(CommandType.ReportGpuInfo, "command1") + # wait command have enough time to write before closed. + + runner2_file_name = "commands/runner_commands_2.txt" + self.args.node_id = 2 + fc2 = FileChannel(self.args) + fc2.send(CommandType.ReportGpuInfo, "command1") + + self.check_timeout(2, lambda: os.path.exists(runner1_file_name) and os.path.exists(runner2_file_name)) + + self.assertTrue(os.path.exists(runner1_file_name)) + with open(runner1_file_name, "rb") as runner: + lines1 = runner.readlines() + self.assertTrue(os.path.exists(runner2_file_name)) + with open(runner2_file_name, "rb") as runner: + lines2 = runner.readlines() + self.assertListEqual(lines1, [b'GI00000000000010"command1"\n']) + self.assertListEqual(lines2, [b'GI00000000000010"command1"\n']) + finally: + if fc1 is not None: + fc1.close() + if fc2 is not None: + fc2.close() + + def test_receive(self): + fc = None + manager_file = None + try: + fc = FileChannel(self.args) + message = fc.receive() + self.assertEqual(message, (None, None)) + + os.mkdir(command_path) + manager_file = open(manager_file_name, "wb") + manager_file.write(b'TR00000000000009"manager"\n') + manager_file.flush() + + self.check_timeout(2, lambda: fc.received()) + message = fc.receive() + self.assertEqual(message, (CommandType.NewTrialJob, "manager")) + + manager_file.write(b'TR00000000000010"manager2"\n') + manager_file.flush() + + self.check_timeout(2, lambda: fc.received()) + message = fc.receive() + self.assertEqual(message, (CommandType.NewTrialJob, "manager2")) + finally: + if fc is not None: + fc.close() + if manager_file is not None: + manager_file.close() + + def check_timeout(self, timeout, callback): + interval = 0.01 + start = datetime.now().timestamp() + count = int(timeout / interval) + for x in range(count): + if callback(): + break + time.sleep(interval) + print("checked {} times, {:3F} seconds".format(x, datetime.now().timestamp()-start)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tools/nni_trial_tool/trial.py b/tools/nni_trial_tool/trial.py new file mode 100644 index 0000000000..e2753b1518 --- /dev/null +++ b/tools/nni_trial_tool/trial.py @@ -0,0 +1,148 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import ctypes +import os +import shlex +import tarfile +import time +from datetime import datetime +from subprocess import Popen + +import psutil + +from .log_utils import LogType, RemoteLogger, StdOutputType, nni_log +from .commands import CommandType + +trial_output_path_name = ".nni" + + +class Trial: + def __init__(self, args, data): + self.process = None + self.data = data + self.args = args + self.command_channel = args.command_channel + self.trial_syslogger_stdout = None + + global NNI_TRIAL_JOB_ID + self.id = data["trialId"] + if self.id is None: + raise Exception("trial_id is not found in %s" % data) + os.environ['NNI_TRIAL_JOB_ID'] = self.id + NNI_TRIAL_JOB_ID = self.id + + # for multiple nodes. If it's None, it means single node. + self.node_id = args.node_id + if self.node_id is None: + self.name = self.id + else: + self.name = "%s_%s" % (self.id, self.node_id) + + def run(self): + # redirect trial's stdout and stderr to syslog + self.trial_syslogger_stdout = RemoteLogger(self.args.nnimanager_ip, self.args.nnimanager_port, 'trial', StdOutputType.Stdout, + self.args.log_collection, self.id, self.args.command_channel) + + nni_log(LogType.Info, "%s: start to run trial" % self.name) + + trial_working_dir = os.path.realpath(os.path.join(os.curdir, "..", "..", "trials", self.id)) + self.trial_output_dir = os.path.join(trial_working_dir, trial_output_path_name) + trial_code_dir = os.path.join(trial_working_dir, "code") + trial_nnioutput_dir = os.path.join(trial_working_dir, "nnioutput") + + environ = os.environ.copy() + environ['NNI_TRIAL_SEQ_ID'] = str(self.data["sequenceId"]) + environ['NNI_OUTPUT_DIR'] = os.path.join(trial_working_dir, "nnioutput") + environ['NNI_SYS_DIR'] = trial_working_dir + self.working_dir = trial_working_dir + + # prepare code and parameters + prepared_flag_file_name = os.path.join(trial_working_dir, "trial_prepared") + if not os.path.exists(trial_working_dir): + os.makedirs(trial_working_dir, exist_ok=True) + + os.makedirs(self.trial_output_dir, exist_ok=True) + os.makedirs(trial_nnioutput_dir, exist_ok=True) + # prepare code + os.makedirs(trial_code_dir, exist_ok=True) + with tarfile.open(os.path.join("..", "nni-code.tar.gz"), "r:gz") as tar: + tar.extractall(trial_code_dir) + + # save parameters + nni_log(LogType.Info, '%s: saving parameter %s' % (self.name, self.data["parameter"]["value"])) + parameter_file_name = os.path.join(trial_working_dir, "parameter.cfg") + with open(parameter_file_name, "w") as parameter_file: + parameter_file.write(self.data["parameter"]["value"]) + + # ready flag + with open(prepared_flag_file_name, "w") as prepared_flag_file: + prepared_flag_file.write("%s" % (int(datetime.now().timestamp() * 1000))) + + # make sure code prepared by other node. + if self.node_id is not None: + while True: + if os.path.exists(prepared_flag_file_name): + break + time.sleep(0.1) + + self.log_pipe_stdout = self.trial_syslogger_stdout.get_pipelog_reader() + self.process = Popen(self.args.trial_command, shell=True, stdout=self.log_pipe_stdout, + stderr=self.log_pipe_stdout, cwd=trial_code_dir, env=dict(environ)) + nni_log(LogType.Info, '{0}: spawns a subprocess (pid {1}) to run command: {2}'. + format(self.name, self.process.pid, shlex.split(self.args.trial_command))) + + def save_parameter_file(self, command_data): + parameters = command_data["parameters"] + file_index = int(parameters["index"]) + if file_index == 0: + parameter_file_name = "parameter.cfg" + else: + parameter_file_name = "parameter_{}.cfg".format(file_index) + parameter_file_name = os.path.join(self.working_dir, parameter_file_name) + with open(parameter_file_name, "w") as parameter_file: + nni_log(LogType.Info, '%s: saving parameter %s' % (self.name, parameters["value"])) + parameter_file.write(parameters["value"]) + + def is_running(self): + if (self.process is None): + return False + + retCode = self.process.poll() + # child worker process exits and all stdout data is read + if retCode is not None and self.log_pipe_stdout.set_process_exit() and self.log_pipe_stdout.is_read_completed == True: + # In Windows, the retCode -1 is 4294967295. It's larger than c_long, and raise OverflowError. + # So covert it to int32. + retCode = ctypes.c_long(retCode).value + nni_log(LogType.Info, '{0}: subprocess terminated. Exit code is {1}.'.format(self.name, retCode)) + + end_time = int(datetime.now().timestamp() * 1000) + end_message = { + "code": retCode, + "time": end_time, + "trial": self.id, + } + self.command_channel.send(CommandType.TrialEnd, end_message) + self.cleanup() + return False + else: + return True + + def kill(self, trial_id=None): + if trial_id == self.id or trial_id is None: + if self.process is not None: + nni_log(LogType.Info, "%s: killing trial" % self.name) + for child in psutil.Process(self.process.pid).children(True): + child.kill() + self.process.kill() + self.cleanup() + + def cleanup(self): + nni_log(LogType.Info, "%s: clean up trial" % self.name) + self.process = None + if self.log_pipe_stdout is not None: + self.log_pipe_stdout.set_process_exit() + self.log_pipe_stdout = None + if self.trial_syslogger_stdout is not None: + self.trial_syslogger_stdout.close() + self.trial_syslogger_stdout = None diff --git a/tools/nni_trial_tool/trial_keeper.py b/tools/nni_trial_tool/trial_keeper.py index 10ee7af211..08688973e0 100644 --- a/tools/nni_trial_tool/trial_keeper.py +++ b/tools/nni_trial_tool/trial_keeper.py @@ -117,7 +117,7 @@ def main_loop(args): nni_log(LogType.Error, 'HDFS copy directory got exception: ' + str(e)) raise e - ## Exit as the retCode of subprocess(trial) + # Exit as the retCode of subprocess(trial) exit(retCode) break diff --git a/tools/nni_trial_tool/trial_runner.py b/tools/nni_trial_tool/trial_runner.py new file mode 100644 index 0000000000..87db8fda2b --- /dev/null +++ b/tools/nni_trial_tool/trial_runner.py @@ -0,0 +1,247 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import argparse +import json +import os +import random +import re +import sys +import time +import traceback +from datetime import datetime, timedelta + +import pkg_resources + +from .gpu import collect_gpu_usage + +idle_timeout_seconds = 10 * 60 +gpu_refressh_interval_seconds = 5 +regular = re.compile('v?(?P[0-9](\.[0-9]){0,1}).*') +trial_runner_syslogger = None + + +def main_loop(args): + '''main loop logic for trial runner''' + idle_last_time = datetime.now() + gpu_refresh_last_time = datetime.now() - timedelta(minutes=1) + + try: + trials = dict() + + command_channel = args.command_channel + # command loop + while True: + command_type, command_data = command_channel.receive() + if command_type == CommandType.NewTrialJob: + trial_id = command_data["trialId"] + if trial_id in trials.keys(): + trial = trials[trial_id] + if trial.is_running(): + raise Exception('trial %s is running already, cannot start a new one' % trial.id) + else: + del trials[trial_id] + trial = Trial(args, command_data) + trial.run() + trials[trial_id] = trial + elif command_type == CommandType.KillTrialJob: + trial_id = command_data + if trial_id in trials.keys(): + trial = trials[trial_id] + trial.kill(command_data) + elif command_type == CommandType.SendTrialJobParameter: + trial_id = command_data["trialId"] + if trial_id in trials.keys(): + trial = trials[trial_id] + trial.save_parameter_file(command_data) + elif command_type is not None: + raise Exception("unknown command %s" % command_type) + + trial_list = list(trials.values()) + for trial in trial_list: + if trial is not None and trial.is_running(): + idle_last_time = datetime.now() + else: + del trials[trial.id] + + if (datetime.now() - idle_last_time).seconds > idle_timeout_seconds: + nni_log(LogType.Info, "trial runner is idle more than {0} seconds, so exit.".format( + idle_timeout_seconds)) + break + + if args.enable_gpu_collect and (datetime.now() - gpu_refresh_last_time).seconds > gpu_refressh_interval_seconds: + # collect gpu information + gpu_info = collect_gpu_usage(args.node_id) + command_channel.send(CommandType.ReportGpuInfo, gpu_info) + gpu_refresh_last_time = datetime.now() + time.sleep(0.5) + except Exception as ex: + traceback.print_exc() + raise ex + finally: + nni_log(LogType.Info, "main_loop exits.") + + trial_list = list(trials.values()) + for trial in trial_list: + trial.kill() + del trials[trial.id] + # wait to send commands + for _ in range(10): + if command_channel.sent(): + break + time.sleep(1) + command_channel.close() + + +def trial_runner_help_info(*args): + print('please run --help to see guidance') + + +def check_version(args): + try: + trial_runner_version = pkg_resources.get_distribution('nni').version + except pkg_resources.ResolutionError as err: + # package nni does not exist, try nni-tool package + nni_log(LogType.Error, 'Package nni does not exist!') + os._exit(1) + if not args.nni_manager_version: + # skip version check + nni_log(LogType.Warning, 'Skipping version check!') + else: + try: + command_channel = args.command_channel + trial_runner_version = regular.search(trial_runner_version).group('version') + nni_log(LogType.Info, '{0}: runner_version is {1}'.format(args.node_id, trial_runner_version)) + nni_manager_version = regular.search(args.nni_manager_version).group('version') + nni_log(LogType.Info, '{0}: nni_manager_version is {1}'.format(args.node_id, nni_manager_version)) + log_entry = {} + if trial_runner_version != nni_manager_version: + nni_log(LogType.Error, '{0}: Version does not match!'.format(args.node_id)) + error_message = '{0}: NNIManager version is {1}, Trial runner version is {2}, NNI version does not match!'.format( + args.node_id, nni_manager_version, trial_runner_version) + log_entry['tag'] = 'VCFail' + log_entry['msg'] = error_message + command_channel.send(CommandType.VersionCheck, log_entry) + while not command_channel.sent(): + time.sleep(1) + os._exit(1) + else: + nni_log(LogType.Info, '{0}: Version match!'.format(args.node_id)) + log_entry['tag'] = 'VCSuccess' + command_channel.send(CommandType.VersionCheck, log_entry) + except AttributeError as err: + nni_log(LogType.Error, '{0}: {1}'.format(args.node_id, err)) + +if __name__ == '__main__': + + '''NNI Trial Runner main function''' + PARSER = argparse.ArgumentParser() + PARSER.set_defaults(func=trial_runner_help_info) + PARSER.add_argument('--trial_command', type=str, help='Command to launch trial process') + PARSER.add_argument('--nnimanager_ip', type=str, help='NNI manager rest server IP') + PARSER.add_argument('--nnimanager_port', type=str, help='NNI manager rest server port') + PARSER.add_argument('--nni_manager_version', type=str, help='the nni version transmitted from nniManager') + PARSER.add_argument('--log_collection', type=str, help='set the way to collect log in trial runner') + PARSER.add_argument('--node_count', type=int, help='number of nodes, it determines how to consume command and save code file') + args, unknown = PARSER.parse_known_args() + + setting_file = "settings.json" + if not os.path.exists(setting_file): + setting_file = "../{}".format(setting_file) + if os.path.exists(setting_file): + with open(setting_file, 'r') as fp: + settings = json.load(fp) + print("setting is {}".format(settings)) + else: + print("not found setting file") + + args.exp_id = settings["experimentId"] + args.platform = settings["platform"] + # runner_id is unique runner in experiment + args.runner_id = os.path.basename(os.path.realpath(os.path.curdir)) + args.runner_name = "runner_"+args.runner_id + args.enable_gpu_collect = settings["enableGpuCollector"] + args.command_channel = settings["commandChannel"] + + if args.trial_command is None: + args.trial_command = settings["command"] + if args.nnimanager_ip is None: + args.nnimanager_ip = settings["nniManagerIP"] + if args.nnimanager_port is None: + args.nnimanager_port = settings["nniManagerPort"] + if args.nni_manager_version is None: + args.nni_manager_version = settings["nniManagerVersion"] + if args.log_collection is None: + args.log_collection = settings["logCollection"] + if args.node_count is None: + # default has only one node. + args.node_count = 1 + + os.environ['NNI_OUTPUT_DIR'] = os.curdir + "/nnioutput" + os.environ['NNI_PLATFORM'] = args.platform + os.environ['NNI_SYS_DIR'] = os.curdir + os.environ['NNI_EXP_ID'] = args.exp_id + os.environ['MULTI_PHASE'] = "true" + os.environ['NNI_TRIAL_JOB_ID'] = "runner" + + from .log_utils import LogType, RemoteLogger, StdOutputType, nni_log + from .trial import Trial + from .file_channel import FileChannel + from .web_channel import WebChannel + from .commands import CommandType + + is_multi_node = args.node_count > 1 + + if (is_multi_node): + # for multiple nodes, create a file to get a unique id. + while True: + node_id = random.randint(0, 10000) + unique_check_file_name = "node_%s" % (node_id) + if not os.path.exists(unique_check_file_name): + break + with open(unique_check_file_name, "w") as unique_check_file: + unique_check_file.write("%s" % (int(datetime.now().timestamp() * 1000))) + args.node_id = node_id + else: + # node id is unique in the runner + args.node_id = None + + # init command channel + command_channel = None + if args.command_channel == "file": + command_channel = FileChannel(args) + else: + command_channel = WebChannel(args) + command_channel.open() + + nni_log(LogType.Info, "command channel is {}, actual type is {}".format(args.command_channel, type(command_channel))) + args.command_channel = command_channel + + trial_runner_syslogger = RemoteLogger(args.nnimanager_ip, args.nnimanager_port, 'runner', + StdOutputType.Stdout, args.log_collection, args.runner_name, command_channel) + sys.stdout = sys.stderr = trial_runner_syslogger + nni_log(LogType.Info, "{}: merged args is {}".format(args.node_id, args)) + + if args.trial_command is None: + nni_log(LogType.Error, "{}: no command is found.".format(args.node_id)) + os._exit(1) + check_version(args) + try: + main_loop(args) + except SystemExit as se: + nni_log(LogType.Info, '{}: NNI trial runner exit with code {}'.format(args.node_id, se.code)) + + # try best to send latest errors to server + timeout = 10 + while not command_channel.sent() and timeout > 0: + timeout -= 1 + time.sleep(1) + os._exit(se.code) + finally: + if trial_runner_syslogger is not None: + if trial_runner_syslogger.pipeReader is not None: + trial_runner_syslogger.pipeReader.set_process_exit() + trial_runner_syslogger.close() + + # the process doesn't exit even main loop exit. So exit it explictly. + os._exit(0) diff --git a/tools/nni_trial_tool/url_utils.py b/tools/nni_trial_tool/url_utils.py index 1fa926cb8c..7942c62fb5 100644 --- a/tools/nni_trial_tool/url_utils.py +++ b/tools/nni_trial_tool/url_utils.py @@ -3,14 +3,17 @@ from .constants import API_ROOT_URL, BASE_URL, STDOUT_API, NNI_TRIAL_JOB_ID, NNI_EXP_ID, VERSION_API, PARAMETER_META_API + def gen_send_stdout_url(ip, port): '''Generate send stdout url''' return '{0}:{1}{2}{3}/{4}/{5}'.format(BASE_URL.format(ip), port, API_ROOT_URL, STDOUT_API, NNI_EXP_ID, NNI_TRIAL_JOB_ID) + def gen_send_version_url(ip, port): '''Generate send error url''' return '{0}:{1}{2}{3}/{4}/{5}'.format(BASE_URL.format(ip), port, API_ROOT_URL, VERSION_API, NNI_EXP_ID, NNI_TRIAL_JOB_ID) + def gen_parameter_meta_url(ip, port): '''Generate send error url''' return '{0}:{1}{2}{3}'.format(BASE_URL.format(ip), port, API_ROOT_URL, PARAMETER_META_API) diff --git a/tools/nni_trial_tool/web_channel.py b/tools/nni_trial_tool/web_channel.py new file mode 100644 index 0000000000..752a303cb0 --- /dev/null +++ b/tools/nni_trial_tool/web_channel.py @@ -0,0 +1,53 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import asyncio + +import websockets + +from .base_channel import BaseChannel +from .log_utils import LogType, nni_log + + +class WebChannel(BaseChannel): + + def __init__(self, args): + self.node_id = args.node_id + self.args = args + self.client = None + self.in_cache = b"" + + super(WebChannel, self).__init__(args) + + self._event_loop = None + + def _inner_open(self): + url = "ws://{}:{}".format(self.args.nnimanager_ip, self.args.nnimanager_port) + nni_log(LogType.Info, 'WebChannel: connected with info %s' % url) + + connect = websockets.connect(url) + self._event_loop = asyncio.get_event_loop() + client = self._event_loop.run_until_complete(connect) + self.client = client + + def _inner_close(self): + if self.client is not None: + self.client.close() + if self._event_loop.is_running(): + self._event_loop.close() + self.client = None + self._event_loop = None + + def _inner_send(self, message): + loop = asyncio.new_event_loop() + loop.run_until_complete(self.client.send(message)) + + def _inner_receive(self): + messages = [] + if self.client is not None: + received = self._event_loop.run_until_complete(self.client.recv()) + # receive message is string, to get consistent result, encode it here. + self.in_cache += received.encode("utf8") + messages, self.in_cache = self._fetch_message(self.in_cache) + + return messages diff --git a/tools/setup.py b/tools/setup.py index 3c0513e607..48d6923dca 100644 --- a/tools/setup.py +++ b/tools/setup.py @@ -17,7 +17,8 @@ 'schema', 'PythonWebHDFS', 'colorama', - 'netifaces' + 'netifaces', + 'websockets' ], author = 'Microsoft NNI Team', From 19eabd696638a6000cfb0bfb6afd6c270c6f794c Mon Sep 17 00:00:00 2001 From: chicm-ms <38930155+chicm-ms@users.noreply.github.com> Date: Tue, 30 Jun 2020 14:25:37 +0800 Subject: [PATCH 17/17] Refactor model compression documentation (#2612) --- docs/en_US/Compressor/CustomizeCompressor.md | 181 ++++++++++ docs/en_US/Compressor/Framework.md | 249 +++++--------- docs/en_US/Compressor/Pruner.md | 330 +++++++++---------- docs/en_US/model_compression.rst | 3 +- docs/img/compressor_framework.jpg | Bin 0 -> 105277 bytes 5 files changed, 415 insertions(+), 348 deletions(-) create mode 100644 docs/en_US/Compressor/CustomizeCompressor.md create mode 100644 docs/img/compressor_framework.jpg diff --git a/docs/en_US/Compressor/CustomizeCompressor.md b/docs/en_US/Compressor/CustomizeCompressor.md new file mode 100644 index 0000000000..407e103677 --- /dev/null +++ b/docs/en_US/Compressor/CustomizeCompressor.md @@ -0,0 +1,181 @@ +# Customize New Compression Algorithm + +```eval_rst +.. contents:: +``` + +In order to simplify the process of writing new compression algorithms, we have designed simple and flexible programming interface, which covers pruning and quantization. Below, we first demonstrate how to customize a new pruning algorithm and then demonstrate how to customize a new quantization algorithm. + +**Important Note** To better understand how to customize new pruning/quantization algorithms, users should first understand the framework that supports various pruning algorithms in NNI. Reference [Framework overview of model compression](https://nni.readthedocs.io/en/latest/Compressor/Framework.html) + + +## Customize a new pruning algorithm + +Implementing a new pruning algorithm requires implementing a `weight masker` class which shoud be a subclass of `WeightMasker`, and a `pruner` class, which should be a subclass `Pruner`. + +An implementation of `weight masker` may look like this: + +```python +class MyMasker(WeightMasker): + def __init__(self, model, pruner): + super().__init__(model, pruner) + # You can do some initialization here, such as collecting some statistics data + # if it is necessary for your algorithms to calculate the masks. + + def calc_mask(self, sparsity, wrapper, wrapper_idx=None): + # calculate the masks based on the wrapper.weight, and sparsity, + # and anything else + # mask = ... + return {'weight_mask': mask} +``` + +You can reference nni provided [weight masker](https://github.com/microsoft/nni/blob/master/src/sdk/pynni/nni/compression/torch/pruning/structured_pruning.py) implementations to implement your own weight masker. + +A basic `pruner` looks likes this: + +```python +class MyPruner(Pruner): + def __init__(self, model, config_list, optimizer): + super().__init__(model, config_list, optimizer) + self.set_wrappers_attribute("if_calculated", False) + # construct a weight masker instance + self.masker = MyMasker(model, self) + + def calc_mask(self, wrapper, wrapper_idx=None): + sparsity = wrapper.config['sparsity'] + if wrapper.if_calculated: + # Already pruned, do not prune again as a one-shot pruner + return None + else: + # call your masker to actually calcuate the mask for this layer + masks = self.masker.calc_mask(sparsity=sparsity, wrapper=wrapper, wrapper_idx=wrapper_idx) + wrapper.if_calculated = True + return masks + +``` + +Reference nni provided [pruner](https://github.com/microsoft/nni/blob/master/src/sdk/pynni/nni/compression/torch/pruning/one_shot.py) implementations to implement your own pruner class. + + +*** + +## Customize a new quantization algorithm + +To write a new quantization algorithm, you can write a class that inherits `nni.compression.torch.Quantizer`. Then, override the member functions with the logic of your algorithm. The member function to override is `quantize_weight`. `quantize_weight` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask. + +```python +from nni.compression.torch import Quantizer + +class YourQuantizer(Quantizer): + def __init__(self, model, config_list): + """ + Suggest you to use the NNI defined spec for config + """ + super().__init__(model, config_list) + + def quantize_weight(self, weight, config, **kwargs): + """ + quantize should overload this method to quantize weight tensors. + This method is effectively hooked to :meth:`forward` of the model. + + Parameters + ---------- + weight : Tensor + weight that needs to be quantized + config : dict + the configuration for weight quantization + """ + + # Put your code to generate `new_weight` here + + return new_weight + + def quantize_output(self, output, config, **kwargs): + """ + quantize should overload this method to quantize output. + This method is effectively hooked to `:meth:`forward` of the model. + + Parameters + ---------- + output : Tensor + output that needs to be quantized + config : dict + the configuration for output quantization + """ + + # Put your code to generate `new_output` here + + return new_output + + def quantize_input(self, *inputs, config, **kwargs): + """ + quantize should overload this method to quantize input. + This method is effectively hooked to :meth:`forward` of the model. + + Parameters + ---------- + inputs : Tensor + inputs that needs to be quantized + config : dict + the configuration for inputs quantization + """ + + # Put your code to generate `new_input` here + + return new_input + + def update_epoch(self, epoch_num): + pass + + def step(self): + """ + Can do some processing based on the model or weights binded + in the func bind_model + """ + pass +``` + +### Customize backward function + +Sometimes it's necessary for a quantization operation to have a customized backward function, such as [Straight-Through Estimator](https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste), user can customize a backward function as follow: + +```python +from nni.compression.torch.compressor import Quantizer, QuantGrad, QuantType + +class ClipGrad(QuantGrad): + @staticmethod + def quant_backward(tensor, grad_output, quant_type): + """ + This method should be overrided by subclass to provide customized backward function, + default implementation is Straight-Through Estimator + Parameters + ---------- + tensor : Tensor + input of quantization operation + grad_output : Tensor + gradient of the output of quantization operation + quant_type : QuantType + the type of quantization, it can be `QuantType.QUANT_INPUT`, `QuantType.QUANT_WEIGHT`, `QuantType.QUANT_OUTPUT`, + you can define different behavior for different types. + Returns + ------- + tensor + gradient of the input of quantization operation + """ + + # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1 + if quant_type == QuantType.QUANT_OUTPUT: + grad_output[torch.abs(tensor) > 1] = 0 + return grad_output + + +class YourQuantizer(Quantizer): + def __init__(self, model, config_list): + super().__init__(model, config_list) + # set your customized backward function to overwrite default backward function + self.quant_grad = ClipGrad + +``` + +If you do not customize `QuantGrad`, the default backward is Straight-Through Estimator. +_Coming Soon_ ... diff --git a/docs/en_US/Compressor/Framework.md b/docs/en_US/Compressor/Framework.md index 66083197be..6b271343b5 100644 --- a/docs/en_US/Compressor/Framework.md +++ b/docs/en_US/Compressor/Framework.md @@ -1,18 +1,18 @@ -# Customize A New Compression Algorithm +# Framework overview of model compression ```eval_rst .. contents:: ``` -To simplify writing a new compression algorithm, we design programming interfaces which are simple but flexible enough. There are interfaces for pruning and quantization respectively. Below, we first demonstrate how to customize a new pruning algorithm and then demonstrate how to customize a new quantization algorithm. +Below picture shows the components overview of model compression framework. -## Customize a new pruning algorithm +![](../../img/compressor_framework.jpg) -To better demonstrate how to customize a new pruning algorithm, it is necessary for users to first understand the framework for supporting various pruning algorithms in NNI. +There are 3 major components/classes in NNI model compression framework: `Compressor`, `Pruner` and `Quantizer`. Let's look at them in detail one by one: -### Framework overview for pruning algorithms +## Compressor -Following example shows how to use a pruner: +Compressor is the base class for pruner and quntizer, it provides a unified interface for pruner and quantizer for end users, so that pruner and quantizer can be used in the same way. For example, to use a pruner: ```python from nni.compression.torch import LevelPruner @@ -32,82 +32,25 @@ model = pruner.compress() # the model will be pruned during training automatically ``` -A pruner receives `model`, `config_list` and `optimizer` as arguments. It prunes the model per the `config_list` during training loop by adding a hook on `optimizer.step()`. - -From implementation perspective, a pruner consists of a `weight masker` instance and multiple `module wrapper` instances. - -#### Weight masker - -A `weight masker` is the implementation of pruning algorithms, it can prune a specified layer wrapped by `module wrapper` with specified sparsity. - -#### Module wrapper - -A `module wrapper` is a module containing: - -1. the origin module -2. some buffers used by `calc_mask` -3. a new forward method that applies masks before running the original forward method. - -the reasons to use `module wrapper`: - -1. some buffers are needed by `calc_mask` to calculate masks and these buffers should be registered in `module wrapper` so that the original modules are not contaminated. -2. a new `forward` method is needed to apply masks to weight before calling the real `forward` method. - -#### Pruner - -A `pruner` is responsible for: - -1. Manage / verify config_list. -2. Use `module wrapper` to wrap the model layers and add hook on `optimizer.step` -3. Use `weight masker` to calculate masks of layers while pruning. -4. Export pruned model weights and masks. - -### Implement a new pruning algorithm - -Implementing a new pruning algorithm requires implementing a `weight masker` class which shoud be a subclass of `WeightMasker`, and a `pruner` class, which should be a subclass `Pruner`. - -An implementation of `weight masker` may look like this: - +To use a quantizer: ```python -class MyMasker(WeightMasker): - def __init__(self, model, pruner): - super().__init__(model, pruner) - # You can do some initialization here, such as collecting some statistics data - # if it is necessary for your algorithms to calculate the masks. - - def calc_mask(self, sparsity, wrapper, wrapper_idx=None): - # calculate the masks based on the wrapper.weight, and sparsity, - # and anything else - # mask = ... - return {'weight_mask': mask} -``` - -You can reference nni provided [weight masker](https://github.com/microsoft/nni/blob/master/src/sdk/pynni/nni/compression/torch/pruning/structured_pruning.py) implementations to implement your own weight masker. - -A basic `pruner` looks likes this: +from nni.compression.torch import DoReFaQuantizer -```python -class MyPruner(Pruner): - def __init__(self, model, config_list, optimizer): - super().__init__(model, config_list, optimizer) - self.set_wrappers_attribute("if_calculated", False) - # construct a weight masker instance - self.masker = MyMasker(model, self) - - def calc_mask(self, wrapper, wrapper_idx=None): - sparsity = wrapper.config['sparsity'] - if wrapper.if_calculated: - # Already pruned, do not prune again as a one-shot pruner - return None - else: - # call your masker to actually calcuate the mask for this layer - masks = self.masker.calc_mask(sparsity=sparsity, wrapper=wrapper, wrapper_idx=wrapper_idx) - wrapper.if_calculated = True - return masks +configure_list = [{ + 'quant_types': ['weight'], + 'quant_bits': { + 'weight': 8, + }, + 'op_types':['Conv2d', 'Linear'] +}] +optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4) +quantizer = DoReFaQuantizer(model, configure_list, optimizer) +quantizer.compress() ``` +View [example code](https://github.com/microsoft/nni/tree/master/examples/model_compress) for more information. -Reference nni provided [pruner](https://github.com/microsoft/nni/blob/master/src/sdk/pynni/nni/compression/torch/pruning/one_shot.py) implementations to implement your own pruner class. +`Compressor` class provides some utility methods for subclass and users: ### Set wrapper attribute @@ -148,130 +91,104 @@ collector_id = self.pruner.add_activation_collector(collector) self.pruner.remove_activation_collector(collector_id) ``` -### Multi-GPU support +*** + +## Pruner + +A pruner receives `model`, `config_list` and `optimizer` as arguments. It prunes the model per the `config_list` during training loop by adding a hook on `optimizer.step()`. + +Pruner class is a subclass of Compressor, so it contains everything in the Compressor class and some additional components only for pruning, it contains: + +### Weight masker + +A `weight masker` is the implementation of pruning algorithms, it can prune a specified layer wrapped by `module wrapper` with specified sparsity. + +### Pruning module wrapper + +A `pruning module wrapper` is a module containing: + +1. the origin module +2. some buffers used by `calc_mask` +3. a new forward method that applies masks before running the original forward method. + +the reasons to use `module wrapper`: + +1. some buffers are needed by `calc_mask` to calculate masks and these buffers should be registered in `module wrapper` so that the original modules are not contaminated. +2. a new `forward` method is needed to apply masks to weight before calling the real `forward` method. + +### Pruning hook + +A pruning hook is installed on a pruner when the pruner is constructed, it is used to call pruner's calc_mask method at `optimizer.step()` is invoked. -On multi-GPU training, buffers and parameters are copied to multiple GPU every time the `forward` method runs on multiple GPU. If buffers and parameters are updated in the `forward` method, an `in-place` update is needed to ensure the update is effective. -Since `calc_mask` is called in the `optimizer.step` method, which happens after the `forward` method and happens only on one GPU, it supports multi-GPU naturally. *** -## Customize a new quantization algorithm +## Quantizer -To write a new quantization algorithm, you can write a class that inherits `nni.compression.torch.Quantizer`. Then, override the member functions with the logic of your algorithm. The member function to override is `quantize_weight`. `quantize_weight` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask. +Quantizer class is also a subclass of `Compressor`, it is used to compress models by reducing the number of bits required to represent weights or activations, which can reduce the computations and the inference time. It contains: -```python -from nni.compression.torch import Quantizer +### Quantization module wrapper -class YourQuantizer(Quantizer): - def __init__(self, model, config_list): - """ - Suggest you to use the NNI defined spec for config - """ - super().__init__(model, config_list) +Each module/layer of the model to be quantized is wrapped by a quantization module wrapper, it provides a new `forward` method to quantize the original module's weight, input and output. + +### Quantization hook + +A quantization hook is installed on a quntizer when it is constructed, it is call at `optimizer.step()`. + +### Quantization methods + +`Quantizer` class provides following methods for subclass to implement quantization algorithms: - def quantize_weight(self, weight, config, **kwargs): +```python +class Quantizer(Compressor): + """ + Base quantizer for pytorch quantizer + """ + def quantize_weight(self, weight, wrapper, **kwargs): """ - quantize should overload this method to quantize weight tensors. + quantize should overload this method to quantize weight. This method is effectively hooked to :meth:`forward` of the model. - Parameters ---------- weight : Tensor weight that needs to be quantized - config : dict - the configuration for weight quantization + wrapper : QuantizerModuleWrapper + the wrapper for origin module """ + raise NotImplementedError('Quantizer must overload quantize_weight()') - # Put your code to generate `new_weight` here - - return new_weight - - def quantize_output(self, output, config, **kwargs): + def quantize_output(self, output, wrapper, **kwargs): """ quantize should overload this method to quantize output. - This method is effectively hooked to `:meth:`forward` of the model. - + This method is effectively hooked to :meth:`forward` of the model. Parameters ---------- output : Tensor output that needs to be quantized - config : dict - the configuration for output quantization + wrapper : QuantizerModuleWrapper + the wrapper for origin module """ + raise NotImplementedError('Quantizer must overload quantize_output()') - # Put your code to generate `new_output` here - - return new_output - - def quantize_input(self, *inputs, config, **kwargs): + def quantize_input(self, *inputs, wrapper, **kwargs): """ quantize should overload this method to quantize input. This method is effectively hooked to :meth:`forward` of the model. - Parameters ---------- inputs : Tensor inputs that needs to be quantized - config : dict - the configuration for inputs quantization + wrapper : QuantizerModuleWrapper + the wrapper for origin module """ + raise NotImplementedError('Quantizer must overload quantize_input()') - # Put your code to generate `new_input` here - - return new_input - - def update_epoch(self, epoch_num): - pass - - def step(self): - """ - Can do some processing based on the model or weights binded - in the func bind_model - """ - pass ``` -### Customize backward function - -Sometimes it's necessary for a quantization operation to have a customized backward function, such as [Straight-Through Estimator](https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste), user can customize a backward function as follow: - -```python -from nni.compression.torch.compressor import Quantizer, QuantGrad, QuantType - -class ClipGrad(QuantGrad): - @staticmethod - def quant_backward(tensor, grad_output, quant_type): - """ - This method should be overrided by subclass to provide customized backward function, - default implementation is Straight-Through Estimator - Parameters - ---------- - tensor : Tensor - input of quantization operation - grad_output : Tensor - gradient of the output of quantization operation - quant_type : QuantType - the type of quantization, it can be `QuantType.QUANT_INPUT`, `QuantType.QUANT_WEIGHT`, `QuantType.QUANT_OUTPUT`, - you can define different behavior for different types. - Returns - ------- - tensor - gradient of the input of quantization operation - """ - - # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1 - if quant_type == QuantType.QUANT_OUTPUT: - grad_output[torch.abs(tensor) > 1] = 0 - return grad_output - +*** -class YourQuantizer(Quantizer): - def __init__(self, model, config_list): - super().__init__(model, config_list) - # set your customized backward function to overwrite default backward function - self.quant_grad = ClipGrad +## Multi-GPU support -``` +On multi-GPU training, buffers and parameters are copied to multiple GPU every time the `forward` method runs on multiple GPU. If buffers and parameters are updated in the `forward` method, an `in-place` update is needed to ensure the update is effective. +Since `calc_mask` is called in the `optimizer.step` method, which happens after the `forward` method and happens only on one GPU, it supports multi-GPU naturally. -If you do not customize `QuantGrad`, the default backward is Straight-Through Estimator. -_Coming Soon_ ... \ No newline at end of file diff --git a/docs/en_US/Compressor/Pruner.md b/docs/en_US/Compressor/Pruner.md index fcc17fe2b0..0d86d7bc70 100644 --- a/docs/en_US/Compressor/Pruner.md +++ b/docs/en_US/Compressor/Pruner.md @@ -1,27 +1,26 @@ # Supported Pruning Algorithms on NNI -We provide several pruning algorithms that support fine-grained weight pruning and structural filter pruning. **Weight pruning** generally results in unstructured models, which need specialized haredware or software to speed up the sparse network. **Filter Pruning** achieves acceleratation by removing the entire filter. We also provide an algorithm to control the **pruning schedule**. +We provide several pruning algorithms that support fine-grained weight pruning and structural filter pruning. **Fine-grained Pruning** generally results in unstructured models, which need specialized haredware or software to speed up the sparse network. **Filter Pruning** achieves acceleratation by removing the entire filter. We also provide an algorithm to control the **pruning schedule**. -**Weight Pruning** +**Fine-grained Pruning** * [Level Pruner](#level-pruner) -* [Lottery Ticket Hypothesis](#lottery-ticket-hypothesis) **Filter Pruning** * [Slim Pruner](#slim-pruner) -* [Filter Pruners with Weight Rank](#weightrankfilterpruner) - * [FPGM Pruner](#fpgm-pruner) - * [L1Filter Pruner](#l1filter-pruner) - * [L2Filter Pruner](#l2filter-pruner) -* [Filter Pruners with Activation Rank](#activationrankfilterpruner) - * [APoZ Rank Pruner](#activationapozrankfilterpruner) - * [Activation Mean Rank Pruner](#activationmeanrankfilterpruner) -* [Filter Pruners with Gradient Rank](#gradientrankfilterpruner) - * [Taylor FO On Weight Pruner](#taylorfoweightfilterpruner) - +* [FPGM Pruner](#fpgm-pruner) +* [L1Filter Pruner](#l1filter-pruner) +* [L2Filter Pruner](#l2filter-pruner) +* [APoZ Rank Pruner](#activationapozrankfilterpruner) +* [Activation Mean Rank Pruner](#activationmeanrankfilterpruner) +* [Taylor FO On Weight Pruner](#taylorfoweightfilterpruner) + **Pruning Schedule** * [AGP Pruner](#agp-pruner) +**Others** +* [Lottery Ticket Hypothesis](#lottery-ticket-hypothesis) + ## Level Pruner This is one basic one-shot pruner: you can set a target sparsity level (expressed as a fraction, 0.6 means we will prune 60%). @@ -51,126 +50,6 @@ pruner.compress() *** -## AGP Pruner -This is an iterative pruner, In [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878), authors Michael Zhu and Suyog Gupta provide an algorithm to prune the weight gradually. - ->We introduce a new automated gradual pruning algorithm in which the sparsity is increased from an initial sparsity value si (usually 0) to a final sparsity value sf over a span of n pruning steps, starting at training step t0 and with pruning frequency ∆t: -![](../../img/agp_pruner.png) ->The binary weight masks are updated every ∆t steps as the network is trained to gradually increase the sparsity of the network while allowing the network training steps to recover from any pruning-induced loss in accuracy. In our experience, varying the pruning frequency ∆t between 100 and 1000 training steps had a negligible impact on the final model quality. Once the model achieves the target sparsity sf , the weight masks are no longer updated. The intuition behind this sparsity function in equation - -### Usage -You can prune all weight from 0% to 80% sparsity in 10 epoch with the code below. - -Tensorflow code -```python -from nni.compression.tensorflow import AGP_Pruner -config_list = [{ - 'initial_sparsity': 0, - 'final_sparsity': 0.8, - 'start_epoch': 0, - 'end_epoch': 10, - 'frequency': 1, - 'op_types': 'default' -}] -pruner = AGP_Pruner(tf.get_default_graph(), config_list) -pruner.compress() -``` -PyTorch code -```python -from nni.compression.torch import AGP_Pruner -config_list = [{ - 'initial_sparsity': 0, - 'final_sparsity': 0.8, - 'start_epoch': 0, - 'end_epoch': 10, - 'frequency': 1, - 'op_types': ['default'] -}] -pruner = AGP_Pruner(model, config_list, pruning_algorithm='level') -pruner.compress() -``` - -AGP pruner uses `LevelPruner` algorithms to prune the weight by default, however you can set `pruning_algorithm` parameter to other values to use other pruning algorithms: -* `level`: LevelPruner -* `slim`: SlimPruner -* `l1`: L1FilterPruner -* `l2`: L2FilterPruner -* `fpgm`: FPGMPruner -* `taylorfo`: TaylorFOWeightFilterPruner -* `apoz`: ActivationAPoZRankFilterPruner -* `mean_activation`: ActivationMeanRankFilterPruner - -You should add code below to update epoch number when you finish one epoch in your training code. - -Tensorflow code -```python -pruner.update_epoch(epoch, sess) -``` -PyTorch code -```python -pruner.update_epoch(epoch) -``` -You can view example for more information - -#### User configuration for AGP Pruner -* **initial_sparsity:** This is to specify the sparsity when compressor starts to compress -* **final_sparsity:** This is to specify the sparsity when compressor finishes to compress -* **start_epoch:** This is to specify the epoch number when compressor starts to compress, default start from epoch 0 -* **end_epoch:** This is to specify the epoch number when compressor finishes to compress -* **frequency:** This is to specify every *frequency* number epochs compressor compress once, default frequency=1 - -*** - -## Lottery Ticket Hypothesis -[The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://arxiv.org/abs/1803.03635), authors Jonathan Frankle and Michael Carbin,provides comprehensive measurement and analysis, and articulate the *lottery ticket hypothesis*: dense, randomly-initialized, feed-forward networks contain subnetworks (*winning tickets*) that -- when trained in isolation -- reach test accuracy comparable to the original network in a similar number of iterations. - -In this paper, the authors use the following process to prune a model, called *iterative prunning*: ->1. Randomly initialize a neural network f(x;theta_0) (where theta_0 follows D_{theta}). ->2. Train the network for j iterations, arriving at parameters theta_j. ->3. Prune p% of the parameters in theta_j, creating a mask m. ->4. Reset the remaining parameters to their values in theta_0, creating the winning ticket f(x;m*theta_0). ->5. Repeat step 2, 3, and 4. - -If the configured final sparsity is P (e.g., 0.8) and there are n times iterative pruning, each iterative pruning prunes 1-(1-P)^(1/n) of the weights that survive the previous round. - -### Usage - -PyTorch code -```python -from nni.compression.torch import LotteryTicketPruner -config_list = [{ - 'prune_iterations': 5, - 'sparsity': 0.8, - 'op_types': ['default'] -}] -pruner = LotteryTicketPruner(model, config_list, optimizer) -pruner.compress() -for _ in pruner.get_prune_iterations(): - pruner.prune_iteration_start() - for epoch in range(epoch_num): - ... -``` - -The above configuration means that there are 5 times of iterative pruning. As the 5 times iterative pruning are executed in the same run, LotteryTicketPruner needs `model` and `optimizer` (**Note that should add `lr_scheduler` if used**) to reset their states every time a new prune iteration starts. Please use `get_prune_iterations` to get the pruning iterations, and invoke `prune_iteration_start` at the beginning of each iteration. `epoch_num` is better to be large enough for model convergence, because the hypothesis is that the performance (accuracy) got in latter rounds with high sparsity could be comparable with that got in the first round. - - -*Tensorflow version will be supported later.* - -#### User configuration for LotteryTicketPruner - -* **prune_iterations:** The number of rounds for the iterative pruning, i.e., the number of iterative pruning. -* **sparsity:** The final sparsity when the compression is done. - -### Reproduced Experiment - -We try to reproduce the experiment result of the fully connected network on MNIST using the same configuration as in the paper. The code can be referred [here](https://github.com/microsoft/nni/tree/master/examples/model_compress/lottery_torch_mnist_fc.py). In this experiment, we prune 10 times, for each pruning we train the pruned model for 50 epochs. - -![](../../img/lottery_ticket_mnist_fc.png) - -The above figure shows the result of the fully connected network. `round0-sparsity-0.0` is the performance without pruning. Consistent with the paper, pruning around 80% also obtain similar performance compared to non-pruning, and converges a little faster. If pruning too much, e.g., larger than 94%, the accuracy becomes lower and convergence becomes a little slower. A little different from the paper, the trend of the data in the paper is relatively more clear. - -*** - ## Slim Pruner This is an one-shot pruner, In ['Learning Efficient Convolutional Networks through Network Slimming'](https://arxiv.org/pdf/1708.06519.pdf), authors Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan and Changshui Zhang. @@ -208,10 +87,7 @@ The experiments code can be found at [examples/model_compress]( https://github.c *** -## WeightRankFilterPruner -WeightRankFilterPruner is a series of pruners which prune the filters with the smallest importance criterion calculated from the weights in convolution layers to achieve a preset level of network sparsity - -### FPGM Pruner +## FPGM Pruner This is an one-shot pruner, FPGM Pruner is an implementation of paper [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://arxiv.org/pdf/1811.00250.pdf) @@ -221,7 +97,7 @@ FPGMPruner prune filters with the smallest geometric median >Previous works utilized “smaller-norm-less-important” criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small. To solve this problem, we propose a novel filter pruning method, namely Filter Pruning via Geometric Median (FPGM), to compress the model regardless of those two requirements. Unlike previous methods, FPGM compresses CNN models by pruning filters with redundancy, rather than those with “relatively less” importance. -#### Usage +### Usage Tensorflow code ```python @@ -243,26 +119,14 @@ config_list = [{ pruner = FPGMPruner(model, config_list) pruner.compress() ``` -Note: FPGM Pruner is used to prune convolutional layers within deep neural networks, therefore the `op_types` field supports only convolutional layers. - -You should add code below to update epoch number at beginning of each epoch. - -Tensorflow code -```python -pruner.update_epoch(epoch, sess) -``` -PyTorch code -```python -pruner.update_epoch(epoch) -``` -You can view example for more information #### User configuration for FPGM Pruner -* **sparsity:** How much percentage of convolutional filters are to be pruned. +- **sparsity:** How much percentage of convolutional filters are to be pruned. +- **op_types:** Only Conv2d is supported in L1Filter Pruner *** -### L1Filter Pruner +## L1Filter Pruner This is an one-shot pruner, In ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https://arxiv.org/abs/1608.08710), authors Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet and Hans Peter Graf. @@ -280,7 +144,7 @@ This is an one-shot pruner, In ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https: > 4. A new kernel matrix is created for both the ![](http://latex.codecogs.com/gif.latex?i)th and ![](http://latex.codecogs.com/gif.latex?i+1)th layers, and the remaining kernel > weights are copied to the new model. -#### Usage +### Usage PyTorch code @@ -294,9 +158,9 @@ pruner.compress() #### User configuration for L1Filter Pruner - **sparsity:** This is to specify the sparsity operations to be compressed to -- **op_types:** Only Conv1d and Conv2d is supported in L1Filter Pruner +- **op_types:** Only Conv2d is supported in L1Filter Pruner -#### Reproduced Experiment +### Reproduced Experiment We implemented one of the experiments in ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https://arxiv.org/abs/1608.08710) with **L1FilterPruner**, we pruned **VGG-16** for CIFAR-10 to **VGG-16-pruned-A** in the paper, in which $64\%$ parameters are pruned. Our experiments results are as follows: @@ -309,11 +173,11 @@ The experiments code can be found at [examples/model_compress]( https://github.c *** -### L2Filter Pruner +## L2Filter Pruner This is a structured pruning algorithm that prunes the filters with the smallest L2 norm of the weights. It is implemented as a one-shot pruner. -#### Usage +### Usage PyTorch code @@ -324,25 +188,22 @@ pruner = L2FilterPruner(model, config_list) pruner.compress() ``` -#### User configuration for L2Filter Pruner +### User configuration for L2Filter Pruner - **sparsity:** This is to specify the sparsity operations to be compressed to -- **op_types:** Only Conv1d and Conv2d is supported in L2Filter Pruner +- **op_types:** Only Conv2d is supported in L2Filter Pruner *** -## ActivationRankFilterPruner -ActivationRankFilterPruner is a series of pruners which prune the filters with the smallest importance criterion calculated from the output activations of convolution layers to achieve a preset level of network sparsity. +## ActivationAPoZRankFilterPruner -### ActivationAPoZRankFilterPruner - -We implemented it as a one-shot pruner, it prunes convolutional layers based on the criterion `APoZ` which is explained in the paper [Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures](https://arxiv.org/abs/1607.03250). Iterative pruning based on `APoZ` will be supported in future release. +ActivationAPoZRankFilterPruner is a pruner which prunes the filters with the smallest importance criterion `APoZ` calculated from the output activations of convolution layers to achieve a preset level of network sparsity. The pruning criterion `APoZ` is explained in the paper [Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures](https://arxiv.org/abs/1607.03250). The APoZ is defined as: ![](../../img/apoz.png) -#### Usage +### Usage PyTorch code @@ -360,18 +221,18 @@ Note: ActivationAPoZRankFilterPruner is used to prune convolutional layers withi You can view example for more information -#### User configuration for ActivationAPoZRankFilterPruner +### User configuration for ActivationAPoZRankFilterPruner - **sparsity:** How much percentage of convolutional filters are to be pruned. - **op_types:** Only Conv2d is supported in ActivationAPoZRankFilterPruner *** -### ActivationMeanRankFilterPruner +## ActivationMeanRankFilterPruner -We implemented it as a one-shot pruner, it prunes convolutional layers based on the criterion `mean activation` which is explained in section 2.2 of the paper[Pruning Convolutional Neural Networks for Resource Efficient Inference](https://arxiv.org/abs/1611.06440). Other pruning criteria mentioned in this paper will be supported in future release. +ActivationMeanRankFilterPruner is a pruner which prunes the filters with the smallest importance criterion `mean activation` calculated from the output activations of convolution layers to achieve a preset level of network sparsity. The pruning criterion `mean activation` is explained in section 2.2 of the paper[Pruning Convolutional Neural Networks for Resource Efficient Inference](https://arxiv.org/abs/1611.06440). Other pruning criteria mentioned in this paper will be supported in future release. -#### Usage +### Usage PyTorch code @@ -381,7 +242,7 @@ config_list = [{ 'sparsity': 0.5, 'op_types': ['Conv2d'] }] -pruner = ActivationMeanRankFilterPruner(model, config_list) +pruner = ActivationMeanRankFilterPruner(model, config_list, statistics_batch_num=1) pruner.compress() ``` @@ -389,26 +250,22 @@ Note: ActivationMeanRankFilterPruner is used to prune convolutional layers withi You can view example for more information -#### User configuration for ActivationMeanRankFilterPruner +### User configuration for ActivationMeanRankFilterPruner - **sparsity:** How much percentage of convolutional filters are to be pruned. - **op_types:** Only Conv2d is supported in ActivationMeanRankFilterPruner. *** -## GradientRankFilterPruner - -GradientRankFilterPruner is a series of pruners which prune the filters with the smallest importance criterion calculated from the gradients of convolution layers to achieve a preset level of network sparsity. +## TaylorFOWeightFilterPruner -### TaylorFOWeightFilterPruner - -We implemented it as a one-shot pruner, it prunes convolutional layers based on the first order taylor expansion on weights. The estimated importance of filters is defined as the paper [Importance Estimation for Neural Network Pruning](http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf). Other pruning criteria mentioned in this paper will be supported in future release. +TaylorFOWeightFilterPruner is a pruner which prunes convolutional layers based on estimated importance calculated from the first order taylor expansion on weights to achieve a preset level of network sparsity. The estimated importance of filters is defined as the paper [Importance Estimation for Neural Network Pruning](http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf). Other pruning criteria mentioned in this paper will be supported in future release. > ![](../../img/importance_estimation_sum.png) -#### Usage +### Usage PyTorch code @@ -418,17 +275,128 @@ config_list = [{ 'sparsity': 0.5, 'op_types': ['Conv2d'] }] -pruner = TaylorFOWeightFilterPruner(model, config_list, optimizer) +pruner = TaylorFOWeightFilterPruner(model, config_list, statistics_batch_num=1) pruner.compress() ``` You can view example for more information -#### User configuration for GradientWeightSumFilterPruner +### User configuration for TaylorFOWeightFilterPruner - **sparsity:** How much percentage of convolutional filters are to be pruned. - **op_types:** Currently only Conv2d is supported in TaylorFOWeightFilterPruner. +*** + +## AGP Pruner +This is an iterative pruner, In [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878), authors Michael Zhu and Suyog Gupta provide an algorithm to prune the weight gradually. + +>We introduce a new automated gradual pruning algorithm in which the sparsity is increased from an initial sparsity value si (usually 0) to a final sparsity value sf over a span of n pruning steps, starting at training step t0 and with pruning frequency ∆t: +![](../../img/agp_pruner.png) +>The binary weight masks are updated every ∆t steps as the network is trained to gradually increase the sparsity of the network while allowing the network training steps to recover from any pruning-induced loss in accuracy. In our experience, varying the pruning frequency ∆t between 100 and 1000 training steps had a negligible impact on the final model quality. Once the model achieves the target sparsity sf , the weight masks are no longer updated. The intuition behind this sparsity function in equation + +### Usage +You can prune all weight from 0% to 80% sparsity in 10 epoch with the code below. + +PyTorch code +```python +from nni.compression.torch import AGP_Pruner +config_list = [{ + 'initial_sparsity': 0, + 'final_sparsity': 0.8, + 'start_epoch': 0, + 'end_epoch': 10, + 'frequency': 1, + 'op_types': ['default'] +}] + +# load a pretrained model or train a model before using a pruner +# model = MyModel() +# model.load_state_dict(torch.load('mycheckpoint.pth')) + +# AGP pruner prunes model while fine tuning the model by adding a hook on +# optimizer.step(), so an optimizer is required to prune the model. +optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4) + +pruner = AGP_Pruner(model, config_list, optimizer, pruning_algorithm='level') +pruner.compress() +``` + +AGP pruner uses `LevelPruner` algorithms to prune the weight by default, however you can set `pruning_algorithm` parameter to other values to use other pruning algorithms: +* `level`: LevelPruner +* `slim`: SlimPruner +* `l1`: L1FilterPruner +* `l2`: L2FilterPruner +* `fpgm`: FPGMPruner +* `taylorfo`: TaylorFOWeightFilterPruner +* `apoz`: ActivationAPoZRankFilterPruner +* `mean_activation`: ActivationMeanRankFilterPruner + +You should add code below to update epoch number when you finish one epoch in your training code. + +PyTorch code +```python +pruner.update_epoch(epoch) +``` +You can view example for more information + +#### User configuration for AGP Pruner +* **initial_sparsity:** This is to specify the sparsity when compressor starts to compress +* **final_sparsity:** This is to specify the sparsity when compressor finishes to compress +* **start_epoch:** This is to specify the epoch number when compressor starts to compress, default start from epoch 0 +* **end_epoch:** This is to specify the epoch number when compressor finishes to compress +* **frequency:** This is to specify every *frequency* number epochs compressor compress once, default frequency=1 + +*** + +## Lottery Ticket Hypothesis +[The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://arxiv.org/abs/1803.03635), authors Jonathan Frankle and Michael Carbin,provides comprehensive measurement and analysis, and articulate the *lottery ticket hypothesis*: dense, randomly-initialized, feed-forward networks contain subnetworks (*winning tickets*) that -- when trained in isolation -- reach test accuracy comparable to the original network in a similar number of iterations. + +In this paper, the authors use the following process to prune a model, called *iterative prunning*: +>1. Randomly initialize a neural network f(x;theta_0) (where theta_0 follows D_{theta}). +>2. Train the network for j iterations, arriving at parameters theta_j. +>3. Prune p% of the parameters in theta_j, creating a mask m. +>4. Reset the remaining parameters to their values in theta_0, creating the winning ticket f(x;m*theta_0). +>5. Repeat step 2, 3, and 4. + +If the configured final sparsity is P (e.g., 0.8) and there are n times iterative pruning, each iterative pruning prunes 1-(1-P)^(1/n) of the weights that survive the previous round. + +### Usage + +PyTorch code +```python +from nni.compression.torch import LotteryTicketPruner +config_list = [{ + 'prune_iterations': 5, + 'sparsity': 0.8, + 'op_types': ['default'] +}] +pruner = LotteryTicketPruner(model, config_list, optimizer) +pruner.compress() +for _ in pruner.get_prune_iterations(): + pruner.prune_iteration_start() + for epoch in range(epoch_num): + ... +``` + +The above configuration means that there are 5 times of iterative pruning. As the 5 times iterative pruning are executed in the same run, LotteryTicketPruner needs `model` and `optimizer` (**Note that should add `lr_scheduler` if used**) to reset their states every time a new prune iteration starts. Please use `get_prune_iterations` to get the pruning iterations, and invoke `prune_iteration_start` at the beginning of each iteration. `epoch_num` is better to be large enough for model convergence, because the hypothesis is that the performance (accuracy) got in latter rounds with high sparsity could be comparable with that got in the first round. + + +*Tensorflow version will be supported later.* + +#### User configuration for LotteryTicketPruner + +* **prune_iterations:** The number of rounds for the iterative pruning, i.e., the number of iterative pruning. +* **sparsity:** The final sparsity when the compression is done. + +### Reproduced Experiment + +We try to reproduce the experiment result of the fully connected network on MNIST using the same configuration as in the paper. The code can be referred [here](https://github.com/microsoft/nni/tree/master/examples/model_compress/lottery_torch_mnist_fc.py). In this experiment, we prune 10 times, for each pruning we train the pruned model for 50 epochs. + +![](../../img/lottery_ticket_mnist_fc.png) + +The above figure shows the result of the fully connected network. `round0-sparsity-0.0` is the performance without pruning. Consistent with the paper, pruning around 80% also obtain similar performance compared to non-pruning, and converges a little faster. If pruning too much, e.g., larger than 94%, the accuracy becomes lower and convergence becomes a little slower. A little different from the paper, the trend of the data in the paper is relatively more clear. +   \ No newline at end of file diff --git a/docs/en_US/model_compression.rst b/docs/en_US/model_compression.rst index a1d4f01b8c..e594ba1fb7 100644 --- a/docs/en_US/model_compression.rst +++ b/docs/en_US/model_compression.rst @@ -22,4 +22,5 @@ For details, please refer to the following tutorials: Automatic Model Compression Model Speedup Compression Utilities - Customize Compression Algorithms + Compression Framework + Customize Compression Algorithms diff --git a/docs/img/compressor_framework.jpg b/docs/img/compressor_framework.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4528f3a49eb1e54281f3c7c166c249e6b1e040df GIT binary patch literal 105277 zcmeFa2|QGN-#>mVp@b6Ijj~1v$r6TS%T-cYvLqqPWG7=CC1ejpDSIUektOTcmt@IW zB*va4V;_u}-&b8-cU;$f-S_=G*Ykhw`~Spi%$zyrd(QcM&Ug7N@6Q|YJ#i4)b4pc1 z6(S)afhxc+M8xcwS3z1|g`m@?AwdX&s33BZ;}9wML<2TX1zJPo;4=yMtp6x}^BrP^ 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