pruning.yaml

#
# Copyright (c) 2021 Intel Corporation
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# Licensed under the Apache License, Version 2.0 (the "License");
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#   http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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version: 1.0                                         # optional. reserved for future use. if not specified, a supported version would be written back to user yaml.

model:                                               # mandatory. neural_compressor uses this module name and framework name to decide where to save tuning history and deploy yaml.
  name: resnet50v1.5
  framework: pytorch                                 # mandatory. supported values are tensorflow, pytorch, pytorch_fx, pytorch_ipex, onnxrt_integer, onnxrt_qlinear or mxnet; allow new framework backend extension.

device: cpu                                          # optional. default value is cpu. other value is gpu.

pruning:                                             # mandatory only for pruning.
  train:
    start_epoch: 0
    end_epoch: 10
    iteration: 100
    frequency: 2
    hostfile: /path/to/host/file                     # reserved for multi-node training.

    dataloader:
      batch_size: 256
      dataset:
        ImageFolder:
          root: /path/to/imagenet/train
      transform:
        RandomResizedCrop:
          size: 224
        RandomHorizontalFlip:
        ToTensor:
        Normalize:
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225] 
    criterion:
      CrossEntropyLoss:
        reduction: None
    optimizer:
      SGD:
        learning_rate: 0.1
        momentum: 0.9
        weight_decay: 0.0004
        nesterov: False

  approach:
    weight_compression:
      initial_sparsity: 0.0
      target_sparsity: 0.3
      pruners:
        - !Pruner
            initial_sparsity: 0.0
            target_sparsity: 0.97
            prune_type: basic_magnitude
            names: ['layer1.0.conv1.weight']         # tensor name to be pruned.
            start_epoch: 0
            end_epoch: 2
            update_frequency: 0.1

evaluation:                                          # optional. used to config evaluation process. 
  accuracy:                                          # optional. used to evaluate accuracy of passing model.
    metric:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Pruning.
      topk: 1                                        # built-in metrics are topk, map, f1, allow user to register new metric.
    configs:                                         # optional. if not specified, use all cores in 1 socket.
      cores_per_instance: 28
      num_of_instance: 1
    dataloader:                                      # optional. if not specified, user need construct a q_dataloader in code for neural_compressor.Pruning.
      batch_size: 256
      dataset:
        ImageFolder:
          root: /path/to/imagenet/train
      transform:
        RandomResizedCrop:
          size: 224
        RandomHorizontalFlip:
        ToTensor:
        Normalize:
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
  performance:                                       # optional. used to benchmark performance of passing model.
    warmup: 10
    iteration: 100
    configs:
      cores_per_instance: 4
      num_of_instance: 7
    dataloader:
      dataset:
        dummy:
          shape: [[128, 3, 224, 224], [128, 1, 1, 1]]

tuning:
  accuracy_criterion:
    relative:  0.01                                  # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 1%.
  objective: performance                             # optional. objective with accuracy constraint guaranteed. default value is performance. other values are modelsize and footprint.

  exit_policy:
    timeout: 0                                       # optional. tuning timeout (seconds). default value is 0 which means early stop. combine with max_trials field to decide when to exit.
    max_trials: 100                                  # optional. max tune times. default value is 100. combine with timeout field to decide when to exit.

  random_seed: 9527                                  # optional. random seed for deterministic tuning.
  tensorboard: True                                  # optional. dump tensor distribution in evaluation phase for debug purpose. default value is False.

  workspace:
    path: /path/to/saving/directory                  # optional. default workspace is ./nc_workspace/current_time_stamp, saving tuning history and deploy yaml.
    resume: /path/to/a/specified/snapshot/file       # optional. if specified, resume from tuning history.