Add quantization and pruning config yaml files

prune.yml

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+#
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+version: 1.0
+
+model:
+  name: bert_prune
+  framework: pytorch
+
+pruning:
+  approach:
+    weight_compression:
+      initial_sparsity: 0.0
+      target_sparsity: 0.1                           # targeted sparsity of 10%
+      start_epoch: 0
+      end_epoch: 2
+      pruners:
+        - !Pruner
+            prune_type: basic_magnitude
+tuning:
+  accuracy_criterion:
+    relative: 0.1                                    # only verifying workflow, accuracy loss percentage: 10%
+  exit_policy:
+    timeout: 0                                       # tuning timeout (seconds)
+  random_seed: 9527                                  # random seed

quantization.yml

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+#
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+version: 1.0
+
+model:                                               # mandatory.
+  name: bert
+  framework: pytorch                                 # mandatory. possible values are pytorch and pytorch_fx.
+
+device: cpu
+
+quantization:                                        # optional.
+  approach: post_training_dynamic_quant
+
+tuning:
+  accuracy_criterion:
+    relative:  0.03                                  # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 3%.
+  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: 30
+  random_seed: 9527                                  # optional. random seed for deterministic tuning.