add scripts

examples/movement-pruning/bertarize.py

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+# Copyright 2020-present, the HuggingFace Inc. team.
+#
+# 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.
+"""
+Once a model has been fine-pruned, the weights that are masked during the forward pass can be pruned once for all.
+For instance, once the a model from the :class:`~emmental.MaskedBertForSequenceClassification` is trained, it can be saved (and then loaded)
+as a standard :class:`~transformers.BertForSequenceClassification`.
+"""
+
+import os
+import shutil
+import argparse
+
+import torch
+
+from emmental.modules import MagnitudeBinarizer, TopKBinarizer, ThresholdBinarizer
+
+
+def main(args):
+    pruning_method = args.pruning_method
+    threshold = args.threshold
+
+    model_name_or_path = args.model_name_or_path.rstrip("/")
+    target_model_path = args.target_model_path
+
+    print(f"Load fine-pruned model from {model_name_or_path}")
+    model = torch.load(os.path.join(model_name_or_path, "pytorch_model.bin"))
+    pruned_model = {}
+
+    for name, tensor in model.items():
+        if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
+            pruned_model[name] = tensor
+            print(f"Pruned layer {name}")
+        elif "classifier" in name or "qa_output" in name:
+            pruned_model[name] = tensor
+            print(f"Pruned layer {name}")
+        elif "bias" in name:
+            pruned_model[name] = tensor
+            print(f"Pruned layer {name}")
+        else:
+            if pruning_method == "magnitude":
+                mask = MagnitudeBinarizer.apply(inputs=tensor, threshold=threshold)
+                pruned_model[name] = tensor * mask
+                print(f"Pruned layer {name}")
+            elif pruning_method == "topK":
+                if "mask_scores" in name:
+                    continue
+                prefix_ = name[:-6]
+                scores = model[f"{prefix_}mask_scores"]
+                mask = TopKBinarizer.apply(scores, threshold)
+                pruned_model[name] = tensor * mask
+                print(f"Pruned layer {name}")
+            elif pruning_method == "sigmoied_threshold":
+                if "mask_scores" in name:
+                    continue
+                prefix_ = name[:-6]
+                scores = model[f"{prefix_}mask_scores"]
+                mask = ThresholdBinarizer.apply(scores, threshold, True)
+                pruned_model[name] = tensor * mask
+                print(f"Pruned layer {name}")
+            elif pruning_method == "l0":
+                if "mask_scores" in name:
+                    continue
+                prefix_ = name[:-6]
+                scores = model[f"{prefix_}mask_scores"]
+                l, r = -0.1, 1.1
+                s = torch.sigmoid(scores)
+                s_bar = s * (r - l) + l
+                mask = s_bar.clamp(min=0.0, max=1.0)
+                pruned_model[name] = tensor * mask
+                print(f"Pruned layer {name}")
+            else:
+                raise ValueError("Unknown pruning method")
+
+    if target_model_path is None:
+        target_model_path = os.path.join(
+            os.path.dirname(model_name_or_path), f"bertarized_{os.path.basename(model_name_or_path)}"
+        )
+
+    if not os.path.isdir(target_model_path):
+        shutil.copytree(model_name_or_path, target_model_path)
+        print(f"\nCreated folder {target_model_path}")
+
+    torch.save(pruned_model, os.path.join(target_model_path, "pytorch_model.bin"))
+    print("\nPruned model saved! See you later!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--pruning_method",
+        choices=["l0", "magnitude", "topK", "sigmoied_threshold", ],
+        type=str,
+        required=True,
+        help="Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning, sigmoied_threshold = Soft movement pruning)",
+    )
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        required=False,
+        help="For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
+        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
+        "Not needed for `l0`",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        required=True,
+        help="Folder containing the model that was previously fine-pruned",
+    )
+    parser.add_argument(
+        "--target_model_path",
+        default=None,
+        type=str,
+        required=False,
+        help="Folder containing the model that was previously fine-pruned",
+    )
+
+    args = parser.parse_args()
+
+    main(args)

examples/movement-pruning/counts_parameters.py

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+# Copyright 2020-present, the HuggingFace Inc. team.
+#
+# 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.
+"""
+Count remaining (non-zero) weights in the encoder (i.e. the transformer layers).
+Sparsity and remaining weights levels are equivalent: sparsity % = 100 - remaining weights %.
+"""
+import os
+import argparse
+
+import torch
+
+from emmental.modules import TopKBinarizer, ThresholdBinarizer
+
+
+def main(args):
+    serialization_dir = args.serialization_dir
+    pruning_method = args.pruning_method
+    threshold = args.threshold
+
+    st = torch.load(os.path.join(serialization_dir, "pytorch_model.bin"), map_location="cpu")
+
+    remaining_count = 0  # Number of remaining (not pruned) params in the encoder
+    encoder_count = 0  # Number of params in the encoder
+
+    print("name".ljust(60, " "), "Remaining Weights %", "Remaning Weight")
+    for name, param in st.items():
+        if "encoder" not in name:
+            continue
+
+        if "mask_scores" in name:
+            if pruning_method == "topK":
+                mask_ones = TopKBinarizer.apply(param, threshold).sum().item()
+            elif pruning_method == "sigmoied_threshold":
+                mask_ones = ThresholdBinarizer.apply(param, threshold, True).sum().item()
+            elif pruning_method == "l0":
+                l, r = -0.1, 1.1
+                s = torch.sigmoid(param)
+                s_bar = s * (r - l) + l
+                mask = s_bar.clamp(min=0.0, max=1.0)
+                mask_ones = (mask > 0.0).sum().item()
+            else:
+                raise ValueError("Unknown pruning method")
+            remaining_count += mask_ones
+            print(name.ljust(60, " "), str(round(100 * mask_ones / param.numel(), 3)).ljust(20, " "), str(mask_ones))
+        else:
+            encoder_count += param.numel()
+            if "bias" in name or "LayerNorm" in name:
+                remaining_count += param.numel()
+
+    print("")
+    print("Remaining Weights (global) %: ", 100 * remaining_count / encoder_count)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--pruning_method",
+        choices=["l0", "topK", "sigmoied_threshold", ],
+        type=str,
+        required=True,
+        help="Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement pruning)",
+    )
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        required=False,
+        help="For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
+        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
+        "Not needed for `l0`",
+    )
+    parser.add_argument(
+        "--serialization_dir",
+        type=str,
+        required=True,
+        help="Folder containing the model that was previously fine-pruned",
+    )
+
+    args = parser.parse_args()
+
+    main(args)