update extract

examples/distillation/scripts/extract.py

@@ -0,0 +1,89 @@
+# coding=utf-8
+# Copyright 2019-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.
+"""
+Preprocessing script before training the distilled model.
+Specific to RoBERTa -> DistilRoBERTa and GPT2 -> DistilGPT2.
+"""
+from transformers import BertForMaskedLM, RobertaForMaskedLM, GPT2LMHeadModel
+import torch
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned Distillation")
+    parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
+    parser.add_argument("--model_name", default='roberta-large', type=str)
+    parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_roberta_048131723.pth', type=str)
+    parser.add_argument("--vocab_transform", action='store_true')
+    args = parser.parse_args()
+
+
+    if args.model_type == 'roberta':
+        model = RobertaForMaskedLM.from_pretrained(args.model_name)
+        prefix = 'roberta'
+    elif args.model_type == 'gpt2':
+        model = GPT2LMHeadModel.from_pretrained(args.model_name)
+        prefix = 'transformer'
+
+    state_dict = model.state_dict()
+    compressed_sd = {}
+
+    ### Embeddings ###
+    if args.model_type == 'gpt2':
+        for param_name in ['wte.weight', 'wpe.weight']:
+            compressed_sd[f'{prefix}.{param_name}'] = state_dict[f'{prefix}.{param_name}']
+    else:
+        for w in ['word_embeddings', 'position_embeddings', 'token_type_embeddings']:
+            param_name = f'{prefix}.embeddings.{w}.weight'
+            compressed_sd[param_name] = state_dict[param_name]
+        for w in ['weight', 'bias']:
+            param_name = f'{prefix}.embeddings.LayerNorm.{w}'
+            compressed_sd[param_name] = state_dict[param_name]
+
+    ### Transformer Blocks ###
+    std_idx = 0
+    for teacher_idx in [0, 2, 4, 7, 9, 11]:
+        if args.model_type == 'gpt2':
+            for layer in ['ln_1', 'attn.c_attn', 'attn.c_proj', 'ln_2', 'mlp.c_fc', 'mlp.c_proj']:
+                for w in ['weight', 'bias']:
+                    compressed_sd[f'{prefix}.h.{std_idx}.{layer}.{w}'] = \
+                        state_dict[f'{prefix}.h.{teacher_idx}.{layer}.{w}']
+            compressed_sd[f'{prefix}.h.{std_idx}.attn.bias'] = state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
+        else:
+            for layer in ['attention.self.query', 'attention.self.key', 'attention.self.value',
+                        'attention.output.dense', 'attention.output.LayerNorm',
+                        'intermediate.dense', 'output.dense', 'output.LayerNorm']:
+                for w in ['weight', 'bias']:
+                    compressed_sd[f'{prefix}.encoder.layer.{std_idx}.{layer}.{w}'] = \
+                        state_dict[f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}']
+        std_idx += 1
+
+    ### Language Modeling Head ###s
+    if args.model_type == 'roberta':
+        for layer in ['lm_head.decoder.weight', 'lm_head.bias']:
+            compressed_sd[f'{layer}'] = state_dict[f'{layer}']
+        if args.vocab_transform:
+            for w in ['weight', 'bias']:
+                compressed_sd[f'lm_head.dense.{w}'] = state_dict[f'lm_head.dense.{w}']
+                compressed_sd[f'lm_head.layer_norm.{w}'] = state_dict[f'lm_head.layer_norm.{w}']
+    elif args.model_type == 'gpt2':
+        for w in ['weight', 'bias']:
+            compressed_sd[f'{prefix}.ln_f.{w}'] = state_dict[f'{prefix}.ln_f.{w}']
+        compressed_sd[f'lm_head.weight'] = state_dict[f'lm_head.weight']
+
+    print(f'N layers selected for distillation: {std_idx}')
+    print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
+
+    print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
+    torch.save(compressed_sd, args.dump_checkpoint)