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bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt

Transformers
PyTorch
ONNX
bert
Inference Endpoints
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Chua, Vui Seng commited on
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  1. .gitattributes +6 -0
  2. README.md +112 -0
  3. XP_layer_wise_sparsity_global_rate_26.51.csv +200 -0
  4. XP_layer_wise_sparsity_global_rate_26.51.md +201 -0
  5. XP_linear_layer_sparsity_20M_params_57.92_sparsity.csv +73 -0
  6. XP_linear_layer_sparsity_20M_params_57.92_sparsity.md +74 -0
  7. all_results.json +5 -0
  8. bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt.onnx +3 -0
  9. checkpoint-20000/config.json +115 -0
  10. checkpoint-20000/optimizer.pt +3 -0
  11. checkpoint-20000/pytorch_model.bin +3 -0
  12. checkpoint-20000/rng_state.pth +3 -0
  13. checkpoint-20000/scheduler.pt +3 -0
  14. checkpoint-20000/special_tokens_map.json +1 -0
  15. checkpoint-20000/tokenizer.json +0 -0
  16. checkpoint-20000/tokenizer_config.json +1 -0
  17. checkpoint-20000/trainer_state.json +3 -0
  18. checkpoint-20000/training_args.bin +3 -0
  19. checkpoint-20000/vocab.txt +0 -0
  20. compressed_graph.dot +0 -0
  21. config.json +115 -0
  22. eval_XP_results.json +5 -0
  23. eval_nbest_predictions.json +3 -0
  24. eval_predictions.json +0 -0
  25. nncf_bert_squad_sparsity.json +72 -0
  26. original_graph.dot +0 -0
  27. pytorch_model.bin +3 -0
  28. special_tokens_map.json +1 -0
  29. tokenizer.json +0 -0
  30. tokenizer_config.json +1 -0
  31. train_results.json +8 -0
  32. trainer_state.json +3 -0
  33. training_args.bin +3 -0
  34. vocab.txt +0 -0
.gitattributes CHANGED
@@ -25,3 +25,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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+ trainer_state.json filter=lfs diff=lfs merge=lfs -text
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+ checkpoint-20000/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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+ checkpoint-20000/optimizer.pt filter=lfs diff=lfs merge=lfs -text
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+ bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt.onnx filter=lfs diff=lfs merge=lfs -text
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+ eval_nbest_predictions.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
2
+ 1. magnitude sparsification at 57.92% upon initialization so that sparsity over all linear layers of bert-base is at 90%. Parameters are ranked globally via thier absolute norm. Only linear layers of self-attention and ffnn are targeted.
3
+ 2. Custom distillation with large model ```bert-large-uncased-whole-word-masking-finetuned-squad```
4
+
5
+ ```
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+ eval_exact_match = 80.4447
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+ eval_f1 = 87.7678
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+ eval_samples = 10784
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+ ```
10
+
11
+ # Setup
12
+ ```bash
13
+ # OpenVINO/NNCF
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+ git clone https://github.com/vuiseng9/nncf && cd nncf
15
+ git checkout tld-poc
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+ git reset --hard 1dec7afe7a4b567c059fcf287ea2c234980fded2
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+ python setup.py develop
18
+ pip install -r examples/torch/requirements.txt
19
+
20
+ # Huggingface nn_pruning
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+ git clone https://github.com/vuiseng9/nn_pruning && cd nn_pruning
22
+ git checkout reproduce-evaluation
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+ git reset --hard 2d4e196d694c465e43e5fbce6c3836d0a60e1446
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+ pip install -e ".[dev]"
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+
26
+ # Huggingface Transformers
27
+ git clone https://github.com/vuiseng9/transformers && cd transformers
28
+ git checkout tld-poc
29
+ git reset --hard 10a1e29d84484e48fd106f58957d9ffc89dc43c5
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+ pip install -e .
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+ head -n 1 examples/pytorch/question-answering/requirements.txt | xargs -i pip install {}
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+
33
+ # Additional dependencies
34
+ pip install onnx
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+ ```
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+
37
+ # Train
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+
39
+ ```bash
40
+ git clone https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt
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+ BASE_MODEL=/path/to/cloned_repo_above #to-revise
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+
43
+ wget https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt/raw/main/nncf_bert_squad_sparsity.json
44
+ NNCF_CFG=/path/to/downloaded_nncf_cfg_above #to-revise
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+
46
+ OUTROOT=/path/to/train_output_root #to-revise
47
+ WORKDIR=transformers/examples/pytorch/question-answering #to-revise
48
+ RUNID=bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt
49
+
50
+ cd $WORKDIR
51
+
52
+ OUTDIR=$OUTROOT/$RUNID
53
+ mkdir -p $OUTDIR
54
+
55
+ export CUDA_VISIBLE_DEVICES=0
56
+ NEPOCH=5
57
+
58
+ python run_qa.py \
59
+ --model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid \
60
+ --optimize_model_before_eval \
61
+ --optimized_checkpoint $BASE_MODEL \
62
+ --dataset_name squad \
63
+ --do_eval \
64
+ --do_train \
65
+ --evaluation_strategy steps \
66
+ --eval_steps 250 \
67
+ --learning_rate 3e-5 \
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+ --lr_scheduler_type cosine_with_restarts \
69
+ --warmup_ratio 0.25 \
70
+ --cosine_cycles 1 \
71
+ --teacher bert-large-uncased-whole-word-masking-finetuned-squad \
72
+ --teacher_ratio 0.9 \
73
+ --num_train_epochs $NEPOCH \
74
+ --per_device_eval_batch_size 128 \
75
+ --per_device_train_batch_size 16 \
76
+ --max_seq_length 384 \
77
+ --doc_stride 128 \
78
+ --save_steps 250 \
79
+ --nncf_config $NNCF_CFG \
80
+ --logging_steps 1 \
81
+ --overwrite_output_dir \
82
+ --run_name $RUNID \
83
+ --output_dir $OUTDIR
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+ ```
85
+
86
+ # Eval
87
+ This repo must be cloned locally.
88
+ ```bash
89
+ git clone https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt
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+ MODELROOT=/path/to/cloned_repo_above #to-revise
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+
92
+ export CUDA_VISIBLE_DEVICES=0
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+
94
+ OUTDIR=eval-bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt
95
+ WORKDIR=transformers/examples/pytorch/question-answering #to-revise
96
+ cd $WORKDIR
97
+ mkdir $OUTDIR
98
+
99
+ nohup python run_qa.py \
100
+ --model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid \
101
+ --dataset_name squad \
102
+ --optimize_model_before_eval \
103
+ --qat_checkpoint $MODELROOT/checkpoint-20000 \
104
+ --nncf_config $MODELROOT/nncf_bert_squad_sparsity.json \
105
+ --to_onnx $OUTDIR/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt.onnx \
106
+ --do_eval \
107
+ --per_device_eval_batch_size 128 \
108
+ --max_seq_length 384 \
109
+ --doc_stride 128 \
110
+ --overwrite_output_dir \
111
+ --output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
112
+ ```
XP_layer_wise_sparsity_global_rate_26.51.csv ADDED
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+ ,layer_id,layer_type,param_type,shape,nparam,nnz,sparsity
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+ 1,nncf_module.bert.embeddings.position_embeddings,NNCFEmbedding,weight,"[512, 768]",393216,393216,0.0
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+ 2,nncf_module.bert.embeddings.token_type_embeddings,NNCFEmbedding,weight,"[2, 768]",1536,1536,0.0
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+ 3,nncf_module.bert.embeddings.LayerNorm,LayerNorm,weight,[768],768,768,0.0
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+ 4,nncf_module.bert.embeddings.LayerNorm,LayerNorm,bias,[768],768,768,0.0
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+ 5,nncf_module.bert.encoder.layer.0.attention.self.query,NNCFLinear,weight,"[320, 768]",245760,93583,0.6192097663879395
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XP_layer_wise_sparsity_global_rate_26.51.md ADDED
@@ -0,0 +1,201 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ | | layer_id | layer_type | param_type | shape | nparam | nnz | sparsity |
2
+ |----:|:-------------------------------------------------------------|:--------------|:-------------|:-------------|---------:|---------:|-----------:|
3
+ | 0 | nncf_module.bert.embeddings.word_embeddings | NNCFEmbedding | weight | [30522, 768] | 23440896 | 23440896 | 0 |
4
+ | 1 | nncf_module.bert.embeddings.position_embeddings | NNCFEmbedding | weight | [512, 768] | 393216 | 393216 | 0 |
5
+ | 2 | nncf_module.bert.embeddings.token_type_embeddings | NNCFEmbedding | weight | [2, 768] | 1536 | 1536 | 0 |
6
+ | 3 | nncf_module.bert.embeddings.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
7
+ | 4 | nncf_module.bert.embeddings.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
8
+ | 5 | nncf_module.bert.encoder.layer.0.attention.self.query | NNCFLinear | weight | [320, 768] | 245760 | 93583 | 0.61921 |
9
+ | 6 | nncf_module.bert.encoder.layer.0.attention.self.query | NNCFLinear | bias | [320] | 320 | 320 | 0 |
10
+ | 7 | nncf_module.bert.encoder.layer.0.attention.self.key | NNCFLinear | weight | [320, 768] | 245760 | 98270 | 0.600138 |
11
+ | 8 | nncf_module.bert.encoder.layer.0.attention.self.key | NNCFLinear | bias | [320] | 320 | 320 | 0 |
12
+ | 9 | nncf_module.bert.encoder.layer.0.attention.self.value | NNCFLinear | weight | [320, 768] | 245760 | 113605 | 0.53774 |
13
+ | 10 | nncf_module.bert.encoder.layer.0.attention.self.value | NNCFLinear | bias | [320] | 320 | 320 | 0 |
14
+ | 11 | nncf_module.bert.encoder.layer.0.attention.output.dense | NNCFLinear | weight | [768, 320] | 245760 | 117208 | 0.523079 |
15
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16
+ | 13 | nncf_module.bert.encoder.layer.0.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
17
+ | 14 | nncf_module.bert.encoder.layer.0.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
18
+ | 15 | nncf_module.bert.encoder.layer.0.intermediate.dense | NNCFLinear | weight | [185, 768] | 142080 | 97073 | 0.316772 |
19
+ | 16 | nncf_module.bert.encoder.layer.0.intermediate.dense | NNCFLinear | bias | [185] | 185 | 185 | 0 |
20
+ | 17 | nncf_module.bert.encoder.layer.0.output.dense | NNCFLinear | weight | [768, 185] | 142080 | 94692 | 0.33353 |
21
+ | 18 | nncf_module.bert.encoder.layer.0.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
22
+ | 19 | nncf_module.bert.encoder.layer.0.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
23
+ | 20 | nncf_module.bert.encoder.layer.0.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
24
+ | 21 | nncf_module.bert.encoder.layer.1.attention.self.query | NNCFLinear | weight | [320, 768] | 245760 | 118436 | 0.518083 |
25
+ | 22 | nncf_module.bert.encoder.layer.1.attention.self.query | NNCFLinear | bias | [320] | 320 | 320 | 0 |
26
+ | 23 | nncf_module.bert.encoder.layer.1.attention.self.key | NNCFLinear | weight | [320, 768] | 245760 | 118116 | 0.519385 |
27
+ | 24 | nncf_module.bert.encoder.layer.1.attention.self.key | NNCFLinear | bias | [320] | 320 | 320 | 0 |
28
+ | 25 | nncf_module.bert.encoder.layer.1.attention.self.value | NNCFLinear | weight | [320, 768] | 245760 | 107511 | 0.562537 |
29
+ | 26 | nncf_module.bert.encoder.layer.1.attention.self.value | NNCFLinear | bias | [320] | 320 | 320 | 0 |
30
+ | 27 | nncf_module.bert.encoder.layer.1.attention.output.dense | NNCFLinear | weight | [768, 320] | 245760 | 111189 | 0.547571 |
31
+ | 28 | nncf_module.bert.encoder.layer.1.attention.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
32
+ | 29 | nncf_module.bert.encoder.layer.1.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
33
+ | 30 | nncf_module.bert.encoder.layer.1.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
34
+ | 31 | nncf_module.bert.encoder.layer.1.intermediate.dense | NNCFLinear | weight | [315, 768] | 241920 | 148783 | 0.384991 |
35
+ | 32 | nncf_module.bert.encoder.layer.1.intermediate.dense | NNCFLinear | bias | [315] | 315 | 315 | 0 |
36
+ | 33 | nncf_module.bert.encoder.layer.1.output.dense | NNCFLinear | weight | [768, 315] | 241920 | 143166 | 0.408209 |
37
+ | 34 | nncf_module.bert.encoder.layer.1.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
38
+ | 35 | nncf_module.bert.encoder.layer.1.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
39
+ | 36 | nncf_module.bert.encoder.layer.1.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
40
+ | 37 | nncf_module.bert.encoder.layer.2.attention.self.query | NNCFLinear | weight | [576, 768] | 442368 | 162735 | 0.632128 |
41
+ | 38 | nncf_module.bert.encoder.layer.2.attention.self.query | NNCFLinear | bias | [576] | 576 | 576 | 0 |
42
+ | 39 | nncf_module.bert.encoder.layer.2.attention.self.key | NNCFLinear | weight | [576, 768] | 442368 | 164795 | 0.627471 |
43
+ | 40 | nncf_module.bert.encoder.layer.2.attention.self.key | NNCFLinear | bias | [576] | 576 | 576 | 0 |
44
+ | 41 | nncf_module.bert.encoder.layer.2.attention.self.value | NNCFLinear | weight | [576, 768] | 442368 | 135670 | 0.69331 |
45
+ | 42 | nncf_module.bert.encoder.layer.2.attention.self.value | NNCFLinear | bias | [576] | 576 | 576 | 0 |
46
+ | 43 | nncf_module.bert.encoder.layer.2.attention.output.dense | NNCFLinear | weight | [768, 576] | 442368 | 138445 | 0.687037 |
47
+ | 44 | nncf_module.bert.encoder.layer.2.attention.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
48
+ | 45 | nncf_module.bert.encoder.layer.2.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
49
+ | 46 | nncf_module.bert.encoder.layer.2.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
50
+ | 47 | nncf_module.bert.encoder.layer.2.intermediate.dense | NNCFLinear | weight | [339, 768] | 260352 | 154035 | 0.408359 |
51
+ | 48 | nncf_module.bert.encoder.layer.2.intermediate.dense | NNCFLinear | bias | [339] | 339 | 339 | 0 |
52
+ | 49 | nncf_module.bert.encoder.layer.2.output.dense | NNCFLinear | weight | [768, 339] | 260352 | 150816 | 0.420723 |
53
+ | 50 | nncf_module.bert.encoder.layer.2.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
54
+ | 51 | nncf_module.bert.encoder.layer.2.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
55
+ | 52 | nncf_module.bert.encoder.layer.2.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
56
+ | 53 | nncf_module.bert.encoder.layer.3.attention.self.query | NNCFLinear | weight | [576, 768] | 442368 | 170623 | 0.614296 |
57
+ | 54 | nncf_module.bert.encoder.layer.3.attention.self.query | NNCFLinear | bias | [576] | 576 | 576 | 0 |
58
+ | 55 | nncf_module.bert.encoder.layer.3.attention.self.key | NNCFLinear | weight | [576, 768] | 442368 | 178401 | 0.596714 |
59
+ | 56 | nncf_module.bert.encoder.layer.3.attention.self.key | NNCFLinear | bias | [576] | 576 | 576 | 0 |
60
+ | 57 | nncf_module.bert.encoder.layer.3.attention.self.value | NNCFLinear | weight | [576, 768] | 442368 | 171905 | 0.611398 |
61
+ | 58 | nncf_module.bert.encoder.layer.3.attention.self.value | NNCFLinear | bias | [576] | 576 | 576 | 0 |
62
+ | 59 | nncf_module.bert.encoder.layer.3.attention.output.dense | NNCFLinear | weight | [768, 576] | 442368 | 169172 | 0.617576 |
63
+ | 60 | nncf_module.bert.encoder.layer.3.attention.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
64
+ | 61 | nncf_module.bert.encoder.layer.3.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
65
+ | 62 | nncf_module.bert.encoder.layer.3.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
66
+ | 63 | nncf_module.bert.encoder.layer.3.intermediate.dense | NNCFLinear | weight | [368, 768] | 282624 | 163163 | 0.422685 |
67
+ | 64 | nncf_module.bert.encoder.layer.3.intermediate.dense | NNCFLinear | bias | [368] | 368 | 368 | 0 |
68
+ | 65 | nncf_module.bert.encoder.layer.3.output.dense | NNCFLinear | weight | [768, 368] | 282624 | 157506 | 0.442701 |
69
+ | 66 | nncf_module.bert.encoder.layer.3.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
70
+ | 67 | nncf_module.bert.encoder.layer.3.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
71
+ | 68 | nncf_module.bert.encoder.layer.3.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
72
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73
+ | 70 | nncf_module.bert.encoder.layer.4.attention.self.query | NNCFLinear | bias | [576] | 576 | 576 | 0 |
74
+ | 71 | nncf_module.bert.encoder.layer.4.attention.self.key | NNCFLinear | weight | [576, 768] | 442368 | 177087 | 0.599684 |
75
+ | 72 | nncf_module.bert.encoder.layer.4.attention.self.key | NNCFLinear | bias | [576] | 576 | 576 | 0 |
76
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77
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78
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79
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80
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81
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82
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83
+ | 80 | nncf_module.bert.encoder.layer.4.intermediate.dense | NNCFLinear | bias | [386] | 386 | 386 | 0 |
84
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85
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86
+ | 83 | nncf_module.bert.encoder.layer.4.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
87
+ | 84 | nncf_module.bert.encoder.layer.4.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
88
+ | 85 | nncf_module.bert.encoder.layer.5.attention.self.query | NNCFLinear | weight | [384, 768] | 294912 | 114186 | 0.612813 |
89
+ | 86 | nncf_module.bert.encoder.layer.5.attention.self.query | NNCFLinear | bias | [384] | 384 | 384 | 0 |
90
+ | 87 | nncf_module.bert.encoder.layer.5.attention.self.key | NNCFLinear | weight | [384, 768] | 294912 | 132782 | 0.549757 |
91
+ | 88 | nncf_module.bert.encoder.layer.5.attention.self.key | NNCFLinear | bias | [384] | 384 | 384 | 0 |
92
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93
+ | 90 | nncf_module.bert.encoder.layer.5.attention.self.value | NNCFLinear | bias | [384] | 384 | 384 | 0 |
94
+ | 91 | nncf_module.bert.encoder.layer.5.attention.output.dense | NNCFLinear | weight | [768, 384] | 294912 | 131941 | 0.552609 |
95
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96
+ | 93 | nncf_module.bert.encoder.layer.5.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
97
+ | 94 | nncf_module.bert.encoder.layer.5.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
98
+ | 95 | nncf_module.bert.encoder.layer.5.intermediate.dense | NNCFLinear | weight | [336, 768] | 258048 | 153916 | 0.403537 |
99
+ | 96 | nncf_module.bert.encoder.layer.5.intermediate.dense | NNCFLinear | bias | [336] | 336 | 336 | 0 |
100
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101
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102
+ | 99 | nncf_module.bert.encoder.layer.5.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
103
+ | 100 | nncf_module.bert.encoder.layer.5.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
104
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105
+ | 102 | nncf_module.bert.encoder.layer.6.attention.self.query | NNCFLinear | bias | [448] | 448 | 448 | 0 |
106
+ | 103 | nncf_module.bert.encoder.layer.6.attention.self.key | NNCFLinear | weight | [448, 768] | 344064 | 144502 | 0.580014 |
107
+ | 104 | nncf_module.bert.encoder.layer.6.attention.self.key | NNCFLinear | bias | [448] | 448 | 448 | 0 |
108
+ | 105 | nncf_module.bert.encoder.layer.6.attention.self.value | NNCFLinear | weight | [448, 768] | 344064 | 130911 | 0.619516 |
109
+ | 106 | nncf_module.bert.encoder.layer.6.attention.self.value | NNCFLinear | bias | [448] | 448 | 448 | 0 |
110
+ | 107 | nncf_module.bert.encoder.layer.6.attention.output.dense | NNCFLinear | weight | [768, 448] | 344064 | 125928 | 0.633998 |
111
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112
+ | 109 | nncf_module.bert.encoder.layer.6.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
113
+ | 110 | nncf_module.bert.encoder.layer.6.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
114
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115
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116
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117
+ | 114 | nncf_module.bert.encoder.layer.6.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
118
+ | 115 | nncf_module.bert.encoder.layer.6.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
119
+ | 116 | nncf_module.bert.encoder.layer.6.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
120
+ | 117 | nncf_module.bert.encoder.layer.7.attention.self.query | NNCFLinear | weight | [448, 768] | 344064 | 132120 | 0.616002 |
121
+ | 118 | nncf_module.bert.encoder.layer.7.attention.self.query | NNCFLinear | bias | [448] | 448 | 448 | 0 |
122
+ | 119 | nncf_module.bert.encoder.layer.7.attention.self.key | NNCFLinear | weight | [448, 768] | 344064 | 152223 | 0.557574 |
123
+ | 120 | nncf_module.bert.encoder.layer.7.attention.self.key | NNCFLinear | bias | [448] | 448 | 448 | 0 |
124
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125
+ | 122 | nncf_module.bert.encoder.layer.7.attention.self.value | NNCFLinear | bias | [448] | 448 | 448 | 0 |
126
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127
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128
+ | 125 | nncf_module.bert.encoder.layer.7.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
129
+ | 126 | nncf_module.bert.encoder.layer.7.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
130
+ | 127 | nncf_module.bert.encoder.layer.7.intermediate.dense | NNCFLinear | weight | [211, 768] | 162048 | 109590 | 0.323719 |
131
+ | 128 | nncf_module.bert.encoder.layer.7.intermediate.dense | NNCFLinear | bias | [211] | 211 | 211 | 0 |
132
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133
+ | 130 | nncf_module.bert.encoder.layer.7.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
134
+ | 131 | nncf_module.bert.encoder.layer.7.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
135
+ | 132 | nncf_module.bert.encoder.layer.7.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
136
+ | 133 | nncf_module.bert.encoder.layer.8.attention.self.query | NNCFLinear | weight | [448, 768] | 344064 | 129148 | 0.62464 |
137
+ | 134 | nncf_module.bert.encoder.layer.8.attention.self.query | NNCFLinear | bias | [448] | 448 | 448 | 0 |
138
+ | 135 | nncf_module.bert.encoder.layer.8.attention.self.key | NNCFLinear | weight | [448, 768] | 344064 | 130060 | 0.621989 |
139
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140
+ | 137 | nncf_module.bert.encoder.layer.8.attention.self.value | NNCFLinear | weight | [448, 768] | 344064 | 108162 | 0.685634 |
141
+ | 138 | nncf_module.bert.encoder.layer.8.attention.self.value | NNCFLinear | bias | [448] | 448 | 448 | 0 |
142
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143
+ | 140 | nncf_module.bert.encoder.layer.8.attention.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
144
+ | 141 | nncf_module.bert.encoder.layer.8.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
145
+ | 142 | nncf_module.bert.encoder.layer.8.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
146
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147
+ | 144 | nncf_module.bert.encoder.layer.8.intermediate.dense | NNCFLinear | bias | [108] | 108 | 108 | 0 |
148
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149
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150
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151
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152
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153
+ | 150 | nncf_module.bert.encoder.layer.9.attention.self.query | NNCFLinear | bias | [320] | 320 | 320 | 0 |
154
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155
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156
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157
+ | 154 | nncf_module.bert.encoder.layer.9.attention.self.value | NNCFLinear | bias | [320] | 320 | 320 | 0 |
158
+ | 155 | nncf_module.bert.encoder.layer.9.attention.output.dense | NNCFLinear | weight | [768, 320] | 245760 | 53210 | 0.783488 |
159
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160
+ | 157 | nncf_module.bert.encoder.layer.9.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
161
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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176
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177
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183
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192
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193
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XP_linear_layer_sparsity_20M_params_57.92_sparsity.csv ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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