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

Transformers
PyTorch
ONNX
bert
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xet
 Community
Chua, Vui Seng commited on
Commit
51b709b
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1 Parent(s): 7b5d626

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  1. .gitattributes +8 -0
  2. README.md +113 -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. XP_onnx_sparsity.csv +77 -0
  8. XP_onnx_sparsity.md +78 -0
  9. all_results.json +5 -0
  10. bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt.onnx +3 -0
  11. checkpoint-21750/config.json +115 -0
  12. checkpoint-21750/optimizer.pt +3 -0
  13. checkpoint-21750/pytorch_model.bin +3 -0
  14. checkpoint-21750/rng_state.pth +3 -0
  15. checkpoint-21750/scheduler.pt +3 -0
  16. checkpoint-21750/special_tokens_map.json +1 -0
  17. checkpoint-21750/tokenizer.json +0 -0
  18. checkpoint-21750/tokenizer_config.json +1 -0
  19. checkpoint-21750/trainer_state.json +3 -0
  20. checkpoint-21750/training_args.bin +3 -0
  21. checkpoint-21750/vocab.txt +0 -0
  22. compressed_graph.dot +0 -0
  23. config.json +115 -0
  24. eval_XP_results.json +5 -0
  25. eval_nbest_predictions.json +3 -0
  26. eval_predictions.json +0 -0
  27. nncf_bert_squad_sparsity.json +72 -0
  28. original_graph.dot +0 -0
  29. pytorch_model.bin +3 -0
  30. special_tokens_map.json +1 -0
  31. tokenizer.json +0 -0
  32. tokenizer_config.json +1 -0
  33. train_results.json +8 -0
  34. trainer_state.json +3 -0
  35. training_args.bin +3 -0
  36. vocab.txt +0 -0
.gitattributes CHANGED
@@ -25,3 +25,11 @@ 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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+ bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt.onnx 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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+ sparsified_model/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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+ eval_nbest_predictions.json filter=lfs diff=lfs merge=lfs -text
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+ checkpoint-21750/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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+ checkpoint-21750/trainer_state.json filter=lfs diff=lfs merge=lfs -text
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+ checkpoint-21750/optimizer.pt filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,113 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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. NNCF Quantize-Aware Training - Symmetric 8-bit for both weight and activation on all learnable layers.
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+ 3. Custom distillation with large model ```bert-large-uncased-whole-word-masking-finetuned-squad```
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+
6
+ ```
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+ eval_exact_match = 80.4541
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+ eval_f1 = 87.6832
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+ eval_samples = 10784
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+ ```
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+
12
+ # Setup
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+ ```bash
14
+ # OpenVINO/NNCF
15
+ git clone https://github.com/vuiseng9/nncf && cd nncf
16
+ git checkout tld-poc
17
+ git reset --hard 1dec7afe7a4b567c059fcf287ea2c234980fded2
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+ python setup.py develop
19
+ pip install -r examples/torch/requirements.txt
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+
21
+ # Huggingface nn_pruning
22
+ git clone https://github.com/vuiseng9/nn_pruning && cd nn_pruning
23
+ git checkout reproduce-evaluation
24
+ git reset --hard 2d4e196d694c465e43e5fbce6c3836d0a60e1446
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+ pip install -e ".[dev]"
26
+
27
+ # Huggingface Transformers
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+ git clone https://github.com/vuiseng9/transformers && cd transformers
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+ git checkout tld-poc
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+ 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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+
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+ # Additional dependencies
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+ pip install onnx
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+ ```
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+
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+ # Train
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+
40
+ ```bash
41
+ 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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+
44
+ wget https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt/raw/main/nncf_bert_squad_sparsity.json
45
+ NNCF_CFG=/path/to/downloaded_nncf_cfg_above #to-revise
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+
47
+ OUTROOT=/path/to/train_output_root #to-revise
48
+ WORKDIR=transformers/examples/pytorch/question-answering #to-revise
49
+ RUNID=bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt
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+
51
+ cd $WORKDIR
52
+
53
+ OUTDIR=$OUTROOT/$RUNID
54
+ mkdir -p $OUTDIR
55
+
56
+ export CUDA_VISIBLE_DEVICES=0
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+ NEPOCH=5
58
+
59
+ python run_qa.py \
60
+ --model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid \
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+ --optimize_model_before_eval \
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+ --optimized_checkpoint $BASE_MODEL \
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+ --dataset_name squad \
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+ --do_eval \
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+ --do_train \
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+ --evaluation_strategy steps \
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+ --eval_steps 250 \
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+ --learning_rate 3e-5 \
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+ --lr_scheduler_type cosine_with_restarts \
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+ --warmup_ratio 0.25 \
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+ --cosine_cycles 1 \
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+ --teacher bert-large-uncased-whole-word-masking-finetuned-squad \
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+ --teacher_ratio 0.9 \
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+ --num_train_epochs $NEPOCH \
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+ --per_device_eval_batch_size 128 \
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+ --per_device_train_batch_size 16 \
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+ --max_seq_length 384 \
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+ --doc_stride 128 \
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+ --save_steps 250 \
80
+ --nncf_config $NNCF_CFG \
81
+ --logging_steps 1 \
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+ --overwrite_output_dir \
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+ --run_name $RUNID \
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+ --output_dir $OUTDIR
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+ ```
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+
87
+ # Eval
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+ This repo must be cloned locally.
89
+ ```bash
90
+ git clone https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt
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+ MODELROOT=/path/to/cloned_repo_above #to-revise
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+
93
+ export CUDA_VISIBLE_DEVICES=0
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+
95
+ OUTDIR=eval-bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt
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+ WORKDIR=transformers/examples/pytorch/question-answering #to-revise
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+ cd $WORKDIR
98
+ mkdir $OUTDIR
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+
100
+ nohup python run_qa.py \
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+ --model_name_or_path vuiseng9/bert-base-squadv1-block-pruning-hybrid \
102
+ --dataset_name squad \
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+ --optimize_model_before_eval \
104
+ --qat_checkpoint $MODELROOT/checkpoint-21750 \
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+ --nncf_config $MODELROOT/nncf_bert_squad_sparsity.json \
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+ --to_onnx $OUTDIR/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt.onnx \
107
+ --do_eval \
108
+ --per_device_eval_batch_size 128 \
109
+ --max_seq_length 384 \
110
+ --doc_stride 128 \
111
+ --overwrite_output_dir \
112
+ --output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
113
+ ```
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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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 | 93507 | 0.619519 |
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 | 98224 | 0.600325 |
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 | 113596 | 0.537777 |
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 | 117320 | 0.522624 |
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 | 97047 | 0.316955 |
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 | 94629 | 0.333974 |
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 | 118358 | 0.5184 |
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 | 118132 | 0.51932 |
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 | 107518 | 0.562508 |
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 | 111172 | 0.54764 |
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 | 148727 | 0.385222 |
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 | 143174 | 0.408176 |
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 | 162784 | 0.632017 |
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 | 164797 | 0.627466 |
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 | 135705 | 0.693231 |
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 | 138448 | 0.68703 |
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 | 154043 | 0.408328 |
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 | 150923 | 0.420312 |
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 | 170686 | 0.614154 |
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 | 178480 | 0.596535 |
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 | 172200 | 0.610731 |
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 | 169194 | 0.617527 |
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 | 163234 | 0.422434 |
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 | 157364 | 0.443204 |
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
+ | 69 | nncf_module.bert.encoder.layer.4.attention.self.query | NNCFLinear | weight | [576, 768] | 442368 | 176138 | 0.601829 |
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 | 177351 | 0.599087 |
75
+ | 72 | nncf_module.bert.encoder.layer.4.attention.self.key | NNCFLinear | bias | [576] | 576 | 576 | 0 |
76
+ | 73 | nncf_module.bert.encoder.layer.4.attention.self.value | NNCFLinear | weight | [576, 768] | 442368 | 164248 | 0.628707 |
77
+ | 74 | nncf_module.bert.encoder.layer.4.attention.self.value | NNCFLinear | bias | [576] | 576 | 576 | 0 |
78
+ | 75 | nncf_module.bert.encoder.layer.4.attention.output.dense | NNCFLinear | weight | [768, 576] | 442368 | 159471 | 0.639506 |
79
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80
+ | 77 | nncf_module.bert.encoder.layer.4.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
81
+ | 78 | nncf_module.bert.encoder.layer.4.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
82
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83
+ | 80 | nncf_module.bert.encoder.layer.4.intermediate.dense | NNCFLinear | bias | [386] | 386 | 386 | 0 |
84
+ | 81 | nncf_module.bert.encoder.layer.4.output.dense | NNCFLinear | weight | [768, 386] | 296448 | 159961 | 0.460408 |
85
+ | 82 | nncf_module.bert.encoder.layer.4.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
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 | 114241 | 0.612627 |
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 | 132821 | 0.549625 |
91
+ | 88 | nncf_module.bert.encoder.layer.5.attention.self.key | NNCFLinear | bias | [384] | 384 | 384 | 0 |
92
+ | 89 | nncf_module.bert.encoder.layer.5.attention.self.value | NNCFLinear | weight | [384, 768] | 294912 | 135092 | 0.541924 |
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 | 132344 | 0.551242 |
95
+ | 92 | nncf_module.bert.encoder.layer.5.attention.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
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 | 153822 | 0.403902 |
99
+ | 96 | nncf_module.bert.encoder.layer.5.intermediate.dense | NNCFLinear | bias | [336] | 336 | 336 | 0 |
100
+ | 97 | nncf_module.bert.encoder.layer.5.output.dense | NNCFLinear | weight | [768, 336] | 258048 | 145684 | 0.435438 |
101
+ | 98 | nncf_module.bert.encoder.layer.5.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
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
+ | 101 | nncf_module.bert.encoder.layer.6.attention.self.query | NNCFLinear | weight | [448, 768] | 344064 | 131784 | 0.616978 |
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 | 144539 | 0.579907 |
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 | 131107 | 0.618946 |
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 | 126145 | 0.633368 |
111
+ | 108 | nncf_module.bert.encoder.layer.6.attention.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
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
+ | 111 | nncf_module.bert.encoder.layer.6.intermediate.dense | NNCFLinear | weight | [280, 768] | 215040 | 135219 | 0.371191 |
115
+ | 112 | nncf_module.bert.encoder.layer.6.intermediate.dense | NNCFLinear | bias | [280] | 280 | 280 | 0 |
116
+ | 113 | nncf_module.bert.encoder.layer.6.output.dense | NNCFLinear | weight | [768, 280] | 215040 | 131559 | 0.388211 |
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 | 132226 | 0.615694 |
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 | 152327 | 0.557271 |
123
+ | 120 | nncf_module.bert.encoder.layer.7.attention.self.key | NNCFLinear | bias | [448] | 448 | 448 | 0 |
124
+ | 121 | nncf_module.bert.encoder.layer.7.attention.self.value | NNCFLinear | weight | [448, 768] | 344064 | 141141 | 0.589783 |
125
+ | 122 | nncf_module.bert.encoder.layer.7.attention.self.value | NNCFLinear | bias | [448] | 448 | 448 | 0 |
126
+ | 123 | nncf_module.bert.encoder.layer.7.attention.output.dense | NNCFLinear | weight | [768, 448] | 344064 | 135857 | 0.60514 |
127
+ | 124 | nncf_module.bert.encoder.layer.7.attention.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
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 | 109376 | 0.32504 |
131
+ | 128 | nncf_module.bert.encoder.layer.7.intermediate.dense | NNCFLinear | bias | [211] | 211 | 211 | 0 |
132
+ | 129 | nncf_module.bert.encoder.layer.7.output.dense | NNCFLinear | weight | [768, 211] | 162048 | 107132 | 0.338887 |
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 | 129219 | 0.624433 |
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 | 130088 | 0.621908 |
139
+ | 136 | nncf_module.bert.encoder.layer.8.attention.self.key | NNCFLinear | bias | [448] | 448 | 448 | 0 |
140
+ | 137 | nncf_module.bert.encoder.layer.8.attention.self.value | NNCFLinear | weight | [448, 768] | 344064 | 108043 | 0.68598 |
141
+ | 138 | nncf_module.bert.encoder.layer.8.attention.self.value | NNCFLinear | bias | [448] | 448 | 448 | 0 |
142
+ | 139 | nncf_module.bert.encoder.layer.8.attention.output.dense | NNCFLinear | weight | [768, 448] | 344064 | 103567 | 0.698989 |
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
+ | 143 | nncf_module.bert.encoder.layer.8.intermediate.dense | NNCFLinear | weight | [108, 768] | 82944 | 63183 | 0.238245 |
147
+ | 144 | nncf_module.bert.encoder.layer.8.intermediate.dense | NNCFLinear | bias | [108] | 108 | 108 | 0 |
148
+ | 145 | nncf_module.bert.encoder.layer.8.output.dense | NNCFLinear | weight | [768, 108] | 82944 | 62633 | 0.244876 |
149
+ | 146 | nncf_module.bert.encoder.layer.8.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
150
+ | 147 | nncf_module.bert.encoder.layer.8.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
151
+ | 148 | nncf_module.bert.encoder.layer.8.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
152
+ | 149 | nncf_module.bert.encoder.layer.9.attention.self.query | NNCFLinear | weight | [320, 768] | 245760 | 107216 | 0.563737 |
153
+ | 150 | nncf_module.bert.encoder.layer.9.attention.self.query | NNCFLinear | bias | [320] | 320 | 320 | 0 |
154
+ | 151 | nncf_module.bert.encoder.layer.9.attention.self.key | NNCFLinear | weight | [320, 768] | 245760 | 101848 | 0.585579 |
155
+ | 152 | nncf_module.bert.encoder.layer.9.attention.self.key | NNCFLinear | bias | [320] | 320 | 320 | 0 |
156
+ | 153 | nncf_module.bert.encoder.layer.9.attention.self.value | NNCFLinear | weight | [320, 768] | 245760 | 52063 | 0.788155 |
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 | 53127 | 0.783826 |
159
+ | 156 | nncf_module.bert.encoder.layer.9.attention.output.dense | NNCFLinear | bias | [768] | 768 | 768 | 0 |
160
+ | 157 | nncf_module.bert.encoder.layer.9.attention.output.LayerNorm | LayerNorm | weight | [768] | 768 | 768 | 0 |
161
+ | 158 | nncf_module.bert.encoder.layer.9.attention.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
162
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163
+ | 160 | nncf_module.bert.encoder.layer.9.intermediate.dense | NNCFLinear | bias | [53] | 53 | 53 | 0 |
164
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165
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166
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167
+ | 164 | nncf_module.bert.encoder.layer.9.output.LayerNorm | LayerNorm | bias | [768] | 768 | 768 | 0 |
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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175
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176
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177
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178
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181
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182
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183
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184
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185
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186
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188
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190
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191
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192
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193
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194
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199
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201
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XP_linear_layer_sparsity_20M_params_57.92_sparsity.csv ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 143,nncf_module.bert.encoder.layer.8.intermediate.dense,NNCFLinear,weight,"[108, 768]",82944,63183,0.23824506998062134
55
+ 145,nncf_module.bert.encoder.layer.8.output.dense,NNCFLinear,weight,"[768, 108]",82944,62633,0.24487602710723877
56
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+ 161,nncf_module.bert.encoder.layer.9.output.dense,NNCFLinear,weight,"[768, 53]",40704,32340,0.20548349618911743
62
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63
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+ 169,nncf_module.bert.encoder.layer.10.attention.self.value,NNCFLinear,weight,"[384, 768]",294912,61630,0.7910224199295044
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+ 177,nncf_module.bert.encoder.layer.10.output.dense,NNCFLinear,weight,"[768, 86]",66048,49494,0.25063592195510864
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+ 181,nncf_module.bert.encoder.layer.11.attention.self.query,NNCFLinear,weight,"[384, 768]",294912,88056,0.701416015625
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+ 185,nncf_module.bert.encoder.layer.11.attention.self.value,NNCFLinear,weight,"[384, 768]",294912,47126,0.8402031660079956
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+ 187,nncf_module.bert.encoder.layer.11.attention.output.dense,NNCFLinear,weight,"[768, 384]",294912,49010,0.8338148593902588
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+ 191,nncf_module.bert.encoder.layer.11.intermediate.dense,NNCFLinear,weight,"[105, 768]",80640,62069,0.2302951216697693
73
+ 193,nncf_module.bert.encoder.layer.11.output.dense,NNCFLinear,weight,"[768, 105]",80640,61476,0.23764878511428833
XP_linear_layer_sparsity_20M_params_57.92_sparsity.md ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ | | layer_id | layer_type | param_type | shape | nparam | nnz | sparsity |
2
+ |----:|:---------------------------------------------------------|:-------------|:-------------|:-----------|---------:|-------:|-----------:|
3
+ | 5 | nncf_module.bert.encoder.layer.0.attention.self.query | NNCFLinear | weight | [320, 768] | 245760 | 93507 | 0.619519 |
4
+ | 7 | nncf_module.bert.encoder.layer.0.attention.self.key | NNCFLinear | weight | [320, 768] | 245760 | 98224 | 0.600325 |
5
+ | 9 | nncf_module.bert.encoder.layer.0.attention.self.value | NNCFLinear | weight | [320, 768] | 245760 | 113596 | 0.537777 |
6
+ | 11 | nncf_module.bert.encoder.layer.0.attention.output.dense | NNCFLinear | weight | [768, 320] | 245760 | 117320 | 0.522624 |
7
+ | 15 | nncf_module.bert.encoder.layer.0.intermediate.dense | NNCFLinear | weight | [185, 768] | 142080 | 97047 | 0.316955 |
8
+ | 17 | nncf_module.bert.encoder.layer.0.output.dense | NNCFLinear | weight | [768, 185] | 142080 | 94629 | 0.333974 |
9
+ | 21 | nncf_module.bert.encoder.layer.1.attention.self.query | NNCFLinear | weight | [320, 768] | 245760 | 118358 | 0.5184 |
10
+ | 23 | nncf_module.bert.encoder.layer.1.attention.self.key | NNCFLinear | weight | [320, 768] | 245760 | 118132 | 0.51932 |
11
+ | 25 | nncf_module.bert.encoder.layer.1.attention.self.value | NNCFLinear | weight | [320, 768] | 245760 | 107518 | 0.562508 |
12
+ | 27 | nncf_module.bert.encoder.layer.1.attention.output.dense | NNCFLinear | weight | [768, 320] | 245760 | 111172 | 0.54764 |
13
+ | 31 | nncf_module.bert.encoder.layer.1.intermediate.dense | NNCFLinear | weight | [315, 768] | 241920 | 148727 | 0.385222 |
14
+ | 33 | nncf_module.bert.encoder.layer.1.output.dense | NNCFLinear | weight | [768, 315] | 241920 | 143174 | 0.408176 |
15
+ | 37 | nncf_module.bert.encoder.layer.2.attention.self.query | NNCFLinear | weight | [576, 768] | 442368 | 162784 | 0.632017 |
16
+ | 39 | nncf_module.bert.encoder.layer.2.attention.self.key | NNCFLinear | weight | [576, 768] | 442368 | 164797 | 0.627466 |
17
+ | 41 | nncf_module.bert.encoder.layer.2.attention.self.value | NNCFLinear | weight | [576, 768] | 442368 | 135705 | 0.693231 |
18
+ | 43 | nncf_module.bert.encoder.layer.2.attention.output.dense | NNCFLinear | weight | [768, 576] | 442368 | 138448 | 0.68703 |
19
+ | 47 | nncf_module.bert.encoder.layer.2.intermediate.dense | NNCFLinear | weight | [339, 768] | 260352 | 154043 | 0.408328 |
20
+ | 49 | nncf_module.bert.encoder.layer.2.output.dense | NNCFLinear | weight | [768, 339] | 260352 | 150923 | 0.420312 |
21
+ | 53 | nncf_module.bert.encoder.layer.3.attention.self.query | NNCFLinear | weight | [576, 768] | 442368 | 170686 | 0.614154 |
22
+ | 55 | nncf_module.bert.encoder.layer.3.attention.self.key | NNCFLinear | weight | [576, 768] | 442368 | 178480 | 0.596535 |
23
+ | 57 | nncf_module.bert.encoder.layer.3.attention.self.value | NNCFLinear | weight | [576, 768] | 442368 | 172200 | 0.610731 |
24
+ | 59 | nncf_module.bert.encoder.layer.3.attention.output.dense | NNCFLinear | weight | [768, 576] | 442368 | 169194 | 0.617527 |
25
+ | 63 | nncf_module.bert.encoder.layer.3.intermediate.dense | NNCFLinear | weight | [368, 768] | 282624 | 163234 | 0.422434 |
26
+ | 65 | nncf_module.bert.encoder.layer.3.output.dense | NNCFLinear | weight | [768, 368] | 282624 | 157364 | 0.443204 |
27
+ | 69 | nncf_module.bert.encoder.layer.4.attention.self.query | NNCFLinear | weight | [576, 768] | 442368 | 176138 | 0.601829 |
28
+ | 71 | nncf_module.bert.encoder.layer.4.attention.self.key | NNCFLinear | weight | [576, 768] | 442368 | 177351 | 0.599087 |
29
+ | 73 | nncf_module.bert.encoder.layer.4.attention.self.value | NNCFLinear | weight | [576, 768] | 442368 | 164248 | 0.628707 |
30
+ | 75 | nncf_module.bert.encoder.layer.4.attention.output.dense | NNCFLinear | weight | [768, 576] | 442368 | 159471 | 0.639506 |
31
+ | 79 | nncf_module.bert.encoder.layer.4.intermediate.dense | NNCFLinear | weight | [386, 768] | 296448 | 167744 | 0.434154 |
32
+ | 81 | nncf_module.bert.encoder.layer.4.output.dense | NNCFLinear | weight | [768, 386] | 296448 | 159961 | 0.460408 |
33
+ | 85 | nncf_module.bert.encoder.layer.5.attention.self.query | NNCFLinear | weight | [384, 768] | 294912 | 114241 | 0.612627 |
34
+ | 87 | nncf_module.bert.encoder.layer.5.attention.self.key | NNCFLinear | weight | [384, 768] | 294912 | 132821 | 0.549625 |
35
+ | 89 | nncf_module.bert.encoder.layer.5.attention.self.value | NNCFLinear | weight | [384, 768] | 294912 | 135092 | 0.541924 |
36
+ | 91 | nncf_module.bert.encoder.layer.5.attention.output.dense | NNCFLinear | weight | [768, 384] | 294912 | 132344 | 0.551242 |
37
+ | 95 | nncf_module.bert.encoder.layer.5.intermediate.dense | NNCFLinear | weight | [336, 768] | 258048 | 153822 | 0.403902 |
38
+ | 97 | nncf_module.bert.encoder.layer.5.output.dense | NNCFLinear | weight | [768, 336] | 258048 | 145684 | 0.435438 |
39
+ | 101 | nncf_module.bert.encoder.layer.6.attention.self.query | NNCFLinear | weight | [448, 768] | 344064 | 131784 | 0.616978 |
40
+ | 103 | nncf_module.bert.encoder.layer.6.attention.self.key | NNCFLinear | weight | [448, 768] | 344064 | 144539 | 0.579907 |
41
+ | 105 | nncf_module.bert.encoder.layer.6.attention.self.value | NNCFLinear | weight | [448, 768] | 344064 | 131107 | 0.618946 |
42
+ | 107 | nncf_module.bert.encoder.layer.6.attention.output.dense | NNCFLinear | weight | [768, 448] | 344064 | 126145 | 0.633368 |
43
+ | 111 | nncf_module.bert.encoder.layer.6.intermediate.dense | NNCFLinear | weight | [280, 768] | 215040 | 135219 | 0.371191 |
44
+ | 113 | nncf_module.bert.encoder.layer.6.output.dense | NNCFLinear | weight | [768, 280] | 215040 | 131559 | 0.388211 |
45
+ | 117 | nncf_module.bert.encoder.layer.7.attention.self.query | NNCFLinear | weight | [448, 768] | 344064 | 132226 | 0.615694 |
46
+ | 119 | nncf_module.bert.encoder.layer.7.attention.self.key | NNCFLinear | weight | [448, 768] | 344064 | 152327 | 0.557271 |
47
+ | 121 | nncf_module.bert.encoder.layer.7.attention.self.value | NNCFLinear | weight | [448, 768] | 344064 | 141141 | 0.589783 |
48
+ | 123 | nncf_module.bert.encoder.layer.7.attention.output.dense | NNCFLinear | weight | [768, 448] | 344064 | 135857 | 0.60514 |
49
+ | 127 | nncf_module.bert.encoder.layer.7.intermediate.dense | NNCFLinear | weight | [211, 768] | 162048 | 109376 | 0.32504 |
50
+ | 129 | nncf_module.bert.encoder.layer.7.output.dense | NNCFLinear | weight | [768, 211] | 162048 | 107132 | 0.338887 |
51
+ | 133 | nncf_module.bert.encoder.layer.8.attention.self.query | NNCFLinear | weight | [448, 768] | 344064 | 129219 | 0.624433 |
52
+ | 135 | nncf_module.bert.encoder.layer.8.attention.self.key | NNCFLinear | weight | [448, 768] | 344064 | 130088 | 0.621908 |
53
+ | 137 | nncf_module.bert.encoder.layer.8.attention.self.value | NNCFLinear | weight | [448, 768] | 344064 | 108043 | 0.68598 |
54
+ | 139 | nncf_module.bert.encoder.layer.8.attention.output.dense | NNCFLinear | weight | [768, 448] | 344064 | 103567 | 0.698989 |
55
+ | 143 | nncf_module.bert.encoder.layer.8.intermediate.dense | NNCFLinear | weight | [108, 768] | 82944 | 63183 | 0.238245 |
56
+ | 145 | nncf_module.bert.encoder.layer.8.output.dense | NNCFLinear | weight | [768, 108] | 82944 | 62633 | 0.244876 |
57
+ | 149 | nncf_module.bert.encoder.layer.9.attention.self.query | NNCFLinear | weight | [320, 768] | 245760 | 107216 | 0.563737 |
58
+ | 151 | nncf_module.bert.encoder.layer.9.attention.self.key | NNCFLinear | weight | [320, 768] | 245760 | 101848 | 0.585579 |
59
+ | 153 | nncf_module.bert.encoder.layer.9.attention.self.value | NNCFLinear | weight | [320, 768] | 245760 | 52063 | 0.788155 |
60
+ | 155 | nncf_module.bert.encoder.layer.9.attention.output.dense | NNCFLinear | weight | [768, 320] | 245760 | 53127 | 0.783826 |
61
+ | 159 | nncf_module.bert.encoder.layer.9.intermediate.dense | NNCFLinear | weight | [53, 768] | 40704 | 33339 | 0.18094 |
62
+ | 161 | nncf_module.bert.encoder.layer.9.output.dense | NNCFLinear | weight | [768, 53] | 40704 | 32340 | 0.205483 |
63
+ | 165 | nncf_module.bert.encoder.layer.10.attention.self.query | NNCFLinear | weight | [384, 768] | 294912 | 112357 | 0.619015 |
64
+ | 167 | nncf_module.bert.encoder.layer.10.attention.self.key | NNCFLinear | weight | [384, 768] | 294912 | 109640 | 0.628228 |
65
+ | 169 | nncf_module.bert.encoder.layer.10.attention.self.value | NNCFLinear | weight | [384, 768] | 294912 | 61630 | 0.791022 |
66
+ | 171 | nncf_module.bert.encoder.layer.10.attention.output.dense | NNCFLinear | weight | [768, 384] | 294912 | 63912 | 0.783284 |
67
+ | 175 | nncf_module.bert.encoder.layer.10.intermediate.dense | NNCFLinear | weight | [86, 768] | 66048 | 50252 | 0.239159 |
68
+ | 177 | nncf_module.bert.encoder.layer.10.output.dense | NNCFLinear | weight | [768, 86] | 66048 | 49494 | 0.250636 |
69
+ | 181 | nncf_module.bert.encoder.layer.11.attention.self.query | NNCFLinear | weight | [384, 768] | 294912 | 88056 | 0.701416 |
70
+ | 183 | nncf_module.bert.encoder.layer.11.attention.self.key | NNCFLinear | weight | [384, 768] | 294912 | 85229 | 0.711002 |
71
+ | 185 | nncf_module.bert.encoder.layer.11.attention.self.value | NNCFLinear | weight | [384, 768] | 294912 | 47126 | 0.840203 |
72
+ | 187 | nncf_module.bert.encoder.layer.11.attention.output.dense | NNCFLinear | weight | [768, 384] | 294912 | 49010 | 0.833815 |
73
+ | 191 | nncf_module.bert.encoder.layer.11.intermediate.dense | NNCFLinear | weight | [105, 768] | 80640 | 62069 | 0.230295 |
74
+ | 193 | nncf_module.bert.encoder.layer.11.output.dense | NNCFLinear | weight | [768, 105] | 80640 | 61476 | 0.237649 |
XP_onnx_sparsity.csv ADDED
@@ -0,0 +1,77 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ,layer_id,shape,nparam,nnz,sparsity
2
+ 0,Constant_15,"[30522, 768]",23440896,23440896,0.0
3
+ 1,Constant_23,"[2, 768]",1536,1536,0.0
4
+ 2,Constant_35,"[512, 768]",393216,393216,0.0
5
+ 3,Constant_61,"[320, 768]",245760,93507,0.61951904296875
6
+ 4,Constant_71,"[320, 768]",245760,98224,0.6003255208333333
7
+ 5,Constant_91,"[320, 768]",245760,113596,0.5377766927083334
8
+ 6,Constant_150,"[768, 320]",245760,117320,0.5226236979166667
9
+ 7,Constant_178,"[185, 768]",142080,97047,0.3169552364864865
10
+ 8,Constant_196,"[768, 185]",142080,94629,0.3339738175675676
11
+ 9,Constant_224,"[320, 768]",245760,118358,0.5184000651041667
12
+ 10,Constant_234,"[320, 768]",245760,118132,0.5193196614583333
13
+ 11,Constant_254,"[320, 768]",245760,107518,0.5625081380208333
14
+ 12,Constant_313,"[768, 320]",245760,111172,0.5476399739583333
15
+ 13,Constant_341,"[315, 768]",241920,148727,0.3852223875661376
16
+ 14,Constant_359,"[768, 315]",241920,143174,0.40817625661375656
17
+ 15,Constant_387,"[576, 768]",442368,162784,0.6320167824074074
18
+ 16,Constant_397,"[576, 768]",442368,164797,0.6274662724247686
19
+ 17,Constant_417,"[576, 768]",442368,135705,0.6932305230034722
20
+ 18,Constant_476,"[768, 576]",442368,138448,0.6870298032407407
21
+ 19,Constant_504,"[339, 768]",260352,154043,0.4083279559980334
22
+ 20,Constant_522,"[768, 339]",260352,150923,0.42031173180924286
23
+ 21,Constant_550,"[576, 768]",442368,170686,0.6141538266782407
24
+ 22,Constant_560,"[576, 768]",442368,178480,0.5965350115740741
25
+ 23,Constant_580,"[576, 768]",442368,172200,0.6107313368055556
26
+ 24,Constant_639,"[768, 576]",442368,169194,0.6175265842013888
27
+ 25,Constant_667,"[368, 768]",282624,163234,0.4224340466485508
28
+ 26,Constant_685,"[768, 368]",282624,157364,0.44320369112318836
29
+ 27,Constant_713,"[576, 768]",442368,176138,0.6018292462384259
30
+ 28,Constant_723,"[576, 768]",442368,177351,0.5990871853298612
31
+ 29,Constant_743,"[576, 768]",442368,164248,0.6287073206018519
32
+ 30,Constant_802,"[768, 576]",442368,159471,0.6395060221354167
33
+ 31,Constant_830,"[386, 768]",296448,167744,0.43415371329879104
34
+ 32,Constant_848,"[768, 386]",296448,159961,0.4604078961571675
35
+ 33,Constant_876,"[384, 768]",294912,114241,0.6126268174913194
36
+ 34,Constant_886,"[384, 768]",294912,132821,0.5496249728732638
37
+ 35,Constant_906,"[384, 768]",294912,135092,0.5419243706597222
38
+ 36,Constant_965,"[768, 384]",294912,132344,0.5512424045138888
39
+ 37,Constant_993,"[336, 768]",258048,153822,0.40390159970238093
40
+ 38,Constant_1011,"[768, 336]",258048,145684,0.4354383680555556
41
+ 39,Constant_1039,"[448, 768]",344064,131784,0.6169782366071428
42
+ 40,Constant_1049,"[448, 768]",344064,144539,0.5799066452752977
43
+ 41,Constant_1069,"[448, 768]",344064,131107,0.6189458937872023
44
+ 42,Constant_1128,"[768, 448]",344064,126145,0.6333676292782738
45
+ 43,Constant_1156,"[280, 768]",215040,135219,0.37119140625
46
+ 44,Constant_1174,"[768, 280]",215040,131559,0.3882114955357143
47
+ 45,Constant_1202,"[448, 768]",344064,132226,0.6156935918898809
48
+ 46,Constant_1212,"[448, 768]",344064,152327,0.5572713216145833
49
+ 47,Constant_1232,"[448, 768]",344064,141141,0.58978271484375
50
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+ | | layer_id | shape | nparam | nnz | sparsity |
2
+ |---:|:--------------|:-------------|---------:|---------:|-----------:|
3
+ | 0 | Constant_15 | [30522, 768] | 23440896 | 23440896 | 0 |
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+ | 55 | Constant_1482 | [108, 768] | 82944 | 63183 | 0.238245 |
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+ | 56 | Constant_1500 | [768, 108] | 82944 | 62633 | 0.244876 |
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+ | 57 | Constant_1528 | [320, 768] | 245760 | 107216 | 0.563737 |
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+ | 60 | Constant_1617 | [768, 320] | 245760 | 53127 | 0.783826 |
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+ | 61 | Constant_1645 | [53, 768] | 40704 | 33339 | 0.18094 |
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+ | 62 | Constant_1663 | [768, 53] | 40704 | 32340 | 0.205483 |
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+ | 63 | Constant_1691 | [384, 768] | 294912 | 112357 | 0.619015 |
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+ | 64 | Constant_1701 | [384, 768] | 294912 | 109640 | 0.628228 |
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+ | 65 | Constant_1721 | [384, 768] | 294912 | 61630 | 0.791022 |
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+ | 66 | Constant_1780 | [768, 384] | 294912 | 63912 | 0.783285 |
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+ | 67 | Constant_1808 | [86, 768] | 66048 | 50252 | 0.239159 |
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+ | 68 | Constant_1826 | [768, 86] | 66048 | 49494 | 0.250636 |
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+ | 69 | Constant_1854 | [384, 768] | 294912 | 88056 | 0.701416 |
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+ | 70 | Constant_1864 | [384, 768] | 294912 | 85229 | 0.711002 |
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+ | 71 | Constant_1884 | [384, 768] | 294912 | 47126 | 0.840203 |
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+ | 72 | Constant_1943 | [768, 384] | 294912 | 49010 | 0.833815 |
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+ | 73 | Constant_1971 | [105, 768] | 80640 | 62069 | 0.230295 |
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+ | 74 | Constant_1989 | [768, 105] | 80640 | 61476 | 0.237649 |
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+ | 75 | Constant_2017 | [2, 768] | 1536 | 1536 | 0 |
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