upload model

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+trainer_state.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+model-index:
+- name: yujiepan/bert-base-uncased-sst2-int8-unstructured80-17epoch
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE SST2
+      type: glue
+      config: sst2
+      split: validation
+      args: sst2
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.91284
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Joint magnitude pruning, quantization and distillation on BERT-base/SST-2
+
+This model conducts unstructured magnitude pruning, quantization and distillation at the same time when finetuning on the GLUE SST2 dataset.
+It achieves the following results on the evaluation set:
+- Torch loss: 0.3858
+- Torch accuracy: 0.9128
+- OpenVINO IR accuracy: 0.9128
+- Sparsity in transformer block linear layers: 0.80
+
+## Setup
+
+```
+conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
+git clone https://github.com/yujiepan-work/optimum-intel.git
+git checkout -b "magnitude-pruning" 01927af543eaea8678671bf8f4eb78fdb29f8930
+cd optimum-intel
+pip install -e .[openvino,nncf]
+
+cd examples/openvino/text-classification/
+pip install -r requirements.txt
+pip install wandb # optional
+```
+
+## NNCF config
+
+See `nncf_config.json` in this repo.
+
+
+## Run
+
+We use one card for training.
+
+```
+NNCFCFG=/path/to/nncf/config
+python run_glue.py \
+--lr_scheduler_type cosine_with_restarts \
+--cosine_cycle_ratios 11,6 \
+--cosine_cycle_decays 1,1 \
+--save_best_model_after_epoch -1 \
+--save_best_model_after_sparsity 0.7999 \
+--model_name_or_path textattack/bert-base-uncased-SST-2 \
+--teacher_model_or_path yoshitomo-matsubara/bert-large-uncased-sst2 \
+--distillation_temperature 2 \
+--task_name sst2 \
+--nncf_compression_config $NNCFCFG \
+--distillation_weight 0.95 \
+--output_dir /tmp/bert-base-uncased-sst2-int8-unstructured80-17epoch \
+--run_name bert-base-uncased-sst2-int8-unstructured80-17epoch \
+--overwrite_output_dir \
+--do_train \
+--do_eval \
+--max_seq_length 128 \
+--per_device_train_batch_size 32 \
+--per_device_eval_batch_size 32 \
+--learning_rate 5e-05 \
+--optim adamw_torch \
+--num_train_epochs 17 \
+--logging_steps 1 \
+--evaluation_strategy steps \
+--eval_steps 250 \
+--save_strategy steps \
+--save_steps 250 \
+--save_total_limit 1 \
+--fp16 \
+--seed 1
+```
+
+The best model checkpoint is stored in the `best_model` folder. Here we only upload that checkpoint folder together with some config files.
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2
+
+For a full description of the environment, please refer to `pip-requirements.txt` and `conda-requirements.txt`.

conda-requirements.txt

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+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: linux-64
+_libgcc_mutex=0.1=main
+_openmp_mutex=5.1=1_gnu
+addict=2.4.0=pypi_0
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config.json

@@ -0,0 +1,35 @@
+{
+  "_name_or_path": "textattack/bert-base-uncased-SST-2",
+  "architectures": [
+    "NNCFNetwork"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "sst2",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "negative",
+    "1": "positive"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "negative": 0,
+    "positive": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

eval_results.json

@@ -0,0 +1,8 @@
+{
+    "eval_accuracy": 0.9128440366972477,
+    "eval_loss": 0.38576310873031616,
+    "eval_runtime": 6.8627,
+    "eval_samples_per_second": 127.064,
+    "eval_steps_per_second": 4.08
+}
+

nncf_config.json

@@ -0,0 +1,49 @@
+[
+    {
+        "algorithm": "quantization",
+        "preset": "mixed",
+        "overflow_fix": "disable",
+        "initializer": {
+            "range": {
+                "num_init_samples": 300,
+                "type": "mean_min_max"
+            },
+            "batchnorm_adaptation": {
+                "num_bn_adaptation_samples": 0
+            }
+        },
+        "scope_overrides": {
+            "activations": {
+                "{re}.*matmul_0": {
+                    "mode": "symmetric"
+                }
+            }
+        },
+        "ignored_scopes": [
+            "{re}.*Embeddings.*",
+            "{re}.*__add___[0-1]",
+            "{re}.*layer_norm_0",
+            "{re}.*matmul_1",
+            "{re}.*__truediv__*"
+        ]
+    },
+    {
+        "algorithm": "magnitude_sparsity",
+        "ignored_scopes": [
+            "{re}.*NNCFEmbedding.*",
+            "{re}.*LayerNorm.*",
+            "{re}.*pooler.*",
+            "{re}.*classifier.*"
+        ],
+        "sparsity_init": 0.0,
+        "params": {
+            "power": 3,
+            "schedule": "polynomial",
+            "sparsity_freeze_epoch": 10,
+            "sparsity_target": 0.8,
+            "sparsity_target_epoch": 9,
+            "steps_per_epoch": 2105,
+            "update_per_optimizer_step": true
+        }
+    }
+]

openvino_config.json

@@ -0,0 +1,82 @@
+{
+  "compression": [
+    {
+      "algorithm": "quantization",
+      "export_to_onnx_standard_ops": false,
+      "ignored_scopes": [
+        "{re}.*Embeddings.*",
+        "{re}.*__add___[0-1]",
+        "{re}.*layer_norm_0",
+        "{re}.*matmul_1",
+        "{re}.*__truediv__*"
+      ],
+      "initializer": {
+        "batchnorm_adaptation": {
+          "num_bn_adaptation_samples": 0
+        },
+        "range": {
+          "num_init_samples": 300,
+          "type": "mean_min_max"
+        }
+      },
+      "overflow_fix": "disable",
+      "preset": "mixed",
+      "scope_overrides": {
+        "activations": {
+          "{re}.*matmul_0": {
+            "mode": "symmetric"
+          }
+        }
+      }
+    },
+    {
+      "algorithm": "magnitude_sparsity",
+      "ignored_scopes": [
+        "{re}.*NNCFEmbedding.*",
+        "{re}.*LayerNorm.*",
+        "{re}.*pooler.*",
+        "{re}.*classifier.*"
+      ],
+      "params": {
+        "power": 3,
+        "schedule": "polynomial",
+        "sparsity_freeze_epoch": 10,
+        "sparsity_target": 0.8,
+        "sparsity_target_epoch": 9,
+        "steps_per_epoch": 2105,
+        "update_per_optimizer_step": true
+      },
+      "sparsity_init": 0.0
+    }
+  ],
+  "input_info": [
+    {
+      "keyword": "input_ids",
+      "sample_size": [
+        32,
+        128
+      ],
+      "type": "long"
+    },
+    {
+      "keyword": "token_type_ids",
+      "sample_size": [
+        32,
+        128
+      ],
+      "type": "long"
+    },
+    {
+      "keyword": "attention_mask",
+      "sample_size": [
+        32,
+        128
+      ],
+      "type": "long"
+    }
+  ],
+  "log_dir": "/nvme2/yujiepan/workspace/jpqd-test/LOGS/optimum-magnitude/0209_jp0w_QMaP80_LR5e-05_COS11,6_EPO17_PerStp_INIT0.0_END9_BS32_LT0.95TMP2_SEED1_dgx1",
+  "optimum_version": "1.6.3",
+  "save_onnx_model": false,
+  "transformers_version": "4.26.0"
+}

openvino_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6bc46883c54fe9cf7d9ed6d172ee9622273d1c081c186aae0da2755d30bc0861
+size 181696972

pip-requirements.txt

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+asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1670263926556/work
+async-timeout==4.0.2
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+backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work
+backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1618230623929/work
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+coloredlogs==15.0.1
+colorlog==6.7.0
+comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1670575068857/work
+cycler==0.11.0
+datasets==2.8.0
+debugpy==1.6.4
+decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
+defusedxml==0.7.1
+dill==0.3.6
+docker-pycreds==0.4.0
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+et-xmlfile==1.1.0
+evaluate==0.4.0
+executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1667317341051/work
+fastjsonschema==2.16.2
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+flake8==6.0.0
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+ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1674911957283/work
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+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1669134318875/work
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+nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1664684991461/work
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+nncf @ git+https://github.com/openvinotoolkit/nncf@c02f9f3bc0d77677b8cacbeedf102bb8ca93c0ac
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+-e git+https://github.com/yujiepan-work/optimum-intel.git@01927af543eaea8678671bf8f4eb78fdb29f8930#egg=optimum_intel
+optuna==3.1.0
+packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1673482170163/work
+pandas==1.3.5
+pandocfilters==1.5.0
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+parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1638334955874/work
+pathspec==0.10.3
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+pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1667297516076/work
+pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
+Pillow==9.4.0
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+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1672264874562/work
+pooch==1.6.0
+prometheus-client==0.16.0
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+prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1670414775770/work
+protobuf==3.20.2
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+ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
+pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work
+py==1.11.0
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+pydot==1.4.2
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+Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1672682006896/work
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+rfc3986-validator==0.1.1
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+six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
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+stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
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+traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1673359992537/work
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+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1665144421445/work
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+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1673864653149/work
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pytorch_model.bin

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special_tokens_map.json

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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
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+}

tokenizer_config.json

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+  "do_lower_case": true,
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+  "name_or_path": "textattack/bert-base-uncased-SST-2",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
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+}

train_results.json

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trainer_state.json

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training_args.bin

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