Tutorial on sparsifying BERT on SQuAD

integrations/huggingface-transformers/recipes/bert-base-12layers_prune80.md

@@ -15,18 +15,18 @@ limitations under the License.
 -->
 
 ---
-# General variables
+# General Variables
 num_epochs: &num_epochs 30
 
-# pruning hyperparameters
+# Pruning Hyperparameters
 init_sparsity: &init_sparsity 0.00
 final_sparsity: &final_sparsity 0.80
 pruning_start_epoch: &pruning_start_epoch 2
 pruning_end_epoch: &pruning_end_epoch 20
 update_frequency: &pruning_update_frequency 0.01
 
 
-# modifiers:
+# Modifiers
 training_modifiers:
   - !EpochRangeModifier
     end_epoch: 30
@@ -35,12 +35,12 @@ training_modifiers:
 pruning_modifiers:
   - !GMPruningModifier
     params:
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.query.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.key.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.value.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.output.dense.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).intermediate.dense.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).output.dense.weight
+      - re:bert.encoder.layer.*.attention.self.query.weight
+      - re:bert.encoder.layer.*.attention.self.key.weight
+      - re:bert.encoder.layer.*.attention.self.value.weight
+      - re:bert.encoder.layer.*.attention.output.dense.weight
+      - re:bert.encoder.layer.*.intermediate.dense.weight
+      - re:bert.encoder.layer.*.output.dense.weight
     start_epoch: *pruning_start_epoch
     end_epoch: *pruning_end_epoch
     init_sparsity: *init_sparsity
@@ -52,21 +52,21 @@ pruning_modifiers:
     log_types: __ALL__
 ---
 
-# Bert model with pruned encoder layers
+# BERT Model with Pruned Encoder Layers
 
-This recipe defines a pruning strategy to sparsify all encoder layers of a Bert model at 80% sparsity. It was used together with knowledge distillation to create sparse model that achives 100% recovery from its baseline accuracy on the Squad dataset. 
-Training was done using 1 GPU at half precision using a training batch size of 16 with the
+This recipe defines a pruning strategy to sparsify all encoder layers of a BERT model at 80% sparsity. It was used together with knowledge distillation to create sparse model that completely recovers the F1 metric (88.596) of the baseline model by on the SQuAD dataset. (We use the checkpoint at the end of the first 2 epochs as the baseline model for comparison, right before the pruning takes effect.)
+Training was done using one V100 GPU at half precision using a training batch size of 16 with the
 [SparseML integration with huggingface/transformers](https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers).
 
 ## Weights and Biases
 
-- [Sparse Bert on Squad](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/18qdx7b3?workspace=user-neuralmagic)
+- [Sparse BERT on SQuAD](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/18qdx7b3?workspace=user-neuralmagic)
 
 ## Training
 
 To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/huggingface-transformers/README.md).
 Using the `run_qa.py` script from the question-answering examples, the following command can be used to launch this recipe with distillation.
-Adjust the training command below with your setup for GPU device, checkpoint saving frequency and logging options.
+Adjust the training command below with your setup for GPU device, checkpoint saving frequency, and logging options.
 
 *training command*
 ```
@@ -91,7 +91,7 @@ python transformers/examples/pytorch/question-answering/run_qa.py \
   --distill_temperature 2.0 \
   --save_steps 1000 \
   --save_total_limit 2 \
-  --recipe ../recipes/uni_80sparse_freq0.01_18prune10fine.md \
+  --recipe ../recipes/bert-base-12layers_prune80.md \
   --onnx_export_path MODELS_DIR/sparse80/onnx \
   --report_to wandb
 ```

integrations/huggingface-transformers/recipes/bert-base-12layers_prune90.md

@@ -15,17 +15,17 @@ limitations under the License.
 -->
 
 ---
-# General variables
+# General Variables
 num_epochs: &num_epochs 30
 
-# pruning hyperparameters
+# Pruning Hyperparameters
 init_sparsity: &init_sparsity 0.00
 final_sparsity: &final_sparsity 0.90
 pruning_start_epoch: &pruning_start_epoch 2
 pruning_end_epoch: &pruning_end_epoch 20
 update_frequency: &pruning_update_frequency 0.01
 
-# modifiers:
+# Modifiers
 training_modifiers:
   - !EpochRangeModifier
     end_epoch: 30
@@ -34,12 +34,12 @@ training_modifiers:
 pruning_modifiers:
   - !GMPruningModifier
     params:
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.query.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.key.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.value.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.output.dense.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).intermediate.dense.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).output.dense.weight
+      - re:bert.encoder.layer.*.attention.self.query.weight
+      - re:bert.encoder.layer.*.attention.self.key.weight
+      - re:bert.encoder.layer.*.attention.self.value.weight
+      - re:bert.encoder.layer.*.attention.output.dense.weight
+      - re:bert.encoder.layer.*.intermediate.dense.weight
+      - re:bert.encoder.layer.*.output.dense.weight
     start_epoch: *pruning_start_epoch
     end_epoch: *pruning_end_epoch
     init_sparsity: *init_sparsity
@@ -50,21 +50,21 @@ pruning_modifiers:
     mask_type: unstructured
     log_types: __ALL__
 ---
-# Bert model with pruned encoder layers
+# BERT Model with Pruned Encoder Layers
 
-This recipe defines a pruning strategy to sparsify all encoder layers of a Bert model at 90% sparsity. It was used together with knowledge distillation to create sparse model that achives 98.4% recovery from its baseline accuracy on the Squad dataset. 
-Training was done using 1 GPU at half precision using a training batch size of 16 with the
+This recipe defines a pruning strategy to sparsify all encoder layers of a BERT model at 90% sparsity. It was used together with knowledge distillation to create sparse model that achieves 98.4% recovery from the F1 metric (88.596) of the baseline model on the SQuAD dataset. (We use the checkpoint at the end of the first 2 epochs as the baseline model for comparison, right before the pruning takes effect.)
+Training was done using one V100 GPU at half precision using a training batch size of 16 with the
 [SparseML integration with huggingface/transformers](https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers).
 
 ## Weights and Biases
 
-- [Sparse Bert on Squad](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/2ht2eqsn?workspace=user-neuralmagic)
+- [Sparse BERT on SQuAD](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/2ht2eqsn?workspace=user-neuralmagic)
 
 ## Training
 
 To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/huggingface-transformers/README.md).
 Using the `run_qa.py` script from the question-answering examples, the following command can be used to launch this recipe with distillation.
-Adjust the training command below with your setup for GPU device, checkpoint saving frequency and logging options.
+Adjust the training command below with your setup for GPU device, checkpoint saving frequency, and logging options.
 
 *training command*
 ```
@@ -89,7 +89,7 @@ python transformers/examples/pytorch/question-answering/run_qa.py \
   --distill_temperature 2.0 \
   --save_steps 1000 \
   --save_total_limit 2 \
-  --recipe ../recipes/uni_90sparse_freq0.01_18prune10fine.md \
+  --recipe ../recipes/bert-base-12layers_prune90.md \
   --onnx_export_path MODELS_DIR/sparse90/onnx \
   --report_to wandb
 ```

integrations/huggingface-transformers/recipes/bert-base-12layers_prune95.md

@@ -15,17 +15,17 @@ limitations under the License.
 -->
 
 ---
-# General variables
+# General Variables
 num_epochs: &num_epochs 30
 
-# pruning hyperparameters
+# Pruning Hyperparameters
 init_sparsity: &init_sparsity 0.00
 final_sparsity: &final_sparsity 0.95
 pruning_start_epoch: &pruning_start_epoch 2
 pruning_end_epoch: &pruning_end_epoch 20
 update_frequency: &pruning_update_frequency 0.01
 
-# modifiers:
+# Modifiers
 training_modifiers:
   - !EpochRangeModifier
     end_epoch: 30
@@ -34,12 +34,12 @@ training_modifiers:
 pruning_modifiers:
   - !GMPruningModifier
     params:
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.query.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.key.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.self.value.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).attention.output.dense.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).intermediate.dense.weight
-      - re:bert.encoder.layer.([0,2,4,6,8]|11).output.dense.weight
+      - re:bert.encoder.layer.*.attention.self.query.weight
+      - re:bert.encoder.layer.*.attention.self.key.weight
+      - re:bert.encoder.layer.*.attention.self.value.weight
+      - re:bert.encoder.layer.*.attention.output.dense.weight
+      - re:bert.encoder.layer.*.intermediate.dense.weight
+      - re:bert.encoder.layer.*.output.dense.weight
     start_epoch: *pruning_start_epoch
     end_epoch: *pruning_end_epoch
     init_sparsity: *init_sparsity
@@ -51,21 +51,21 @@ pruning_modifiers:
     log_types: __ALL__
 ---
 
-# Bert model with pruned encoder layers
+# BERT Model with Pruned Encoder Layers
 
-This recipe defines a pruning strategy to sparsify all encoder layers of a Bert model at 95% sparsity. It was used together with knowledge distillation to create sparse model that achives 94.7% recovery from its baseline accuracy on the Squad dataset. 
+This recipe defines a pruning strategy to sparsify all encoder layers of a BERT model at 95% sparsity. It was used together with knowledge distillation to create sparse model that achieves 94.7% recovery from the F1 metric of the baseline model on the SQuAD dataset. (We use the checkpoint at the end of the first 2 epochs as the baseline model for comparison, right before the pruning takes effect.)
 Training was done using 1 GPU at half precision using a training batch size of 16 with the
 [SparseML integration with huggingface/transformers](https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers).
 
 ## Weights and Biases
 
-- [Sparse Bert on Squad](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/3gv0arxd?workspace=user-neuralmagic)
+- [Sparse BERT on SQuAD](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/3gv0arxd?workspace=user-neuralmagic)
 
 ## Training
 
 To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/huggingface-transformers/README.md).
 Using the `run_qa.py` script from the question-answering examples, the following command can be used to launch this recipe with distillation.
-Adjust the training command below with your setup for GPU device, checkpoint saving frequency and logging options.
+Adjust the training command below with your setup for GPU device, checkpoint saving frequency, and logging options.
 
 *training command*
 ```
@@ -90,7 +90,7 @@ python transformers/examples/pytorch/question-answering/run_qa.py \
   --distill_temperature 2.0 \
   --save_steps 1000 \
   --save_total_limit 2 \
-  --recipe ../recipes/uni_95sparse_freq0.01_18prune10fine.md \
+  --recipe ../recipes/bert-base-12layers_prune95.md \
   --onnx_export_path MODELS_DIR/sparse95/onnx \
   --report_to wandb
 ```

integrations/huggingface-transformers/recipes/bert-base-6layers_prune80.md

@@ -15,17 +15,17 @@ limitations under the License.
 -->
 
 ---
-# General variables
+# General Variables
 num_epochs: &num_epochs 30
 
-# pruning hyperparameters
+# Pruning Hyperparameters
 init_sparsity: &init_sparsity 0.00
 final_sparsity: &final_sparsity 0.80
 pruning_start_epoch: &pruning_start_epoch 2
 pruning_end_epoch: &pruning_end_epoch 20
 update_frequency: &pruning_update_frequency 0.01
 
-# modifiers:
+# Modifiers
 training_modifiers:
   - !EpochRangeModifier
     end_epoch: 30
@@ -60,21 +60,21 @@ pruning_modifiers:
       - bert.encoder.layer.10
 ---
 
-# Bert model with dropped and pruned encoder layers
+# BERT Model with Dropped and Pruned Encoder Layers
 
-This recipe defines a dropping and pruning strategy to sparsify 6 encoder layers of a Bert model at 80% sparsity. It was used together with knowledge distillation to create sparse model that achives 97% recovery from its (teacher) baseline accuracy on the Squad dataset. 
-Training was done using 1 GPU at half precision using a training batch size of 16 with the
+This recipe defines a dropping and pruning strategy to sparsify six encoder layers of a BERT model at 80% sparsity. It was used together with knowledge distillation to create sparse model that exceeds the F1 metric (83.632) of the baseline model by 0.02% on the SQuAD dataset. (We use the checkpoint at the end of the first 2 epochs as the baseline model for comparison, right before the pruning takes effect.)
+Training was done using one V100 GPU at half precision using a training batch size of 16 with the
 [SparseML integration with huggingface/transformers](https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers).
 
 ## Weights and Biases
 
-- [Sparse Bert on Squad](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/ebab4np4?workspace=user-neuralmagic)
+- [Sparse BERT on SQuAD](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/ebab4np4?workspace=user-neuralmagic)
 
 ## Training
 
 To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/huggingface-transformers/README.md).
 Using the `run_qa.py` script from the question-answering examples, the following command can be used to launch this recipe with distillation.
-Adjust the training command below with your setup for GPU device, checkpoint saving frequency and logging options.
+Adjust the training command below with your setup for GPU device, checkpoint saving frequency, and logging options.
 
 *training command*
 ```
@@ -99,7 +99,7 @@ python transformers/examples/pytorch/question-answering/run_qa.py \
   --distill_temperature 2.0 \
   --save_steps 1000 \
   --save_total_limit 2 \
-  --recipe ../recipes/uni_80sparse_freq0.01_18prune10fine_6layers.md \
+  --recipe ../recipes/bert-base-6layers_prune80.md \
   --onnx_export_path MODELS_DIR/sparse80_6layers/onnx \
   --report_to wandb
 ```

integrations/huggingface-transformers/recipes/bert-base-6layers_prune90.md

@@ -15,17 +15,17 @@ limitations under the License.
 -->
 
 ---
-# General Epoch/LR variables
+# General Variables
 num_epochs: &num_epochs 30
 
-# pruning hyperparameters
+# Pruning Hyperparameters
 init_sparsity: &init_sparsity 0.00
 final_sparsity: &final_sparsity 0.90
 pruning_start_epoch: &pruning_start_epoch 2
 pruning_end_epoch: &pruning_end_epoch 20
 update_frequency: &pruning_update_frequency 0.01
 
-# modifiers:
+# Modifiers
 training_modifiers:
   - !EpochRangeModifier
     end_epoch: 30
@@ -60,21 +60,21 @@ pruning_modifiers:
       - bert.encoder.layer.10
 ---
 
-# Bert model with dropped and pruned encoder layers
+# BERT Model with Dropped and Pruned Encoder Layers
 
-This recipe defines a dropping and pruning strategy to sparsify 6 encoder layers of a Bert model at 90% sparsity. It was used together with knowledge distillation to create sparse model that achives 94.5% recovery from its (teacher) baseline accuracy on the Squad dataset. 
-Training was done using 1 GPU at half precision using a training batch size of 16 with the
+This recipe defines a dropping and pruning strategy to sparsify six encoder layers of a BERT model at 90% sparsity. It was used together with knowledge distillation to create sparse model that achieves 99.9% recovery from the F1 metric (83.632) of the baseline model on the SQuAD dataset. (We use the checkpoint at the end of the first 2 epochs as the baseline model for comparison, right before the pruning takes effect.)
+Training was done using one V100 GPU at half precision using a training batch size of 16 with the
 [SparseML integration with huggingface/transformers](https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers).
 
 ## Weights and Biases
 
-- [Sparse Bert on Squad](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/3qvxoroz?workspace=user-neuralmagic)
+- [Sparse BERT on SQuAD](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/3qvxoroz?workspace=user-neuralmagic)
 
 ## Training
 
 To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/huggingface-transformers/README.md).
 Using the `run_qa.py` script from the question-answering examples, the following command can be used to launch this recipe with distillation.
-Adjust the training command below with your setup for GPU device, checkpoint saving frequency and logging options.
+Adjust the training command below with your setup for GPU device, checkpoint saving frequency, and logging options.
 
 *training command*
 ```
@@ -99,7 +99,7 @@ python transformers/examples/pytorch/question-answering/run_qa.py \
   --distill_temperature 2.0 \
   --save_steps 1000 \
   --save_total_limit 2 \
-  --recipe ../recipes/uni_90sparse_freq0.01_18prune10fine_6layers.md \
+  --recipe ../recipes/bert-base-6layers_prune90.md \
   --onnx_export_path MODELS_DIR/sparse90_6layers/onnx \
   --report_to wandb
 ```

integrations/huggingface-transformers/recipes/bert-base-6layers_prune95.md

@@ -15,17 +15,17 @@ limitations under the License.
 -->
 
 ---
-# General Epoch/LR variables
+# General Variables
 num_epochs: &num_epochs 30
 
-# pruning hyperparameters
+# Pruning Hyperparameters
 init_sparsity: &init_sparsity 0.00
 final_sparsity: &final_sparsity 0.95
 pruning_start_epoch: &pruning_start_epoch 2
 pruning_end_epoch: &pruning_end_epoch 20
 update_frequency: &pruning_update_frequency 0.01
 
-# modifiers:
+# Modifiers
 training_modifiers:
   - !EpochRangeModifier
     end_epoch: 30
@@ -60,21 +60,21 @@ pruning_modifiers:
       - bert.encoder.layer.10
 ---
 
-# Bert model with dropped and pruned encoder layers
+# BERT Model with Dropped and Pruned Encoder Layers
 
-This recipe defines a dropping and pruning strategy to sparsify 6 encoder layers of a Bert model at 95% sparsity. It was used together with knowledge distillation to create sparse model that achives 90% recovery from its (teacher) baseline accuracy on the Squad dataset. 
-Training was done using 1 GPU at half precision using a training batch size of 16 with the
+This recipe defines a dropping and pruning strategy to sparsify six encoder layers of a BERT model at 95% sparsity. It was used together with knowledge distillation to create sparse model that achives 96.2% recovery from the F1 metric of the baseline model on the SQuAD dataset. (We use the checkpoint at the end of the first 2 epochs as the baseline model for comparison, right before the pruning takes effect.)
+Training was done using one V100 GPU at half precision using a training batch size of 16 with the
 [SparseML integration with huggingface/transformers](https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers).
 
 ## Weights and Biases
 
-- [Sparse Bert on Squad](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/3plynclw?workspace=user-neuralmagic)
+- [Sparse BERT on SQuAD](https://wandb.ai/neuralmagic/sparse-bert-squad/runs/3plynclw?workspace=user-neuralmagic)
 
 ## Training
 
 To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/huggingface-transformers/README.md).
 Using the `run_qa.py` script from the question-answering examples, the following command can be used to launch this recipe with distillation.
-Adjust the training command below with your setup for GPU device, checkpoint saving frequency and logging options.
+Adjust the training command below with your setup for GPU device, checkpoint saving frequency, and logging options.
 
 *training command*
 ```
@@ -99,7 +99,7 @@ python transformers/examples/pytorch/question-answering/run_qa.py \
   --distill_temperature 2.0 \
   --save_steps 1000 \
   --save_total_limit 2 \
-  --recipe ../recipes/uni_95sparse_freq0.01_18prune10fine_6layers.md \
+  --recipe ../recipes/bert-base-6layers_prune95.md \
   --onnx_export_path MODELS_DIR/sparse95_6layers/onnx \
   --report_to wandb
 ```