### Install Example Requirements

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Install dependencies required for running example tests. This is typically needed only once.

```bash
pip install -r examples/requirements.txt  # only needed the first time
```

--------------------------------

### Run Example Tests with Pytest

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Execute tests for the examples using pytest. Ensure example requirements are installed first.

```bash
python -m pytest -n auto --dist=loadfile -s -v ./examples/
```

--------------------------------

### Run Example Tests with Unittest

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Execute tests for the examples using the standard unittest module. This command discovers and runs tests from the 'examples' directory.

```bash
python -m unittest discover -s examples -t examples -v
```

--------------------------------

### Install Dependencies and EdgeBERT

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/README.md

Follow these commands to set up the Python environment, install necessary libraries, and install the EdgeBERT transformers package.

```bash
conda create --name test-edge python=3.7
source activate test-edge

pip install torch
pip install tensorboard
pip install -U scikit-learn

cd EdgeBERT/transformers
python setup.py install
```

--------------------------------

### Install Dependencies

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Installs the required version of urllib3.

```python
!pip install urllib3==1.25.4
```

--------------------------------

### Install Development Dependencies

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Install the project in editable mode with development dependencies. This command ensures all necessary packages for development are available in your virtual environment.

```bash
$ pip install -e ".[dev]"
```

--------------------------------

### Install EdgeBERT Transformers

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Navigates to the transformers directory and installs the package.

```python
%cd ./EdgeBERT/EdgeBERT/transformers/
!python setup.py install
%cd ../../..
```

--------------------------------

### Install EdgeBERT Dependencies

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Commands to set up the conda environment and install required Python packages and the EdgeBERT library.

```bash
# Create conda environment
conda create --name edgebert python=3.7
source activate edgebert

# Install dependencies
pip install torch
pip install tensorboard
pip install -U scikit-learn

# Install EdgeBERT transformers library
cd sw/EdgeBERT/transformers
python setup.py install
```

--------------------------------

### Install Unreleased isort Version

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Install a specific unreleased version of 'isort' to avoid a known bug. This is a temporary workaround until the bug is fixed in a stable release.

```bash
$ pip install -U git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort
```

--------------------------------

### Get Average Predict Layer

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Calculates the average prediction layer using entropy prediction without early exit before.

```python
%%shell

ENTROPIES="0.09 0.16 0.28"

for ENTROPY in $ENTROPIES; do
    echo $ENTROPY
    python ../examples/masked_run_highway_glue.py --model_type albert \
      --model_name_or_path ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6 \
      --task_name SST-2 \
      --do_eval \
      --do_lower_case \
      --data_dir ./glue_data/SST-2 \
      --max_seq_length 128 \
      --per_gpu_eval_batch_size=1 \
      --overwrite_output_dir \
      --output_dir ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6  \
      --plot_data_dir ./plotting/ \
      --early_exit_entropy $ENTROPY \
      --eval_highway \
      --entropy_predictor \
      --predict_layer 1 \
      --lookup_table_file ./sst2_lookup_table_opt.csv \
      --no_ee_before \
      --overwrite_cache 
done
```

--------------------------------

### Download GLUE Data

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Clones the GLUE baselines repository and downloads the necessary datasets.

```python
!git clone https://github.com/nyu-mll/GLUE-baselines.git
!python GLUE-baselines/download_glue_data.py
```

--------------------------------

### Build and Run EdgeBERT Simulation

Source: https://github.com/harvard-acc/edgebert/blob/main/hw/README.md

Commands to clone the repository and execute the C++ compilation and simulation of the Top-level accelerator.

```bash
git clone --recursive https://github.com/harvard-acc/EdgeBERT.git
cd EdgeBERT/hw/cmod/TopAccel
make
make run
```

--------------------------------

### Run Library Tests with Unittest

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Execute all library tests using the standard unittest module. This command discovers and runs tests from the 'tests' directory.

```bash
python -m unittest discover -s tests -t . -v
```

--------------------------------

### Clone and Build EdgeBERT Hardware Accelerator

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Clones the EdgeBERT repository and builds the SystemC simulation for the hardware accelerator. Ensure tool paths are configured in the Makefile.

```bash
# Clone repository with submodules (NVIDIA MatchLib)
git clone --recursive https://github.com/harvard-acc/EdgeBERT.git
cd EdgeBERT/hw/cmod/TopAccel

# Configure tool paths in cmod_Makefile
# Set BOOST_HOME, SYSTEMC_HOME, CATAPULT_HOME

# Build the accelerator simulation
make

# Run simulation
make run
```

--------------------------------

### Train Teacher Model

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Trains the teacher model using the run_glue script.

```python
!python ../examples/run_glue.py \
  --model_type albert \
  --model_name_or_path albert-base-v2 \
  --task_name SST-2 \
  --do_train \
  --do_eval \
  --do_lower_case \
  --data_dir ./glue_data/SST-2 \
  --max_seq_length 128 \
  --per_gpu_eval_batch_size 1 \
  --per_gpu_train_batch_size 32 \
  --learning_rate 3e-5 \
  --num_train_epochs 3 \
  --save_steps 0 \
  --seed 42 \
  --output_dir ./saved_models/albert-base/SST-2/teacher \
  --overwrite_cache \
  --overwrite_output_dir
```

--------------------------------

### Add and Commit Changes

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Stage your modified files using 'git add' and then commit them locally with 'git commit'. Remember to write clear and informative commit messages.

```bash
$ git add modified_file.py
$ git commit
```

--------------------------------

### Train EdgeBERT with Distillation and Pruning

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Execute the training pipeline for the EdgeBERT model using knowledge distillation, adaptive attention, and magnitude pruning.

```bash
#!/bin/bash
export CUDA_VISIBLE_DEVICES=0

python examples/masked_run_highway_glue.py \
  --model_type masked_albert \
  --model_name_or_path albert-base-v2 \
  --task_name SST-2 \
  --do_train \
  --do_eval \
  --do_lower_case \
  --data_dir ./glue_data/SST-2 \
  --max_seq_length 128 \
  --per_gpu_eval_batch_size 1 \
  --per_gpu_train_batch_size 64 \
  --learning_rate 3e-5 \
  --num_train_epochs 30 \
  --overwrite_output_dir \
  --seed 42 \
  --output_dir ./saved_models/masked_albert/SST-2/two_stage_pruned_0.5 \
  --plot_data_dir ./plotting/ \
  --save_steps 0 \
  --overwrite_cache \
  --eval_after_first_stage \
  --adaptive \
  --adaptive_span_ramp 256 \
  --max_span 512 \
  --warmup_steps 1000 \
  --mask_scores_learning_rate 1e-2 \
  --initial_threshold 1 \
  --final_threshold 0.5 \
  --initial_warmup 2 \
  --final_warmup 3 \
  --pruning_method magnitude \
  --mask_init constant \
  --mask_scale 0. \
  --fxp_and_prune \
  --prune_percentile 60 \
  --teacher_type albert_teacher \
  --teacher_name_or_path ./saved_models/albert-base/SST-2/teacher \
  --alpha_ce 0.1 \
  --alpha_distil 0.9
```

--------------------------------

### Check Code Quality with Flake8

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Run 'flake8' to check for coding mistakes and style issues. While CI also performs these checks, running it locally helps catch errors early.

```bash
$ make quality
```

--------------------------------

### Run Test Suite

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Execute the project's test suite to ensure your changes have not introduced regressions. This command verifies the overall stability of the codebase.

```bash
$ make test
```

--------------------------------

### Load Dataset with NumPy

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/Entropy_LUT/entropypredictor.ipynb

Loads training and evaluation datasets from specified text files using NumPy's loadtxt function. Assumes data is comma-separated.

```python
#load dataset
fn2 = "dataset_sst2_eval.txt"
fn = "dataset_sst2_train.txt"

nparr2 = np.loadtxt(fn2, delimiter=",")
nparr = np.loadtxt(fn, delimiter=",")
```

--------------------------------

### Format Code with Black and isort

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Format your code changes using 'black' and 'isort' to maintain consistent code style across the project. This command should be run before committing your changes.

```bash
$ make style
```

--------------------------------

### Implement Weight Binarization Classes

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Defines MagnitudeBinarizer for pruning based on absolute value and TopKBinarizer for movement pruning during training.

```python
from torch import autograd

class MagnitudeBinarizer(object):
    """
    Magnitude Binarizer - prunes weights below threshold based on absolute value.
    Keeps the k% highest magnitude weights.
    """
    @staticmethod
    def apply(inputs: torch.tensor, threshold: float):
        mask = inputs.clone()
        _, idx = inputs.abs().flatten().sort(descending=True)
        j = int(threshold * inputs.numel())

        flat_out = mask.flatten()
        flat_out[idx[j:]] = 0
        flat_out[idx[:j]] = 1
        return mask

class TopKBinarizer(autograd.Function):
    """
    Top-k Binarizer - keeps top k% of weights by score value.
    Used for movement pruning during training.
    """
    @staticmethod
    def forward(ctx, inputs: torch.tensor, threshold: float):
        mask = inputs.clone()
        _, idx = inputs.flatten().sort(descending=True)
        j = int(threshold * inputs.numel())

        flat_out = mask.flatten()
        flat_out[idx[j:]] = 0
        flat_out[idx[:j]] = 1
        return mask
```

--------------------------------

### Create a New Development Branch

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Always create a new branch for your development changes. Do not work directly on the 'master' branch to keep your contributions organized and prevent conflicts.

```bash
$ git checkout -b a-descriptive-name-for-my-changes
```

--------------------------------

### Evaluate Model with Entropy Prediction

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Uses a lookup table to predict exit layers based on early-layer entropy for DVFS-aware decision making.

```bash
#!/bin/bash
export CUDA_VISIBLE_DEVICES=0

ENTROPIES="0.09 0.16 0.28"

for ENTROPY in $ENTROPIES; do
    echo "Testing with entropy prediction, threshold: $ENTROPY"
    python examples/masked_run_highway_glue.py \
      --model_type albert \
      --model_name_or_path ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6 \
      --task_name SST-2 \
      --do_eval \
      --do_lower_case \
      --data_dir ./glue_data/SST-2 \
      --max_seq_length 128 \
      --per_gpu_eval_batch_size 1 \
      --overwrite_output_dir \
      --output_dir ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6 \
      --plot_data_dir ./plotting/ \
      --early_exit_entropy $ENTROPY \
      --eval_highway \
      --entropy_predictor \
      --predict_layer 1 \
      --lookup_table_file ./sst2_lookup_table_opt.csv \
      --overwrite_cache
done
```

--------------------------------

### Gather environment information for bug reports

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Use this snippet to collect OS, Python, PyTorch, and TensorFlow versions when reporting bugs.

```python
import platform; print("Platform", platform.platform())
import sys; print("Python", sys.version)
import torch; print("PyTorch", torch.__version__)
import tensorflow; print("Tensorflow", tensorflow.__version__)
```

--------------------------------

### Train Teacher Model

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Train a baseline ALBERT teacher model on the SST-2 task to generate soft labels for distillation.

```bash
#!/bin/bash
export CUDA_VISIBLE_DEVICES=0

python examples/run_glue.py \
  --model_type albert \
  --model_name_or_path albert-base-v2 \
  --task_name SST-2 \
  --do_train \
  --do_eval \
  --do_lower_case \
  --data_dir ./glue_data/SST-2 \
  --max_seq_length 128 \
  --per_gpu_eval_batch_size 1 \
  --per_gpu_train_batch_size 32 \
  --learning_rate 3e-5 \
  --num_train_epochs 3 \
  --save_steps 0 \
  --seed 42 \
  --output_dir ./saved_models/albert-base/SST-2/teacher \
  --overwrite_cache \
  --overwrite_output_dir
```

--------------------------------

### Enpy Hardware Entropy Calculator Module

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Implements the Enpy module for hardware entropy calculation. It includes state machine logic for finding maximum values, computing sums, and evaluating entropy for early exit decisions.

```cpp
// Enpy.h - Hardware entropy calculator
SC_MODULE(Enpy) {
  public:
    sc_in_clk     clk;
    sc_in<bool>   rst;

    // Configuration inputs
    Connections::In<bool> start;
    Connections::In<spec::EnpyConfig> enpy_config;
    Connections::In<spec::VectorType> act_rsp;  // Activation from memory

    // Outputs
    Connections::Out<NVUINT2> enpy_status;      // 2=above threshold, 3=below
    Connections::Out<spec::ActScalarType> enpy_val_out;
    Connections::Out<bool> done;

    // State machine states
    enum FSM { IDLE, FINDMAX, FINDMAX2, RSUM, RSUMb, EVAL, NEXT };

    void EnpyRun() {
        Reset();
        while(1) {
            switch (state) {
                case FINDMAX:
                    // Request activation vector from memory
                    mask_rd_req.Push(mask_reg);
                    break;

                case FINDMAX2:
                    // Find maximum value for numerical stability
                    act_reg = act_rsp.Pop();
                    Adpfloat2Fixed(act_reg, x_vector, adpbias_act1);
                    UpdateMax(x_vector);
                    break;

                case RSUM:
                    // Compute sum(exp(x-max)) and sum(x*exp(x-max))
                    for (int i = 0; i < N; i++) {
                        exp_vector_max[i] = exp_vector[i] - maximum_value;
                    }
                    Exponential(exp_vector_max, exp_vector_max);
                    sum_exp += tmp_sum;
                    sum_xexp += tmp_xsum;
                    break;

                case EVAL:
                    // Compute entropy: log(sum_exp) + max - sum_xexp/sum_exp
                    LogN(sum_exp, log_tmp);
                    tmp_div = sum_xexp / sum_exp;
                    enpy_result = log_tmp + maximum_value - tmp_div;

                    // Compare with threshold
                    if (enpy_result <= enpy_threshold)
                        enpy_status.Push(3);  // Exit early
                    else
                        enpy_status.Push(2);  // Continue
                    break;
            }
            wait();
        }
    }
};

```

--------------------------------

### Run Library Tests with Pytest

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Execute all library tests using pytest. This command enables parallel execution and verbose output.

```bash
python -m pytest -n auto --dist=loadfile -s -v ./tests/
```

--------------------------------

### Set Working Directory

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Changes the current working directory to the scripts folder.

```python
%cd ./EdgeBERT/EdgeBERT/scripts/
```

--------------------------------

### Import Libraries for TensorFlow and NumPy

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/Entropy_LUT/entropypredictor.ipynb

Imports necessary libraries for numerical operations and deep learning model building.

```python
import tensorflow as tf
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from matplotlib.pyplot import plot
```

--------------------------------

### Train Entropy Prediction Lookup Table

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Python script for training an entropy prediction lookup table using provided datasets. This table maps early-layer entropy values to predicted entropy for DVFS decisions.

```python
# Training entropy prediction lookup table (entropypredictor.ipynb)
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

# Load entropy datasets collected during evaluation
train_data = np.loadtxt('dataset_sst2_train.txt')
eval_data = np.loadtxt('dataset_sst2_eval.txt')

# Format: [layer1_entropy, layer2_entropy, ..., layerN_entropy]

```

--------------------------------

### Clone Repository and Set Upstream Remote

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Clone your forked repository and add the base repository as a remote named 'upstream'. This is essential for keeping your local copy synchronized with the main project.

```bash
$ git clone git@github.com:<your Github handle>/transformers.git
$ cd transformers
$ git remote add upstream https://github.com/huggingface/transformers.git
```

--------------------------------

### Evaluate Entropy Prediction Performance

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Evaluates the model performance using entropy prediction and a lookup table.

```python
%%shell

ENTROPIES="0.09 0.16 0.28"

for ENTROPY in $ENTROPIES; do
    echo $ENTROPY
    python ../examples/masked_run_highway_glue.py --model_type albert \
      --model_name_or_path ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6 \
      --task_name SST-2 \
      --do_eval \
      --do_lower_case \
      --data_dir ./glue_data/SST-2 \
      --max_seq_length 128 \
      --per_gpu_eval_batch_size=1 \
      --overwrite_output_dir \
      --output_dir ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6  \
      --plot_data_dir ./plotting/ \
      --early_exit_entropy $ENTROPY \
      --eval_highway \
      --entropy_predictor \
      --predict_layer 1 \
      --lookup_table_file ./sst2_lookup_table_opt.csv \
      --overwrite_cache 
done
```

--------------------------------

### Data Splitting and Feature Extraction

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/Entropy_LUT/entropypredictor.ipynb

Splits the loaded dataset into training and testing sets, and extracts features based on whether to average entropies or use a specific layer's entropy. The `start_layer` variable determines the split point.

```python
#split 80/20
#whether to average entropies up to start_layer or just use entropy at start layer
avg = 0 
#layer after which you make entropy prediction
start_layer = 6 #indexed 0-11

test = nparr2[:,:11]
train = nparr[:,:11]

if avg:
  (x_train, y_train) = (train[:,:start_layer], train[:,start_layer+1:])
  (x_test, y_test) = (test[:,:start_layer], test[:,start_layer+1:])
  x_train = np.mean(x_train, axis=-1)
  x_test = np.mean(x_test, axis=-1)
else:
  (x_train, y_train) = (train[:,start_layer], train[:,start_layer+1:])
  (x_test, y_test) = (test[:,start_layer], test[:,start_layer+1:])
```

--------------------------------

### Generate and Save Lookup Table

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/Entropy_LUT/entropypredictor.ipynb

Creates a lookup table by predicting values for a range of inputs using the trained model. The table is then saved to a CSV file after setting negative values to zero.

```python
#produce lookup table
table = np.zeros((100, 11-start_layer))
for i in range(0,100):
  xval = i*0.01;
  yval = model.predict(np.array([xval]), batch_size=1)
  table[i,0] = xval
  table[i,1:] = yval[0]

threshold_indices = table < 0
table[threshold_indices] = 0

np.savetxt("sst2_lookup_table_opt.csv", table, delimiter=",")
```

--------------------------------

### Sync Local Code with Upstream Repository

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Fetch changes from the upstream repository and rebase your local branch onto the latest master. This helps to resolve potential conflicts before pushing.

```bash
$ git fetch upstream
$ git rebase upstream/master
```

--------------------------------

### Evaluate Model with Entropy-Based Early Exit

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Runs evaluation on a highway model using specified entropy thresholds to trigger early exits.

```bash
#!/bin/bash
export CUDA_VISIBLE_DEVICES=0

# Test multiple entropy thresholds
ENTROPIES="0.23 0.28 0.46"

for ENTROPY in $ENTROPIES; do
    echo "Testing entropy threshold: $ENTROPY"
    python examples/masked_run_highway_glue.py \
      --model_type albert \
      --model_name_or_path ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6 \
      --task_name SST-2 \
      --do_eval \
      --do_lower_case \
      --data_dir ./glue_data/SST-2 \
      --max_seq_length 128 \
      --per_gpu_eval_batch_size 1 \
      --overwrite_output_dir \
      --output_dir ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6 \
      --plot_data_dir ./plotting/ \
      --early_exit_entropy $ENTROPY \
      --eval_highway \
      --overwrite_cache
done
```

--------------------------------

### Set CUDA Device

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Configures the visible CUDA devices.

```python
!export CUDA_VISIBLE_DEVICES=0 
```

--------------------------------

### Push Changes to Your Fork

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Push your local branch with the new changes to your fork on GitHub. This makes your contributions visible and ready for a pull request.

```bash
$ git push -u origin a-descriptive-name-for-my-changes
```

--------------------------------

### Train Keras Model

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/Entropy_LUT/entropypredictor.ipynb

Trains the compiled Keras model using the prepared training data (x_train, y_train) for 3 epochs. Verbose output is enabled.

```python
model.fit(x_train, y_train, epochs=3, verbose=1)
```

--------------------------------

### Evaluate Early Exit Performance

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Evaluates the model performance using early exit thresholds.

```python
%%shell
ENTROPIES="0.23 0.28 0.46"

for ENTROPY in $ENTROPIES; do
    echo $ENTROPY
    python ../examples/masked_run_highway_glue.py --model_type albert \
      --model_name_or_path ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6 \
      --task_name SST-2 \
      --do_eval \
      --do_lower_case \
      --data_dir ./glue_data/SST-2 \
      --max_seq_length 128 \
      --per_gpu_eval_batch_size=1 \
      --overwrite_output_dir \
      --output_dir ./saved_models/masked_albert/SST-2/bertarized_two_stage_pruned_0.6  \
      --plot_data_dir ./plotting/ \
      --early_exit_entropy $ENTROPY \
      --eval_highway \
      --overwrite_cache 
done
```

--------------------------------

### Clone EdgeBERT Repository

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Clones the EdgeBERT repository from GitHub.

```python
!git clone https://$GITHUB_AUTH@github.com/chooper1/EdgeBERT.git
```

--------------------------------

### Run Custom Tokenizer Tests with Pytest

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Execute tests for custom tokenizers by setting the RUN_CUSTOM_TOKENIZERS environment variable to 'yes'. These tests do not run by default.

```bash
RUN_CUSTOM_TOKENIZERS=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
```

--------------------------------

### Dvfs Hardware Dynamic Voltage Frequency Scaling Controller

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Implements the Dvfs module for dynamic voltage and frequency scaling. It uses an entropy value to select appropriate V/F settings from lookup tables.

```cpp
// Dvfs.h - Dynamic Voltage Frequency Scaling controller
SC_MODULE(Dvfs) {
  public:
    sc_in_clk     clk;
    sc_in<bool>   rst;

    // Inputs
    Connections::In<bool> start;
    Connections::In<spec::DvfsConfig> dvfs_config;
    Connections::In<spec::ActScalarType> enpy_val_in;

    // DCO and LDO lookup table configurations
    Connections::In<spec::DCOConfigA> dco_config_a;  // 5 DCO values
    Connections::In<spec::DCOConfigB> dco_config_b;  // 5 DCO values
    Connections::In<spec::LDOConfigA> ldo_config_a;  // 4 LDO values
    Connections::In<spec::LDOConfigB> ldo_config_b;  // 4 LDO values

    // Outputs to DCO and LDO
    Connections::Out<NVUINT6> dco_sel_out;  // 6-bit DCO selector
    Connections::Out<NVUINT8> ldo_sel_out;  // 8-bit LDO selector

    void DvfsRun() {
        Reset();
        while(1) {
            switch (state) {
                case SCALE_ENPY:
                    // Scale entropy value for LUT indexing
                    enpy_val_scaled = enpy_val_reg / enpy_scale;
                    break;

                case PUSH_VF:
                    // Lookup table maps entropy to V/F settings
                    // Lower entropy = higher confidence = can use lower V/F
                    if (enpy_val_scaled < 256) {
                        dco_sel_out.Push(dco_val_config_a[0]);
                        ldo_sel_out.Push(ldo_val_config_a[0]);
                    }
                    else if (enpy_val_scaled < 512) {
                        dco_sel_out.Push(dco_val_config_a[1]);
                        ldo_sel_out.Push(ldo_val_config_a[1]);
                    }
                    // ... additional thresholds for 16 V/F levels
                    else if (enpy_val_scaled >= 28672) {
                        dco_sel_out.Push(dco_val_config_c[4]);
                        ldo_sel_out.Push(ldo_val_config_d[3]);
                    }
                    break;
            }
            wait();
        }
    }
};

```

--------------------------------

### Evaluate Model Performance

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/Entropy_LUT/entropypredictor.ipynb

Evaluates the trained model on the test dataset (x_test, y_test) to determine the loss. The model summary is commented out.

```python
#check prediction
val_loss = model.evaluate(x_test, y_test)
#model.summary()
```

--------------------------------

### Calculate Prediction Entropy

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Computes entropy from logits to measure prediction confidence for early exit logic.

```python
import torch

def entropy(x):
    """
    Compute entropy from logits (before softmax).
    Lower entropy indicates higher prediction confidence.

    Args:
        x: torch.Tensor of logits with shape (batch_size, num_classes)

    Returns:
        torch.Tensor of entropy values with shape (batch_size,)
    """
    exp_x = torch.exp(x)
    A = torch.sum(exp_x, dim=1)    # sum of exp(x_i)
    B = torch.sum(x * exp_x, dim=1)  # sum of x_i * exp(x_i)
    return torch.log(A) - B / A

# Usage example:
logits = torch.tensor([[2.0, 1.0, 0.1], [0.5, 0.5, 0.5]])
ent = entropy(logits)
```

--------------------------------

### Run Slow Tests with Pytest

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/EdgeBERT/transformers/CONTRIBUTING.md

Execute all tests, including slow ones, by setting the RUN_SLOW environment variable to 'yes'. This may require significant disk space and bandwidth.

```bash
RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
```

```bash
RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/
```

--------------------------------

### Train entropy lookup table for early exit prediction

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Trains linear regression models to predict entropy at later layers based on early layer entropy, then saves the results to a CSV file.

```python
def train_lookup_table(data, predict_layer=1):
    """
    Train models to predict entropy at each layer from early layer entropy.

    Args:
        data: Array of shape (num_samples, num_layers)
        predict_layer: Which layer's entropy to use as predictor

    Returns:
        lookup_table: Array mapping entropy bins to predicted layer entropies
    """
    X = data[:, :predict_layer]

    models = []
    for target_layer in range(predict_layer, data.shape[1]):
        y = data[:, target_layer]
        model = LinearRegression()
        model.fit(X, y)
        models.append(model)

    # Create discretized lookup table
    entropy_bins = np.linspace(0, 2, 100)
    lookup_table = np.zeros((len(entropy_bins), len(models) + 1))
    lookup_table[:, 0] = entropy_bins

    for i, ent in enumerate(entropy_bins):
        X_pred = np.array([[ent]])
        for j, model in enumerate(models):
            lookup_table[i, j+1] = model.predict(X_pred)[0]

    return lookup_table

# Generate and save lookup table
lookup_table = train_lookup_table(train_data, predict_layer=1)
np.savetxt('sst2_lookup_table_opt.csv', lookup_table, delimiter=',')
```

--------------------------------

### Prune Model Weights

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Apply binary masks to remove zero-valued weights from the trained model to create a deployment-ready version.

```bash
#!/bin/bash
export CUDA_VISIBLE_DEVICES=0

python examples/bertarize.py \
  --pruning_method magnitude \
  --threshold 0.5 \
  --model_name_or_path ./saved_models/masked_albert/SST-2/two_stage_pruned_0.5
```

--------------------------------

### TopAccel SystemC Module Definition

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Defines the top-level SystemC module for the EdgeBERT accelerator, connecting various internal modules and interfaces.

```cpp
// TopAccel.h - Top-level accelerator module
SC_MODULE(TopAccel) {
  public:
    sc_in_clk     clk;
    sc_in<bool>   rst;

    // IRQ and DVFS outputs
    sc_out<bool> interrupt;
    sc_out<NVUINT6> edgebert_dco_cc_sel;  // DCO frequency control
    sc_out<NVUINT8> edgebert_ldo_res_sel; // LDO voltage control

    // AXI interfaces for data, mask, input, and auxiliary memory
    typename spec::AxiData::axi4_data::read::template master<>  if_data_rd;
    typename spec::AxiData::axi4_data::write::template master<> if_data_wr;

    // Internal modules
    PUModule    pu_inst;    // Processing Unit with sparse PE
    GBModule    gb_inst;    // Global Buffer with LayerNorm, Softmax
    Control     ct_inst;    // Configuration and control
    MaskAxi     ma_inst;    // Mask memory AXI interface
    InputAxi    ia_inst;    // Input memory AXI interface
    AuxAxi      aa_inst;    // Auxiliary memory AXI interface

    SC_HAS_PROCESS(TopAccel);
    TopAccel(sc_module_name name_) : sc_module(name_) {
        // Connect processing unit
        pu_inst.clk(clk);
        pu_inst.rst(rst);
        pu_inst.accel_config(accel_config);

        // Connect global buffer with DVFS
        gb_inst.clk(clk);
        gb_inst.rst(rst);
        gb_inst.dco_sel_out(dco_sel_out);
        gb_inst.ldo_sel_out(ldo_sel_out);
        gb_inst.enpy_status(enpy_status);

        // Connect control unit
        ct_inst.clk(clk);
        ct_inst.rst(rst);
        ct_inst.interrupt(interrupt);
        ct_inst.edgebert_dco_cc_sel(edgebert_dco_cc_sel);
        ct_inst.edgebert_ldo_res_sel(edgebert_ldo_res_sel);
    }
};

```

--------------------------------

### AlbertTransformer Early Exit Implementation

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Implements early exit in AlbertTransformer using highway classifiers. Requires configuration for entropy prediction and lookup tables.

```python
class AlbertTransformer(nn.Module):
    def __init__(self, config, params):
        super().__init__()
        self.config = config
        self.embedding_hidden_mapping_in = nn.Linear(
            config.embedding_size, config.hidden_size
        )
        self.albert_layer_groups = nn.ModuleList([
            AlbertLayerGroup(config, params)
            for _ in range(config.num_hidden_groups)
        ])

        # Highway classifiers for early exit
        self.highway = nn.ModuleList([
            AlbertHighway(config)
            for _ in range(config.num_hidden_layers)
        ])
        self.early_exit_entropy = [-1] * config.num_hidden_layers

        # Entropy prediction configuration
        if config.entropy_predictor:
            self.lookup_table = np.loadtxt(
                config.lookup_table_file, delimiter=","
            )
            self.predict_layer = config.predict_layer

    def set_early_exit_entropy(self, threshold):
        """Set entropy threshold for all layers."""
        for i in range(len(self.early_exit_entropy)):
            self.early_exit_entropy[i] = threshold

    def forward(self, hidden_states, attention_mask=None, head_mask=None):
        hidden_states = self.embedding_hidden_mapping_in(hidden_states)

        for i in range(self.config.num_hidden_layers):
            group_idx = int(i / (self.config.num_hidden_layers /
                                  self.config.num_hidden_groups))

            # Process through layer group
            layer_output = self.albert_layer_groups[group_idx](
                hidden_states, attention_mask, head_mask
            )
            hidden_states = layer_output[0]

            # Compute highway exit
            highway_exit = self.highway[i]((hidden_states,))
            highway_logits = highway_exit[0]
            highway_entropy = entropy(highway_logits)

            # Early exit if entropy below threshold
            if not self.training:
                if highway_entropy < self.early_exit_entropy[i]:
                    raise HighwayException(
                        (highway_logits,), exit_layer=i+1
                    )

        return (hidden_states,)
```

--------------------------------

### Configure Masked ALBERT for EdgeBERT

Source: https://context7.com/harvard-acc/edgebert/llms.txt

Defines the configuration class for EdgeBERT models, including parameters for pruning, early exit, and entropy prediction.

```python
from transformers import AlbertConfig

class MaskedAlbertConfig(AlbertConfig):
    """Configuration for EdgeBERT masked ALBERT model."""

    def __init__(
        self,
        # Standard ALBERT config
        vocab_size=30000,
        embedding_size=128,
        hidden_size=768,
        num_hidden_layers=12,
        num_hidden_groups=1,
        num_attention_heads=12,

        # Pruning configuration
        pruning_method="magnitude",  # magnitude, topK, l0, sigmoied_threshold
        mask_init="constant",
        mask_scale=0.0,

        # Early exit configuration
        one_class=False,  # Share highway classifier across layers
        entropy_predictor=False,
        predict_layer=1,
        predict_average_layers=0,
        lookup_table_file="./lookup_table.csv",
        extra_layer=False,
        get_predict_acc=False,
        no_ee_before=False,

        **kwargs
    ):
        super().__init__(
            vocab_size=vocab_size,
            embedding_size=embedding_size,
            hidden_size=hidden_size,
            num_hidden_layers=num_hidden_layers,
            num_hidden_groups=num_hidden_groups,
            num_attention_heads=num_attention_heads,
            **kwargs
        )

        self.pruning_method = pruning_method
        self.mask_init = mask_init
        self.mask_scale = mask_scale
        self.one_class = one_class
        self.entropy_predictor = entropy_predictor
        self.predict_layer = predict_layer
        self.lookup_table_file = lookup_table_file

# Usage example:
config = MaskedAlbertConfig(
    pruning_method="magnitude",
    entropy_predictor=True,
    predict_layer=1,
    lookup_table_file="./sst2_lookup_table_opt.csv"
)
```

--------------------------------

### Train Pruned Model

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Trains a pruned model using the masked_run_highway_glue script.

```python
!python ../examples/masked_run_highway_glue.py --model_type masked_albert \
  --model_name_or_path albert-base-v2 \
  --task_name SST-2 \
  --do_train \
  --do_eval \
  --do_lower_case \
  --data_dir ./glue_data/SST-2 \
  --max_seq_length 128 \
  --per_gpu_eval_batch_size=1 \
  --per_gpu_train_batch_size=64 \
  --learning_rate 3e-5 \
  --num_train_epochs 5 \
  --overwrite_output_dir \
  --seed 42 \
  --output_dir ./saved_models/masked_albert/SST-2/two_stage_pruned_0.6 \
  --plot_data_dir ./plotting/ \
  --save_steps 0 \
  --overwrite_cache \
  --eval_after_first_stage \
  --warmup_steps 1000 \
  --mask_scores_learning_rate 1e-2 \
  --initial_threshold 1 --final_threshold 0.6 \
  --initial_warmup 2 --final_warmup 3 \
  --pruning_method magnitude --mask_init constant --mask_scale 0. \
  --teacher_type albert_teacher --teacher_name_or_path ./saved_models/albert-base/SST-2/teacher \
  --alpha_ce 0.1 --alpha_distil 0.9
```

--------------------------------

### Define and Compile Keras Sequential Model

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/Entropy_LUT/entropypredictor.ipynb

Defines a Keras Sequential model with multiple Dense layers. The model is compiled using the Adam optimizer and Mean Squared Error loss function.

```python
model = Sequential()
model.add(Dense(64, activation='relu', input_dim = 1)) #input layer
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
#model.add(Dense(64, activation='linear'))
model.add(Dense(10-start_layer)) # output layer

model.compile(
    optimizer='adam',
    loss='mean_squared_error'
)
```

--------------------------------

### Bertarize Model

Source: https://github.com/harvard-acc/edgebert/blob/main/sw/edgebert.ipynb

Converts the pruned model into a bertarized format.

```python
!python ../examples/bertarize.py \
    --pruning_method magnitude \
    --threshold 0.6 \
    --model_name_or_path ./saved_models/masked_albert/SST-2/two_stage_pruned_0.6 \
```

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