Initialize demo

.editorconfig

@@ -0,0 +1,9 @@
+root = true
+
+[*]
+indent_style = space
+tab_width = 4
+end_of_line = lf
+
+[*.yml]
+tab_width = 2

.gitignore

@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

README.md

@@ -1,12 +1,16 @@
 ---
-title: Debiasing Lms
-emoji: 💻
-colorFrom: red
+title: Debiasing LMs
+emoji: ⚖️
+colorFrom: yellow
 colorTo: purple
 sdk: gradio
 sdk_version: 3.0.3
 app_file: app.py
-pinned: false
+pinned: true
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+
+Official demo for _Debiasing Pre-Trained Language Models via Efficient Fine-Tuning_ published in the [Second Workshop on Language Technology for Equality, Diversity, Inclusion](https://sites.google.com/view/lt-edi-2022) at ACL 2022. [View the code here.](https://github.com/michaelgira23/debiasing-lms)
+
+**WARNING: MODEL OUTPUTS MAY CONTAIN SENSITIVE MATERIAL.**

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from load_model import device, load_gpt2, load_unprejudiced, tokenizer
+
+model_names = {
+    'gpt2': 'Original GPT-2',
+    'unprejudiced_ln': 'LN',
+    'unprejudiced_ln_wpe': 'LN + WPE',
+    'unprejudiced_ln_wpe_wte': 'LN + WPE + WTE',
+    'unprejudiced_ln_wpe_wte_io': 'LN + WPE + WTE + IO',
+    'unprejudiced_full': 'FULL',
+}
+
+models = []
+for model_name in model_names:
+    if model_name == 'gpt2':
+        models.append(load_gpt2())
+    else:
+        models.append(load_unprejudiced(model_name))
+
+
+def generate(prompt):
+
+    encoded_prompt = tokenizer.encode(
+        prompt, add_special_tokens=False, return_tensors="pt").to(device)
+
+    output = []
+    for model in models:
+        output_sequences = model.generate(
+            input_ids=encoded_prompt, do_sample=True, max_length=100)
+        if len(output_sequences.shape) > 2:
+            output_sequences.squeeze_()
+
+        for output_sequence in output_sequences:
+            output.append(tokenizer.decode(
+                output_sequence, clean_up_tokenization_spaces=True))
+
+    return tuple(output)
+
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.Textbox(label='Prompt'),
+    outputs=[
+        gr.Textbox(label=model_names[model_name]) for model_name in model_names
+    ],
+    examples=[
+        'Classmates of the schoolgirl found her to be',
+        'That award-winning chess player is',
+        'I think Bangladesh is a',
+        'The Bibles I looked at were very'
+    ],
+    title='Debiasing LMs GPT-2 Demo',
+    description='Official demo for _Debiasing Pre-Trained Language Models via Efficient Fine-Tuning_ published in the [Second Workshop on Language Technology for Equality, Diversity, Inclusion](https://sites.google.com/view/lt-edi-2022) at ACL 2022. [View the code here.](https://github.com/michaelgira23/debiasing-lms)<br />WARNING: MODEL OUTPUTS MAY CONTAIN SENSITIVE MATERIAL.'
+)
+
+demo.launch()

load_model.py

@@ -0,0 +1,48 @@
+import json
+import os
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from model import get_model
+
+device = 'cuda'
+models_path = 'models'
+
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+
+def load_gpt2():
+
+    model = GPT2LMHeadModel.from_pretrained('gpt2').to(device)
+    return model
+
+
+def load_unprejudiced(model_name):
+    model_path = os.path.join(
+        models_path, f'{model_name}.pth'
+    )
+    model_json_path = os.path.join(
+        models_path, f'{model_name}.json'
+    )
+
+    with open(model_json_path) as f:
+        config = json.loads(f.read())
+        combination = config['combination']
+
+        unprejudiced_model = get_model(
+            device=device,
+            gpt2_name='gpt2',
+            in_net=combination['in_net'],
+            in_net_init_identity=combination['in_net_init_identity'],
+            out_net=combination['out_net'],
+            out_net_init_identity=combination['out_net_init_identity'],
+            freeze_ln=combination['freeze_ln'],
+            freeze_pos=combination['freeze_pos'],
+            freeze_wte=combination['freeze_wte'],
+            freeze_ff=combination['freeze_ff'],
+            freeze_attn=combination['freeze_attn'],
+            dup_lm_head=combination['dup_lm_head'],
+            dup_lm_head_bias=combination['dup_lm_head_bias']
+        )
+        checkpoint = torch.load(model_path, map_location=device)
+        unprejudiced_model.load_state_dict(checkpoint['model_state_dict'])
+        unprejudiced_model = unprejudiced_model.to(device)
+        return unprejudiced_model

model.py

@@ -0,0 +1,250 @@
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2DoubleHeadsModel
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+from types import MethodType
+
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+
+
+def get_model(device='cpu', gpt2_name='gpt2', in_net=False, in_net_init_identity=True, out_net=False, out_net_init_identity=True, freeze_ln=False, freeze_pos=True,
+              freeze_wte=True, freeze_ff=True, freeze_attn=True, dup_lm_head=False, dup_lm_head_bias=False):
+
+    # ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl']
+    model = GPT2LMHeadModel.from_pretrained(gpt2_name).to(device)
+    # model = GPT2DoubleHeadsModel.from_pretrained('gpt2')
+
+    """
+    Initialize linear input layer
+    """
+
+    in_layer_sizes = []
+    out_layer_sizes = []
+    input_dim = model.config.n_embd
+    dropout = 0.1
+    orth_gain = 1.41
+    # orth_gain = None
+    in_net_init_identity = True
+
+    #Model - in_net
+    if in_net:
+        in_layers = []
+        last_output_size = input_dim
+
+        for size in in_layer_sizes:
+            layer = nn.Linear(last_output_size, size)
+            if orth_gain is not None:
+                torch.nn.init.orthogonal_(layer.weight, gain=orth_gain)
+            layer.bias.data.zero_()
+
+            in_layers.append(layer)
+            in_layers.append(nn.ReLU())
+            in_layers.append(nn.Dropout(dropout))
+            last_output_size = size
+
+        in_final_linear = nn.Linear(last_output_size, model.config.n_embd)
+        # if orth_gain is not None:
+        #     torch.nn.init.orthogonal_(in_final_linear.weight, gain=orth_gain)
+        # in_final_linear.bias.data.zero_()
+
+        # Initialize final_linear layer to identity transformation
+        if in_net_init_identity:
+            nn.init.eye_(in_final_linear.weight)
+            in_final_linear.bias.data.zero_()
+
+        in_layers.append(in_final_linear)
+        in_layers.append(nn.Dropout(dropout))
+
+        model.in_net = nn.Sequential(*in_layers)
+
+        model.in_net.requires_grad = True
+
+    """
+    Initialize linear output layer
+    """
+    if out_net:
+        output_dim = model.config.n_embd
+        out_layers = []
+        last_output_size = model.config.n_embd
+        for size in out_layer_sizes:
+            out_layers.append(nn.Linear(last_output_size, size))
+            out_layers.append(nn.ReLU())
+            out_layers.append(nn.Dropout(dropout))
+            last_output_size = size
+
+        out_final_linear = nn.Linear(last_output_size, output_dim)
+
+        if out_net_init_identity:
+            nn.init.eye_(out_final_linear.weight)
+            out_final_linear.bias.data.zero_()
+
+        out_layers.append(out_final_linear)
+        model.out_net = nn.Sequential(*out_layers)
+
+        model.out_net.requires_grad = True
+
+    """
+    out layer on top of lm_head
+    """
+    # out_net_top = nn.Linear(model.config.vocab_size, model.config.vocab_size)
+    # nn.init.eye_(out_net_top.weight)
+    # model.out_net_top = out_net_top
+    # model.out_net_top.requires_grad = True
+
+    if dup_lm_head:
+        lm_head_new = nn.Linear(model.config.n_embd,
+                                model.config.vocab_size, bias=dup_lm_head_bias)
+        lm_head_new.weight = torch.nn.Parameter(
+            model.lm_head.weight.data.detach().clone(), requires_grad=True)
+        # lm_head_new.bias.data.zero_()
+        model.lm_head_new = lm_head_new
+        model.lm_head_new.requires_grad = True
+
+    """
+    Freeze transformer layers
+    """
+
+    total_parameters = 0
+    target_parameters = 0
+
+    for name, p in model.transformer.named_parameters():
+        name = name.lower()
+
+        size = p.size()
+        param_count = 1
+        for dimension in size:
+            param_count *= dimension
+
+        total_parameters += param_count
+
+        if 'ln' in name or 'norm' in name:
+            p.requires_grad = not freeze_ln
+        elif 'wpe' in name or 'position_embeddings' in name or 'pos_drop' in name:
+            p.requires_grad = not freeze_pos
+            target_parameters += param_count
+        elif 'mlp' in name:
+            p.requires_grad = not freeze_ff
+        elif 'attn' in name:
+            p.requires_grad = not freeze_attn
+        elif 'wte' in name:
+            p.requires_grad = not freeze_wte
+        else:
+            p.requires_grad = False
+
+    # print(f'Total params: {total_parameters}')
+    # print(
+    #     f'Target params: {target_parameters} ({target_parameters / total_parameters * 100:.2f}%)')
+
+    def forward(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
+            ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Convert from input ids to word embeddings so that we can apply a linear layer
+        x = self.transformer.wte(input_ids)
+
+        try:
+            x = self.in_net(x)
+        except AttributeError:
+            pass
+
+        transformer_outputs = self.transformer(
+            inputs_embeds=x,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            **kwargs
+        )
+        hidden_states = transformer_outputs[0]
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.transformer.first_device)
+            hidden_states = hidden_states.to(self.lm_head.weight.device)
+
+        try:
+            hidden_states = self.out_net(hidden_states)
+        except AttributeError:
+            pass
+
+        try:
+            lm_logits = self.lm_head_new(hidden_states)
+        except AttributeError:
+            lm_logits = self.lm_head(hidden_states)
+
+        # lm_logits = self.out_net_top(lm_logits)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+            cross_attentions=transformer_outputs.cross_attentions,
+        )
+
+    model.forward = MethodType(forward, model)
+
+    return model
+
+
+# model = get_model()
+'''
+    only for testing purpose
+'''
+if __name__ == "__main__":
+    model = get_model(gpt2_name='gpt2', in_net=False, in_net_init_identity=True, out_net=False, out_net_init_identity=False, freeze_ln=True, freeze_pos=True,
+                      freeze_wte=True, freeze_ff=True, freeze_attn=True)
+    for name, p in model.named_parameters():
+        if p.requires_grad:
+            print(name, p.requires_grad)
+
+    for p in model.lm_head_new.parameters():
+        print('lm_head_new', p)
+
+    # for p in model.out_net.parameters():
+    #     print('out_net',p)

models/unprejudiced_full.json

@@ -0,0 +1,402 @@
+{
+    "combination": {
+        "num_epoches": 2,
+        "lr": 0.0002,
+        "batch_size": 50,
+        "optimizer": "adam",
+        "in_net": false,
+        "in_net_init_identity": false,
+        "out_net": false,
+        "out_net_init_identity": false,
+        "freeze_ln": false,
+        "freeze_pos": false,
+        "freeze_wte": false,
+        "dup_lm_head": false,
+        "dup_lm_head_bias": false,
+        "freeze_ff": false,
+        "freeze_attn": false,
+        "model_save_path": "official-matrix-results/unprejudiced_full"
+    },
+    "results": {
+        "intrasentence": {
+            "gender": {
+                "Count": [
+                    765.0,
+                    765.0,
+                    765.0,
+                    765.0,
+                    765.0,
+                    765.0,
+                    765.0,
+                    765.0,
+                    765.0,
+                    765.0
+                ],
+                "LM Score": [
+                    93.36633422937771,
+                    91.7658506549811,
+                    91.22898454202802,
+                    92.60683133291829,
+                    93.59360695665043,
+                    92.54365248278292,
+                    92.62846340455037,
+                    91.79493027101724,
+                    92.65281867890563,
+                    93.15924059837104
+                ],
+                "SS Score": [
+                    61.748979683762286,
+                    63.02738388825345,
+                    61.830647291516854,
+                    61.25860081947038,
+                    60.53624909711865,
+                    61.08708232186493,
+                    62.20106576193532,
+                    61.863980624850186,
+                    60.2713399322095,
+                    60.87978076673729
+                ],
+                "ICAT Score": [
+                    71.42715094921135,
+                    67.85647136868963,
+                    69.64302576442849,
+                    71.7543643902513,
+                    73.87109582078871,
+                    72.02287061392943,
+                    70.02514393603161,
+                    70.0138647871208,
+                    73.61944675233744,
+                    72.88819831625086
+                ]
+            },
+            "profession": {
+                "Count": [
+                    2430.0,
+                    2430.0,
+                    2430.0,
+                    2430.0,
+                    2430.0,
+                    2430.0,
+                    2430.0,
+                    2430.0,
+                    2430.0,
+                    2430.0
+                ],
+                "LM Score": [
+                    92.66118513274773,
+                    92.89712157285084,
+                    93.11904559125475,
+                    92.47266481564515,
+                    92.63307771972653,
+                    93.04684117530033,
+                    93.46754217502432,
+                    92.52513513017473,
+                    92.03004251317432,
+                    93.1777837549873
+                ],
+                "SS Score": [
+                    62.42811341795197,
+                    62.88681524499281,
+                    64.36028780555368,
+                    61.657931946889086,
+                    61.184202874815426,
+                    62.408674761343185,
+                    62.23745612964913,
+                    61.68922936855322,
+                    61.88738643629987,
+                    63.866052170013695
+                ],
+                "ICAT Score": [
+                    69.62911076731505,
+                    68.95416072283155,
+                    66.37471969387688,
+                    70.91186414827962,
+                    71.9125350370072,
+                    69.95508138100719,
+                    70.59144323676452,
+                    70.89418459231486,
+                    70.15010893111015,
+                    67.33762354232914
+                ]
+            },
+            "race": {
+                "Count": [
+                    2886.0,
+                    2886.0,
+                    2886.0,
+                    2886.0,
+                    2886.0,
+                    2886.0,
+                    2886.0,
+                    2886.0,
+                    2886.0,
+                    2886.0
+                ],
+                "LM Score": [
+                    90.01315387609708,
+                    89.95538806609045,
+                    89.69226125140138,
+                    89.57123039836956,
+                    90.55240696045361,
+                    89.83527794097476,
+                    90.60476921789747,
+                    89.46430372117965,
+                    89.19705463444784,
+                    89.99810499557233
+                ],
+                "SS Score": [
+                    61.32607349049196,
+                    61.21386666644415,
+                    61.193511846868645,
+                    59.70670331340266,
+                    61.58789876404254,
+                    60.846074325689365,
+                    60.80182496222662,
+                    60.362391660400576,
+                    61.28258396894039,
+                    60.40630213866781
+                ],
+                "ICAT Score": [
+                    69.62324195786435,
+                    69.78043351206286,
+                    69.6128334736014,
+                    72.18240322050141,
+                    69.56616446649126,
+                    70.34807590863927,
+                    71.03083206120414,
+                    70.92302062550172,
+                    69.0695894605414,
+                    71.26715554574284
+                ]
+            },
+            "religion": {
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