added language generation

.github/workflows/push_to_hf_hub.yml

@@ -17,4 +17,4 @@ jobs:
       - name: Push to hub
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        run: git push --force https://wissamantoun:[email protected]/spaces/wissamantoun/Arabic-NLP main
+        run: git push --force https://aubmindlab:[email protected]/spaces/aubmindlab/Arabic-NLP main

.gitignore

@@ -129,4 +129,5 @@ dmypy.json
 .pyre/
 
 
-.vscode/
+.vscode/
+add_key.bat

app.py

@@ -1,24 +1,36 @@
-import streamlit as st
 import awesome_streamlit as ast
-import pages.home
-import pages.processor
+import streamlit as st
 
+import backend.aragpt
+import backend.home
+import backend.processor
 
 st.set_page_config(
     page_title="TEST", page_icon="📖", initial_sidebar_state="expanded", layout="wide"
 )
 
-PAGES = {"Home": pages.home, "Arabic Text Preprocessor": pages.processor}
+PAGES = {
+    "Home": backend.home,
+    "Arabic Text Preprocessor": backend.processor,
+    "Arabic Language Generation": backend.aragpt,
+}
 
 
 st.sidebar.title("Navigation")
 selection = st.sidebar.radio("Pages", list(PAGES.keys()))
 
 page = PAGES[selection]
-with st.spinner(f"Loading {selection} ..."):
-    ast.shared.components.write_page(page)
+# with st.spinner(f"Loading {selection} ..."):
+ast.shared.components.write_page(page)
 
 st.sidebar.header("Info")
 st.sidebar.write("Made by [Wissam Antoun](https://twitter.com/wissam_antoun)")
-st.sidebar.write("[Models Repo](https://github.com/aub-mind/arabert)")
-st.sidebar.write("Source Code [GitHub](https://github.com/WissamAntoun/Arabic-NLP-app)")
+st.sidebar.write(
+    "Pre-trained models are available on [HF Hub](https://huggingface.co/aubmindlab)"
+)
+st.sidebar.write(
+    "Models source code available on [GitHub](https://github.com/aub-mind/arabert)"
+)
+st.sidebar.write(
+    "App source code available on [GitHub](https://github.com/WissamAntoun/Arabic-NLP-app)"
+)

backend/aragpt.py

@@ -0,0 +1,182 @@
+import streamlit as st
+from .services import TextGeneration
+from tokenizers import Tokenizer
+from functools import lru_cache
+
+# @st.cache(allow_output_mutation=False, hash_funcs={Tokenizer: str})
+@lru_cache(maxsize=1)
+def load_text_generator():
+    generator = TextGeneration()
+    generator.load()
+    return generator
+
+
+generator = load_text_generator()
+
+qa_prompt = """
+            أجب عن السؤال التالي:
+            """
+qa_prompt_post = """ الجواب هو  """
+qa_prompt_post_year = """ في سنة: """
+
+
+def write():
+    # Sidebar
+
+    # Taken from https://huggingface.co/spaces/flax-community/spanish-gpt2/blob/main/app.py
+    st.sidebar.subheader("Configurable parameters")
+
+    model_name = st.sidebar.selectbox(
+        "Model Selector",
+        options=[
+            "AraGPT2-Base",
+            "AraGPT2-Medium",
+            "Aragpt2-Large",
+            "AraGPT2-Mega",
+        ],
+        index=0,
+    )
+
+    max_new_tokens = st.sidebar.number_input(
+        "Maximum length",
+        min_value=0,
+        max_value=1024,
+        value=100,
+        help="The maximum length of the sequence to be generated.",
+    )
+    temp = st.sidebar.slider(
+        "Temperature",
+        value=1.0,
+        min_value=0.1,
+        max_value=100.0,
+        help="The value used to module the next token probabilities.",
+    )
+    top_k = st.sidebar.number_input(
+        "Top k",
+        value=10,
+        help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
+    )
+    top_p = st.sidebar.number_input(
+        "Top p",
+        value=0.95,
+        help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
+    )
+    do_sample = st.sidebar.selectbox(
+        "Sampling?",
+        (True, False),
+        help="Whether or not to use sampling; use greedy decoding otherwise.",
+    )
+    num_beams = st.sidebar.number_input(
+        "Number of beams",
+        min_value=1,
+        max_value=10,
+        value=3,
+        help="The number of beams to use for beam search.",
+    )
+    repetition_penalty = st.sidebar.number_input(
+        "Repetition Penalty",
+        min_value=0.0,
+        value=3.0,
+        step=0.1,
+        help="The parameter for repetition penalty. 1.0 means no penalty",
+    )
+    no_repeat_ngram_size = st.sidebar.number_input(
+        "No Repear N-Gram Size",
+        min_value=0,
+        value=3,
+        help="If set to int > 0, all ngrams of that size can only occur once.",
+    )
+
+    st.write("#")
+
+    col = st.beta_columns(2)
+
+    col[0].image("images/AraGPT2.png", width=200)
+
+    st.markdown(
+        """
+
+        <h3 style="text-align:left;">AraGPT2 is GPT2 model trained from scratch on 77GB of Arabic text.</h3>
+        <h4 style="text-align:left;"> More details in our <a href="https://github.com/aub-mind/arabert/tree/master/aragpt2">repo</a>.</h4>
+
+        <p style="text-align:left;"><p>
+        <p style="text-align:left;">Use the generation paramters on the sidebar to adjust generation quality.</p>
+        <p style="text-align:right;"><p>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    # col[0].write(
+    #     "AraGPT2 is trained from screatch on 77GB of Arabic text. More details in our [repo](https://github.com/aub-mind/arabert/tree/master/aragpt2)."
+    # )
+    # st.write("## Generate Arabic Text")
+
+    st.markdown(
+        """
+        <style>
+        p, div, input, label, textarea{
+        text-align: right;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    prompt = st.text_area(
+        "Prompt",
+        "يحكى أن مزارعا مخادعا قام ببيع بئر الماء الموجود في أرضه لجاره مقابل مبلغ كبير من المال",
+    )
+    if st.button("Generate"):
+        with st.spinner("Generating..."):
+            generated_text = generator.generate(
+                prompt=prompt,
+                model_name=model_name,
+                max_new_tokens=max_new_tokens,
+                temperature=temp,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                do_sample=do_sample,
+                num_beams=num_beams,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+            )
+            st.write(generated_text)
+
+    st.markdown("---")
+    st.subheader("")
+    st.markdown(
+        """
+        <p style="text-align:left;"><p>
+        <h2 style="text-align:left;">Zero-Shot Question Answering</h2>
+
+        <p style="text-align:left;">Adjust the maximum length to closely match the expected output length. Setting the Sampling paramter to False is recommended</p>
+        <p style="text-align:left;"><p>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    question = st.text_input(
+        "Question", "من كان رئيس ألمانيا النازية في الحرب العالمية ��لثانية ؟"
+    )
+    is_date = st.checkbox("Help the model: Is the answer a date?")
+    if st.button("Answer"):
+
+        prompt = qa_prompt + question + qa_prompt_post
+        if is_date:
+            prompt += qa_prompt_post_year
+        else:
+            prompt += " : "
+        with st.spinner("Thinking..."):
+            answer = generator.generate(
+                prompt=prompt,
+                model_name=model_name,
+                max_new_tokens=max_new_tokens,
+                temperature=temp,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                do_sample=do_sample,
+                num_beams=num_beams,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+            )
+            st.write(answer)

backend/modeling_gpt2.py

@@ -0,0 +1,1196 @@
+# coding=utf-8
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+PyTorch OpenAI GPT-2 model.
+Adapted from https://github.com/huggingface/transformers/blob/v4.0.1/src/transformers/models/gpt2/modeling_gpt2.py
+and https://github.com/ghosthamlet/gpt2-ml-torch/blob/master/gpt2_ml_torch/modeling_gpt2.py
+"""
+
+
+import logging
+import os
+
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss, MSELoss
+
+
+
+from transformers.activations import ACT2FN
+from transformers import GPT2Config
+
+from transformers.modeling_utils import (
+    Conv1D,
+    PreTrainedModel,
+    SequenceSummary,
+    prune_conv1d_layer,
+    find_pruneable_heads_and_indices
+)
+
+from transformers import CONFIG_NAME, WEIGHTS_NAME, GPT2Config, GPT2Model
+
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    SequenceClassifierOutputWithPast
+)
+
+from transformers.file_utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    add_code_sample_docstrings,
+    replace_return_docstrings
+)
+
+# THe Difference from Transformers is code under _USE_GROVER
+_USE_GROVER = True
+
+logger = logging.getLogger(__name__)
+
+_CONFIG_FOR_DOC = "GPT2Config"
+_TOKENIZER_FOR_DOC = "GPT2Tokenizer"
+
+GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "gpt2",
+    "gpt2-medium",
+    "gpt2-large",
+    "gpt2-xl",
+    "distilgpt2",
+    # See all GPT-2 models at https://huggingface.co/models?filter=gpt2
+]
+
+logger.setLevel(logging.INFO)
+console = logging.StreamHandler()
+console.setLevel(logging.INFO)
+logger.addHandler(console)
+
+_GPT2_ML_TF_TO_TORCH = {
+        'LayerNorm_embed_norm': 'emb_norm',
+        'pos_embed': 'wpe.weight',
+        'word_embed': 'wte.weight',
+
+        'layer': 'h',
+        # Most importently This two layer norm must be put on the same position as gpt2-ml
+        # or generated data is bad, just repeat the last token
+        'LayerNorm_mlp_ln0': 'ln_1',
+        'LayerNorm_mlp_ln1': 'ln_2',
+        'intermediate': 'mlp.c_fc',
+        'output': 'mlp.c_proj',
+        'query_layer': 'attn.c_attn',
+        'key_layer': 'attn.c_attn',
+        'value_layer': 'attn.c_attn',
+        'context_projection_layer': 'attn.c_proj',
+
+        'gamma': 'weight',
+        'kernel': 'weight',
+        'beta': 'bias',
+        'bias': 'bias',
+}
+
+
+def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
+    # Construct model
+    if gpt2_config_file == "":
+        config = GPT2Config()
+    else:
+        config = GPT2Config.from_json_file(gpt2_config_file)
+    model = GPT2Model(config)
+
+    # Load weights from numpy
+    load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
+
+    # Save pytorch-model
+    pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
+    pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
+    print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
+    torch.save(model.state_dict(), pytorch_weights_dump_path)
+    print("Save configuration file to {}".format(pytorch_config_dump_path))
+    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
+        f.write(config.to_json_string())
+
+
+# XXX: MUST do like: convert_gpt2_checkpoint_to_pytorch('./model.ckpt-100000', './mega.json', './')
+#      https://github.com/tensorflow/models/issues/2675#issuecomment-516595597
+def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
+    """ Load tf checkpoints in a pytorch model
+    """
+    try:
+        import re
+        import tensorflow as tf
+    except ImportError:
+        logger.error(
+            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+            "https://www.tensorflow.org/install/ for installation instructions."
+        )
+        raise
+    tf_path = os.path.abspath(gpt2_checkpoint_path)
+    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
+    # Load weights from TF model
+    init_vars = tf.train.list_variables(tf_path)
+    names = []
+    arrays = []
+    for name, shape in init_vars:
+        logger.info("Loading TF weight {} with shape {}".format(name, shape))
+        array = tf.train.load_variable(tf_path, name)
+        names.append(name)
+        arrays.append(array.squeeze())
+
+    import copy
+    orig_model = copy.deepcopy(model)
+
+    for name, array in zip(names, arrays):
+        name = name[6:]  # skip "model/"
+        name = name.split("/")
+        pointer = model
+
+        attn_layer = ''
+        for m_name in name:
+            if re.fullmatch(r"[A-Za-z]+\d+", m_name):
+                scope_names = re.split(r"(\d+)", m_name)
+            else:
+                scope_names = [m_name]
+            sname = scope_names[0]
+
+            if sname == '' or sname == 'embeddings':
+                continue
+            elif sname not in _GPT2_ML_TF_TO_TORCH:
+                print('=========================================================')
+                logger.info('Skip var name {}'.format(scope_names))
+                pointer = None
+                break
+            else:
+                tname = _GPT2_ML_TF_TO_TORCH[sname]
+                if '.' in tname:
+                    parent, child = tname.split('.')
+                    pointer = getattr(pointer, parent)
+                    pointer = getattr(pointer, child)
+                else:
+                    pointer = getattr(pointer, tname)
+
+                if tname == 'attn.c_attn':
+                    attn_layer = sname
+
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
+                pointer = pointer[num]
+
+        if pointer is None:
+            continue
+        if attn_layer == '':
+            try:
+                assert pointer.shape == array.shape
+            except AssertionError as e:
+                e.args += (pointer.shape, array.shape)
+                raise
+        logger.info("Initialize PyTorch weight {}, {}, {}".format(name, array.mean(), pointer.mean()))
+        if attn_layer == '':
+            pointer.data = torch.from_numpy(array)
+        else:
+            shape = pointer.shape
+            d = torch.from_numpy(array)
+            is_bias = len(shape) == 1
+            end = int(shape[0 if is_bias else 1]/3)
+            m = dict(
+                    query_layer=0,
+                    key_layer=end,
+                    value_layer=end*2,
+                    )
+            start = m[attn_layer]
+            end = start + end
+            if is_bias:
+                pointer.data[start:end] = d
+            else:
+                pointer.data[:, start:end] = d
+        logger.info("Initialize PyTorch weight {}, {}, {}".format(name, array.mean(), pointer.mean()))
+
+    for name, params in orig_model.named_parameters():
+        for n, p in model.named_parameters():
+            if name == n:
+                if params.equal(p):
+                    print('--------------------------')
+                    print(' %s not changed!' % n)
+    return model
+
+
+class Attention(nn.Module):
+    def __init__(self, nx, n_ctx, config, scale=False, is_cross_attention=False):
+        super().__init__()
+
+        n_state = nx  # in Attention: n_state=768 (nx=n_embd)
+        # [switch nx => n_state from Block to Attention to keep identical to TF implem]
+        assert n_state % config.n_head == 0
+        self.register_buffer(
+            "bias", torch.tril(torch.ones((n_ctx, n_ctx), dtype=torch.uint8)).view(1, 1, n_ctx, n_ctx)
+        )
+        self.register_buffer("masked_bias", torch.tensor(-1e4))
+        self.n_head = config.n_head
+        self.split_size = n_state
+        self.scale = scale
+        self.is_cross_attention = is_cross_attention
+        if self.is_cross_attention:
+            self.c_attn = Conv1D(2 * n_state, nx)
+            self.q_attn = Conv1D(n_state, nx)
+        else:
+            self.c_attn = Conv1D(3 * n_state, nx)
+        self.c_proj = Conv1D(n_state, nx)
+        self.attn_dropout = nn.Dropout(config.attn_pdrop)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.n_head, self.split_size // self.n_head, self.pruned_heads
+        )
+        index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
+
+        # Prune conv1d layers
+        self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
+        self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
+
+        # Update hyper params
+        self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
+        self.n_head = self.n_head - len(heads)
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def _attn(self, q, k, v, attention_mask=None, head_mask=None, output_attentions=False):
+        w = torch.matmul(q, k)
+        if self.scale:
+            w = w / (float(v.size(-1)) ** 0.5)
+        nd, ns = w.size(-2), w.size(-1)
+
+        if not self.is_cross_attention:
+            # if only "normal" attention layer implements causal mask
+            mask = self.bias[:, :, ns - nd : ns, :ns]
+            w = torch.where(mask.bool(), w, self.masked_bias.to(w.dtype))
+
+        if attention_mask is not None:
+            # Apply the attention mask
+            w = w + attention_mask
+
+        w = nn.Softmax(dim=-1)(w)
+        w = self.attn_dropout(w)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            w = w * head_mask
+
+        outputs = [torch.matmul(w, v)]
+        if output_attentions:
+            outputs.append(w)
+        return outputs
+
+    def merge_heads(self, x):
+        x = x.permute(0, 2, 1, 3).contiguous()
+        new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
+        return x.view(*new_x_shape)  # in Tensorflow implem: fct merge_states
+
+    def split_heads(self, x, k=False):
+        new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
+        x = x.view(*new_x_shape)  # in Tensorflow implem: fct split_states
+        if k:
+            return x.permute(0, 2, 3, 1)  # (batch, head, head_features, seq_length)
+        else:
+            return x.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)
+
+    def forward(
+        self,
+        hidden_states,
+        layer_past=None,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        if encoder_hidden_states is not None:
+            assert hasattr(
+                self, "q_attn"
+            ), "If class is used as cross attention, the weights `q_attn` have to be defined. Please make sure to instantiate class with `Attention(..., is_cross_attention=True)`."
+            query = self.q_attn(hidden_states)
+            key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
+            attention_mask = encoder_attention_mask
+        else:
+            query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
+
+        query = self.split_heads(query)
+        key = self.split_heads(key, k=True)
+        value = self.split_heads(value)
+        if layer_past is not None:
+            past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1]  # transpose back cf below
+            key = torch.cat((past_key, key), dim=-1)
+            value = torch.cat((past_value, value), dim=-2)
+
+        if use_cache is True:
+            present = torch.stack((key.transpose(-2, -1), value))  # transpose to have same shapes for stacking
+        else:
+            present = (None,)
+
+        attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions)
+        a = attn_outputs[0]
+
+        a = self.merge_heads(a)
+        a = self.c_proj(a)
+        a = self.resid_dropout(a)
+
+        outputs = [a, present] + attn_outputs[1:]
+        return outputs  # a, present, (attentions)
+
+
+class MLP(nn.Module):
+    def __init__(self, n_state, config):  # in MLP: n_state=3072 (4 * n_embd)
+        super().__init__()
+        nx = config.n_embd
+        self.c_fc = Conv1D(n_state, nx)
+        self.c_proj = Conv1D(nx, n_state)
+        self.act = ACT2FN[config.activation_function]
+        self.dropout = nn.Dropout(config.resid_pdrop)
+
+    def forward(self, x):
+        h = self.act(self.c_fc(x))
+        h2 = self.c_proj(h)
+        return self.dropout(h2)
+
+
+class Block(nn.Module):
+    def __init__(self, n_ctx, config, scale=False):
+        super().__init__()
+        hidden_size = config.n_embd
+        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = Attention(hidden_size, n_ctx, config, scale)
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        if config.add_cross_attention:
+            self.crossattention = Attention(hidden_size, n_ctx, config, scale, is_cross_attention=True)
+            self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = MLP(inner_dim, config)
+
+    def forward(
+        self,
+        hidden_states,
+        layer_past=None,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        attn_outputs = self.attn(
+            hidden_states,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
+        outputs = attn_outputs[1:]
+        # residual connection
+        hidden_states = attn_output + hidden_states
+
+        if encoder_hidden_states is not None:
+            # add one self-attention block for cross-attention
+            assert hasattr(
+                self, "crossattention"
+            ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
+            cross_attn_outputs = self.crossattention(
+                self.ln_cross_attn(hidden_states),
+                attention_mask=attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                output_attentions=output_attentions,
+            )
+            attn_output = cross_attn_outputs[0]
+            # residual connection
+            hidden_states = hidden_states + attn_output
+            outputs = outputs + cross_attn_outputs[2:]  # add cross attentions if we output attention weights
+
+        feed_forward_hidden_states = self.mlp(self.ln_1(hidden_states))
+        # residual connection
+        hidden_states = hidden_states + feed_forward_hidden_states
+
+        hidden_states = self.ln_2(hidden_states)
+
+        outputs = [hidden_states] + outputs
+        return outputs  # hidden_states, present, (attentions, cross_attentions)
+
+
+class GPT2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = GPT2Config
+    load_tf_weights = load_tf_weights_in_gpt2
+    base_model_prefix = "transformer"
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, (nn.Linear, nn.Embedding, Conv1D)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+@dataclass
+class GPT2DoubleHeadsModelOutput(ModelOutput):
+    """
+    Base class for outputs of models predicting if two sentences are consecutive or not.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided):
+            Language modeling loss.
+        mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`mc_labels` is provided):
+            Multiple choice classification loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        mc_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
+            Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
+        past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(2,
+            batch_size, num_heads, sequence_length, embed_size_per_head)`).
+
+            Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
+            :obj:`past_key_values` input) to speed up sequential decoding.
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    mc_loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    mc_logits: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+GPT2_START_DOCSTRING = r"""
+
+    This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
+    methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
+    pruning heads etc.)
+
+    This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
+    subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
+    general usage and behavior.
+
+    Parameters:
+        config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
+            weights.
+"""
+
+GPT2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
+            :obj:`input_ids_length` = ``sequence_length`` if :obj:`past_key_values` is ``None`` else
+            ``past_key_values[0].shape[-2]`` (``sequence_length`` of input past key value states). Indices of input
+            sequence tokens in the vocabulary.
+
+            If :obj:`past_key_values` is used, only ``input_ids`` that do not have their past calculated should be
+            passed as ``input_ids``.
+
+            Indices can be obtained using :class:`~transformers.GPT2Tokenizer`. See
+            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
+            details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        past_key_values (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
+            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
+            :obj:`past_key_values` output below). Can be used to speed up sequential decoding. The ``input_ids`` which
+            have their past given to this model should not be passed as ``input_ids`` as they have already been
+            computed.
+        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            `What are attention masks? <../glossary.html#attention-mask>`__
+        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`, `optional`):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
+            1]``:
+
+            - 0 corresponds to a `sentence A` token,
+            - 1 corresponds to a `sentence B` token.
+
+            `What are token type IDs? <../glossary.html#token-type-ids>`_
+        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
+            config.max_position_embeddings - 1]``.
+
+            `What are position IDs? <../glossary.html#position-ids>`_
+        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
+            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
+            vectors than the model's internal embedding lookup matrix.
+
+            If :obj:`past_key_values` is used, optionally only the last :obj:`inputs_embeds` have to be input (see
+            :obj:`past_key_values`).
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        output_attentions (:obj:`bool`, `optional`):
+            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
+            tensors for more detail.
+        output_hidden_states (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
+    GPT2_START_DOCSTRING,
+)
+class GPT2Model(GPT2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.wpe = nn.Embedding(config.n_positions, config.n_embd)
+        if _USE_GROVER:
+            self.emb_norm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+
+        self.drop = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
+        if not _USE_GROVER:
+            self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
+        """
+        for layer, heads in heads_to_prune.items():
+            self.h[layer].attn.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="gpt2",
+        output_type=BaseModelOutputWithPastAndCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    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,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+            batch_size = input_ids.shape[0]
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size = inputs_embeds.shape[0]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids.view(-1, input_shape[-1])
+        if position_ids is not None:
+            position_ids = position_ids.view(-1, input_shape[-1])
+
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = [None] * len(self.h)
+        else:
+            past_length = past_key_values[0][0].size(-2)
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+
+        # Attention mask.
+        if attention_mask is not None:
+            assert batch_size > 0, "batch_size has to be defined and > 0"
+            attention_mask = attention_mask.view(batch_size, -1)
+            # We create a 3D attention mask from a 2D tensor mask.
+            # Sizes are [batch_size, 1, 1, to_seq_length]
+            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+            # this attention mask is more simple than the triangular masking of causal attention
+            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+            attention_mask = attention_mask[:, None, None, :]
+
+            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+            # masked positions, this operation will create a tensor which is 0.0 for
+            # positions we want to attend and -10000.0 for masked positions.
+            # Since we are adding it to the raw scores before the softmax, this is
+            # effectively the same as removing these entirely.
+            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * -10000.0
+
+        # If a 2D ou 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.add_cross_attention and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # head_mask has shape n_layer x batch x n_heads x N x N
+        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = inputs_embeds + position_embeds
+
+        if token_type_ids is not None:
+            token_type_embeds = self.wte(token_type_ids)
+            hidden_states = hidden_states + token_type_embeds
+
+        hidden_states = self.drop(hidden_states)
+        if _USE_GROVER:
+            hidden_states = self.emb_norm(hidden_states)
+        output_shape = input_shape + (hidden_states.size(-1),)
+
+        presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+        all_hidden_states = () if output_hidden_states else None
+        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
+
+            if getattr(self.config, "gradient_checkpointing", False):
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # checkpointing only works with tuple returns, not with lists
+                        return tuple(output for output in module(*inputs, use_cache, output_attentions))
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    layer_past,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    attention_mask=attention_mask,
+                    head_mask=head_mask[i],
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states, present = outputs[:2]
+            if use_cache is True:
+                presents = presents + (present,)
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (outputs[2],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (outputs[3],)
+
+        if not _USE_GROVER:
+            hidden_states = self.ln_f(hidden_states)
+
+        hidden_states = hidden_states.view(*output_shape)
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
+
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input
+    embeddings).
+    """,
+    GPT2_START_DOCSTRING,
+)
+class GPT2LMHeadModel(GPT2PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = GPT2Model(config)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        if past:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past,
+            "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+    @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="gpt2",
+        output_type= CausalLMOutputWithCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    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,
+    ):
+        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
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            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,
+        )
+        hidden_states = transformer_outputs[0]
+
+        lm_logits = self.lm_head(hidden_states)
+
+        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,
+        )
+
+
+@add_start_docstrings(
+    """
+The GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
+RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
+input embeddings, the classification head takes as input the input of a specified classification token index in the
+input sequence).
+""",
+    GPT2_START_DOCSTRING,
+)
+class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        config.num_labels = 1
+        self.transformer = GPT2Model(config)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.multiple_choice_head = SequenceSummary(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        if past:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past,
+            "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+    @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=GPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
+    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,
+        mc_token_ids=None,
+        labels=None,
+        mc_labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        r"""
+        mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input):
+            Index of the classification token in each input sequence. Selected in the range ``[0, input_ids.size(-1) -
+            1[``.
+        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 ``[-1, 0, ..., config.vocab_size]`` All labels set to
+            ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
+        mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`):
+            Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
+            num_choices]`` where `num_choices` is the size of the second dimension of the input tensors. (see
+            `input_ids` above)
+
+        Return:
+
+        Example::
+
+            >>> import torch
+            >>> from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel
+
+            >>> tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+            >>> model = GPT2DoubleHeadsModel.from_pretrained('gpt2')
+
+            >>> # Add a [CLS] to the vocabulary (we should train it also!)
+            >>> num_added_tokens = tokenizer.add_special_tokens({'cls_token': '[CLS]'})
+
+            >>> embedding_layer = model.resize_token_embeddings(len(tokenizer))  # Update the model embeddings with the new vocabulary size
+
+            >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
+            >>> encoded_choices = [tokenizer.encode(s) for s in choices]
+            >>> cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices]
+
+            >>> input_ids = torch.tensor(encoded_choices).unsqueeze(0)  # Batch size: 1, number of choices: 2
+            >>> mc_token_ids = torch.tensor([cls_token_location])  # Batch size: 1
+
+            >>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
+            >>> lm_logits = outputs.lm_logits
+            >>> mc_logits = outputs.mc_logits
+
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = transformer_outputs[0]
+
+        lm_logits = self.lm_head(hidden_states)
+        mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
+
+        mc_loss = None
+        if mc_labels is not None:
+            loss_fct = CrossEntropyLoss()
+            mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
+        lm_loss = None
+        if labels is not None:
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits, mc_logits) + transformer_outputs[1:]
+            if mc_loss is not None:
+                output = (mc_loss,) + output
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return GPT2DoubleHeadsModelOutput(
+            loss=lm_loss,
+            mc_loss=mc_loss,
+            logits=lm_logits,
+            mc_logits=mc_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The GPT2 Model transformer with a sequence classification head on top (linear layer).
+
+    :class:`~transformers.GPT2ForSequenceClassification` uses the last token in order to do the classification, as
+    other causal models (e.g. GPT-1) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    :obj:`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each
+    row. If no :obj:`pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot
+    guess the padding tokens when :obj:`inputs_embeds` are passed instead of :obj:`input_ids`, it does the same (take
+    the last value in each row of the batch).
+    """,
+    GPT2_START_DOCSTRING,
+)
+class GPT2ForSequenceClassification(GPT2PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = GPT2Model(config)
+        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="microsoft/dialogrpt",
+        output_type=SequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    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,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size, sequence_length = input_ids.shape[:2]
+        else:
+            batch_size, sequence_length = inputs_embeds.shape[:2]
+
+        assert (
+            self.config.pad_token_id is not None or batch_size == 1
+        ), "Cannot handle batch sizes > 1 if no padding token is defined."
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
+            else:
+                sequence_lengths = -1
+                logger.warning(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    f"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+                )
+
+        pooled_logits = logits[range(batch_size), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(pooled_logits.view(-1), labels.to(self.dtype).view(-1))
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+

{pages → backend}/processor.py

@@ -122,8 +122,7 @@ def write():
 
     st.sidebar.title("Model Selector")
     model_selector = st.sidebar.selectbox(
-        """Select None to enable further filters""",
-        options=MODELS_to_SELECT,
+        """Select None to enable further filters""", options=MODELS_to_SELECT, index=3
     )
     if model_selector == "None":
         keep_emojis = st.sidebar.checkbox("Keep emojis", False)

backend/services.py

@@ -0,0 +1,174 @@
+import json
+import os
+
+import requests
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, pipeline, set_seed
+from .modeling_gpt2 import GPT2LMHeadModel as GROVERLMHeadModel
+from .preprocess import ArabertPreprocessor
+
+
+# Taken and Modified from https://huggingface.co/spaces/flax-community/chef-transformer/blob/main/app.py
+class TextGeneration:
+    def __init__(self):
+        self.debug = False
+        self.generation_pipline = {}
+        self.preprocessor = ArabertPreprocessor(model_name="aragpt2-mega")
+        self.tokenizer = GPT2Tokenizer.from_pretrained(
+            "aubmindlab/aragpt2-mega", use_fast=False
+        )
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+        self.API_KEY = os.getenv("API_KEY")
+        self.headers = {"Authorization": f"Bearer {self.API_KEY}"}
+        # self.model_names_or_paths = {
+        #     "aragpt2-medium": "D:/ML/Models/aragpt2-medium",
+        #     "aragpt2-base": "D:/ML/Models/aragpt2-base",
+        # }
+        self.model_names_or_paths = {
+            "aragpt2-medium": "https://huggingface.co/aubmindlab/aragpt2-medium",
+            "aragpt2-base": "https://huggingface.co/aubmindlab/aragpt2-base",
+            "aragpt2-large": "https://huggingface.co/aubmindlab/aragpt2-large",
+            "aragpt2-mega": "https://huggingface.co/aubmindlab/aragpt2-mega",
+        }
+        set_seed(42)
+
+    def load_pipeline(self):
+        for model_name, model_path in self.model_names_or_paths.items():
+            if "base" in model_name or "medium" in model_name:
+                self.generation_pipline[model_name] = pipeline(
+                    "text-generation",
+                    model=GPT2LMHeadModel.from_pretrained(model_path),
+                    tokenizer=self.tokenizer,
+                    device=-1,
+                )
+            else:
+                self.generation_pipline[model_name] = pipeline(
+                    "text-generation",
+                    model=GROVERLMHeadModel.from_pretrained(model_path),
+                    tokenizer=self.tokenizer,
+                    device=-1,
+                )
+
+    def load(self):
+        if not self.debug:
+            self.load_pipeline()
+
+    def generate(
+        self,
+        model_name,
+        prompt,
+        max_new_tokens: int,
+        temperature: float,
+        top_k: int,
+        top_p: float,
+        repetition_penalty: float,
+        no_repeat_ngram_size: int,
+        do_sample: bool,
+        num_beams: int,
+    ):
+        prompt = self.preprocessor.preprocess(prompt)
+        return_full_text = False
+        return_text = True
+        num_return_sequences = 1
+        pad_token_id = 0
+        eos_token_id = 0
+        input_tok = self.tokenizer.tokenize(prompt)
+        max_length = len(input_tok) + max_new_tokens
+        if max_length > 1024:
+            max_length = 1024
+        if not self.debug:
+            generated_text = self.generation_pipline[model_name.lower()](
+                prompt,
+                max_length=max_length,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+                return_full_text=return_full_text,
+                return_text=return_text,
+                do_sample=do_sample,
+                num_beams=num_beams,
+                num_return_sequences=num_return_sequences,
+            )[0]["generated_text"]
+        else:
+            generated_text = self.generate_by_query(
+                prompt,
+                model_name,
+                max_length=max_length,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+                return_full_text=return_full_text,
+                return_text=return_text,
+                do_sample=do_sample,
+                num_beams=num_beams,
+                num_return_sequences=num_return_sequences,
+            )
+            # print(generated_text)
+            if isinstance(generated_text, dict):
+                if "error" in generated_text:
+                    if "is currently loading" in generated_text["error"]:
+                        return f"Model is currently loading, estimated time is {generated_text['estimated_time']}"
+                    return generated_text["error"]
+                else:
+                    return "Something happened 🤷‍♂️!!"
+            else:
+                generated_text = generated_text[0]["generated_text"]
+        return self.preprocessor.unpreprocess(generated_text)
+
+    def query(self, payload, model_name):
+        data = json.dumps(payload)
+        url = (
+            "https://api-inference.huggingface.co/models/aubmindlab/"
+            + model_name.lower()
+        )
+        response = requests.request("POST", url, headers=self.headers, data=data)
+        return json.loads(response.content.decode("utf-8"))
+
+    def generate_by_query(
+        self,
+        prompt: str,
+        model_name: str,
+        max_length: int,
+        temperature: float,
+        top_k: int,
+        top_p: float,
+        repetition_penalty: float,
+        no_repeat_ngram_size: int,
+        pad_token_id: int,
+        eos_token_id: int,
+        return_full_text: int,
+        return_text: int,
+        do_sample: bool,
+        num_beams: int,
+        num_return_sequences: int,
+    ):
+        payload = {
+            "inputs": prompt,
+            "parameters": {
+                "max_length ": max_length,
+                "top_k": top_k,
+                "top_p": top_p,
+                "temperature": temperature,
+                "repetition_penalty": repetition_penalty,
+                "no_repeat_ngram_size": no_repeat_ngram_size,
+                "pad_token_id": pad_token_id,
+                "eos_token_id": eos_token_id,
+                "return_full_text": return_full_text,
+                "return_text": return_text,
+                "pad_token_id": pad_token_id,
+                "do_sample": do_sample,
+                "num_beams": num_beams,
+                "num_return_sequences": num_return_sequences,
+            },
+            "options": {
+                "use_cache": True,
+            },
+        }
+        return self.query(payload, model_name)

requirements.txt

@@ -4,4 +4,6 @@ python-bidi==0.4.2
 PyArabic
 farasapy==0.0.14
 emoji==1.4.2
-awesome_streamlit
+awesome_streamlit
+torch
+transformers==4.10.0

test.py

@@ -0,0 +1,10 @@
+#%%
+from transformers import GPT2Tokenizer
+
+# %%
+tok = GPT2Tokenizer.from_pretrained("D:/ML/Models/aragpt2-medium", use_fast=False)
+# %%
+tok.pad_token = tok.eos_token
+#%%
+tok.pad_token_id = [tok.eos_token_id]
+# %%