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config.json

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+{
+  "architectures": [
+      "GptBertFoCausalLM"
+  ],
+  "auto_map": {
+      "AutoConfig": "configuration_gptbert.GptBertConfig",
+      "AutoModel": "modeling_gptbert.GptBertModel",
+      "AutoModelForCausalLM": "modeling_gptbert.GptBertForCausalLM",
+      "AutoModelForMaskedLM": "modeling_gptbert.GptBertForMaskedLM",
+      "AutoModelForSequenceClassification": "modeling_gptbert.GptBertForSequenceClassification",
+      "AutoModelForTokenClassification": "modeling_gptbert.GptBertForTokenClassification",
+      "AutoModelForQuestionAnswering": "modeling_gptbert.GptBertForQuestionAnswering",
+      "AutoModelForMultipleChoice": "modeling_gptbert.GptBertForMultipleChoice"
+  },
+  "attention_dropout": 0.0,
+  "attention_output_dropout_p": 0.0,
+  "attention_inter_norm_affine": false,
+  "attention_inter_norm_eps": 1e-07,
+  "attention_pre_norm_affine": false,
+  "attention_pre_norm_eps": 1e-07,
+  "attention_probabilities_dropout_p": 0.0,
+  "classifier_post_norm_affine": false,
+  "classifier_post_norm_eps": 1e-07,
+  "classifier_pre_norm_affine": false,
+  "classifier_pre_norm_eps": 1e-07,
+  "d_qk": 64,
+  "d_v": 64,
+  "embedding_dropout_p": 0.1,
+  "feed_forward_dropout_p": 0.0,
+  "feed_forward_inter_norm_affine": false,
+  "feed_forward_inter_norm_eps": 1e-07,
+  "feed_forward_pre_norm_affine": false,
+  "feed_forward_pre_norm_eps": 1e-07,
+  "hidden_size": 640,
+  "intermediate_size": 1664,
+  "max_sequence_length": 16384,
+  "num_attention_heads": 10,
+  "num_kv_heads": 10,
+  "num_layers": 24,
+  "rope_theta": 160000,
+  "vocab_size": 51200,
+  "word_norm_affine": true,
+  "word_norm_eps": 1e-07,
+  "short_long_ratio": 4,
+  "window_length": 8192,
+  "is_decoder": false,
+  "not_flex": true,
+  "hidden_dropout_prob": 0.2
+}

configuration_gptbert.py

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+from __future__ import annotations
+
+import json
+from pathlib import Path
+import copy
+from transformers.configuration_utils import PretrainedConfig
+
+
+class GptBertConfig(PretrainedConfig):
+
+    def __init__(
+        self,
+        config_file: Path | str | None = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.model: str
+
+        # General information
+        self.model = "base"
+
+        # Vocabulary
+        self.vocab_size = 16384
+        self.max_sequence_length = 512
+
+        # Model dimensions
+        self.hidden_size = 768
+        self.intermediate_size = 2048
+        self.num_attention_heads = 12
+        self.num_layers = 12
+        self.d_qk = 64
+
+        # Dropout probabilities
+        self.embedding_dropout_p = 0.1
+        self.attention_probabilities_dropout_p = 0.1
+        self.attention_output_dropout_p = 0.1
+        self.feed_forward_dropout_p = 0.1
+        self.attention_dropout = 0.1
+        self.hidden_dropout_prob = 0.2
+
+        # Position Emebedding
+        self.rope_theta = 160_000
+
+        # Norms
+        self.word_norm_eps = 1e-7
+        self.word_norm_affine = False
+
+        self.attention_pre_norm_eps = 1e-7
+        self.attention_pre_norm_affine = False
+
+        self.attention_inter_norm_eps = 1e-7
+        self.attention_inter_norm_affine = True
+
+        self.feed_forward_pre_norm_eps = 1e-7
+        self.feed_forward_pre_norm_affine = False
+
+        self.feed_forward_inter_norm_eps = 1e-7
+        self.feed_forward_inter_norm_affine = False
+
+        self.classifier_pre_norm_eps = 1e-7
+        self.classifier_pre_norm_affine = False
+
+        self.classifier_post_norm_eps = 1e-7
+        self.classifier_post_norm_affine = False
+
+        if config_file is not None:
+            if type(config_file) is str:
+                config_file = Path(config_file)
+            assert type(config_file) is not Path, "The config_file should either be a Path or str"
+            with config_file.open("r") as file:
+                config = json.load(file)
+
+            for attr, value in config.items():
+                if isinstance(value, str):
+                    value = value.lower()
+                setattr(self, attr, value)
+
+        for attr, value in kwargs.items():
+            if isinstance(value, str):
+                value = value.lower()
+            setattr(self, attr, value)
+
+    def __repr__(self) -> str:
+        return str(self.to_json_string())
+
+    def to_dict(self) -> dict:
+        """Serializes this instance to a Python dictionary."""
+        output: dict
+
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self) -> str:
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+    def to_json_file(self, json_file_path: Path | str) -> None:
+        """Save this instance to a json file."""
+        if isinstance(json_file_path, str):
+            json_file_path: Path = Path(json_file_path)
+        with json_file_path.open("w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())
+
+    @classmethod
+    def create_base_config(cls, json_file_path: Path | str | None = None) -> GptBertConfig:
+        config: GptBertConfig
+
+        config = GptBertConfig()
+        if json_file_path is not None:
+            config.to_json_file(json_file_path)
+
+        return config

modeling_gptbert.py

@@ -0,0 +1,1216 @@
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch import _softmax_backward_data as _softmax_backward_data
+
+from functools import partial
+
+from .configuration_gptbert import GptBertConfig
+from transformers.modeling_utils import PreTrainedModel
+from transformers.activations import gelu_new
+from transformers.modeling_outputs import (
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+    BaseModelOutput,
+    CausalLMOutput
+)
+import math
+from typing import TYPE_CHECKING, Optional, Union, Tuple, List
+
+try:
+    from torch.nn.attention.flex_attention import flex_attention, create_block_mask
+except ImportError:
+    pass
+
+
+class ModelOutput:
+
+    def __init__(
+        self,
+        logits: torch.Tensor | None = None,
+        loss: torch.Tensor | float | None = None,
+        perplexity: torch.Tensor | float | None = None,
+        accuracy: float | None = None,
+        z_loss: torch.Tensor | float | None = None,
+        **kwargs
+    ):
+        self.logits: torch.Tensor | None
+        self.loss: torch.Tensor | float | None
+        self.perplexity: torch.Tensor | float | None
+        self.accuracy: float | None
+        self.z_loss: torch.Tensor | float | None
+
+        self.logits = logits
+        self.loss = loss
+        self.perplexity = perplexity
+        self.accuracy = accuracy
+        self.z_loss = z_loss
+
+        for attr, value in kwargs.items():
+            setattr(self, attr, value)
+
+
+class CastedLinear(nn.Linear):
+
+    def __init__(self, in_features, out_features, bias):
+        super().__init__(in_features, out_features, bias=bias)
+
+    def reset_parameters(self) -> None:
+        std: float = math.sqrt(2.0 / (self.in_features + self.out_features))
+        nn.init.trunc_normal_(self.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, x):
+        return F.linear(x, self.weight.type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class CastedLinearIn(nn.Linear):
+
+    def __init__(self, in_features, out_features, bias):
+        super().__init__(in_features, out_features, bias=bias)
+        self.scale = nn.Parameter(torch.ones(in_features))
+
+    def reset_parameters(self) -> None:
+        std: float = math.sqrt(2.0 / (self.in_features + self.out_features))
+        nn.init.trunc_normal_(self.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, x):
+        return F.linear(x, (self.weight * (self.scale + 1.0).unsqueeze(0)).type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class CastedLinearOut(nn.Linear):
+
+    def __init__(self, in_features, out_features, bias):
+        super().__init__(in_features, out_features, bias=bias)
+        self.scale = nn.Parameter(torch.ones(out_features))
+
+    def reset_parameters(self) -> None:
+        std: float = math.sqrt(2.0 / (self.in_features + self.out_features))
+        nn.init.trunc_normal_(self.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, x):
+        return F.linear(x, (self.scale.unsqueeze(1) * self.weight).type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class MultiCastedLinearOrtho(nn.Module):
+
+    def __init__(self, in_features, out_features, bias):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+
+        self.weights = nn.ParameterList()
+        for out_feature in out_features:
+            self.weights.append(nn.Parameter(torch.empty((out_feature, in_features))))
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(sum(out_features)))
+        else:
+            self.bias = self.register_parameter("bias", None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        for i, weight in enumerate(self.weights):
+            std: float = math.sqrt(2.0 / (self.in_features + self.out_features[i]))
+            nn.init.trunc_normal_(weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, x):
+        return F.linear(x, torch.cat([weight for weight in self.weights], dim=0).type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class MultiCastedLinearOrthoIn(nn.Module):
+
+    def __init__(self, in_features, out_features, bias):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+
+        self.weights = nn.ParameterList()
+        for out_feature in out_features:
+            self.weights.append(nn.Parameter(torch.empty((out_feature, in_features))))
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(sum(out_features)))
+        else:
+            self.bias = self.register_parameter("bias", None)
+
+        self.scale = nn.Parameter(torch.ones(in_features))
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        for weight in self.weights:
+            std = 0.5 * (self.in_features ** -0.5)
+            bound = (3 ** 0.5) * std
+            with torch.no_grad():
+                weight.uniform_(-bound, bound)
+
+    def forward(self, x):
+        return F.linear(x, (torch.cat([weight for weight in self.weights], dim=0) * (self.scale + 1.0).unsqueeze(0)).type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class MultiCastedLinearOrthoOut(nn.Module):
+
+    def __init__(self, in_features, out_features, bias):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+
+        self.weights = nn.ParameterList()
+        for out_feature in out_features:
+            self.weights.append(nn.Parameter(torch.empty((out_feature, in_features))))
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(sum(out_features)))
+        else:
+            self.bias = self.register_parameter("bias", None)
+
+        self.scale = nn.Parameter(torch.ones(sum(out_features)))
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        for weight in self.weights:
+            std = 0.5 * (self.in_features ** -0.5)
+            bound = (3 ** 0.5) * std
+            with torch.no_grad():
+                weight.uniform_(-bound, bound)
+
+    def forward(self, x):
+        return F.linear(x, (self.scale.unsqueeze(1) * torch.cat([weight for weight in self.weights], dim=0)).type_as(x), bias=self.bias.type_as(x) if self.bias is not None else None)
+
+
+class GeGLU(nn.Module):
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        x = x * gelu_new(gate)
+        return x
+
+
+class MaskedSoftmax(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: torch.Tensor, mask: torch.BoolTensor, dim: int) -> torch.Tensor:
+        ctx.dim: int
+
+        ctx.dim = dim
+        x.masked_fill_(mask, float('-inf'))
+        x = torch.softmax(x, ctx.dim)
+        x.masked_fill_(mask, 0.0)
+        ctx.save_for_backward(x)
+        return x
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> tuple[torch.Tensor, None, None]:
+        output: torch.Tensor
+
+        output, = ctx.saved_tensors
+        inputGrad: torch.Tensor = _softmax_backward_data(grad_output, output, ctx.dim, output.dtype)
+        return inputGrad, None, None
+
+
+class Encoder(nn.Module):
+
+    def __init__(self, config) -> None:
+        super().__init__()
+
+        self.layers: nn.ModuleList[Layer]
+
+        self.layers = nn.ModuleList([Layer(config, i) for i in range(config.num_layers)])
+
+        for i, layer in enumerate(self.layers):
+            for weight in layer.mlp.up_proj.weights:
+                weight.data *= math.sqrt(1.0 / (2.0 * (i + 1)))
+            layer.mlp.down_proj.weight.data *= math.sqrt(1.0 / (2.0 * (i + 1)))
+
+        self.short_long_ratio = config.short_long_ratio
+
+    def set_window_length(self, config) -> None:
+        for i, layer in enumerate(self.layers):
+            if (i+1) % self.short_long_ratio == 0:
+                layer.set_window_length(config.window_length, config.not_flex)
+            else:
+                layer.set_window_length(256, config.not_flex)
+
+    def forward(self, hidden_layer: torch.Tensor, embeddings: torch.Tensor, mask: torch.Tensor | None = None) -> torch.Tensor:
+        hidden_layer: List[torch.Tensor]
+        attention_probs: List[torch.Tensor]
+
+        hidden_states = []
+        attention_probs = []
+        v1 = None
+
+        for layer in self.layers:
+            hidden_layer, v1, attention_p = layer(hidden_layer, embeddings, v1, mask)
+            hidden_states.append(hidden_layer)
+            attention_probs.append(attention_p)
+
+        return hidden_states, attention_probs
+
+
+class Layer(nn.Module):
+
+    def __init__(self, config, layer_idx: int) -> None:
+        super().__init__()
+
+        self.attention: SelfAttention
+        self.mlp: FeedForward
+
+        self.attention = SelfAttention(config, layer_idx)
+        self.mlp = FeedForward(config)
+        self.lambdas = nn.Parameter(torch.tensor([0., 0., 1., 0., 1., 0.]))
+
+    def set_window_length(self, window_length: int, not_flex: bool) -> None:
+        self.attention.set_window_length(window_length, not_flex)
+
+    def forward(self, hidden_layer: torch.Tensor, embeddings: torch.Tensor, v1: torch.Tensor | None, mask: torch.Tensor | None = None) -> Tuple[torch.Tensor, torch.Tensor]:
+        output: torch.Tensor
+        attention_p: torch.Tensor
+
+        attention_output = (1 - self.lambdas[0]) * hidden_layer + self.lambdas[0] * embeddings
+        qk_layer = (1 - self.lambdas[1]) * hidden_layer + self.lambdas[1] * embeddings
+        mlp_layer = F.softplus(self.lambdas[2]) * ((1 - self.lambdas[3]) * hidden_layer + self.lambdas[3] * embeddings)
+
+        attention_output, v1, attention_p = self.attention(attention_output, qk_layer, v1, mask)
+        mlp_layer = mlp_layer + attention_output
+        hidden_layer = F.softplus(self.lambdas[4]) * ((1 - self.lambdas[5]) * hidden_layer + self.lambdas[5] * embeddings)
+        output = hidden_layer + attention_output + self.mlp(mlp_layer)
+
+        return output, v1, attention_p
+
+
+class Embedding(nn.Module):
+
+    def __init__(self, config) -> None:
+        super().__init__()
+
+        assert hasattr(config, "vocab_size"), "The config must have a vocab_size attribute!"
+        assert hasattr(config, "hidden_size"), "The config must have a hidden_size attribute!"
+        assert hasattr(config, "embedding_dropout_p"), "The model must have a embedding_dropout_p attribute!"
+
+        self.word_embedding: nn.Embedding
+        self.word_norm: nn.LayerNorm
+        self.dropout: nn.Dropout
+
+        self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.word_norm = nn.LayerNorm(config.hidden_size, eps=config.word_norm_eps, elementwise_affine=False, bias=False)
+        self.word_scale = nn.Parameter(torch.zeros(config.hidden_size))
+
+        self.dropout = nn.Dropout(config.embedding_dropout_p)
+
+        self.initialize(config.hidden_size, config.vocab_size)
+
+    @torch.no_grad()
+    def initialize(self, hidden_size: int, vocab_size: int) -> None:
+        std: float
+
+        std = math.sqrt(2.0 / (hidden_size + vocab_size))
+        nn.init.trunc_normal_(self.word_embedding.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
+        word_embedding: torch.Tensor
+
+        word_embedding = self.word_embedding(input_ids)
+        word_embedding = self.word_norm(word_embedding)
+        word_embedding = (word_embedding * (self.word_scale + 1.0).unsqueeze(0).unsqueeze(0))
+
+        return self.dropout(word_embedding)
+
+
+class MaskClassifier(nn.Module):
+
+    def __init__(self, config, embedding_weights: nn.Parameter) -> None:
+        super().__init__()
+
+        self.projection: CastedLinear
+        self.emb2vocab: CastedLinear
+        self.pre_norm: nn.LayerNorm
+        self.post_norm: nn.LayerNorm
+
+        self.pre_norm = nn.LayerNorm(config.hidden_size, eps=config.classifier_pre_norm_eps, elementwise_affine=config.classifier_pre_norm_affine)
+        self.projection = CastedLinearIn(config.hidden_size, config.hidden_size, bias=False)
+        self.post_norm = nn.LayerNorm(config.hidden_size, eps=config.classifier_post_norm_eps, elementwise_affine=config.classifier_post_norm_affine)
+        self.emb2vocab = CastedLinearIn(config.hidden_size, config.vocab_size, bias=True)
+
+        self.initialize(config.hidden_size, config.vocab_size, embedding_weights)
+
+    @torch.no_grad()
+    def initialize(self, hidden_size: int, vocab_size: int, embedding_weights: nn.Parameter) -> None:
+        proj_std: float = math.sqrt(2.0 / (hidden_size + 4*hidden_size))
+
+        nn.init.trunc_normal_(self.projection.weight, mean=0.0, std=proj_std, a=-2*proj_std, b=2*proj_std)
+        self.emb2vocab.weight = embedding_weights
+        self.emb2vocab.bias.zero_()
+
+    def project(self, hidden_layer: torch.Tensor) -> torch.Tensor:
+        projection: torch.Tensor
+
+        projection = self.projection(hidden_layer)
+        projection = gelu_new(projection)
+        projection = self.post_norm(projection)
+
+        return projection
+
+    def calculate_output(self, hidden_layer: torch.Tensor) -> torch.Tensor:
+        return self.emb2vocab(hidden_layer)
+
+    def forward(self, hidden_layer: torch.Tensor, labels: torch.Tensor | None = None) -> torch.Tensor:
+        output: torch.Tensor
+
+        if labels is not None:
+            hidden_layer = torch.index_select(hidden_layer.flatten(0, 1), 0, torch.nonzero(labels.flatten() != -100).squeeze())
+
+        hidden_layer = self.pre_norm(hidden_layer)
+        hidden_layer = self.project(hidden_layer)
+        output = self.calculate_output(hidden_layer)
+
+        return output
+
+
+class SelfAttention(nn.Module):
+
+    def __init__(self, config, layer_idx) -> None:
+        super().__init__()
+        self.d_qk = config.d_qk
+        self.d_v = config.d_v
+        self.num_attention_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_kv_heads
+        self.hidden_size = config.hidden_size
+
+        self.q_out_dim = self.d_qk * self.num_attention_heads
+        self.k_out_dim = self.d_qk * self.num_kv_heads
+        self.v_out_dim = self.d_v * self.num_kv_heads
+
+        self.qk_proj = MultiCastedLinearOrthoIn(self.hidden_size, [self.q_out_dim, self.k_out_dim], bias=False)
+        self.v_proj = CastedLinearIn(self.hidden_size, self.v_out_dim, bias=False)
+        self.out_proj = CastedLinearIn(self.d_v*self.num_attention_heads, self.hidden_size, bias=False)
+
+        self.pre_v_norm = nn.LayerNorm(config.hidden_size, eps=config.attention_pre_norm_eps, elementwise_affine=config.attention_pre_norm_affine)
+        self.pre_qk_norm = nn.LayerNorm(config.hidden_size, eps=config.attention_pre_norm_eps, elementwise_affine=config.attention_pre_norm_affine)
+        self.inter_norm = nn.LayerNorm(self.d_v * self.num_attention_heads, eps=config.attention_inter_norm_eps, elementwise_affine=config.attention_inter_norm_affine)
+        self.q_norm = nn.LayerNorm(config.d_qk, eps=config.attention_pre_norm_eps, elementwise_affine=False, bias=False)
+        self.k_norm = nn.LayerNorm(config.d_qk, eps=config.attention_pre_norm_eps, elementwise_affine=False, bias=False)
+        self.k_scale = nn.Parameter(torch.ones(self.num_kv_heads, config.d_qk))
+        self.q_scale = nn.Parameter(torch.ones(self.num_attention_heads, config.d_qk))
+
+        self.dropout = nn.Dropout(config.attention_output_dropout_p)
+
+        theta = 160_000 if (layer_idx + 1) % config.short_long_ratio == 0 else 10_000
+
+        self.rope_embedding = RotaryPositionalEmbeddings(config, theta)
+        self.scale: float = 1.0 / math.sqrt(self.d_qk)
+
+        self.dropout = nn.Dropout(config.attention_dropout if hasattr(config, "attention_dropout") else 0.0)
+
+        self.lambdas = nn.Parameter(torch.tensor([0.5]))
+
+        self.initialize()
+
+        self.sequence_length = config.max_sequence_length
+        self.is_causal = config.is_decoder
+        self.not_flex = config.not_flex
+
+    @torch.no_grad()
+    def initialize(self) -> None:
+        std: float = math.sqrt(2.0 / (self.hidden_size + 4*self.hidden_size))
+        for weight in self.qk_proj.weights:
+            nn.init.trunc_normal_(weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.v_proj.weight, mean=0.0, std=std, a=2*std, b=2*std)
+        self.out_proj.weight.data.zero_()
+
+    def set_window_length(self, window_length: int, not_flex: bool) -> None:
+        self.window_length: int = window_length
+        if not not_flex:
+            self.block_mask = self.create_block_mask(window_length)
+
+    def causal_mask_mode(self, window_length, b, _, q_idx, kv_idx):
+        return (q_idx >= kv_idx) & ((q_idx - kv_idx) < window_length)
+
+    def bidirectional_mask_mode(self, window_length, b, _, q_idx, kv_idx):
+        return ((q_idx - kv_idx) < window_length) & ((kv_idx - q_idx) < window_length)
+
+    def create_block_mask(self, window_length: int) -> torch.Tensor:
+        if self.is_causal:
+            return create_block_mask(
+                partial(self.causal_mask_mode, self.window_length),
+                1, 1, self.sequence_length, self.sequence_length, device=self.k_scale.device
+            )
+        else:
+            return create_block_mask(
+                partial(self.bidirectional_mask_mode, self.window_length),
+                1, 1, self.sequence_length, self.sequence_length, device=self.k_scale.device
+            )
+
+    def attention_operation(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, padding_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        attention_scores: torch.Tensor
+        attention_probabilities: torch.Tensor
+        batch_size: int
+        query_length: int
+        key_length: int
+
+        batch_size, _, query_length, _ = query.size()
+        _, _, key_length, _ = key.size()
+
+        if self.is_causal:
+            window_mask = ~torch.ones(query_length, key_length, dtype=torch.bool, device=self.k_scale.device).tril().triu(diagonal=-self.window_length).view(1, 1, query_length, key_length)
+        else:
+            window_mask = ~torch.ones(query_length, key_length, dtype=torch.bool, device=self.k_scale.device).tril(diagonal=self.window_length).triu(diagonal=-self.window_length).view(1, 1, query_length, key_length)
+        
+        if padding_mask is not None:
+            attention_mask = padding_mask | window_mask
+        else:
+            attention_mask = window_mask
+
+        attention_scores = torch.bmm(query.flatten(0, 1), key.transpose(-1, -2).flatten(0, 1)) * self.scale  # shape: [B*H, T, T]
+        attention_scores = attention_scores.view(batch_size, self.num_attention_heads, query_length, key_length)
+
+        attention_probabilities = MaskedSoftmax.apply(attention_scores, attention_mask, -1)
+        attention_probabilities = self.dropout(attention_probabilities)
+
+        value = torch.bmm(attention_probabilities.flatten(0, 1), value.flatten(0, 1))
+        value = value.view(batch_size, self.num_attention_heads, query_length, self.d_v)
+
+        return value, attention_probabilities.detach()
+
+    def forward(self, hidden_layer: torch.Tensor, qk_layer: torch.Tensor, v1: torch.Tensor | None, mask: torch.Tensor | None = None, doc_ids:  torch.Tensor | None = None) -> Tuple[torch.Tensor, torch.Tensor]:
+        hidden_layer = self.pre_v_norm(hidden_layer)
+        qk_layer = self.pre_qk_norm(qk_layer)
+
+        query, key = self.qk_proj(qk_layer).tensor_split([self.q_out_dim], dim=-1)
+        value = self.v_proj(hidden_layer)
+
+        query_length: int = hidden_layer.size(0)
+        key_length: int = hidden_layer.size(0)
+        batch_size: int = hidden_layer.size(1)
+
+        query = query.reshape(query_length, batch_size, self.num_attention_heads, self.d_qk).permute(1, 2, 0, 3)  # shape: [B, H, T, D]
+        key = key.reshape(key_length, batch_size, self.num_kv_heads, self.d_qk).permute(1, 2, 0, 3)  # shape: [B, H, T, D]
+        value = value.reshape(key_length, batch_size, self.num_kv_heads, self.d_qk).permute(1, 2, 0, 3)  # shape: [B, H, T, D]
+
+        query, key = ((self.q_scale + 1.0).unsqueeze(1).unsqueeze(0) * self.q_norm(query.float())).type_as(query), ((self.k_scale + 1.0).unsqueeze(1).unsqueeze(0) * self.k_norm(key.float())).type_as(key)
+
+        if v1 is None:
+            v1 = value
+        value = (1 - self.lambdas[0]) * value + self.lambdas[0] * v1
+
+        query = self.rope_embedding(query)
+        key = self.rope_embedding(key)
+
+        if self.not_flex:
+            output, attention_probabilities = self.attention_operation(query, key, value, mask)
+        else:
+            def document_score_mod(score, b, _, q_idx, kv_idx):
+                return torch.where(doc_ids[q_idx] == doc_ids[kv_idx], score, -float("inf"))
+
+            if self.is_causal:
+                block_mask = create_block_mask(
+                    partial(self.causal_mask_mode, self.window_length),
+                    1, 1, query_length, key_length, device=self.k_scale.device
+                )
+            else:
+                block_mask = create_block_mask(
+                    partial(self.bidirectional_mask_mode, self.window_length),
+                    1, 1, query_length, key_length, device=self.k_scale.device
+                )
+
+            output = flex_attention(
+                query, key, value, block_mask=block_mask, enable_gqa=True
+            )
+            attention_probabilities = None
+
+        output = output.permute(2, 0, 1, 3).flatten(2, 3)  # shape: [T, B, H*D]
+        output = self.inter_norm(output)
+        output = self.out_proj(output)
+
+        return self.dropout(output), v1, attention_probabilities
+
+
+class FeedForward(nn.Module):
+
+    def __init__(self, config) -> None:
+        super().__init__()
+
+        self.up_proj: CastedLinear
+        self.down_proj: CastedLinear
+        self.pre_norm: nn.LayerNorm
+        self.inter_norm: nn.LayerNorm
+        self.activation: GeGLU
+        self.dropout: nn.Dropout
+
+        self.pre_norm = nn.LayerNorm(config.hidden_size, eps=config.feed_forward_pre_norm_eps, elementwise_affine=config.feed_forward_pre_norm_affine)
+        self.up_proj = MultiCastedLinearOrthoIn(config.hidden_size, [config.intermediate_size, config.intermediate_size], bias=False)
+        self.activation = GeGLU()
+        self.inter_norm = nn.LayerNorm(config.intermediate_size, eps=config.feed_forward_inter_norm_eps, elementwise_affine=config.feed_forward_inter_norm_affine)
+        self.down_proj = CastedLinearIn(config.intermediate_size, config.hidden_size, bias=False)
+        self.dropout = nn.Dropout(config.feed_forward_dropout_p)
+
+        self.initialize(config.hidden_size)
+
+    @torch.no_grad()
+    def initialize(self, hidden_size: int) -> None:
+        std: float = math.sqrt(2.0 / (5*hidden_size))
+
+        for weight in self.up_proj.weights:
+            nn.init.trunc_normal_(weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        self.down_proj.weight.data.zero_()
+
+    def up_project(self, hidden_layer: torch.Tensor) -> torch.Tensor:
+        hidden_layer = self.pre_norm(hidden_layer)
+        return self.up_proj(hidden_layer)
+
+    def activate(self, projection: torch.Tensor) -> torch.Tensor:
+        activated_projection: torch.Tensor
+
+        activated_projection = self.activation(projection)
+        activated_projection = self.inter_norm(activated_projection.float()).type_as(projection)
+
+        return activated_projection
+
+    def down_project(self, activated_projection: torch.Tensor) -> torch.Tensor:
+        output: torch.Tensor
+
+        output = self.down_proj(activated_projection)
+
+        return self.dropout(output)
+
+    def forward(self, hidden_layer: torch.Tensor) -> torch.Tensor:
+        output: torch.Tensor
+
+        output = self.up_project(hidden_layer)
+        output = self.activate(output)
+        output = self.down_project(output)
+
+        return output
+
+
+class RotaryPositionalEmbeddings(nn.Module):
+
+    def __init__(self, config, theta: int) -> None:
+        super().__init__()
+
+        assert hasattr(config, "d_qk"), "The config must have a d_qk attribute!"
+        assert hasattr(config, "max_sequence_length"), "The config must have a max_sequence_length attribute!"
+
+        self.inv_freq: torch.Tensor
+        self.cos_matrix: torch.Tensor
+        self.sin_matrix: torch.Tensor
+        head_size: int
+        max_seq_len: int
+        inv_freq: torch.Tensor
+        pos: torch.Tensor
+        embedding: torch.Tensor
+
+        head_size = config.d_qk
+        assert head_size % 2 == 0
+        max_seq_len = config.max_sequence_length
+
+        inv_freq = 1.0 / (theta ** (torch.arange(0, head_size, 2, dtype=torch.float32) / head_size))
+        pos = torch.arange(max_seq_len, dtype=torch.float32)
+        embedding = torch.einsum('n, d -> nd', pos, inv_freq)
+        embedding = torch.cat([embedding, embedding], dim=-1).unsqueeze(0)
+        self.register_buffer("cos_matrix", embedding.cos(), persistent=False)
+        self.register_buffer("sin_matrix", embedding.sin(), persistent=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        seq_len: int
+        cos_matrix: torch.Tensor
+        sin_matrix: torch.Tensor
+        x_rotate_half: torch.Tensor
+        out: torch.Tensor
+
+        hidden_layer = x.float()
+
+        seq_len = x.shape[2]
+
+        cos_matrix = self.cos_matrix[:, None, :seq_len, :]
+        sin_matrix = self.sin_matrix[:, None, :seq_len, :]
+
+        x_rotate_half = torch.cat(
+            [
+                -hidden_layer[:, :, :, x.size(-1) // 2:],
+                hidden_layer[:, :, :, :x.size(-1) // 2]
+            ],
+            dim=-1
+        )
+
+        out = hidden_layer * cos_matrix + x_rotate_half * sin_matrix
+        return out.type_as(x)
+
+
+#
+# HuggingFace wrappers
+#
+
+class GptBertPreTrainedModel(PreTrainedModel):
+    config_class = GptBertConfig
+    supports_gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        raise NotImplementedError("Gradient checkpointing is not supported by this model")
+
+    def _init_weights(self, module):
+        std = math.sqrt(2.0 / (5.0 * self.hidden_size))
+
+        if isinstance(module, nn.Linear):
+            nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+class GptBertModel(GptBertPreTrainedModel):
+
+    def __init__(self, config, add_mlm_layer=False, **kwargs):
+        super().__init__(config, **kwargs)
+        self.config = config
+        self.hidden_size = config.hidden_size
+
+        self.embedding = Embedding(config)
+        self.encoder = Encoder(config)
+        self.classifier = MaskClassifier(config, self.embedding.word_embedding.weight) if add_mlm_layer else None
+        self.set_window_length(config)
+
+    def set_window_length(self, config) -> None:
+        self.encoder.set_window_length(config)
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embedding
+
+    def set_input_embeddings(self, value):
+        self.embedding.word_embedding = value
+
+    def get_contextualized_embeddings(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None
+    ) -> List[torch.Tensor]:
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            raise ValueError("You have to specify input_ids")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device
+
+        # if attention_mask is None:
+        #     attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device)
+        if attention_mask is not None:
+            attention_mask = ~attention_mask.bool()
+
+            if len(attention_mask.size()) == 2:
+                attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            elif len(attention_mask.size()) == 3:
+                attention_mask = attention_mask.unsqueeze(1)
+
+            if self.config.is_decoder:
+                attention_mask = attention_mask | torch.triu(torch.ones(seq_length, seq_length, dtype=torch.bool, device=device), 1).unsqueeze(0).unsqueeze(0)
+
+        static_embeddings = self.embedding(input_ids.t())
+        contextualized_embeddings, attention_probs = self.encoder(static_embeddings, static_embeddings, attention_mask)
+        contextualized_embeddings = [e.transpose(0, 1) for e in contextualized_embeddings]
+        last_layer = contextualized_embeddings[-1]
+        contextualized_embeddings = [contextualized_embeddings[0]] + [
+            contextualized_embeddings[i] - contextualized_embeddings[i - 1]
+            for i in range(1, len(contextualized_embeddings))
+        ]
+        return last_layer, contextualized_embeddings, attention_probs
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+
+        if not return_dict:
+            return (
+                sequence_output,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+
+        return BaseModelOutput(
+            last_hidden_state=sequence_output,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+class GptBertForMaskedLM(GptBertModel):
+    _keys_to_ignore_on_load_unexpected = ["head"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config, add_mlm_layer=True, **kwargs)
+
+    def get_output_embeddings(self):
+        return self.classifier.emb2vocab.weight
+
+    def set_output_embeddings(self, new_embeddings):
+        self.classifier.emb2vocab.weight = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        subword_prediction = self.classifier(sequence_output)
+        subword_prediction = 30 * torch.sigmoid(subword_prediction / 7.5)
+
+        masked_lm_loss = None
+        if labels is not None:
+            labels_flatten = labels[:, 1:].flatten()
+            subword_prediction_flatten = subword_prediction[:, :-1].flatten(0, 1)
+            masked_lm_loss = F.cross_entropy(subword_prediction_flatten, labels_flatten)
+
+        if not return_dict:
+            output = (
+                subword_prediction,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=subword_prediction,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+class Classifier(nn.Module):
+    def __init__(self, config, num_labels: int):
+        super().__init__()
+
+        drop_out = getattr(config, "cls_dropout", None)
+        drop_out = config.hidden_dropout_prob if drop_out is None else drop_out
+
+        self.projection: CastedLinear
+        self.emb2vocab: CastedLinear
+        self.pre_norm: nn.LayerNorm
+        self.post_norm: nn.LayerNorm
+
+        self.pre_norm = nn.LayerNorm(config.hidden_size, eps=config.classifier_pre_norm_eps, elementwise_affine=config.classifier_pre_norm_affine)
+        self.projection = CastedLinear(config.hidden_size, config.hidden_size, bias=False)
+        self.post_norm = nn.LayerNorm(config.hidden_size, eps=config.classifier_post_norm_eps, elementwise_affine=config.classifier_post_norm_affine)
+        self.emb2vocab = CastedLinear(config.hidden_size, num_labels, bias=True)
+        self.dropout = nn.Dropout(drop_out)
+
+        self.initialize(config.hidden_size, config.intermediate_size, num_labels)
+
+    @torch.no_grad()
+    def initialize(self, hidden_size: int, intermediate_size: int, vocab_size: int) -> None:
+        proj_std: float = math.sqrt(2.0 / (hidden_size + intermediate_size))
+
+        nn.init.trunc_normal_(self.projection.weight, mean=0.0, std=proj_std, a=-2*proj_std, b=2*proj_std)
+        nn.init.trunc_normal_(self.emb2vocab.weight, mean=0.0, std=proj_std, a=-2*proj_std, b=2*proj_std)
+        self.emb2vocab.bias.zero_()
+
+    def project(self, hidden_layer: torch.Tensor) -> torch.Tensor:
+        projection: torch.Tensor
+
+        projection = self.pre_norm(hidden_layer)
+        projection = self.dropout(projection)
+        projection = self.projection(hidden_layer)
+        projection = gelu_new(projection)
+        projection = self.post_norm(projection)
+
+        return projection
+
+    def calculate_output(self, hidden_layer: torch.Tensor) -> torch.Tensor:
+        return self.emb2vocab(hidden_layer)
+
+    def forward(self, hidden_layer: torch.Tensor) -> torch.Tensor:
+        output: torch.Tensor
+        projection: torch.Tensor
+
+        projection = self.project(hidden_layer)
+        output = self.calculate_output(projection)
+
+        return output
+
+
+class GptBertForCausalLM(GptBertModel):
+    _keys_to_ignore_on_load_unexpected = ["head"]
+
+    def __init__(self, config, **kwargs):
+        config.is_decoder = True
+        super().__init__(config, add_mlm_layer=True, **kwargs)
+
+    def get_output_embeddings(self):
+        return self.classifier.emb2vocab.weight
+
+    def set_output_embeddings(self, new_embeddings):
+        self.classifier.emb2vocab.weight = new_embeddings
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embedding
+
+    def set_input_embeddings(self, value):
+        self.embedding.word_embedding = value
+
+    def set_decoder(self, decoder):
+        self.encoder = decoder
+
+    def get_decoder(self):
+        return self.encoder
+
+    def can_generate(self):
+        return True
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None
+    ) -> Union[Tuple, CausalLMOutput]:
+
+        assert inputs_embeds is None, "inputs_embeds is not supported for now"
+        assert past_key_values is None, "past_key_values is not supported for now"
+        assert not use_cache, "use_cache is not supported for now"
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        subword_prediction = self.classifier(sequence_output)
+        subword_prediction = 30 * torch.sigmoid(subword_prediction / 7.5)
+
+        masked_lm_loss = None
+        if labels is not None:
+            labels_flatten = labels[:, 1:].flatten()
+            subword_prediction_flatten = subword_prediction[:, :-1].flatten(0, 1)
+            masked_lm_loss = F.cross_entropy(subword_prediction_flatten, labels_flatten)
+
+        if not return_dict:
+            output = (
+                subword_prediction,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return CausalLMOutput(
+            loss=masked_lm_loss,
+            logits=subword_prediction,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.Tensor,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        use_cache: bool = True,
+        num_logits_to_keep: Optional[int] = None,
+        **kwargs,
+    ):
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+        if past_key_values is not None:
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+
+        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_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids.contiguous()}  # `contiguous()` needed for compilation use cases
+
+        if num_logits_to_keep is not None:
+            model_inputs["num_logits_to_keep"] = num_logits_to_keep
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+
+class GptBertForSequenceClassification(GptBertModel):
+    _keys_to_ignore_on_load_unexpected = ["classifier"]
+    _keys_to_ignore_on_load_missing = ["head"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config, add_mlm_layer=False, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.head = Classifier(config, self.num_labels)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        logits = self.head(sequence_output[:, 0, :])
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = nn.MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (
+                logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+class GptBertForTokenClassification(GptBertModel):
+    _keys_to_ignore_on_load_unexpected = ["classifier"]
+    _keys_to_ignore_on_load_missing = ["head"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config, add_mlm_layer=False, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.head = Classifier(config, self.num_labels)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        logits = self.head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (
+                logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+class GptBertForQuestionAnswering(GptBertModel):
+    _keys_to_ignore_on_load_unexpected = ["classifier"]
+    _keys_to_ignore_on_load_missing = ["head"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config, add_mlm_layer=False, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.head = Classifier(config, self.num_labels)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        logits = self.head(sequence_output)
+
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (
+                start_logits,
+                end_logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+class GptBertForMultipleChoice(GptBertModel):
+    _keys_to_ignore_on_load_unexpected = ["classifier"]
+    _keys_to_ignore_on_load_missing = ["head"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config, add_mlm_layer=False, **kwargs)
+
+        self.num_labels = getattr(config, "num_labels", 2)
+        self.head = Classifier(config, self.num_labels)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        num_choices = input_ids.shape[1]
+
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(flat_input_ids, flat_attention_mask)
+        logits = self.head(sequence_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+
+        if not return_dict:
+            output = (
+                reshaped_logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return MultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7babbe2f0f59391dedc55eb4609596d3981e87bc62f877633851e494720cb95e
+size 597611298

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<oad>", "cls_token": "<s>", "mask_token": "<mask>"}

tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+    "tokenizer_class": "PreTrainedTokenizerFast",
+    "bos_token": "<s>",
+    "eos_token": "</s>",
+    "unk_token": "<unk>",
+    "sep_token": "</s>",
+    "pad_token": "<pad>",
+    "cls_token": "<s>",
+    "mask_token": "<mask>"
+}