modeling_gptbert.py

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
        )