Uploading the modeling file

config.json

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+{
+    "architectures": [
+          "GPTBERTFoCausalLM"
+      ],
+      "auto_map": {
+          "AutoConfig": "configuration_gpt_bert.ModelConfig",
+          "AutoModel": "modeling_gpt_bert.GPTBERT",
+          "AutoModelForCausalLM": "modeling_gpt_bert.GPTBERTForCausalLM",
+          "AutoModelForMaskedLM": "modeling_gpt_bert.GPTBERTForMaskedLM"
+      },
+    "attention_probs_dropout_prob": 0.1,
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "intermediate_size": 2560,
+    "max_position_embeddings": 512,
+    "position_bucket_size": 32,
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "vocab_size": 8192,
+    "layer_norm_eps": 1.0e-5
+  }

configuration_gpt_bert.py

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+from __future__ import annotations
+
+import json
+import pathlib
+import copy
+
+from typing import Any
+from transformers.configuration_utils import PretrainedConfig
+
+
+class ModelConfig(PretrainedConfig):
+
+    def __init__(self: ModelConfig, config_file: pathlib.Path | str | None = None, **kwargs):
+        """
+        """
+        super().__init__(**kwargs)
+        if config_file is None:
+            self.attention_probs_dropout_prob: float = 0.1
+            self.hidden_dropout_prob = 0.1
+            self.hidden_size = 768
+            self.intermediate_size = 2560
+            self.max_sequence_length = 512
+            self.position_bucket_size = 32
+            self.num_attention_heads = 12
+            self.num_layers = 12
+            self.vocab_size = 8192
+            self.layer_norm_eps = 1e-5
+        else:
+            if config_file == "str":
+                config_file = pathlib.Path(config_file)
+
+            config: dict[str, Any] = json.load(config_file.open("r"))
+
+            for key, value in config.items():
+                setattr(self, key, value)
+
+    def __repr__(self) -> str:
+        return str(self.to_json_string())
+
+    def to_dict(self) -> dict[str, Any]:
+        """Serializes this instance to a Python dictionary."""
+        output: dict[str, Any] = 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: pathlib.Path | str) -> None:
+        """Save this instance to a json file."""
+        if isinstance(json_file_path, str):
+            json_file_path: pathlib.Path = pathlib.Path(json_file_path)
+        with json_file_path.open("w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())

modeling_gpt_bert.py

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+from __future__ import annotations
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import _softmax_backward_data as _softmax_backward_data
+from .configuration_gpt_bert import ModelConfig
+
+from transformers.modeling_utils import PreTrainedModel
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    CausalLMOutput
+)
+
+from typing import Optional, Union
+
+
+class Layer(nn.Module):
+
+    def __init__(self: Layer, config: ModelConfig, layer_idx: int = 0):
+        super().__init__()
+        self.attention = Attention(config)
+        self.mlp = FeedForward(config)
+
+        self.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + layer_idx)))
+        self.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + layer_idx)))
+
+    def forward(self: Layer, x: torch.Tensor, attention_mask: torch.Tensor, relative_embedding: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        attention: torch.Tensor
+        attention_probs: torch.Tensor
+        attention, attention_probs = self.attention(x, attention_mask, relative_embedding)
+        x += attention
+        x += self.mlp(x)
+
+        return x, attention_probs
+
+
+class MaskClassifier(nn.Module):
+
+    def __init__(self: MaskClassifier, config: ModelConfig, subword_embedding: nn.Parameter):
+        super().__init__()
+        self.nonlinearity = nn.Sequential(
+            nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.hidden_size, config.hidden_size),
+            nn.GELU(),
+            nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
+            nn.Dropout(config.hidden_dropout_prob),
+            nn.Linear(subword_embedding.size(1), subword_embedding.size(0))
+        )
+        self.initialize(config.hidden_size, subword_embedding)
+
+    def initialize(self: MaskClassifier, hidden_size: int, embedding: nn.Parameter):
+        std: float = math.sqrt(2.0 / (5.0 * hidden_size))
+        nn.init.trunc_normal_(self.nonlinearity[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        self.nonlinearity[-1].weight = embedding
+        self.nonlinearity[1].bias.data.zero_()
+        self.nonlinearity[-1].bias.data.zero_()
+
+    def forward(self: MaskClassifier, x: torch.Tensor, masked_lm_labels: torch.Tensor | None = None) -> torch.Tensor:
+        if masked_lm_labels is not None:
+            x = torch.index_select(x.flatten(0, 1), 0, torch.nonzero(masked_lm_labels.flatten() != -100).squeeze())
+        x = self.nonlinearity(x)
+
+        return x
+
+
+class GeGLU(nn.Module):
+    def forward(self: GeGLU, x: torch.Tensor) -> torch.Tensor:
+        gate: torch.Tensor
+        x, gate = x.chunk(2, dim=-1)
+        x = x * F.gelu(gate, approximate='tanh')
+        return x
+
+
+class FeedForward(nn.Module):
+    def __init__(self: FeedForward, config: ModelConfig) -> None:
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.hidden_size, 2*config.intermediate_size, bias=False),
+            GeGLU(),
+            nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.intermediate_size, config.hidden_size, bias=False),
+            nn.Dropout(config.hidden_dropout_prob)
+        )
+        self.initialize(config.hidden_size)
+
+    def initialize(self: FeedForward, hidden_size: int) -> None:
+        std: float = math.sqrt(2.0 / (5.0 * hidden_size))
+        nn.init.trunc_normal_(self.mlp[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.mlp[-2].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self: FeedForward, x: torch.Tensor) -> torch.Tensor:
+        return self.mlp(x)
+
+
+class MaskedSoftmax(torch.autograd.Function):
+    @staticmethod
+    def forward(self: MaskedSoftmax, x: torch.Tensor, mask: torch.Tensor, dim: int) -> torch.Tensor:
+        self.dim = dim
+        x.masked_fill_(mask, float('-inf'))
+        x = torch.softmax(x, self.dim)
+        x.masked_fill_(mask, 0.0)
+        self.save_for_backward(x)
+        return x
+
+    @staticmethod
+    def backward(self: MaskedSoftmax, grad_output: torch.Tensor) -> tuple[torch.Tensor, None, None]:
+        output: torch.Tensor
+        output, = self.saved_tensors
+        inputGrad: torch.Tensor = _softmax_backward_data(grad_output, output, self.dim, output.dtype)
+        return inputGrad, None, None
+
+
+class Attention(nn.Module):
+    def __init__(self: Attention, config: ModelConfig) -> None:
+        super().__init__()
+
+        self.config: ModelConfig = config
+
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}")
+
+        self.hidden_size: int = config.hidden_size
+        self.num_heads: int = config.num_attention_heads
+        self.head_size: int = config.hidden_size // config.num_attention_heads
+
+        self.in_proj_qk = nn.Linear(config.hidden_size, 2*config.hidden_size, bias=True)
+        self.in_proj_vg = nn.Linear(config.hidden_size, 2*config.hidden_size, bias=True)
+        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+
+        self.pre_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False)
+        self.post_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False)
+
+        position_indices: torch.Tensor = torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(1) \
+            - torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(0)
+        position_indices: torch.Tensor = self.make_log_bucket_position(position_indices, config.position_bucket_size, config.max_position_embeddings)
+        position_indices = config.position_bucket_size - 1 + position_indices
+        self.register_buffer("position_indices", position_indices, persistent=True)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.scale: float = 1.0 / math.sqrt(3 * self.head_size)
+        self.initialize()
+
+    def make_log_bucket_position(self: Attention, relative_pos: torch.Tensor, bucket_size: int, max_position: int) -> torch.Tensor:
+        sign: torch.Tensor = torch.sign(relative_pos)
+        mid: int = bucket_size // 2
+        abs_pos: torch.Tensor = torch.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, torch.abs(relative_pos).clamp(max=max_position - 1))
+        log_pos: torch.Tensor = torch.ceil(torch.log(abs_pos / mid) / math.log((max_position-1) / mid) * (mid - 1)).int() + mid
+        bucket_pos: torch.Tensor = torch.where(abs_pos <= mid, relative_pos, log_pos * sign).long()
+        return bucket_pos
+
+    def initialize(self: Attention) -> None:
+        std: float = math.sqrt(2.0 / (5.0 * self.hidden_size))
+        nn.init.trunc_normal_(self.in_proj_qk.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.in_proj_vg.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.out_proj.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        self.in_proj_qk.bias.data.zero_()
+        self.in_proj_vg.bias.data.zero_()
+        self.out_proj.bias.data.zero_()
+
+    def _create_position_tensors(self: Attention, relative_embedding: torch.Tensor, query_len: int, key_len: int) -> tuple[torch.Tensor, torch.Tensor]:
+        pos = self.in_proj_qk(self.dropout(relative_embedding))  # shape: [2T-1, 2D]
+        pos = F.embedding(self.position_indices[:query_len, :key_len], pos)  # shape: [T, T, 2D]
+        query_pos, key_pos = pos.chunk(2, dim=-1)
+        query_pos = query_pos.view(query_len, key_len, self.num_heads, self.head_size)
+        key_pos = key_pos.view(query_len, key_len, self.num_heads, self.head_size)
+
+        return query_pos, key_pos
+
+    def attention_operation(self: Attention, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: torch.Tensor, query_pos: torch.Tensor, key_pos: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        key_len: int
+        batch_size: int
+        key_len, batch_size, _ = key.size()
+        query_len: int
+        query_len, _, _ = query.size()
+
+        query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
+        key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
+        value = value.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
+
+        attention_probs: torch.Tensor = torch.bmm(query, key.transpose(1, 2) * self.scale)
+
+        query = query.view(batch_size, self.num_heads, query_len, self.head_size)
+        key = key.view(batch_size, self.num_heads, query_len, self.head_size)
+        attention_probs = attention_probs.view(batch_size, self.num_heads, query_len, key_len)
+        attention_probs.add_(torch.einsum("bhqd,qkhd->bhqk", query, key_pos * self.scale))
+        attention_probs.add_(torch.einsum("bhkd,qkhd->bhqk", key * self.scale, query_pos))
+
+        attention_probs = MaskedSoftmax.apply(attention_probs, attention_mask, -1)
+
+        attention_probs = self.dropout(attention_probs)
+        attention_output: torch.Tensor = torch.bmm(attention_probs.flatten(0, 1), value)  # shape: [B*H, Q, D]
+        attention_output = attention_output.transpose(0, 1).reshape(query_len, batch_size, self.hidden_size)  # shape: [Q, B, H*D]
+
+        return attention_output, attention_probs
+
+    def forward(self: Attention, hidden_states: torch.Tensor, attention_mask: torch.Tensor, relative_embedding: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        key_len: int
+        batch_size: int
+        key_len, batch_size, _ = hidden_states.size()
+        query_len: int = key_len
+
+        if self.position_indices.size(0) < query_len:
+            position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \
+                - torch.arange(query_len, dtype=torch.long).unsqueeze(0)
+            position_indices = self.make_log_bucket_position(position_indices, self.config.position_bucket_size, 512)
+            position_indices = self.config.position_bucket_size - 1 + position_indices
+            self.register_buffer("position_indices", position_indices.to(hidden_states.device), persistent=True)
+
+        hidden_states = self.pre_layer_norm(hidden_states)
+        query, key = self.in_proj_qk(hidden_states).chunk(2, dim=2)  # shape: [T, B, D]
+        value, gate = self.in_proj_vg(hidden_states).chunk(2, dim=2)  # shape: [T, B, D]
+        gate = F.gelu(gate)
+
+        query_pos: torch.Tensor
+        key_pos: torch.Tensor
+        query_pos, key_pos = self._create_position_tensors(relative_embedding, query_len, key_len)
+
+        attention_output: torch.Tensor
+        attention_probs: torch.Tensor
+        attention_output, attention_probs = self.attention_operation(query, key, value, attention_mask, query_pos, key_pos)
+        attention_output = attention_output * gate
+        attention_output = self.post_layer_norm(attention_output)
+        attention_output = self.out_proj(attention_output)
+        attention_output = self.dropout(attention_output)
+
+        return attention_output, attention_probs
+
+
+class Embedding(nn.Module):
+    def __init__(self: Embedding, config: ModelConfig):
+        super().__init__()
+        self.hidden_size: int = config.hidden_size
+
+        self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.word_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        self.relative_embedding = nn.Parameter(torch.empty(2 * config.position_bucket_size - 1, config.hidden_size))
+        self.relative_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.initialize()
+
+    def initialize(self: Embedding):
+        std: float = math.sqrt(2.0 / (5.0 * self.hidden_size))
+        nn.init.trunc_normal_(self.relative_embedding, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.word_embedding.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self: Embedding, input_ids: torch.Tensor):
+        word_embedding: torch.Tensor = self.dropout(self.word_layer_norm(self.word_embedding(input_ids)))
+        relative_embeddings: torch.Tensor = self.relative_layer_norm(self.relative_embedding)
+        return word_embedding, relative_embeddings
+
+
+class GPTBERTPreTrainedModel(PreTrainedModel):
+    config_class = ModelConfig
+    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 GPTBERT(GPTBERTPreTrainedModel):
+
+    def __init__(self, config: ModelConfig, is_causal: bool, **kwargs):
+        super().__init__(config, **kwargs)
+        self.config = config
+        self.hidden_size = config.hidden_size
+
+        self.embedding = Embedding(config)
+        self.layers = nn.ModuleList([Layer(config) for _ in range(config.num_layers)])
+        self.is_causal = is_causal
+
+    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: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> list[torch.Tensor]:
+        """
+        """
+        input_shape = input_ids.size()
+
+        batch_size, seq_length = input_shape
+
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones((seq_length, seq_length), dtype=torch.bool).triu(diagonal=1).unsqueeze(0).unsqueeze(0)
+
+        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.is_causal:
+                attention_mask = attention_mask | input_ids.new_ones((seq_length, seq_length), dtype=torch.bool).triu(1).unsqueeze(0).unsqueeze(0)
+
+        static_embeddings, relative_embeddings = self.embedding(input_ids.t())
+        contextualized_embeddings = [static_embeddings]
+        attention_probs = []
+        for layer in self.layers:
+            layer_embeddings, layer_attention_probs = layer(contextualized_embeddings[-1], attention_mask, relative_embeddings)
+            contextualized_embeddings.append(layer_embeddings)
+            attention_probs.append(layer_attention_probs)
+        contextualized_embeddings = [emb.transpose(0, 1) for emb in contextualized_embeddings]
+        last_layer = contextualized_embeddings[-1]
+        return last_layer, contextualized_embeddings, attention_probs
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        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
+        )
+
+# To do Masked Language Modeling instead, you can replace MyModelForCausalLM by MyModelForMaskedLM
+# and change the output type from CausalLMOutput to MaskedLMOutput.
+
+
+class GPTBERTForCausalLM(GPTBERTPreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = ["head"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config, **kwargs)
+        self.model = GPTBERT(config, is_causal=True, **kwargs)
+        self.vocab_size = config.vocab_size
+        self.lm_head = MaskClassifier(config, self.model.embedding.word_embedding.weight)
+        self.hidden_size = config.hidden_size
+
+    def get_output_embeddings(self):
+        return self.lm_head.nonlinearity[-1].weight
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.nonlinearity[-1].weight = new_embeddings
+
+    def get_input_embeddings(self):
+        return self.model.embedding.word_embedding
+
+    def set_input_embeddings(self, value):
+        self.model.embedding.word_embedding = value
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def can_generate(self):
+        return True
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        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, CausalLMOutput]:
+
+        sequence_output, contextualized_embeddings, attention_probs = self.model.get_contextualized_embeddings(input_ids, attention_mask)
+        subword_prediction = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            gold_labels = labels.flatten()
+            gold_labels = gold_labels[gold_labels != -100]
+
+            loss = F.cross_entropy(subword_prediction, gold_labels)
+
+        if not return_dict:
+            output = (
+                subword_prediction,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutput(
+            loss=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 GPTBERTForMaskedLM(GPTBERTPreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = ["head"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config, **kwargs)
+        self.model = GPTBERT(config, is_causal=False, **kwargs)
+        self.vocab_size = config.vocab_size
+        self.lm_head = MaskClassifier(config, self.model.embedding.word_embedding.weight)
+        self.hidden_size = config.hidden_size
+
+    def get_output_embeddings(self):
+        return self.lm_head.nonlinearity[-1].weight
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.nonlinearity[-1].weight = new_embeddings
+
+    def get_input_embeddings(self):
+        return self.model.embedding.word_embedding
+
+    def set_input_embeddings(self, value):
+        self.model.embedding.word_embedding = value
+
+    def set_encoder(self, encoder):
+        self.model = encoder
+
+    def get_encoder(self):
+        return self.model
+
+    def can_generate(self):
+        return True
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        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, CausalLMOutput]:
+
+        sequence_output, contextualized_embeddings, attention_probs = self.model.get_contextualized_embeddings(input_ids, attention_mask)
+        subword_prediction = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            gold_labels = labels.flatten()
+            gold_labels = gold_labels[gold_labels != -100]
+
+            loss = F.cross_entropy(subword_prediction, gold_labels)
+
+        if not return_dict:
+            output = (
+                subword_prediction,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutput(
+            loss=loss,
+            logits=subword_prediction,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )

special_tokens_map.json

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