Duplicate from dmis-lab/UltraBERT-Embed-150M

Co-authored-by: Data Mining and Information Systems Lab <dmis-lab@users.noreply.huggingface.co>

.gitattributes

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+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json

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+{
+    "embedding_dimension": 768,
+    "pooling_mode": "cls",
+    "include_prompt": true
+}

config.json

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+{
+  "architectures": [
+    "UltrabertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_windows": [
+    128,
+    128,
+    null,
+    128,
+    128,
+    null,
+    128,
+    128,
+    null,
+    128,
+    128,
+    null,
+    128,
+    128,
+    null,
+    128,
+    128,
+    null,
+    128,
+    128,
+    null,
+    128,
+    128,
+    null
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_ultrabert.UltrabertConfig",
+    "AutoModel": "modeling_ultrabert.UltrabertModel",
+    "AutoModelForMaskedLM": "modeling_ultrabert.UltrabertForMaskedLM",
+    "AutoModelForQuestionAnswering": "modeling_ultrabert.UltrabertForQuestionAnswering",
+    "AutoModelForSequenceClassification": "modeling_ultrabert.UltrabertForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_ultrabert.UltrabertForTokenClassification"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 0,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "mlp_bias": false,
+  "model_type": "ultrabert",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 6,
+  "pad_token_id": 0,
+  "prediction_head_size": 768,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": [
+    10000.0,
+    10000.0,
+    2560000.0,
+    10000.0,
+    10000.0,
+    2560000.0,
+    10000.0,
+    10000.0,
+    2560000.0,
+    10000.0,
+    10000.0,
+    2560000.0,
+    10000.0,
+    10000.0,
+    2560000.0,
+    10000.0,
+    10000.0,
+    2560000.0,
+    10000.0,
+    10000.0,
+    2560000.0,
+    10000.0,
+    10000.0,
+    2560000.0
+  ],
+  "tie_word_embeddings": false,
+  "transformers_version": "5.16.1",
+  "use_cache": false,
+  "vocab_size": 49152
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.14.0+cu130",
+    "sentence_transformers": "6.0.1",
+    "transformers": "5.16.1"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "Instruct: Given a question, retrieve passages that answer the question\nQuery: ",
+    "document": ""
+  },
+  "similarity_fn_name": "cosine"
+}

configuration_ultrabert.py

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+from __future__ import annotations
+
+from collections.abc import Iterable
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class UltrabertConfig(PretrainedConfig):
+    model_type = "ultrabert"
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        prediction_head_size=None,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        attention_windows=None,
+        hidden_act="silu",
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        pad_token_id=0,
+        bos_token_id=0,
+        eos_token_id=0,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        head_dim=None,
+        classifier_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.prediction_head_size = prediction_head_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads or num_attention_heads
+        self.attention_windows = (
+            attention_windows
+            if isinstance(attention_windows, Iterable)
+            else [attention_windows] * num_hidden_layers
+        )
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = (
+            rope_theta
+            if isinstance(rope_theta, Iterable)
+            else [rope_theta] * num_hidden_layers
+        )
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+        self.head_dim = head_dim or self.hidden_size // self.num_attention_heads
+        self.classifier_dropout = classifier_dropout

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da8be29564b69e4f90fc8f2683e1f369c3d3575e75f7fe798643e28de02e2b9c
+size 604153320

modeling_ultrabert.py

@@ -0,0 +1,766 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from examples/modular_ultrabert.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_ultrabert.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+from collections.abc import Callable
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    MaskedLMOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
+from transformers.utils.generic import merge_with_config_defaults
+from transformers.utils.output_capturing import capture_outputs
+
+from transformers.cache_utils import Cache
+from transformers.integrations import use_kernel_forward_from_hub, use_kernelized_func
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_rope_utils import dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+from transformers.utils.deprecation import deprecate_kwarg
+from transformers.utils.generic import maybe_autocast
+from .configuration_ultrabert import UltrabertConfig
+
+
+class RotaryEmbedding(nn.Module):
+    @deprecate_kwarg("device", version="5.18")
+    def __init__(self, base: float, dim: int, device: torch.device | None = None):
+        super().__init__()
+        self.rope_type = "default"
+        self.attention_scaling = 1
+        self.base, self.dim = base, dim
+
+        inv_freq = torch.arange(0, dim, 2, dtype=torch.int64)
+        inv_freq = 1.0 / (base ** (inv_freq.to(device=device, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @staticmethod
+    @deprecate_kwarg("device", version="5.18")
+    def compute_default_rope_parameters(
+        config: UltrabertConfig, device=None, **kwargs
+    ) -> tuple[torch.Tensor, float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = (
+            getattr(config, "head_dim", None)
+            or config.hidden_size // config.num_attention_heads
+        )
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        return inv_freq.to(device), attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = (
+            self.inv_freq[None, :, None]
+            .expand(position_ids.shape[0], -1, 1)
+            .to(dtype=torch.float, device=x.device)
+        )
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = (
+            x.device.type
+            if isinstance(x.device.type, str) and x.device.type != "mps"
+            else "cpu"
+        )
+        # Disable any outside autocast context if any, to really force fp32
+        with maybe_autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@use_kernel_forward_from_hub("rotary_pos_emb")
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
+        query.dtype
+    )
+    attn_weights = nn.functional.dropout(
+        attn_weights, p=dropout, training=module.training
+    )
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+@use_kernelized_func(apply_rotary_pos_emb)
+class UltrabertAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: UltrabertConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(
+            config, "head_dim", config.hidden_size // config.num_attention_heads
+        )
+        self.num_key_value_groups = (
+            config.num_attention_heads // config.num_key_value_heads
+        )
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = False
+
+        self.q_proj = nn.Linear(
+            config.hidden_size,
+            config.num_attention_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size,
+            config.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size,
+            config.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim,
+            config.hidden_size,
+            bias=config.attention_bias,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        attention_mask: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(
+                key_states, value_states, self.layer_idx
+            )
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class UltrabertRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        UltrabertRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class UltrabertMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(
+            self.hidden_size, self.intermediate_size, bias=config.mlp_bias
+        )
+        self.up_proj = nn.Linear(
+            self.hidden_size, self.intermediate_size, bias=config.mlp_bias
+        )
+        self.down_proj = nn.Linear(
+            self.intermediate_size, self.hidden_size, bias=config.mlp_bias
+        )
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class UltrabertLayer(GradientCheckpointingLayer):
+    def __init__(self, config: UltrabertConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = UltrabertAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = UltrabertMLP(config)
+        self.input_layernorm = UltrabertRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = UltrabertRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class UltrabertPreTrainedModel(PreTrainedModel):
+    config: UltrabertConfig
+    base_model_prefix = "ultrabert"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["UltrabertLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": UltrabertLayer,
+        "attentions": UltrabertAttention,
+    }
+    config_class = UltrabertConfig
+
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, RotaryEmbedding):
+            inv_freq = torch.arange(0, module.dim, 2, dtype=torch.int64)
+            inv_freq = 1.0 / (
+                module.base
+                ** (
+                    inv_freq.to(device=module.inv_freq.device, dtype=torch.float)
+                    / module.dim
+                )
+            )
+            module.inv_freq.copy_(inv_freq)
+
+
+def create_local_attention_mask(
+    seq_len: int,
+    sliding_window: int | None = None,
+    device: torch.device | None = None,
+) -> torch.Tensor:
+    attention_mask = torch.ones(1, seq_len, seq_len, dtype=torch.bool, device=device)
+    if sliding_window:
+        attention_mask = attention_mask.tril(sliding_window).triu(-sliding_window)
+    return attention_mask
+
+
+@auto_docstring
+class UltrabertModel(UltrabertPreTrainedModel):
+    def __init__(self, config: UltrabertConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+
+        self.layers = nn.ModuleList(
+            [UltrabertLayer(config, i) for i in range(config.num_hidden_layers)]
+        )
+        self.norm = UltrabertRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = nn.ModuleDict()
+        self.gradient_checkpointing = False
+        for base in set(config.rope_theta):
+            name = str(base).replace(".", "_")
+            self.rotary_emb[name] = RotaryEmbedding(base, config.head_dim)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutput:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("must specify exactly one of input_ids or inputs_embeds")
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        if position_ids is None:
+            position_ids = torch.arange(inputs_embeds.size(1))
+            position_ids = position_ids.to(inputs_embeds.device).unsqueeze(0)
+        if (
+            attention_mask is None
+            and self.config._attn_implementation != "flash_attention_2"
+        ):
+            attention_mask = inputs_embeds.new_ones(inputs_embeds.shape[:2])
+        varlen_kwargs = {
+            k: kwargs[k]
+            for k in ("cu_seq_lens_q", "cu_seq_lens_k", "max_length_q", "max_length_k")
+            if kwargs.get(k) is not None
+        }
+
+        # Create attention masks for different local window sizes and store by window.
+        attention_mask_dict = {}
+        for local_window in list(set(self.config.attention_windows)) + [None]:
+            if self.config._attn_implementation == "flash_attention_2":
+                # For FlashAttention 2, rely on the `sliding_window` keyword instead of
+                # constructing masks manually.
+                attention_mask_dict[local_window] = attention_mask
+            else:
+                window_mask = create_local_attention_mask(
+                    seq_len=attention_mask.size(1),
+                    sliding_window=local_window,
+                    device=attention_mask.device,
+                )
+                window_mask = window_mask & attention_mask.bool().unsqueeze(1)
+                attention_mask_dict[local_window] = (
+                    torch.where(window_mask, 0.0, torch.finfo(inputs_embeds.dtype).min)
+                    .to(inputs_embeds.dtype)
+                    .unsqueeze(1)
+                )
+
+        # Create position embeddings to be shared across the decoder layers.
+        hidden_states, position_embeddings = inputs_embeds, {}
+        for base in set(self.config.rope_theta):
+            rotary_emb = self.rotary_emb[str(base).replace(".", "_")]
+            position_embeddings[base] = rotary_emb(hidden_states, position_ids)
+
+        for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
+            local_window = self.config.attention_windows[i]
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask_dict[local_window],
+                position_ids=position_ids,
+                position_embeddings=position_embeddings[self.config.rope_theta[i]],
+                sliding_window=(
+                    local_window + 1
+                    if local_window is not None
+                    and self.config._attn_implementation == "flash_attention_2"
+                    else local_window
+                ),
+                **varlen_kwargs,
+            )
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+class UltrabertPredictionHead(nn.Module):
+    def __init__(self, config: UltrabertConfig):
+        super().__init__()
+        if (dim := config.prediction_head_size) is not None:
+            self.linear1 = nn.Linear(config.hidden_size, dim, bias=False)
+            self.linear2 = nn.Linear(dim, config.hidden_size, bias=False)
+            self.act_fn = ACT2FN["gelu_pytorch_tanh"]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "linear1"):
+            return self.act_fn(self.linear2(self.act_fn(self.linear1(x))))
+        return x
+
+
+@auto_docstring
+class UltrabertForMaskedLM(UltrabertPreTrainedModel):
+    _tied_weights_keys = {"cls.weight": "ultrabert.embed_tokens.weight"}
+
+    def __init__(self, config: UltrabertConfig):
+        super().__init__(config)
+        self.ultrabert = UltrabertModel(config)
+        self.head = UltrabertPredictionHead(config)
+        self.cls = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.cls
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls = new_embeddings
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        token_type_ids: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor] | MaskedLMOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        """
+        outputs = self.ultrabert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            **kwargs,
+        )
+        prediction_scores = self.cls(self.head(outputs[0]))
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    Ultrabert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+    output) e.g. for GLUE tasks.
+    """
+)
+class UltrabertForSequenceClassification(UltrabertPreTrainedModel):
+    def __init__(self, config: UltrabertConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+        self.ultrabert = UltrabertModel(config)
+        classifier_dropout = (
+            config.classifier_dropout
+            if config.classifier_dropout is not None
+            else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        token_type_ids: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor] | SequenceClassifierOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        outputs = self.ultrabert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            **kwargs,
+        )
+        logits = self.classifier(self.dropout(outputs[0][:, 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 = 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 = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring
+class UltrabertForQuestionAnswering(UltrabertPreTrainedModel):
+    def __init__(self, config: UltrabertConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.ultrabert = UltrabertModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        token_type_ids: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        start_positions: torch.Tensor | None = None,
+        end_positions: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput:
+        outputs = self.ultrabert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            **kwargs,
+        )
+        logits = self.qa_outputs(outputs[0])
+        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 len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = 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
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring
+class UltrabertForTokenClassification(UltrabertPreTrainedModel):
+    def __init__(self, config: UltrabertConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.ultrabert = UltrabertModel(config)
+        classifier_dropout = (
+            config.classifier_dropout
+            if config.classifier_dropout is not None
+            else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        token_type_ids: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor] | TokenClassifierOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        outputs = self.ultrabert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            **kwargs,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer_config.json

@@ -0,0 +1,37 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<|sep|>",
+    "<|mask|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "mask_token": "<|mask|>",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "sep_token": "<|sep|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "truncation_side": "right",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}