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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

SECURITY.md

@@ -0,0 +1,11 @@
+# Security and privacy
+If you discover potential security issues in the project, or believe you may have found a security issue, please notify the ByteDance security team through our [security center](https://security.bytedance.com/src) or [vulnerability reporting email](mailto:src@bytedance.com). Please **do not** create public GitHub Issues.
+ 
+We will assess the vulnerability based on the Common Vulnerability Scoring System (CVSS 3.1). The security team will keep you updated on key progress and may request further information or guidance from you. You are welcome to contact us via the email or website mentioned above to ask questions or discuss disclosure matters.
+ 
+To protect the security of our customers, ByteDance requests that you do not publish or share information regarding the vulnerability in any public forum, nor publish or share data involving users, until the vulnerability has been remediated and our users have been notified. Please understand that the time required for remediation depends on the severity of the vulnerability and the scope of the impact.
+
+Individuals, companies, and security teams may wish to publish security advisories on their own websites or other forums. Please contact us via the email or website mentioned above prior to publication to discuss the information that can be disclosed and to coordinate the disclosure timeline.
+
+# Bug Bounty Reward
+[For the policy of bug bounty reward](https://bytedance.larkoffice.com/docx/ZstQd7bbooDctqxBCAmcFasOngd), if you have any questions about the rules, please contact [https://src.bytedance.com/home](https://src.bytedance.com/home) for consultation.

chat_template.jinja

@@ -0,0 +1,3 @@
+{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set role = message['role'] %}{{ bos_token + role + '
+' + message['content'] | trim + eos_token }}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant
+'}}{% endif %}

config.json

@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "ILLaDAForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_illada.ILLaDAConfig",
+    "AutoModel": "modeling_illada.ILLaDAForCausalLM",
+    "AutoModelForCausalLM": "modeling_illada.ILLaDAForCausalLM"
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.013975424859373685,
+  "intermediate_size": 14336,
+  "layer_norm_eps": null,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "illada",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 1.0,
+    "rope_type": "default"
+  },
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "torch_dtype": "bfloat16",
+  "vocab_size": 155136
+}

configuration_illada.py

@@ -0,0 +1,87 @@
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""iLLaDA model configuration."""
+
+from transformers import AutoConfig, PretrainedConfig
+
+
+class ILLaDAConfig(PretrainedConfig):
+    model_type = "illada"
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        layer_norm_eps=None,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        resid_pdrop=0.0,
+        **kwargs,
+    ):
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.layer_norm_eps = layer_norm_eps
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+        self.resid_pdrop = resid_pdrop
+
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+
+        if self.hidden_size % self.num_attention_heads != 0:
+            raise ValueError("hidden_size must be divisible by num_attention_heads")
+        if self.num_attention_heads % self.num_key_value_heads != 0:
+            raise ValueError("num_attention_heads must be divisible by num_key_value_heads")
+
+        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,
+        )
+
+
+AutoConfig.register(ILLaDAConfig.model_type, ILLaDAConfig)

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "transformers_version": "4.57.1"
+}

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+}

modeling_illada.py

@@ -0,0 +1,538 @@
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import AutoModel, AutoModelForCausalLM
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutput, CausalLMOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.utils import logging
+
+from .configuration_illada import ILLaDAConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class ILLaDARMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        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}"
+
+
+ALL_LAYERNORM_LAYERS.append(ILLaDARMSNorm)
+
+
+class ILLaDARotaryEmbedding(nn.Module):
+    def __init__(self, config: ILLaDAConfig):
+        super().__init__()
+        rope_scaling = config.rope_scaling or {}
+        rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
+        factor = rope_scaling.get("factor", 1.0)
+        if rope_type != "default" or factor != 1.0:
+            raise ValueError("This iLLaDA checkpoint expects default RoPE without scaling.")
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        inv_freq = 1.0 / (
+            config.rope_theta ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim)
+        )
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        inv_freq = self.inv_freq[None, :, None].to(device=x.device)
+        position_ids = position_ids[:, None, :].to(dtype=torch.float32, device=x.device)
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq.float() @ position_ids.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
+
+
+class ILLaDAMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.dropout = nn.Dropout(config.resid_pdrop)
+
+    def forward(self, x):
+        return self.dropout(self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)))
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    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 _prepare_4d_attention_mask(attention_mask, dtype, device):
+    if attention_mask is None:
+        return None
+
+    attention_mask = attention_mask.to(device=device)
+    min_dtype = torch.finfo(dtype).min
+
+    if attention_mask.dim() == 2:
+        allowed = attention_mask.to(torch.bool)[:, None, None, :]
+        additive_mask = torch.zeros(allowed.shape, dtype=dtype, device=device)
+        return additive_mask.masked_fill(~allowed, min_dtype)
+
+    if attention_mask.dim() == 3:
+        attention_mask = attention_mask[:, None, :, :]
+
+    if attention_mask.dim() != 4:
+        raise ValueError("attention_mask must have shape (batch, seq), (batch, q, k), or (batch, 1, q, k)")
+
+    if attention_mask.dtype == torch.bool:
+        additive_mask = torch.zeros(attention_mask.shape, dtype=dtype, device=device)
+        return additive_mask.masked_fill(~attention_mask, min_dtype)
+
+    return attention_mask.to(dtype=dtype)
+
+
+class ILLaDAAttention(nn.Module):
+    def __init__(self, config: ILLaDAConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.attention_dropout = config.attention_dropout
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+
+    def _project_qkv(self, hidden_states, position_embeddings):
+        batch_size, seq_len, _ = hidden_states.size()
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        return query_states, key_states, value_states
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        batch_size, seq_len, _ = hidden_states.size()
+        query_states, key_states, value_states = self._project_qkv(hidden_states, position_embeddings)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask[:, :, :, : key_states.shape[-2]]
+
+        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = F.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (batch_size, self.num_heads, seq_len, self.head_dim):
+            raise ValueError(
+                f"attn_output should have shape {(batch_size, self.num_heads, seq_len, self.head_dim)}, "
+                f"got {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous().reshape(batch_size, seq_len, -1)
+        attn_output = self.resid_dropout(self.o_proj(attn_output))
+        return attn_output, attn_weights if output_attentions else None
+
+
+class ILLaDASdpaAttention(ILLaDAAttention):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        if output_attentions:
+            logger.warning_once(
+                "torch SDPA does not return attention weights; falling back to the eager attention implementation."
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                position_embeddings=position_embeddings,
+            )
+
+        batch_size, seq_len, _ = hidden_states.size()
+        query_states, key_states, value_states = self._project_qkv(hidden_states, position_embeddings)
+
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = F.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=False,
+        )
+        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, -1)
+        attn_output = self.resid_dropout(self.o_proj(attn_output))
+        return attn_output, None
+
+
+ILLADA_ATTENTION_CLASSES = {
+    "eager": ILLaDAAttention,
+    "sdpa": ILLaDASdpaAttention,
+}
+
+
+class ILLaDADecoderLayer(nn.Module):
+    def __init__(self, config: ILLaDAConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        attn_impl = getattr(config, "_attn_implementation", "sdpa")
+        if attn_impl == "flash_attention_2":
+            logger.warning_once("flash_attention_2 is not implemented in this lightweight iLLaDA code; using sdpa.")
+            attn_impl = "sdpa"
+        if attn_impl not in ILLADA_ATTENTION_CLASSES:
+            attn_impl = "eager"
+        self.self_attn = ILLADA_ATTENTION_CLASSES[attn_impl](config=config, layer_idx=layer_idx)
+        self.mlp = ILLaDAMLP(config)
+        if config.layer_norm_eps is not None:
+            self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+            self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        else:
+            self.input_layernorm = ILLaDARMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            self.post_attention_layernorm = ILLaDARMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            position_embeddings=position_embeddings,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+        return outputs
+
+
+class ILLaDAPreTrainedModel(PreTrainedModel):
+    config_class = ILLaDAConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["ILLaDADecoderLayer"]
+    _supports_sdpa = True
+    _supports_flash_attn_2 = False
+
+    def post_init(self):
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) and any(substring in name for substring in ["o_proj", "down_proj"]):
+                module.std = self.config.initializer_range / math.sqrt(2 * self.config.num_hidden_layers)
+        super().post_init()
+
+    def _init_weights(self, module):
+        std = getattr(module, "std", self.config.initializer_range)
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            std = math.sqrt(1 / (2 * self.config.hidden_size))
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class ILLaDAModel(ILLaDAPreTrainedModel):
+    def __init__(self, config: ILLaDAConfig):
+        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(
+            [ILLaDADecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        if config.layer_norm_eps is not None:
+            self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        else:
+            self.norm = ILLaDARMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = ILLaDARotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        attention_bias: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, BaseModelOutput]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) == (inputs_embeds is None):
+            raise ValueError("Specify exactly one of input_ids or inputs_embeds")
+
+        if input_ids is not None and input_ids.dim() == 1:
+            input_ids = input_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        elif inputs_embeds.dim() == 2:
+            inputs_embeds = inputs_embeds.unsqueeze(0)
+
+        batch_size, seq_len, _ = inputs_embeds.shape
+        if position_ids is None:
+            position_ids = torch.arange(seq_len, device=inputs_embeds.device).unsqueeze(0).expand(batch_size, -1)
+        elif position_ids.dim() == 1:
+            position_ids = position_ids.unsqueeze(0)
+
+        prepared_attention_mask = _prepare_4d_attention_mask(
+            attention_mask,
+            dtype=inputs_embeds.dtype,
+            device=inputs_embeds.device,
+        )
+        prepared_attention_bias = _prepare_4d_attention_mask(
+            attention_bias,
+            dtype=inputs_embeds.dtype,
+            device=inputs_embeds.device,
+        )
+        if prepared_attention_mask is None:
+            prepared_attention_mask = prepared_attention_bias
+        elif prepared_attention_bias is not None:
+            prepared_attention_mask = prepared_attention_mask + prepared_attention_bias
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    prepared_attention_mask,
+                    output_attentions,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=prepared_attention_mask,
+                    output_attentions=output_attentions,
+                    position_embeddings=position_embeddings,
+                )
+
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attns] if v is not None)
+
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class ILLaDAForCausalLM(ILLaDAPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ILLaDAModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.loss_fct = CrossEntropyLoss()
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        attention_bias: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs,
+    ) -> Union[Tuple, CausalLMOutput]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            attention_bias=attention_bias,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        if isinstance(logits_to_keep, int) and logits_to_keep > 0:
+            hidden_states_for_logits = hidden_states[:, -logits_to_keep:, :]
+        elif isinstance(logits_to_keep, torch.Tensor):
+            hidden_states_for_logits = hidden_states[:, logits_to_keep, :]
+        else:
+            hidden_states_for_logits = hidden_states
+        logits = self.lm_head(hidden_states_for_logits)
+
+        loss = None
+        if labels is not None:
+            logits = logits.float()
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous().to(shift_logits.device)
+            loss = self.loss_fct(shift_logits.view(-1, self.vocab_size), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+AutoModel.register(ILLaDAConfig, ILLaDAForCausalLM)
+AutoModelForCausalLM.register(ILLaDAConfig, ILLaDAForCausalLM)

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<[BOS]>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<[EOS]>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<[PAD]>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<[SEP]>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62dd8b84b725abc895c3c845fb2632e53d9ebaabfb49443f9c8a117dec8668c7
+size 11880930

tokenizer_config.json

@@ -0,0 +1,1036 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<[BOS]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<[PAD]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<[EOS]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<[UNK]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<[CLS]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<[MASK]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<[SEP]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<[PLHD7]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<[PLHD8]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<[PLHD9]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "<[PLHD10]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "<[PLHD11]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "<[PLHD12]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "<[PLHD13]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "<[PLHD14]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "<[PLHD15]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "16": {
+      "content": "<[PLHD16]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "17": {
+      "content": "<[PLHD17]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "18": {
+      "content": "<[PLHD18]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "19": {
+      "content": "<[PLHD19]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "20": {
+      "content": "<[PLHD20]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "21": {
+      "content": "<[PLHD21]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "22": {
+      "content": "<[PLHD22]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "23": {
+      "content": "<[PLHD23]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "24": {
+      "content": "<[PLHD24]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "25": {
+      "content": "<[PLHD25]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "26": {
+      "content": "<[PLHD26]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "27": {
+      "content": "<[PLHD27]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "28": {
+      "content": "<[PLHD28]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "29": {
+      "content": "<[PLHD29]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30": {
+      "content": "<[PLHD30]>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "31": {
+      "content": "<[PLHD31]>",
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