Upload 2 files

modeling_hyperclovax.py

@@ -0,0 +1,1259 @@
+# coding=utf-8
+# This file was created for the HyperCLOVA X SEED 14B Think architecture.
+# partially copied and modified from https://github.com/huggingface/transformers
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+from typing import Callable, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.generation.logits_process import LogitsProcessor, LogitsProcessorList
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from typing import List, Iterable, Optional, Union, Tuple
+from collections import deque
+import os
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
+from .configuration_hyperclovax import HyperCLOVAXConfig
+if is_torch_flex_attn_available():
+    from torch.nn.attention.flex_attention import BlockMask
+
+    from transformers.integrations.flex_attention import make_flex_block_causal_mask
+
+logger = logging.get_logger(__name__)
+ 
+# ================= DeepConf: confidence-based online early stop =================
+class DeepConfEOSLogitsProcessor(LogitsProcessor):
+    """
+    Per-sample early stop: at each step, compute token_conf = mean(logprob of top-r),
+    maintain group_conf = mean of last `window` token_conf; if group_conf < threshold,
+    force EOS for THAT sample by setting EOS logprob=0 and others to -inf.
+    """
+
+    def __init__(
+        self,
+        eos_token_ids: List[int],
+        window: int = 512,
+        top_r: int = 5,
+        threshold: float = -3.5,
+        warmup_tokens: int = 0,
+        prefer_eos_ids: Optional[List[int]] = None,
+        require_prev_id: Optional[int] = None,
+        im_end_id: Optional[int] = None,
+        require_im_end_count: int = 0,
+        threshold_think: Optional[float] = None,
+        threshold_answer: Optional[float] = None,
+    ):
+        self.eos_ids: List[int] = sorted({int(i) for i in (eos_token_ids or []) if i is not None and i >= 0})
+        self.window: int = max(int(window), 1)
+        self.top_r: int = max(int(top_r), 1)
+        self.threshold: float = float(threshold)
+        self.warmup_tokens: int = max(int(warmup_tokens), 0)
+        self.prefer_eos_ids: List[int] = sorted({int(i) for i in (prefer_eos_ids or []) if i is not None and i >= 0})
+        self.require_prev_id = require_prev_id
+        self.im_end_id = im_end_id
+        self.require_im_end_count = max(int(require_im_end_count), 0)
+        self.threshold_think = threshold_think
+        self.threshold_answer = threshold_answer
+        self._base_im_end_counts: Optional[List[int]] = None
+        self._buffers: Optional[List[deque]] = None
+        self._verbose: bool = os.getenv("HYPERCLOVA_DEEPCONF_VERBOSE", "0").strip().lower() in {"1", "on", "true"}
+        self._every: int = max(int(os.getenv("HYPERCLOVA_DEEPCONF_REPORT_EVERY", "64")), 1)
+        self._tick: int = 0
+        self._stops: int = 0
+
+    def _ensure(self, bsz: int) -> None:
+        if self._buffers is None or len(self._buffers) != bsz:
+            self._buffers = [deque(maxlen=self.window) for _ in range(bsz)]
+
+    @torch.no_grad()
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        bsz, vocab = scores.shape
+        self._ensure(bsz)
+
+        # --- im_end count (only in generated part) ---
+        gen_counts = [0] * bsz
+        if self.im_end_id is not None and input_ids is not None:
+            # Count im_end in the whole context
+            curr = (input_ids == self.im_end_id).sum(dim=1).tolist()
+            if self._base_im_end_counts is None:
+                self._base_im_end_counts = curr[:]  # Set baseline
+            gen_counts = [curr[i] - self._base_im_end_counts[i] for i in range(bsz)]
+
+        logprobs = torch.log_softmax(scores, dim=-1)
+        k = min(self.top_r, vocab)
+        token_conf = torch.topk(logprobs, k=k, dim=-1).values.mean(dim=-1).tolist()
+
+        for i, c in enumerate(token_conf):
+            buf = self._buffers[i]
+            buf.append(c)
+            group_conf = sum(buf) / len(buf)
+            if len(buf) < self.warmup_tokens:
+                continue
+
+            # phase-aware threshold
+            if self.threshold_think is not None and gen_counts[i] <= 0:
+                thr = self.threshold_think
+            elif self.threshold_answer is not None and gen_counts[i] >= 1:
+                thr = self.threshold_answer
+            else:
+                thr = self.threshold
+
+            # ChatML protection: only force stop after enough im_end tokens
+            im_end_gate_ok = gen_counts[i] >= self.require_im_end_count
+
+            # (Optional) previous token gate
+            prev_ok = True
+            if self.require_prev_id is not None and input_ids is not None and input_ids.size(1) > 0:
+                prev_ok = int(input_ids[i, -1].item()) == self.require_prev_id
+
+            if group_conf < thr and (self.prefer_eos_ids or self.eos_ids) and im_end_gate_ok and prev_ok:
+                targets = self.prefer_eos_ids if self.prefer_eos_ids else self.eos_ids
+                scores[i].fill_(-float("inf"))
+                for eid in targets:
+                    if 0 <= eid < vocab:
+                        scores[i, eid] = 0.0
+                self._stops += 1
+
+        if self._verbose:
+            self._tick += 1
+            if self._tick % self._every == 0:
+                try:
+                    gcs = [(sum(b) / len(b)) if b else float("nan") for b in (self._buffers or [])]
+                    valid = [x for x in gcs if not (x != x)]
+                    mean_gc = float(sum(valid) / max(1, len(valid)))
+                except Exception:
+                    mean_gc = float("nan")
+                
+                if os.getenv("HYPERCLOVA_DEEPCONF_VERBOSE_ATTACH", "0") in {"1", "on", "true"}:
+                    print(f"[DeepConf] step={self._tick} mean_gc={mean_gc:.4f} stops={self._stops}")
+        return scores
+
+# (optional) Offline helper: Lowest Group Confidence (LGC)
+def deepconf_lgc_from_scores(scores_list: Iterable[torch.Tensor], top_r: int = 5, window: int = 2048) -> float:
+    tensors = [s for s in scores_list]
+    if not tensors: return float("-inf")
+    with torch.no_grad():
+        vals = [
+            torch.topk(torch.log_softmax(s, dim=-1), k=min(top_r, s.size(-1)), dim=-1).values.mean(dim=-1)
+            for s in tensors
+        ]  # each (B,)
+        conf = torch.stack(vals).squeeze(-1)  # (T,) if B=1
+        w = min(int(window), conf.numel())
+        kernel = torch.ones(1,1,w, device=conf.device) / w
+        run = torch.nn.functional.conv1d(conf.view(1,1,-1), weight=kernel).squeeze()
+        return float(run.min().item())
+# ============================================================================== 
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class HyperCLOVAXRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        HyperCLOVAXRMSNorm is equivalent to T5LayerNorm
+        """
+        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(HyperCLOVAXRMSNorm)
+class HyperCLOVAXRotaryEmbedding(nn.Module):
+    def __init__(self, config: HyperCLOVAXConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @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].float().expand(position_ids.shape[0], -1, 1).to(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"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).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)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        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
+
+
+class HyperCLOVAXMLP(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
+
+
+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: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    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:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_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
+
+
+class HyperCLOVAXAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: HyperCLOVAXConfig, 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 = getattr(config, "attention_multiplier", self.head_dim**-0.5) # MuP
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        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],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[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_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+
+        if self.config._attn_implementation != "eager":
+            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
+                logger.warning_once(
+                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+                )
+            else:
+                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        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
+
+
+class HyperCLOVAXDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: HyperCLOVAXConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = HyperCLOVAXAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = HyperCLOVAXMLP(config)
+        self.input_layernorm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.use_post_norm = getattr(config, "use_post_norm", False)
+
+        # Peri-LN (post-norm)
+        if self.use_post_norm:
+            self.post_norm1 = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            self.post_norm2 = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.residual_multiplier = getattr(config, "residual_multiplier", 1.0) # MuP
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+
+        if self.use_post_norm: # Peri-LN
+            hidden_states = self.post_norm1(hidden_states)
+
+        hidden_states = residual + hidden_states * self.residual_multiplier # MuP
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        if self.use_post_norm: # Peri-LN
+            hidden_states = self.post_norm2(hidden_states)
+
+        hidden_states = residual + hidden_states * self.residual_multiplier # MuP
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+@auto_docstring
+class HyperCLOVAXPreTrainedModel(PreTrainedModel):
+    config_class = HyperCLOVAXConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["HyperCLOVAXDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+    _supports_attention_backend = True
+
+    def _init_weights(self, 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):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, HyperCLOVAXRMSNorm):
+            module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class HyperCLOVAXModel(HyperCLOVAXPreTrainedModel):
+    def __init__(self, config: HyperCLOVAXConfig):
+        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(
+            [HyperCLOVAXDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = HyperCLOVAXRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # MuP
+        self.embedding_multiplier = getattr(config, "embedding_multiplier", 1.0)
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> BaseModelOutputWithPast:
+        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
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
+        if not isinstance(past_key_values, (type(None), Cache)):
+            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        inputs_embeds = inputs_embeds * self.embedding_multiplier # MuP
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **flash_attn_kwargs,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(
+        self,
+        attention_mask: Union[torch.Tensor, "BlockMask"],
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool = False,
+    ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and (attention_mask == 0.0).any():
+                return attention_mask
+            return None
+        if self.config._attn_implementation == "flex_attention":
+            if isinstance(attention_mask, torch.Tensor):
+                attention_mask = make_flex_block_causal_mask(attention_mask)
+            return attention_mask
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype = input_tensor.dtype
+        sequence_length = input_tensor.shape[1]
+        if using_compilable_cache:
+            target_length = past_key_values.get_max_cache_shape()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type in ["cuda", "xpu", "npu"]
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        cache_position: torch.Tensor,
+        batch_size: int,
+        **kwargs,
+    ):
+        """
+        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+                `(batch_size, 1, query_length, key_value_length)`.
+            sequence_length (`int`):
+                The sequence length being processed.
+            target_length (`int`):
+                The target length: when generating with static cache, the mask should be as long as the static cache,
+                to account for the 0 padding, the part of the cache that is not filled yet.
+            dtype (`torch.dtype`):
+                The dtype to use for the 4D attention mask.
+            cache_position (`torch.Tensor`):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            batch_size (`torch.Tensor`):
+                Batch size.
+        """
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            causal_mask = attention_mask
+        else:
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+                    causal_mask.device
+                )
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+
+        return causal_mask
+
+
+class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
+
+
+@auto_docstring
+class HyperCLOVAXForCausalLM(HyperCLOVAXPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = HyperCLOVAXModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.logits_scaling = getattr(config, "logits_scaling", 1.0)
+
+        # Initialize weights and apply final processing
+        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
+
+    # -------- DeepConf helpers ----------
+    def _dc_collect_eos(self, explicit: Optional[Union[int, List[int]]] = None, **kwargs) -> List[int]:
+        ids: List[int] = []
+        if explicit is not None:
+            ids.extend([int(x) for x in (explicit if isinstance(explicit, (list,tuple)) else [explicit])])
+        else:
+            if getattr(self.config, "eos_token_id", None) is not None:
+                ids.append(int(self.config.eos_token_id))
+            if getattr(self.config, "eos_token_id_list", None):
+                ids.extend(int(x) for x in self.config.eos_token_id_list if x is not None)
+        extra = os.getenv("HYPERCLOVA_DEEPCONF_EOS_IDS", "").strip()
+        if extra:
+            ids.extend(int(tok) for tok in extra.split(",") if tok.strip().isdigit())
+        return sorted({i for i in ids if i >= 0})
+
+    def _dc_enabled(self) -> bool:
+        enabled = True
+        env = os.getenv("HYPERCLOVA_DEEPCONF", "").strip().lower()
+        if env in {"0","off","false"}: enabled = False
+        elif env in {"1","on","true"}: enabled = True
+        cfg_en = getattr(self.config, "deepconf_enable", None)
+        if cfg_en is not None:
+            enabled = bool(cfg_en)           # If config is specified, it takes precedence
+        if getattr(self.config, "deepconf_disable", False):
+            enabled = False                  # Force OFF flag
+        return enabled
+
+    def _dc_params(self) -> Tuple[int,int,float,int]:
+        def env_int(k, d): v=os.getenv(k); return int(v) if v not in (None,"") else d
+        def env_flt(k, d): v=os.getenv(k); return float(v) if v not in (None,"") else d
+        window = env_int("HYPERCLOVA_DEEPCONF_WINDOW", getattr(self.config, "deepconf_window", 512))
+        top_r  = env_int("HYPERCLOVA_DEEPCONF_TOPR",   getattr(self.config, "deepconf_top_r", 5))
+        thr    = env_flt("HYPERCLOVA_DEEPCONF_THRESH", getattr(self.config, "deepconf_threshold", -3.5))
+        warmup = env_int("HYPERCLOVA_DEEPCONF_WARMUP", getattr(self.config, "deepconf_warmup_tokens", 0))
+        return window, top_r, thr, warmup
+
+    def deepconf_generate(self, *args,
+                          eos_token_id: Optional[Union[int, List[int]]] = None,
+                          window: int = 512, top_r: int = 5, threshold: float = -3.5,
+                          warmup_tokens: int = 0,
+                          **kwargs):
+        # Prefer ChatML stop strings if tokenizer+stop_strings are provided
+        prefer_ids: List[int] = []
+        tok = kwargs.get("tokenizer", None)
+        stop_strings = kwargs.get("stop_strings", None)
+        if tok is not None and stop_strings:
+            for s in stop_strings:
+                try:
+                    eid = tok.convert_tokens_to_ids(s)
+                    if isinstance(eid, int) and eid >= 0:
+                        prefer_ids.append(int(eid)); continue
+                except Exception:
+                    pass
+                try:
+                    enc = tok.encode(s, add_special_tokens=False)
+                    if isinstance(enc, list) and len(enc) == 1:
+                        prefer_ids.append(int(enc[0]))
+                except Exception:
+                    pass
+        lp: LogitsProcessorList = kwargs.pop("logits_processor", None) or LogitsProcessorList()
+        lp.append(
+            DeepConfEOSLogitsProcessor(
+                self._dc_collect_eos(eos_token_id, **kwargs),
+                window, top_r, threshold,
+                warmup_tokens=warmup_tokens,
+                prefer_eos_ids=prefer_ids or None
+            )
+        )
+        kwargs["logits_processor"] = lp
+        return super().generate(*args, **kwargs)
+
+    # Override generate() to be default ON (auto-attach DeepConf; merge with external lps)
+    def generate(self, *args, **kwargs):
+        if self._dc_enabled():
+            eos_ids = self._dc_collect_eos(kwargs.get("eos_token_id", None), **kwargs)
+            # Prefer ChatML end tokens if provided
+            prefer_ids: List[int] = []
+            tok = kwargs.get("tokenizer", None)
+            stop_strings = kwargs.get("stop_strings", None)
+            im_end_id = None
+            if tok is not None and stop_strings:
+                for s in stop_strings:
+                    try:
+                        eid = tok.convert_tokens_to_ids(s)
+                        if isinstance(eid, int) and eid >= 0:
+                            prefer_ids.append(int(eid))
+                            continue
+                    except Exception:
+                        pass
+                    try:
+                        enc = tok.encode(s, add_special_tokens=False)
+                        if isinstance(enc, list) and len(enc) == 1:
+                            prefer_ids.append(int(enc[0]))
+                    except Exception:
+                        pass
+
+            # For ChatML protection: extract <|im_end|> id
+            if tok is not None:
+                try:
+                    im_end_id = tok.convert_tokens_to_ids("<|im_end|>")
+                    if not isinstance(im_end_id, int) or im_end_id < 0:
+                        im_end_id = None
+                except Exception:
+                    im_end_id = None
+
+            if eos_ids:
+                window, top_r, thr, warmup = self._dc_params()
+
+                def env_int(k, d):
+                    v = os.getenv(k)
+                    return int(v) if v not in (None, "") else d
+
+                # Phase-aware params from ENV
+                require_count = env_int(
+                    "HYPERCLOVA_DEEPCONF_REQUIRE_IM_END_COUNT", 2 if (prefer_ids and im_end_id is not None) else 0
+                )
+                thr_think_str = os.getenv("HYPERCLOVA_DEEPCONF_THRESH_THINK", None)
+                thr_ans_str = os.getenv("HYPERCLOVA_DEEPCONF_THRESH_ANS", None)
+                thr_think = float(thr_think_str) if thr_think_str is not None and thr_think_str.strip() != "" else None
+                thr_ans = float(thr_ans_str) if thr_ans_str is not None and thr_ans_str.strip() != "" else None
+
+                # require_prev_id is deprecated in favor of require_im_end_count, setting to None as recommended.
+                require_prev = None
+                if os.getenv("HYPERCLOVA_DEEPCONF_REQUIRE_IM_END", "0").lower() in {"1", "on", "true"}:  # Keep for BC, but default off
+                    require_prev = im_end_id
+
+                lp: LogitsProcessorList = kwargs.pop("logits_processor", None) or LogitsProcessorList()
+
+                if os.getenv("HYPERCLOVA_DEEPCONF_VERBOSE_ATTACH", "0") in {"1", "on", "true"}:
+                    print(
+                        f"[DeepConf] attach window={window} top_r={top_r} thr={thr} warmup={warmup} eos={eos_ids} prefer={prefer_ids} "
+                        f"require_prev={require_prev} im_end_id={im_end_id} require_count={require_count} thr_think={thr_think} thr_ans={thr_ans}"
+                    )
+
+                lp.append(
+                    DeepConfEOSLogitsProcessor(
+                        eos_ids,
+                        window,
+                        top_r,
+                        thr,
+                        warmup_tokens=warmup,
+                        prefer_eos_ids=prefer_ids or None,
+                        require_prev_id=require_prev,
+                        im_end_id=im_end_id,
+                        require_im_end_count=require_count,
+                        threshold_think=thr_think,
+                        threshold_answer=thr_ans,
+                    )
+                )
+                kwargs["logits_processor"] = lp
+        return super().generate(*args, **kwargs)
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[KwargsForCausalLM],
+    ) -> CausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (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]`.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, HyperCLOVAXForCausalLM
+
+        >>> model = HyperCLOVAXForCausalLM.from_pretrained("naver-hyperclovax/{model_name}")
+        >>> tokenizer = AutoTokenizer.from_pretrained("naver-hyperclovax/{model_name}")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?
+I'm not conscious, but I can talk to you."
+        ```"""
+        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
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        # MuP
+        logits = self.lm_head(hidden_states[:, slice_indices, :]) * self.logits_scaling
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    The HyperCLOVAX Model transformer with a sequence classification head on top (linear layer).
+
+    [`HyperCLOVAXForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """
+)
+class HyperCLOVAXForSequenceClassification(HyperCLOVAXPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = HyperCLOVAXModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> SequenceClassifierOutputWithPast:
+        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).
+        """
+
+        transformer_outputs: BaseModelOutputWithPast = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        hidden_states = transformer_outputs.last_hidden_state
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            last_non_pad_token = -1
+        elif input_ids is not None:
+            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+        else:
+            last_non_pad_token = -1
+            logger.warning_once(
+                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+            )
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@auto_docstring
+class HyperCLOVAXForQuestionAnswering(HyperCLOVAXPreTrainedModel):
+    base_model_prefix = "transformer"
+
+    # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->HyperCLOVAX
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = HyperCLOVAXModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.transformer.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        **kwargs,
+    ) -> QuestionAnsweringModelOutput:
+        outputs: BaseModelOutputWithPast = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output = outputs.last_hidden_state
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        loss = None
+        if start_positions is not None and end_positions is not None:
+            loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
+
+        return QuestionAnsweringModelOutput(
+            loss=loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring
+class HyperCLOVAXForTokenClassification(HyperCLOVAXPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = HyperCLOVAXModel(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> TokenClassifierOutput:
+        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: BaseModelOutputWithPast = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = outputs.last_hidden_state
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.config)
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = [
+    "HyperCLOVAXForCausalLM",
+    "HyperCLOVAXModel",
+    "HyperCLOVAXPreTrainedModel",
+    "HyperCLOVAXForSequenceClassification",
+    "HyperCLOVAXForQuestionAnswering",
+    "HyperCLOVAXForTokenClassification",
+]

modeling_hyperclovax_old.py

@@ -0,0 +1,1199 @@
+# coding=utf-8
+# This file was created for the HyperCLOVA X SEED 14B Think architecture.
+# partially copied and modified from https://github.com/huggingface/transformers
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+from typing import Callable, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.generation.logits_process import LogitsProcessor, LogitsProcessorList
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from typing import List, Iterable, Optional, Union, Tuple
+from collections import deque
+import os
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
+from .configuration_hyperclovax import HyperCLOVAXConfig
+if is_torch_flex_attn_available():
+    from torch.nn.attention.flex_attention import BlockMask
+
+    from transformers.integrations.flex_attention import make_flex_block_causal_mask
+
+logger = logging.get_logger(__name__)
+ 
+# ================= DeepConf: confidence-based online early stop =================
+class DeepConfEOSLogitsProcessor(LogitsProcessor):
+    """
+    Per-sample early stop: at each step, compute token_conf = mean(logprob of top-r),
+    maintain group_conf = mean of last `window` token_conf; if group_conf < threshold,
+    force EOS for THAT sample by setting EOS logprob=0 and others to -inf.
+    """
+    def __init__(
+        self,
+        eos_token_ids: List[int],
+        window: int = 512,
+        top_r: int = 5,
+        threshold: float = -3.5,
+        warmup_tokens: int = 0,
+        prefer_eos_ids: Optional[List[int]] = None,
+        require_prev_id: Optional[int] = None,
+    ):
+        self.eos_ids: List[int] = sorted({int(i) for i in (eos_token_ids or []) if i is not None and i >= 0})
+        self.window: int = max(int(window), 1)
+        self.top_r: int = max(int(top_r), 1)
+        self.threshold: float = float(threshold)
+        self.warmup_tokens: int = max(int(warmup_tokens), 0)
+        self.prefer_eos_ids: List[int] = sorted({int(i) for i in (prefer_eos_ids or []) if i is not None and i >= 0})
+        self.require_prev_id = require_prev_id
+        self._buffers: Optional[List[deque]] = None
+        self._verbose: bool = os.getenv("HYPERCLOVA_DEEPCONF_VERBOSE", "0").strip().lower() in {"1","on","true"}
+        self._every: int = max(int(os.getenv("HYPERCLOVA_DEEPCONF_REPORT_EVERY", "64")), 1)
+        self._tick: int = 0
+        self._stops: int = 0
+
+    def _ensure(self, bsz: int) -> None:
+        if self._buffers is None or len(self._buffers) != bsz:
+            self._buffers = [deque(maxlen=self.window) for _ in range(bsz)]
+
+    @torch.no_grad()
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        bsz, vocab = scores.shape
+        self._ensure(bsz)
+        logprobs = torch.log_softmax(scores, dim=-1)         # (B, V)
+        k = min(self.top_r, vocab)
+        token_conf = torch.topk(logprobs, k=k, dim=-1).values.mean(dim=-1)  # (B,)
+
+        stopped = False
+        for i, c in enumerate(token_conf.tolist()):
+            buf = self._buffers[i]; buf.append(c)
+            group_conf = sum(buf) / len(buf)
+            # --- warmup gate: do not early-stop until we have enough tokens ---
+            if len(buf) < self.warmup_tokens:
+                continue
+
+            # ChatML protection: only force preferred EOS after the required previous token
+            prev_ok = True
+            if self.require_prev_id is not None:
+                prev_tok = int(input_ids[i, -1].item()) if input_ids is not None and input_ids.size(1) > 0 else -1
+                prev_ok = (prev_tok == self.require_prev_id)
+
+            if group_conf < self.threshold and (self.prefer_eos_ids or self.eos_ids) and prev_ok:
+                # Prefer ChatML end tokens if available; else fall back to config eos
+                targets = self.prefer_eos_ids if self.prefer_eos_ids else self.eos_ids
+                scores[i].fill_(-float("inf"))
+                for eid in targets:
+                    if 0 <= eid < vocab:
+                        scores[i, eid] = 0.0
+                self._stops += 1
+                stopped = True
+
+        if self._verbose:
+            self._tick += 1
+            if self._tick % self._every == 0:
+                try:
+                    gcs = [(sum(b)/len(b)) if b else float("nan") for b in (self._buffers or [])]
+                    valid = [x for x in gcs if not (x != x)]
+                    mean_gc = float(sum(valid)/max(1, len(valid)))
+                except Exception:
+                    mean_gc = float("nan")
+                print(f"[DeepConf] step={self._tick} mean_gc={mean_gc:.4f} stops={self._stops}")
+        return scores
+
+# (optional) Offline helper: Lowest Group Confidence (LGC)
+def deepconf_lgc_from_scores(scores_list: Iterable[torch.Tensor], top_r: int = 5, window: int = 2048) -> float:
+    tensors = [s for s in scores_list]
+    if not tensors: return float("-inf")
+    with torch.no_grad():
+        vals = [
+            torch.topk(torch.log_softmax(s, dim=-1), k=min(top_r, s.size(-1)), dim=-1).values.mean(dim=-1)
+            for s in tensors
+        ]  # each (B,)
+        conf = torch.stack(vals).squeeze(-1)  # (T,) if B=1
+        w = min(int(window), conf.numel())
+        kernel = torch.ones(1,1,w, device=conf.device) / w
+        run = torch.nn.functional.conv1d(conf.view(1,1,-1), weight=kernel).squeeze()
+        return float(run.min().item())
+# ============================================================================== 
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class HyperCLOVAXRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        HyperCLOVAXRMSNorm is equivalent to T5LayerNorm
+        """
+        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(HyperCLOVAXRMSNorm)
+class HyperCLOVAXRotaryEmbedding(nn.Module):
+    def __init__(self, config: HyperCLOVAXConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @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].float().expand(position_ids.shape[0], -1, 1).to(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"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).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)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        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
+
+
+class HyperCLOVAXMLP(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
+
+
+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: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    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:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_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
+
+
+class HyperCLOVAXAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: HyperCLOVAXConfig, 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 = getattr(config, "attention_multiplier", self.head_dim**-0.5) # MuP
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        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],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[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_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+
+        if self.config._attn_implementation != "eager":
+            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
+                logger.warning_once(
+                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+                )
+            else:
+                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        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
+
+
+class HyperCLOVAXDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: HyperCLOVAXConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = HyperCLOVAXAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = HyperCLOVAXMLP(config)
+        self.input_layernorm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.use_post_norm = getattr(config, "use_post_norm", False)
+
+        # Peri-LN (post-norm)
+        if self.use_post_norm:
+            self.post_norm1 = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            self.post_norm2 = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.residual_multiplier = getattr(config, "residual_multiplier", 1.0) # MuP
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+
+        if self.use_post_norm: # Peri-LN
+            hidden_states = self.post_norm1(hidden_states)
+
+        hidden_states = residual + hidden_states * self.residual_multiplier # MuP
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        if self.use_post_norm: # Peri-LN
+            hidden_states = self.post_norm2(hidden_states)
+
+        hidden_states = residual + hidden_states * self.residual_multiplier # MuP
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+@auto_docstring
+class HyperCLOVAXPreTrainedModel(PreTrainedModel):
+    config_class = HyperCLOVAXConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["HyperCLOVAXDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+    _supports_attention_backend = True
+
+    def _init_weights(self, 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):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, HyperCLOVAXRMSNorm):
+            module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class HyperCLOVAXModel(HyperCLOVAXPreTrainedModel):
+    def __init__(self, config: HyperCLOVAXConfig):
+        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(
+            [HyperCLOVAXDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = HyperCLOVAXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = HyperCLOVAXRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # MuP
+        self.embedding_multiplier = getattr(config, "embedding_multiplier", 1.0)
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> BaseModelOutputWithPast:
+        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
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
+        if not isinstance(past_key_values, (type(None), Cache)):
+            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        inputs_embeds = inputs_embeds * self.embedding_multiplier # MuP
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **flash_attn_kwargs,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(
+        self,
+        attention_mask: Union[torch.Tensor, "BlockMask"],
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool = False,
+    ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and (attention_mask == 0.0).any():
+                return attention_mask
+            return None
+        if self.config._attn_implementation == "flex_attention":
+            if isinstance(attention_mask, torch.Tensor):
+                attention_mask = make_flex_block_causal_mask(attention_mask)
+            return attention_mask
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype = input_tensor.dtype
+        sequence_length = input_tensor.shape[1]
+        if using_compilable_cache:
+            target_length = past_key_values.get_max_cache_shape()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type in ["cuda", "xpu", "npu"]
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        cache_position: torch.Tensor,
+        batch_size: int,
+        **kwargs,
+    ):
+        """
+        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+                `(batch_size, 1, query_length, key_value_length)`.
+            sequence_length (`int`):
+                The sequence length being processed.
+            target_length (`int`):
+                The target length: when generating with static cache, the mask should be as long as the static cache,
+                to account for the 0 padding, the part of the cache that is not filled yet.
+            dtype (`torch.dtype`):
+                The dtype to use for the 4D attention mask.
+            cache_position (`torch.Tensor`):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            batch_size (`torch.Tensor`):
+                Batch size.
+        """
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            causal_mask = attention_mask
+        else:
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+                    causal_mask.device
+                )
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+
+        return causal_mask
+
+
+class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
+
+
+@auto_docstring
+class HyperCLOVAXForCausalLM(HyperCLOVAXPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = HyperCLOVAXModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.logits_scaling = getattr(config, "logits_scaling", 1.0)
+
+        # Initialize weights and apply final processing
+        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
+
+    # -------- DeepConf helpers ----------
+    def _dc_collect_eos(self, explicit: Optional[Union[int, List[int]]] = None, **kwargs) -> List[int]:
+        ids: List[int] = []
+        if explicit is not None:
+            ids.extend([int(x) for x in (explicit if isinstance(explicit, (list,tuple)) else [explicit])])
+        else:
+            if getattr(self.config, "eos_token_id", None) is not None:
+                ids.append(int(self.config.eos_token_id))
+            if getattr(self.config, "eos_token_id_list", None):
+                ids.extend(int(x) for x in self.config.eos_token_id_list if x is not None)
+        extra = os.getenv("HYPERCLOVA_DEEPCONF_EOS_IDS", "").strip()
+        if extra:
+            ids.extend(int(tok) for tok in extra.split(",") if tok.strip().isdigit())
+        return sorted({i for i in ids if i >= 0})
+
+    def _dc_enabled(self) -> bool:
+        enabled = True
+        env = os.getenv("HYPERCLOVA_DEEPCONF", "").strip().lower()
+        if env in {"0","off","false"}: enabled = False
+        elif env in {"1","on","true"}: enabled = True
+        cfg_en = getattr(self.config, "deepconf_enable", None)
+        if cfg_en is not None:
+            enabled = bool(cfg_en)           # If config is specified, it takes precedence
+        if getattr(self.config, "deepconf_disable", False):
+            enabled = False                  # Force OFF flag
+        return enabled
+
+    def _dc_params(self) -> Tuple[int,int,float,int]:
+        def env_int(k, d): v=os.getenv(k); return int(v) if v not in (None,"") else d
+        def env_flt(k, d): v=os.getenv(k); return float(v) if v not in (None,"") else d
+        window = env_int("HYPERCLOVA_DEEPCONF_WINDOW", getattr(self.config, "deepconf_window", 512))
+        top_r  = env_int("HYPERCLOVA_DEEPCONF_TOPR",   getattr(self.config, "deepconf_top_r", 5))
+        thr    = env_flt("HYPERCLOVA_DEEPCONF_THRESH", getattr(self.config, "deepconf_threshold", -3.5))
+        warmup = env_int("HYPERCLOVA_DEEPCONF_WARMUP", getattr(self.config, "deepconf_warmup_tokens", 0))
+        return window, top_r, thr, warmup
+
+    def deepconf_generate(self, *args,
+                          eos_token_id: Optional[Union[int, List[int]]] = None,
+                          window: int = 512, top_r: int = 5, threshold: float = -3.5,
+                          warmup_tokens: int = 0,
+                          **kwargs):
+        # Prefer ChatML stop strings if tokenizer+stop_strings are provided
+        prefer_ids: List[int] = []
+        tok = kwargs.get("tokenizer", None)
+        stop_strings = kwargs.get("stop_strings", None)
+        if tok is not None and stop_strings:
+            for s in stop_strings:
+                try:
+                    eid = tok.convert_tokens_to_ids(s)
+                    if isinstance(eid, int) and eid >= 0:
+                        prefer_ids.append(int(eid)); continue
+                except Exception:
+                    pass
+                try:
+                    enc = tok.encode(s, add_special_tokens=False)
+                    if isinstance(enc, list) and len(enc) == 1:
+                        prefer_ids.append(int(enc[0]))
+                except Exception:
+                    pass
+        lp: LogitsProcessorList = kwargs.pop("logits_processor", None) or LogitsProcessorList()
+        lp.append(
+            DeepConfEOSLogitsProcessor(
+                self._dc_collect_eos(eos_token_id, **kwargs),
+                window, top_r, threshold,
+                warmup_tokens=warmup_tokens,
+                prefer_eos_ids=prefer_ids or None
+            )
+        )
+        kwargs["logits_processor"] = lp
+        return super().generate(*args, **kwargs)
+
+    # Override generate() to be default ON (auto-attach DeepConf; merge with external lps)
+    def generate(self, *args, **kwargs):
+        if self._dc_enabled():
+            eos_ids = self._dc_collect_eos(kwargs.get("eos_token_id", None), **kwargs)
+            # Prefer ChatML end tokens if provided
+            prefer_ids: List[int] = []
+            tok = kwargs.get("tokenizer", None)
+            stop_strings = kwargs.get("stop_strings", None)
+            im_end_id = None
+            if tok is not None and stop_strings:
+                for s in stop_strings:
+                    try:
+                        eid = tok.convert_tokens_to_ids(s)
+                        if isinstance(eid, int) and eid >= 0: prefer_ids.append(int(eid)); continue
+                    except Exception: pass
+                    try:
+                        enc = tok.encode(s, add_special_tokens=False)
+                        if isinstance(enc, list) and len(enc) == 1: prefer_ids.append(int(enc[0]))
+                    except Exception: pass
+
+            # For ChatML protection: extract <|im_end|> id
+            if tok is not None:
+                try:
+                    im_end_id = tok.convert_tokens_to_ids("<|im_end|>")
+                    if not isinstance(im_end_id, int) or im_end_id < 0:
+                        im_end_id = None
+                except Exception:
+                    im_end_id = None
+
+            if eos_ids:
+                window, top_r, thr, warmup = self._dc_params()
+                require_prev = None
+                if (os.getenv("HYPERCLOVA_DEEPCONF_REQUIRE_IM_END", "1").lower() in {"1","on","true"}) and prefer_ids and im_end_id is not None:
+                    require_prev = im_end_id
+                
+                lp: LogitsProcessorList = kwargs.pop("logits_processor", None) or LogitsProcessorList()
+
+                if os.getenv("HYPERCLOVA_DEEPCONF_VERBOSE_ATTACH","0") in {"1","on","true"}:
+                    print(f"[DeepConf] attach window={window} top_r={top_r} thr={thr} warmup={warmup} eos={eos_ids} prefer={prefer_ids} require_prev={require_prev}")
+
+                lp.append(
+                    DeepConfEOSLogitsProcessor(
+                        eos_ids, window, top_r, thr,
+                        warmup_tokens=warmup,
+                        prefer_eos_ids=prefer_ids or None,
+                        require_prev_id=require_prev
+                    )
+                )
+                kwargs["logits_processor"] = lp
+        return super().generate(*args, **kwargs)
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[KwargsForCausalLM],
+    ) -> CausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (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]`.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, HyperCLOVAXForCausalLM
+
+        >>> model = HyperCLOVAXForCausalLM.from_pretrained("naver-hyperclovax/{model_name}")
+        >>> tokenizer = AutoTokenizer.from_pretrained("naver-hyperclovax/{model_name}")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        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
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        # MuP
+        logits = self.lm_head(hidden_states[:, slice_indices, :]) * self.logits_scaling
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    The HyperCLOVAX Model transformer with a sequence classification head on top (linear layer).
+
+    [`HyperCLOVAXForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """
+)
+class HyperCLOVAXForSequenceClassification(HyperCLOVAXPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = HyperCLOVAXModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> SequenceClassifierOutputWithPast:
+        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).
+        """
+
+        transformer_outputs: BaseModelOutputWithPast = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        hidden_states = transformer_outputs.last_hidden_state
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            last_non_pad_token = -1
+        elif input_ids is not None:
+            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+        else:
+            last_non_pad_token = -1
+            logger.warning_once(
+                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+            )
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@auto_docstring
+class HyperCLOVAXForQuestionAnswering(HyperCLOVAXPreTrainedModel):
+    base_model_prefix = "transformer"
+
+    # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->HyperCLOVAX
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = HyperCLOVAXModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.transformer.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        **kwargs,
+    ) -> QuestionAnsweringModelOutput:
+        outputs: BaseModelOutputWithPast = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output = outputs.last_hidden_state
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        loss = None
+        if start_positions is not None and end_positions is not None:
+            loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
+
+        return QuestionAnsweringModelOutput(
+            loss=loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring
+class HyperCLOVAXForTokenClassification(HyperCLOVAXPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = HyperCLOVAXModel(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> TokenClassifierOutput:
+        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: BaseModelOutputWithPast = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = outputs.last_hidden_state
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.config)
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = [
+    "HyperCLOVAXForCausalLM",
+    "HyperCLOVAXModel",
+    "HyperCLOVAXPreTrainedModel",
+    "HyperCLOVAXForSequenceClassification",
+    "HyperCLOVAXForQuestionAnswering",
+    "HyperCLOVAXForTokenClassification",
+]