Delete modeling_k2_horizon.py

modeling_k2_horizon.py

@@ -1,1116 +0,0 @@
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-#           This file was automatically generated from src/transformers/models/qwen3_moe/modular_qwen3_moe.py.
-#               Do NOT edit this file manually as any edits will be overwritten by the generation of
-#             the file from the modular. If any change should be done, please apply the change to the
-#                          modular_qwen3_moe.py file directly. One of our CI enforces this.
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-# coding=utf-8
-# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# 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 math
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache
-from transformers.generation import GenerationMixin
-from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_layers import (
-    GenericForQuestionAnswering,
-    GenericForSequenceClassification,
-    GenericForTokenClassification,
-    GradientCheckpointingLayer,
-)
-from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
-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.utils import TransformersKwargs, auto_docstring, can_return_tuple
-from transformers.utils.generic import maybe_autocast
-from transformers.utils.deprecation import deprecate_kwarg
-from transformers.utils.output_capturing import OutputRecorder
-
-from .configuration_k2_horizon import K2HorizonConfig
-
-
-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
-
-
-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 split_to_interleaved(x):
-    # Split halves:  x0 x1 x2 x3 ... y0 y1 y2 y3 ...
-    # Interleaved:   x0 y0 x1 y1 x2 y2 x3 y3 ...
-    return x.reshape(*x.shape[:-1], 2, -1).transpose(-1, -2).reshape(*x.shape[:-1], -1)
-
-
-def interleaved_to_split(x):
-    # Interleaved:   x0 y0 x1 y1 x2 y2 x3 y3 ...
-    # Split halves:  x0 x1 x2 x3 ... y0 y1 y2 y3 ...
-    return x.reshape(*x.shape[:-1], -1, 2).transpose(-1, -2).reshape(*x.shape[:-1], -1)
-
-
-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: Unpack[TransformersKwargs],
-):
-    key_states = repeat_kv(key, module.num_key_value_groups)
-    value_states = repeat_kv(value, module.num_key_value_groups)
-
-    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
-    if attention_mask is not None:
-        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
-
-
-def calc_router_weights(
-    router_logits: torch.Tensor,
-    router_bias: Optional[torch.Tensor],
-    score_func: str,
-    top_k: int,
-    scaling_factor: Optional[float],
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """Return native-XLLM-compatible routing weights and selected experts.
-
-    XLLM applies router bias only to the values used for top-k selection. The
-    selected routes are still weighted by the original router probabilities,
-    then optionally normalized and scaled.
-    """
-    if score_func == "softmax":
-        routing_scores = F.softmax(router_logits, dim=-1, dtype=torch.float32)
-    elif score_func == "sigmoid":
-        routing_scores = torch.sigmoid(router_logits.to(torch.float32))
-    else:
-        raise ValueError(f"Unsupported router score function: {score_func}")
-
-    selection_scores = routing_scores
-    if router_bias is not None:
-        selection_scores = selection_scores + router_bias.to(selection_scores)
-
-    selected_indices = torch.topk(selection_scores, top_k, dim=-1).indices
-    routing_weights = torch.gather(routing_scores, dim=-1, index=selected_indices)
-    if top_k > 1:
-        routing_weights = routing_weights / routing_weights.sum(dim=-1, keepdim=True)
-    if scaling_factor is not None:
-        routing_weights = routing_weights * scaling_factor
-    return routing_weights, selected_indices
-
-
-def combine_routed_experts(
-    hidden_states: torch.Tensor,
-    routing_weights: torch.Tensor,
-    selected_indices: torch.Tensor,
-    experts: nn.ModuleList,
-    activation: Optional[Callable[[torch.Tensor], torch.Tensor]] = None,
-) -> torch.Tensor:
-    num_tokens, hidden_dim = hidden_states.shape
-    final_hidden_states = torch.zeros(
-        (num_tokens, experts[0].out_features),
-        dtype=hidden_states.dtype,
-        device=hidden_states.device,
-    )
-    expert_mask = torch.nn.functional.one_hot(
-        selected_indices, num_classes=len(experts)
-    ).permute(2, 1, 0)
-
-    for expert_idx in torch.nonzero(expert_mask.sum(dim=(-1, -2)), as_tuple=False).flatten():
-        topk_positions, token_positions = torch.where(expert_mask[int(expert_idx)])
-        expert_states = experts[int(expert_idx)](hidden_states[token_positions])
-        if activation is not None:
-            expert_states = activation(expert_states)
-        expert_states = expert_states * routing_weights[token_positions, topk_positions, None].to(expert_states.dtype)
-        final_hidden_states.index_add_(0, token_positions, expert_states.to(hidden_states.dtype))
-
-    return final_hidden_states
-
-
-class K2HorizonAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: K2HorizonConfig, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
-        self.scaling = self.head_dim ** -0.5
-        self.attention_dropout = config.attention_dropout
-        self.is_causal = True
-
-        self.rope_head_dim = self.head_dim if config.rope_head_dim is None else config.rope_head_dim
-
-        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
-        )
-
-        self.gate_func = config.attention_gate_func
-        if self.gate_func is not None:
-            self.gate_proj = nn.Linear(
-                config.hidden_size,
-                config.num_attention_heads * self.head_dim,
-                bias=False)
-
-        if config.query_key_norm:
-            self.q_norm = K2HorizonRMSNorm(
-                hidden_size=config.num_attention_heads * self.head_dim,
-                n_groups=config.num_attention_heads,
-                eps=config.rms_norm_eps)
-            self.k_norm = K2HorizonRMSNorm(
-                hidden_size=config.num_key_value_heads * self.head_dim,
-                n_groups=config.num_key_value_heads,
-                eps=config.rms_norm_eps)
-
-        self.sliding_window = getattr(config, "sliding_window", None)
-
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
-    def forward(
-            self,
-            hidden_states: torch.Tensor,
-            position_embeddings: tuple[torch.Tensor, torch.Tensor],
-            attention_mask: Optional[torch.Tensor],
-            past_key_values: Optional[Cache] = None,
-            cache_position: Optional[torch.LongTensor] = None,
-            **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        if self.config.query_key_norm:
-            query_states = self.q_norm(self.q_proj(hidden_states)).view(hidden_shape).transpose(1, 2)
-            key_states = self.k_norm(self.k_proj(hidden_states)).view(hidden_shape).transpose(1, 2)
-        else:
-            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
-        if self.rope_head_dim == self.head_dim:
-            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-        else:
-            query_states, query_states_ = torch.split(
-                split_to_interleaved(query_states),
-                split_size_or_sections=[self.rope_head_dim, self.head_dim - self.rope_head_dim],
-                dim=-1)
-
-            key_states, key_states_ = torch.split(
-                split_to_interleaved(key_states),
-                split_size_or_sections=[self.rope_head_dim, self.head_dim - self.rope_head_dim],
-                dim=-1)
-
-            query_states, key_states = apply_rotary_pos_emb(
-                interleaved_to_split(query_states),
-                interleaved_to_split(key_states),
-                cos,
-                sin)
-
-            query_states = interleaved_to_split(torch.cat(
-                [split_to_interleaved(query_states), query_states_], dim=-1))
-            key_states = interleaved_to_split(torch.cat(
-                [split_to_interleaved(key_states), key_states_], dim=-1))
-
-        if past_key_values 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_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        attention_interface: Callable = eager_attention_forward
-        if self.config._attn_implementation != "eager":
-            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,
-            sliding_window=self.sliding_window,  # diff with Llama
-            **kwargs,
-        )
-
-        if self.gate_func is not None:
-            gate = self.gate_proj(hidden_states).view(
-                input_shape + (-1, self.head_dim))
-            if self.gate_func == 'silu':
-                gate = F.silu(gate)
-            else:
-                assert self.gate_func == 'softplus'
-                gate = F.softplus(gate, beta=math.log(2))
-
-            attn_output = attn_output * gate
-
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-def apply_rotary_pos_emb_xllm(q, k, freqs_cis):
-    if q.shape[-1] % 2 != 0 or k.shape[-1] % 2 != 0:
-        raise ValueError(f"RoPE dimensions must be even, got q={q.shape[-1]} and k={k.shape[-1]}")
-    q_ = torch.view_as_complex(q.float().reshape(*q.shape[:-1], -1, 2))
-    k_ = torch.view_as_complex(k.float().reshape(*k.shape[:-1], -1, 2))
-    if freqs_cis.ndim == 2:
-        freqs_cis = freqs_cis.unsqueeze(1)
-    elif freqs_cis.ndim == 3:
-        freqs_cis = freqs_cis.unsqueeze(2)
-    else:
-        raise ValueError(f"Unsupported freqs_cis shape: {tuple(freqs_cis.shape)}")
-    if freqs_cis.shape[-1] != q_.shape[-1] or freqs_cis.shape[-1] != k_.shape[-1]:
-        raise ValueError(
-            "RoPE frequency dimension mismatch: "
-            f"q_rope_dim={q.shape[-1]}, k_rope_dim={k.shape[-1]}, "
-            f"q_complex_dim={q_.shape[-1]}, k_complex_dim={k_.shape[-1]}, "
-            f"freqs_complex_dim={freqs_cis.shape[-1]}, freqs_shape={tuple(freqs_cis.shape)}"
-        )
-    q_embed = torch.view_as_real(q_ * freqs_cis).flatten(-2).to(q.dtype)
-    k_embed = torch.view_as_real(k_ * freqs_cis).flatten(-2).to(k.dtype)
-    return q_embed, k_embed
-
-
-class K2HorizonMoVAAttention(nn.Module):
-    """MoVA attention with routed value experts and optional post-attention gate."""
-
-    def __init__(self, config: K2HorizonConfig, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
-        self.scaling = self.head_dim**-0.5
-        self.attention_dropout = config.attention_dropout
-        self.is_causal = True
-
-        self.num_experts_per_tok = config.mova_num_experts_per_tok
-        self.router_score_func = config.router_score_func
-        self.router_scaling_factor = config.router_scaling_factor
-        self.gate_func = config.attention_gate_func
-
-        self.rope_head_dim = self.head_dim if config.rope_head_dim is None else config.rope_head_dim
-
-        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.o_proj = nn.Linear(
-            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
-        )
-        self.v_router = nn.Linear(
-            config.hidden_size,
-            config.mova_num_experts,
-            bias=config.moe_gate_bias)
-        self.v_experts = nn.ModuleList([
-            nn.Linear(
-                config.hidden_size,
-                config.num_key_value_heads * self.head_dim,
-                bias=False
-            ) for _ in range(config.mova_num_experts)
-        ])
-
-        if self.gate_func is not None:
-            self.gate_proj = nn.Linear(
-                config.hidden_size,
-                config.num_attention_heads * self.head_dim,
-                bias=False)
-
-        if config.query_key_norm:
-            self.q_norm = K2HorizonRMSNorm(
-                hidden_size=config.num_attention_heads * self.head_dim,
-                n_groups=config.num_attention_heads,
-                eps=config.rms_norm_eps,
-            )
-            self.k_norm = K2HorizonRMSNorm(
-                hidden_size=config.num_key_value_heads * self.head_dim,
-                n_groups=config.num_key_value_heads,
-                eps=config.rms_norm_eps,
-            )
-
-        self.sliding_window = getattr(config, "sliding_window", None)
-
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: torch.Tensor,
-        attention_mask: Optional[torch.Tensor],
-        past_key_values: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-        flat_hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
-
-        # Match native MOVAttention router semantics exactly: compute logits with
-        # the weight-only linear and apply router bias only to selection scores.
-        router_logits = F.linear(flat_hidden_states, self.v_router.weight)
-        routing_weights, selected_values = calc_router_weights(
-            router_logits=router_logits,
-            router_bias=self.v_router.bias,
-            score_func=self.router_score_func,
-            top_k=self.num_experts_per_tok,
-            scaling_factor=self.router_scaling_factor,
-        )
-
-        mixed_value_states = combine_routed_experts(
-            hidden_states=flat_hidden_states,
-            routing_weights=routing_weights,
-            selected_indices=selected_values,
-            experts=self.v_experts,
-            activation=F.silu)
-
-        value_states = mixed_value_states.view(hidden_shape).transpose(1, 2)
-
-        if self.config.query_key_norm:
-            query_states = self.q_norm(self.q_proj(hidden_states)).view(hidden_shape).transpose(1, 2)
-            key_states = self.k_norm(self.k_proj(hidden_states)).view(hidden_shape).transpose(1, 2)
-        else:
-            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)
-
-        cos, sin = position_embeddings
-        if self.rope_head_dim == self.head_dim:
-            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-        else:
-            query_states, query_states_ = torch.split(
-                split_to_interleaved(query_states),
-                split_size_or_sections=[self.rope_head_dim, self.head_dim - self.rope_head_dim],
-                dim=-1)
-
-            key_states, key_states_ = torch.split(
-                split_to_interleaved(key_states),
-                split_size_or_sections=[self.rope_head_dim, self.head_dim - self.rope_head_dim],
-                dim=-1)
-
-            query_states, key_states = apply_rotary_pos_emb(
-                interleaved_to_split(query_states),
-                interleaved_to_split(key_states),
-                cos,
-                sin)
-
-            query_states = interleaved_to_split(torch.cat(
-                [split_to_interleaved(query_states), query_states_], dim=-1))
-            key_states = interleaved_to_split(torch.cat(
-                [split_to_interleaved(key_states), key_states_], dim=-1))
-
-        if past_key_values is not None:
-            cache_kwargs = {"cache_position": cache_position}
-            key_states, value_states = past_key_values.update(
-                key_states, value_states, self.layer_idx, cache_kwargs
-            )
-
-        attention_interface: Callable = eager_attention_forward
-        if self.config._attn_implementation != "eager":
-            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,
-            sliding_window=self.sliding_window,
-            **kwargs,
-        )
-
-        if self.gate_func is not None:
-            gate = self.gate_proj(hidden_states).view(input_shape + (-1, self.head_dim))
-            if self.gate_func == 'silu':
-                gate = F.silu(gate)
-            else:
-                assert self.gate_func == 'softplus'
-                gate = F.softplus(gate, beta=math.log(2))
-
-            attn_output = attn_output * gate
-
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-class K2HorizonMLP(nn.Module):
-    def __init__(self, config, intermediate_size=None):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, x):
-        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-        return down_proj
-
-
-class K2HorizonSparseMoeBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.num_experts = config.num_experts
-        self.top_k = config.num_experts_per_tok
-        self.norm_topk_prob = config.norm_topk_prob
-        self.num_shared_experts = config.num_shared_experts
-        self.router_score_func = config.router_score_func
-        self.router_scaling_factor = config.router_scaling_factor
-
-        # gating
-        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=config.moe_gate_bias)
-        self.experts = nn.ModuleList(
-            [K2HorizonMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(self.num_experts)]
-        )
-
-        if config.num_shared_experts > 0:
-            self.shared_experts = K2HorizonMLP(
-                config=config,
-                intermediate_size=config.moe_intermediate_size * config.num_shared_experts)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        """ """
-        residuals = hidden_states
-
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        # router_logits: (batch * sequence_length, n_experts)
-        # router_logits = self.gate(hidden_states)
-        router_logits = F.linear(hidden_states, self.gate.weight)
-
-        if self.router_score_func == "softmax":
-            routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        else:
-            assert self.router_score_func == "sigmoid"
-            routing_weights = F.sigmoid(router_logits.to(torch.float32))
-
-        routing_weights_for_choice = routing_weights
-        if self.gate.bias is not None:
-            routing_weights_for_choice = routing_weights + self.gate.bias.to(routing_weights.dtype)
-
-        _, selected_experts = torch.topk(routing_weights_for_choice, self.top_k, dim=-1)
-        routing_weights = torch.gather(routing_weights, dim=-1, index=selected_experts)
-
-        if self.norm_topk_prob:  # only diff with mixtral sparse moe block!
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        routing_weights = routing_weights * self.router_scaling_factor
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
-        )
-
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
-
-        # Loop over all available experts in the model and perform the computation on each expert
-        expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
-        for expert_idx in expert_hit:
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
-
-        if self.num_shared_experts > 0:
-            final_hidden_states = final_hidden_states + self.shared_experts(residuals)
-
-        return final_hidden_states, router_logits
-
-
-# @use_kernel_forward_from_hub("RMSNorm")
-class K2HorizonRMSNorm(nn.Module):
-    def __init__(self, hidden_size: int, n_groups: int, eps=1e-6):
-        """
-        K2HorizonRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.n_groups = n_groups
-        self.hidden_size = hidden_size
-        assert hidden_size % n_groups == 0
-        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)
-
-        hidden_states = hidden_states.reshape(*hidden_states.shape[:-1], self.n_groups, -1)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        hidden_states = hidden_states.reshape(*hidden_states.shape[:-2], -1)
-        hidden_states = self.weight * hidden_states
-
-        return hidden_states.to(input_dtype)
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
-
-
-class K2HorizonDecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: K2HorizonConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        is_sparse_layer = (layer_idx not in config.mlp_only_layers) and (
-                config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0)
-
-        if is_sparse_layer and config.mova_num_experts > 0:
-            self.self_attn = K2HorizonMoVAAttention(config=config, layer_idx=layer_idx)
-        else:
-            self.self_attn = K2HorizonAttention(config, layer_idx)
-
-        if is_sparse_layer:
-            self.mlp = K2HorizonSparseMoeBlock(config)
-        else:
-            self.mlp = K2HorizonMLP(config, intermediate_size=config.intermediate_size)
-
-        assert config.hidden_size % config.layernorm_num_groups == 0
-        self.input_layernorm = K2HorizonRMSNorm(
-            hidden_size=config.hidden_size,
-            n_groups=config.layernorm_num_groups,
-            eps=config.rms_norm_eps)
-        self.post_attention_layernorm = K2HorizonRMSNorm(
-            hidden_size=config.hidden_size,
-            n_groups=config.layernorm_num_groups,
-            eps=config.rms_norm_eps)
-
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
-    def forward(
-            self,
-            hidden_states: torch.Tensor,
-            position_embeddings: tuple[torch.Tensor, torch.Tensor],
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            past_key_values: Optional[Cache] = None,
-            cache_position: Optional[torch.LongTensor] = None,
-            **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> torch.FloatTensor:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, sequence_length)` where padding elements are indicated by 0.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_router_logits (`bool`, *optional*):
-                Whether or not to return the logits of all the routers. They are useful for computing the router loss,
-                and should not be returned during inference.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_values (`Cache`, *optional*): cached past key and value projection states
-            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-                Indices depicting the position of the input sequence tokens in the sequence.
-            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
-                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
-                with `head_dim` being the embedding dimension of each attention head.
-            kwargs (`dict`, *optional*):
-                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
-                into the model
-        """
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, _ = self.self_attn(
-            hidden_states=hidden_states,
-            position_embeddings=position_embeddings,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            cache_position=cache_position,
-            **kwargs,
-        )
-
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        # For the MoE layers, we need to unpack
-        if isinstance(hidden_states, tuple):
-            hidden_states, _ = hidden_states
-        hidden_states = residual + hidden_states
-
-        return hidden_states
-
-
-class K2HorizonRotaryEmbedding(nn.Module):
-    inv_freq: torch.Tensor  # fix linting for `register_buffer`
-
-    def __init__(self, config: K2HorizonConfig, device=None):
-        super().__init__()
-        self.max_seq_len_cached = config.max_position_embeddings
-        self.original_max_seq_len = config.max_position_embeddings
-
-        self.config = config
-
-        self.rope_type = self.config.rope_parameters["rope_type"]
-        rope_init_fn: Callable = self.compute_default_rope_parameters
-        if self.rope_type != "default":
-            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
-
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
-
-    @staticmethod
-    def compute_default_rope_parameters(
-        config: K2HorizonConfig | None = None,
-        device: Optional["torch.device"] = None,
-        seq_len: int | None = None,
-    ) -> tuple["torch.Tensor", float]:
-        """
-        Computes the inverse frequencies according to the original RoPE implementation
-        Args:
-            config ([`~transformers.PreTrainedConfig`]):
-                The model configuration.
-            device (`torch.device`):
-                The device to use for initialization of the inverse frequencies.
-            seq_len (`int`, *optional*):
-                The current sequence length. Unused for this type of RoPE.
-        Returns:
-            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
-            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
-        """
-        base = config.rope_parameters["rope_theta"]
-        # dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
-        dim = (
-            config.rope_head_dim
-            if config.rope_head_dim is not None
-            else getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
-        )
-
-        attention_factor = 1.0  # Unused in this type of RoPE
-
-        # Compute the inverse frequencies
-        inv_freq = 1.0 / (
-            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
-        )
-        return inv_freq, attention_factor
-
-    @torch.no_grad()
-    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
-    def forward(self, x, position_ids):
-        inv_freq_expanded = self.inv_freq[None, :, None].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 maybe_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)
-
-
-@auto_docstring
-class K2HorizonPreTrainedModel(PreTrainedModel):
-    config: K2HorizonConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["K2HorizonDecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn = True
-    _supports_sdpa = True
-    _supports_flex_attn = True
-    _can_compile_fullgraph = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
-    _supports_attention_backend = True
-    _can_record_outputs = {
-        "router_logits": OutputRecorder(K2HorizonSparseMoeBlock, index=1),
-        "hidden_states": K2HorizonDecoderLayer,
-        "attentions": K2HorizonAttention,
-    }
-
-
-@auto_docstring
-class K2HorizonModel(K2HorizonPreTrainedModel):
-    def __init__(self, config: K2HorizonConfig):
-        super().__init__(config)
-        # self.padding_idx = config.pad_token_id
-        self.padding_idx = getattr(config, "padding_idx", None)
-        self.vocab_size = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList(
-            [K2HorizonDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-
-        assert config.hidden_size % config.layernorm_num_groups == 0
-        self.norm = K2HorizonRMSNorm(
-            hidden_size=config.hidden_size,
-            n_groups=config.layernorm_num_groups,
-            eps=config.rms_norm_eps)
-
-        self.rotary_emb = K2HorizonRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @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,
-            cache_position: Optional[torch.LongTensor] = None,
-            **kwargs: Unpack[TransformersKwargs],
-    ) -> MoeModelOutputWithPast:
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-        if use_cache and past_key_values is None:
-            past_key_values = DynamicCache(config=self.config)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        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)
-
-        mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
-        causal_mask = mask_function(
-            config=self.config,
-            inputs_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            past_key_values=past_key_values,
-            position_ids=position_ids,
-        )
-
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        for layer_idx, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
-            hidden_states = decoder_layer(
-                hidden_states,
-                position_embeddings=position_embeddings,
-                attention_mask=causal_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                **kwargs,
-            )
-
-        hidden_states = self.norm(hidden_states)
-
-        return MoeModelOutputWithPast(  # only diff with Mistral is the output type, we need MoE
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values,
-        )
-
-
-def load_balancing_loss_func(
-        gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
-        num_experts: Optional[int] = None,
-        top_k=2,
-        attention_mask: Optional[torch.Tensor] = None,
-) -> Union[torch.Tensor, int]:
-    r"""
-    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
-
-    See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
-    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
-    experts is too unbalanced.
-
-    Args:
-        gate_logits:
-            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
-            shape [batch_size X sequence_length, num_experts].
-        num_experts:
-            Number of experts
-        top_k:
-            The number of experts to route per-token, can be also interpreted as the `top-k` routing
-            parameter.
-        attention_mask (`torch.Tensor`, *optional*):
-            The attention_mask used in forward function
-            shape [batch_size X sequence_length] if not None.
-
-    Returns:
-        The auxiliary loss.
-    """
-    if gate_logits is None or not isinstance(gate_logits, tuple):
-        return 0
-
-    if isinstance(gate_logits, tuple):
-        compute_device = gate_logits[0].device
-        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
-
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
-
-    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
-
-    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
-
-    if attention_mask is None:
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.mean(routing_weights, dim=0)
-    else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
-        expert_attention_mask = (
-            attention_mask[None, :, :, None, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
-            .reshape(-1, top_k, num_experts)
-            .to(compute_device)
-        )
-
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
-        )
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
-        router_per_expert_attention_mask = (
-            attention_mask[None, :, :, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
-            .reshape(-1, num_experts)
-            .to(compute_device)
-        )
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
-            router_per_expert_attention_mask, dim=0
-        )
-
-    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
-    return overall_loss * num_experts
-
-
-@auto_docstring
-class K2HorizonForCausalLM(K2HorizonPreTrainedModel, 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 = K2HorizonModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.router_aux_loss_coef = config.router_aux_loss_coef
-        self.num_experts = config.num_experts
-        self.num_experts_per_tok = config.num_experts_per_tok
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @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_router_logits: Optional[bool] = None,
-            cache_position: Optional[torch.LongTensor] = None,
-            logits_to_keep: Union[int, torch.Tensor] = 0,
-            **kwargs: Unpack[TransformersKwargs],
-    ) -> MoeCausalLMOutputWithPast:
-        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, K2HorizonForCausalLM
-
-        >>> model = K2HorizonForCausalLM.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
-        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
-
-        >>> 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_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs: MoeModelOutputWithPast = 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_router_logits=output_router_logits,
-            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
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
-
-        aux_loss = None
-        if output_router_logits:
-            aux_loss = load_balancing_loss_func(
-                outputs.router_logits,
-                self.num_experts,
-                self.num_experts_per_tok,
-                attention_mask,
-            )
-            if labels is not None:
-                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
-
-        return MoeCausalLMOutputWithPast(
-            loss=loss,
-            aux_loss=aux_loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            router_logits=outputs.router_logits,
-        )
-
-
-class K2HorizonForSequenceClassification(GenericForSequenceClassification, K2HorizonPreTrainedModel):
-    pass
-
-
-class K2HorizonForTokenClassification(GenericForTokenClassification, K2HorizonPreTrainedModel):
-    pass
-
-
-class K2HorizonForQuestionAnswering(GenericForQuestionAnswering, K2HorizonPreTrainedModel):
-    base_model_prefix = "transformer"  # For BC, where `transformer` was used instead of `model`
-
-
-__all__ = [
-    "K2HorizonForCausalLM",
-    "K2HorizonForQuestionAnswering",
-    "K2HorizonModel",
-    "K2HorizonPreTrainedModel",
-    "K2HorizonForSequenceClassification",
-    "K2HorizonForTokenClassification",
-]