Update transformers version to 5.0

README.md

@@ -118,7 +118,7 @@ top_k=50
 Install the required dependencies:
 
 ```bash
-pip install -U transformers kernels torch accelerate
+pip install -U "transformers>=5.0" kernels torch accelerate
 ```
 
 Run inference with the following code:
@@ -136,7 +136,6 @@ model = AutoModelForCausalLM.from_pretrained(
     pretrained_model_name_or_path=MODEL_ID,
     torch_dtype=torch.bfloat16,
     device_map="auto",
-    trust_remote_code=True,
 )
 
 # Prepare input

config.json

@@ -3,11 +3,6 @@
     "architectures": [
         "SolarOpenForCausalLM"
     ],
-    "auto_map": {
-        "AutoConfig": "configuration_solar_open.SolarOpenConfig",
-        "AutoModel": "modeling_solar_open.SolarOpenModel",
-        "AutoModelForCausalLM": "modeling_solar_open.SolarOpenForCausalLM"
-    },
     "pad_token_id": 2,
     "bos_token_id": 1,
     "eos_token_id": 2,

configuration_solar_open.py

@@ -1,242 +0,0 @@
-# coding=utf-8
-# Copyright 2025 Upstage AI.
-# Copyright 2025 The ZhipuAI Inc. team and HuggingFace Inc. team.
-#
-# 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.
-#
-# This file has been modified by Upstage AI including
-# -  Hyperparameter Adjustments: Modified the model architecture by increasing vocab_size and num_hidden_layers, while decreasing num_attention_heads, intermediate_size, and moe_intermediate_size.
-# RoPE Configuration: Replaced the generic rope_parameters argument with explicit rope_theta and rope_scaling parameters to define Rotary Positional Embeddings settings.
-#
-# Based on code from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm4_moe/configuration_glm4_moe.py
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_rope_utils import rope_config_validation
-
-
-class SolarOpenConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`SolarOpenModel`]. It is used to instantiate a
-    SolarOpen model according to the specified arguments, defining the model architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 196608):
-            Vocabulary size of the SolarOpen model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`SolarOpenModel`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 10240):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 48):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 64):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        partial_rotary_factor (`float`, *optional*, defaults to 1.0):
-            The factor of the partial rotary position.
-        num_key_value_heads (`int`, *optional*, defaults to 8):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details, check out [this
-            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
-
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 131072):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 1000000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
-            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
-            accordingly.
-            Expected contents:
-                `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
-                    'llama3'], with 'default' being the original RoPE implementation.
-                `factor` (`float`, *optional*):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
-                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    original maximum pre-trained length.
-                `original_max_position_embeddings` (`int`, *optional*):
-                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
-                    pretraining.
-                `attention_factor` (`float`, *optional*):
-                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation. If unspecified, it defaults to value recommended by the implementation, using the
-                    `factor` field to infer the suggested value.
-                `beta_fast` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 32.
-                `beta_slow` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 1.
-                `short_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `long_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `low_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
-                `high_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        moe_intermediate_size (`int`, *optional*, defaults to 1280):
-            Intermediate size of the routed expert.
-        num_experts_per_tok (`int`, *optional*, defaults to 8):
-            number of experts per token.
-        n_shared_experts (`int`, *optional*, defaults to 1):
-            Number of shared experts.
-        n_routed_experts (`int`, *optional*, defaults to 128):
-            Number of routed experts.
-        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
-            Scaling factor or routed experts.
-        n_group (`int`, *optional*, defaults to 1):
-            Number of groups for routed experts.
-        topk_group (`int`, *optional*, defaults to 1):
-            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
-        first_k_dense_replace (`int`, *optional*, defaults to 0):
-            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
-                                                            \--k dense layers--/
-        norm_topk_prob (`bool`, *optional*, defaults to `True`):
-            Whether to normalize the topk probabilities.
-        use_qk_norm (`bool`, *optional*, defaults to `False`):
-            Whether to use query-key normalization in the attention
-    ```python
-    >>> from transformers import SolarOpenModel, SolarOpenConfig
-
-    >>> # Initializing a SolarOpen style configuration
-    >>> configuration = SolarOpenConfig()
-
-    >>> # Initializing a model from the SolarOpen style configuration
-    >>> model = SolarOpenModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "solar_open"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    # Default tensor parallel plan for base model `SolarOpen`
-    base_model_tp_plan = {
-        "layers.*.self_attn.q_proj": "colwise",
-        "layers.*.self_attn.k_proj": "colwise",
-        "layers.*.self_attn.v_proj": "colwise",
-        "layers.*.self_attn.o_proj": "rowwise",
-        "layers.*.mlp.experts.*.gate_proj": "colwise",
-        "layers.*.mlp.experts.*.up_proj": "colwise",
-        "layers.*.mlp.experts.*.down_proj": "rowwise",
-        "layers.*.mlp.gate_proj": "colwise",
-        "layers.*.mlp.up_proj": "colwise",
-        "layers.*.mlp.down_proj": "rowwise",
-    }
-    base_model_pp_plan = {
-        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
-        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
-        "norm": (["hidden_states"], ["hidden_states"]),
-    }
-
-    def __init__(
-        self,
-        vocab_size=196608,
-        hidden_size=4096,
-        intermediate_size=10240,
-        num_hidden_layers=48,
-        num_attention_heads=64,
-        partial_rotary_factor=1.0,
-        num_key_value_heads=8,
-        hidden_act="silu",
-        max_position_embeddings=131072,
-        initializer_range=0.02,
-        rms_norm_eps=1e-5,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=1000000.0,
-        rope_scaling=None,
-        attention_bias=False,
-        attention_dropout=0.0,
-        moe_intermediate_size=1280,
-        num_experts_per_tok=8,
-        n_shared_experts=1,
-        n_routed_experts=128,
-        routed_scaling_factor=1.0,
-        n_group=1,
-        topk_group=1,
-        first_k_dense_replace=0,
-        norm_topk_prob=True,
-        use_qk_norm=False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.partial_rotary_factor = partial_rotary_factor
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        # Validate the correctness of rotary position embeddings parameters
-        # BC: if there is a 'type' field, move it to 'rope_type'.
-        if self.rope_scaling is not None and "type" in self.rope_scaling:
-            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-        rope_config_validation(self)
-
-        # MoE arguments
-        self.moe_intermediate_size = moe_intermediate_size
-        self.num_experts_per_tok = num_experts_per_tok
-        self.n_group = n_group
-        self.topk_group = topk_group
-        self.n_shared_experts = n_shared_experts
-        self.n_routed_experts = n_routed_experts
-        self.routed_scaling_factor = routed_scaling_factor
-        self.first_k_dense_replace = first_k_dense_replace
-        self.norm_topk_prob = norm_topk_prob
-        self.use_qk_norm = use_qk_norm
-
-        super().__init__(
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-
-__all__ = ["SolarOpenConfig"]

modeling_solar_open.py

@@ -1,605 +0,0 @@
-# coding=utf-8
-# Copyright 2025 Upstage AI.
-# Copyright 2025 The GLM4 & ZhipuAI team and HuggingFace Inc. team.
-#
-# 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.
-#
-# This file has been modified by Upstage AI including:
-# - Hybrid MoE Architecture: Replaced the standard dense structure with a depth-dependent Hybrid MoE, adding `SolarOpenMoE` and `SolarOpenTopkRouter` classes.
-# - RoPE Strategy: Changed the rotary position embedding strategy from GLM4's interleaved rotation to Llama-style block rotation (via modified `rotate_half`).
-# - Normalization Logic: Simplified the layer normalization structure by removing GLM4's extra post-operation norms and adding optional Query-Key Normalization (`use_qk_norm`).
-#
-# Based on code from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm4/modeling_glm4.py
-
-from typing import Callable, Optional, Union
-
-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.integrations import use_kernel_forward_from_hub
-from transformers.masking_utils import create_causal_mask
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_layers import GradientCheckpointingLayer
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-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.deprecation import deprecate_kwarg
-from transformers.utils.generic import check_model_inputs
-from .configuration_solar_open import SolarOpenConfig
-
-
-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: 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 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)
-
-    # Keep half or full tensor for later concatenation
-    rotary_dim = cos.shape[-1]
-    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
-    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
-
-    # Apply rotary embeddings on the first half or full tensor
-    q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
-    k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
-
-    # Concatenate back to full shape
-    q_embed = torch.cat([q_embed, q_pass], dim=-1)
-    k_embed = torch.cat([k_embed, k_pass], dim=-1)
-    return q_embed, k_embed
-
-
-class SolarOpenAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: SolarOpenConfig, layer_idx: Optional[int] = None):
-        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.rope_scaling = config.rope_scaling
-        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=False)
-        self.use_qk_norm = config.use_qk_norm
-        if self.use_qk_norm:
-            self.q_norm = SolarOpenRMSNorm(self.head_dim, eps=config.rms_norm_eps)
-            self.k_norm = SolarOpenRMSNorm(self.head_dim, 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],
-        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)
-
-        query_states = self.q_proj(hidden_states).view(hidden_shape)
-        key_states = self.k_proj(hidden_states).view(hidden_shape)
-        value_states = self.v_proj(hidden_states).view(hidden_shape)
-
-        if self.use_qk_norm:  # main diff from Llama
-            query_states = self.q_norm(query_states)
-            key_states = self.k_norm(key_states)
-
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_values is not None:
-            # sin and cos are specific to RoPE models; position_ids 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,
-            **kwargs,
-        )
-
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-class SolarOpenMLP(nn.Module):
-    def __init__(self, config, hidden_size=None, intermediate_size=None):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
-        self.intermediate_size = config.intermediate_size if intermediate_size is None else 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 SolarOpenTopkRouter(nn.Module):
-    def __init__(self, config: SolarOpenConfig):
-        super().__init__()
-        self.config = config
-        self.top_k = config.num_experts_per_tok
-        self.n_routed_experts = config.n_routed_experts
-        self.routed_scaling_factor = config.routed_scaling_factor
-        self.n_group = config.n_group
-        self.topk_group = config.topk_group
-        self.norm_topk_prob = config.norm_topk_prob
-
-        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
-        self.e_score_correction_bias = nn.Parameter(
-            torch.zeros((self.n_routed_experts), dtype=torch.float32))
-
-    @torch.no_grad()
-    def get_topk_indices(self, scores):
-        scores_for_choice = scores.view(-1, self.n_routed_experts) + self.e_score_correction_bias.unsqueeze(0)
-        group_scores = (
-            scores_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group)
-            .topk(2, dim=-1)[0]
-            .sum(dim=-1)
-        )
-        group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
-        group_mask = torch.zeros_like(group_scores)
-        group_mask.scatter_(1, group_idx, 1)
-        score_mask = (
-            group_mask.unsqueeze(-1)
-            .expand(-1, self.n_group, self.n_routed_experts // self.n_group)
-            .reshape(-1, self.n_routed_experts)
-        )
-        scores_for_choice = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)
-        topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1]
-        return topk_indices
-
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.view(-1, self.config.hidden_size)
-        router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
-        scores = router_logits.sigmoid()
-        topk_indices = self.get_topk_indices(scores)
-        topk_weights = scores.gather(1, topk_indices)
-        if self.norm_topk_prob:
-            denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
-            topk_weights /= denominator
-        topk_weights = topk_weights * self.routed_scaling_factor
-        return topk_indices, topk_weights
-
-
-@use_kernel_forward_from_hub("RMSNorm")
-class SolarOpenRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        SolarOpenRMSNorm 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}"
-
-
-class SolarOpenMoE(nn.Module):
-    """
-    A mixed expert module containing shared experts.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.experts = nn.ModuleList(
-            [
-                SolarOpenMLP(config, intermediate_size=config.moe_intermediate_size)
-                for _ in range(config.n_routed_experts)
-            ]
-        )
-        self.gate = SolarOpenTopkRouter(config)
-        self.shared_experts = SolarOpenMLP(
-            config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
-        )
-
-    @torch.compiler.disable()
-    def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor):
-        r"""
-        MoE forward pass that only executes selected experts.
-        Uses @torch.compiler.disable() to allow dynamic shape operations.
-        Requires --enforce-eager flag when serving with vLLM.
-        """
-        final_hidden_states = torch.zeros_like(hidden_states)
-
-        for expert_idx in range(len(self.experts)):
-            expert = self.experts[expert_idx]
-
-            # Find positions where this expert was selected
-            batch_idx, topk_pos = torch.where(topk_indices == expert_idx)
-
-            if batch_idx.numel() == 0:
-                continue
-
-            # Extract only the tokens routed to this expert
-            expert_input = hidden_states[batch_idx]
-            expert_output = expert(expert_input)
-
-            # Apply weights and accumulate results
-            weights = topk_weights[batch_idx, topk_pos].unsqueeze(-1)
-            final_hidden_states.index_add_(0, batch_idx, (expert_output * weights).to(hidden_states.dtype))
-
-        return final_hidden_states
-
-    def forward(self, hidden_states):
-        residuals = hidden_states
-        orig_shape = hidden_states.shape
-        topk_indices, topk_weights = self.gate(hidden_states)
-        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape)
-        hidden_states = hidden_states + self.shared_experts(residuals)
-        return hidden_states
-
-
-class SolarOpenDecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: SolarOpenConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = SolarOpenAttention(config=config, layer_idx=layer_idx)
-
-        if layer_idx >= config.first_k_dense_replace:
-            self.mlp = SolarOpenMoE(config)
-        else:
-            self.mlp = SolarOpenMLP(config)
-
-        self.input_layernorm = SolarOpenRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = SolarOpenRMSNorm(config.hidden_size, 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,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        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[TransformersKwargs],
-    ) -> torch.Tensor:
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-        # Self Attention
-        hidden_states, _ = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
-            **kwargs,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-        return hidden_states
-
-
-@auto_docstring
-class SolarOpenPreTrainedModel(PreTrainedModel):
-    config: SolarOpenConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["SolarOpenDecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn = True
-    _supports_sdpa = True
-    _supports_flex_attn = True
-    _can_compile_fullgraph = False
-    _supports_attention_backend = True
-    _can_record_outputs = {
-        "hidden_states": SolarOpenDecoderLayer,
-        "attentions": SolarOpenAttention,
-    }
-
-    def _init_weights(self, module):
-        super()._init_weights(module)
-        if isinstance(module, SolarOpenTopkRouter):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-
-
-class SolarOpenRotaryEmbedding(nn.Module):
-    inv_freq: torch.Tensor  # fix linting for `register_buffer`
-
-    def __init__(self, config: SolarOpenConfig, device=None):
-        super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
-            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)
-
-
-@auto_docstring
-class SolarOpenModel(SolarOpenPreTrainedModel):
-    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.92.*", r"model\.layers\.46.*"]
-
-    def __init__(self, config: SolarOpenConfig):
-        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(
-            [SolarOpenDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = SolarOpenRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = SolarOpenRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @check_model_inputs()
-    @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,
-        cache_position: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> BaseModelOutputWithPast:
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
-
-        if use_cache and past_key_values is None:
-            past_key_values = DynamicCache(config=self.config)
-
-        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.Tensor = 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 = create_causal_mask(
-            config=self.config,
-            input_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            cache_position=cache_position,
-            past_key_values=past_key_values,
-            position_ids=position_ids,
-        )
-
-        hidden_states = inputs_embeds
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            hidden_states = decoder_layer(
-                hidden_states,
-                attention_mask=causal_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                **kwargs,
-            )
-
-        hidden_states = self.norm(hidden_states)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values,
-        )
-
-
-@auto_docstring
-class SolarOpenForCausalLM(SolarOpenPreTrainedModel, 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 = SolarOpenModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # 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,
-        cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: Union[int, torch.Tensor] = 0,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> CausalLMOutputWithPast:
-
-        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,
-            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=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,
-        )
-
-
-__all__ = ["SolarOpenPreTrainedModel", "SolarOpenModel", "SolarOpenForCausalLM"]