Upload modeling_gravity_moe.py with huggingface_hub

modeling_gravity_moe.py

@@ -11,718 +11,46 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import math
-from collections.abc import Callable
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from transformers import initialization as init
-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, use_kernel_func_from_hub
-from transformers.masking_utils import create_causal_mask
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_layers import (
-    GenericForSequenceClassification,
-    GenericForTokenClassification,
-    GradientCheckpointingLayer,
+"""
+GravityMoE model — inherits from DeepSeek V3.
+
+GravityMoE shares the same sparse Mixture-of-Experts architecture as DeepSeek V3
+(MLA attention, sigmoid routing with bias correction, shared + routed experts)
+but with different model hyperparameters. All modeling logic is inherited from
+the DeepSeek V3 implementation in `transformers`.
+"""
+
+from transformers.conversion_mapping import _MODEL_TO_CONVERSION_PATTERN
+from transformers.models.deepseek_v3.modeling_deepseek_v3 import (
+    DeepseekV3ForCausalLM,
+    DeepseekV3Model,
+    DeepseekV3PreTrainedModel,
 )
-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.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults
-from transformers.utils.output_capturing import capture_outputs
-from .configuration_gravity_moe import GravityMoEConfig
-
-
-@use_kernel_forward_from_hub("RMSNorm")
-class GravityMoERMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
-        """
-        GravityMoERMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
-
-
-class GravityMoERotaryEmbedding(nn.Module):
-    inv_freq: torch.Tensor  # fix linting for `register_buffer`
-
-    def __init__(self, config: GravityMoEConfig, 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: GravityMoEConfig | 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
-
-        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)
-
-
-class GravityMoEMLP(nn.Module):
-    def __init__(self, config, intermediate_size=None):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.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 GravityMoETopkRouter(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.n_routed_experts = config.n_routed_experts
-
-        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
-        self.register_buffer("e_score_correction_bias", torch.zeros(self.n_routed_experts))
-
-    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))
-        return router_logits
-
-
-class GravityMoENaiveMoe(nn.Module):
-    """Collection of expert weights stored as fused 3D tensors."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.num_experts = config.num_local_experts
-        self.hidden_dim = config.hidden_size
-        self.intermediate_dim = config.moe_intermediate_size
-        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim))
-        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim))
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        top_k_index: torch.Tensor,
-        top_k_weights: torch.Tensor,
-    ) -> torch.Tensor:
-        final_hidden_states = torch.zeros_like(hidden_states)
-        with torch.no_grad():
-            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
-            expert_mask = expert_mask.permute(2, 1, 0)
-            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
-
-        for expert_idx in expert_hit:
-            expert_idx = expert_idx[0]
-            if expert_idx == self.num_experts:
-                continue
-            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
-            current_state = hidden_states[token_idx]
-            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
-            current_hidden_states = self.act_fn(gate) * up
-            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
-            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
-            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))
-
-        return final_hidden_states
-
-
-class GravityMoEMoE(nn.Module):
-    """
-    A mixed expert module containing shared experts.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.experts = GravityMoENaiveMoe(config)
-        self.gate = GravityMoETopkRouter(config)
-        self.shared_experts = GravityMoEMLP(
-            config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
-        )
-        self.n_routed_experts = config.n_routed_experts
-        self.n_group = config.n_group
-        self.topk_group = config.topk_group
-        self.norm_topk_prob = config.norm_topk_prob
-        self.routed_scaling_factor = config.routed_scaling_factor
-        self.top_k = config.num_experts_per_tok
-
-    def route_tokens_to_experts(self, router_logits):
-        router_logits = router_logits.sigmoid()
-        router_logits_for_choice = router_logits + self.gate.e_score_correction_bias
-        group_scores = (
-            router_logits_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 = router_logits_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]
-        topk_weights = router_logits.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
-
-    def forward(self, hidden_states):
-        residuals = hidden_states
-        orig_shape = hidden_states.shape
-        router_logits = self.gate(hidden_states)
-        topk_indices, topk_weights = self.route_tokens_to_experts(router_logits)
-        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        hidden_states = self.experts(hidden_states, topk_indices, topk_weights).view(*orig_shape)
-        hidden_states = hidden_states + self.shared_experts(residuals)
-        return hidden_states
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-@use_kernel_func_from_hub("rotary_pos_emb")
-def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def eager_attention_forward(
-    module: nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: torch.Tensor | None,
-    scaling: float,
-    dropout: float = 0.0,
-    **kwargs: Unpack[TransformersKwargs],
-):
-    key_states = repeat_kv(key, module.num_key_value_groups)
-    value_states = repeat_kv(value, module.num_key_value_groups)
-
-    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
-    if attention_mask is not None:
-        attn_weights = attn_weights + attention_mask
-
-    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
-    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
-    attn_output = torch.matmul(attn_weights, value_states)
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, attn_weights
-
-
-def apply_rotary_pos_emb_interleave(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    r"""
-    TODO let's just use the original freqcis computation to not have the view
-    transpose + reshape! This is not optimized!
-    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`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
-        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)
-
-    b, h, s, d = q.shape
-    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
-
-    b, h, s, d = k.shape
-    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
-
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
 
+from .configuration_gravity_moe import GravityMoEConfig
 
-def yarn_get_mscale(scale=1, mscale=1):
-    if scale <= 1:
-        return 1.0
-    return 0.1 * mscale * math.log(scale) + 1.0
-
-
-class GravityMoEAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: GravityMoEConfig, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
-        self.attention_dropout = config.attention_dropout
-        self.num_heads = config.num_attention_heads
-
-        self.q_lora_rank = config.q_lora_rank
-        self.qk_rope_head_dim = config.qk_rope_head_dim
-        self.kv_lora_rank = config.kv_lora_rank
-        self.v_head_dim = config.v_head_dim
-        self.qk_nope_head_dim = config.qk_nope_head_dim
-        self.qk_head_dim = config.qk_head_dim
-
-        self.is_causal = True
-        if self.q_lora_rank is None:
-            self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
-        else:
-            self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
-            self.q_a_layernorm = GravityMoERMSNorm(config.q_lora_rank)
-            self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
-
-        self.kv_a_proj_with_mqa = nn.Linear(
-            config.hidden_size,
-            self.kv_lora_rank + self.qk_rope_head_dim,
-            bias=config.attention_bias,
-        )
-        self.kv_a_layernorm = GravityMoERMSNorm(self.kv_lora_rank)
-        self.kv_b_proj = nn.Linear(
-            self.kv_lora_rank,
-            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
-            bias=False,
-        )
-
-        self.o_proj = nn.Linear(
-            self.num_heads * self.v_head_dim,
-            config.hidden_size,
-            bias=config.attention_bias,
-        )
-
-        self.scaling = self.qk_head_dim ** (-0.5)
-        if self.config.rope_parameters.get("rope_type", "default") != "default":
-            mscale_all_dim = self.config.rope_parameters.get("mscale_all_dim", 0)
-            scaling_factor = self.config.rope_parameters["factor"]
-            if mscale_all_dim:
-                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
-                self.scaling = self.scaling * mscale * mscale
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: tuple[torch.Tensor, torch.Tensor],
-        attention_mask: torch.Tensor | None,
-        past_key_values: Cache | None = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
-        batch_size, seq_length = hidden_states.shape[:-1]
-        query_shape = (batch_size, seq_length, -1, self.qk_head_dim)
-        key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim)
-
-        if self.q_lora_rank is None:
-            q_states = self.q_proj(hidden_states)
-        else:
-            q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
-        q_states = q_states.view(query_shape).transpose(1, 2)
-        q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
-
-        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
-        k_pass, k_rot = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
-
-        k_pass = self.kv_b_proj(self.kv_a_layernorm(k_pass)).view(key_shape).transpose(1, 2)
-        k_pass, value_states = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
-
-        k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim)
-
-        cos, sin = position_embeddings
-        if self.config.rope_interleave:  # support using interleaved weights for efficiency
-            q_rot, k_rot = apply_rotary_pos_emb_interleave(q_rot, k_rot, cos, sin)
-        else:
-            q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin)
-        k_rot = k_rot.expand(*k_pass.shape[:-1], -1)
-
-        query_states = torch.cat((q_pass, q_rot), dim=-1)
-        key_states = torch.cat((k_pass, k_rot), dim=-1)
-
-        if past_key_values is not None:
-            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
-
-        if is_flash_attention_requested(self.config) and self.qk_head_dim != self.v_head_dim:
-            value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim])
-
-        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
-            self.config._attn_implementation, eager_attention_forward
-        )
-
-        attn_output, attn_weights = attention_interface(
-            self,
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            dropout=0.0 if not self.training else self.attention_dropout,
-            scaling=self.scaling,
-            **kwargs,
-        )
-
-        if is_flash_attention_requested(self.config) and self.qk_head_dim != self.v_head_dim:
-            attn_output = attn_output[:, :, :, : self.v_head_dim]
-
-        attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-class GravityMoEDecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: GravityMoEConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = GravityMoEAttention(config=config, layer_idx=layer_idx)
-
-        if layer_idx >= config.first_k_dense_replace:
-            self.mlp = GravityMoEMoE(config)
-        else:
-            self.mlp = GravityMoEMLP(config)
-
-        self.input_layernorm = GravityMoERMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = GravityMoERMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        past_key_values: Cache | None = None,
-        use_cache: bool | None = False,
-        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> torch.Tensor:
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-        # Self Attention
-        hidden_states, _ = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            position_embeddings=position_embeddings,
-            **kwargs,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-        return hidden_states
-
+# Register weight conversion so that from_pretrained fuses per-expert
+# checkpoint weights (experts.*.gate_proj, etc.) into 3D tensors
+# (experts.gate_up_proj, experts.down_proj), same as DeepSeek V3.
+_MODEL_TO_CONVERSION_PATTERN["gravity_moe"] = "qwen2_moe"
 
-@auto_docstring
-class GravityMoEPreTrainedModel(PreTrainedModel):
-    config: GravityMoEConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["GravityMoEDecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn = True
-    _supports_sdpa = True
-    _supports_flex_attn = True
 
-    _can_compile_fullgraph = True
-    _supports_attention_backend = True
-    _can_record_outputs = {
-        "hidden_states": GravityMoEDecoderLayer,
-        "attentions": GravityMoEAttention,
-    }
+class GravityMoEPreTrainedModel(DeepseekV3PreTrainedModel):
+    config_class = GravityMoEConfig
     _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
     _keys_to_ignore_on_load_unexpected = [r"model\.layers\.28.*"]
 
-    @torch.no_grad()
-    def _init_weights(self, module):
-        super()._init_weights(module)
-        if isinstance(module, GravityMoETopkRouter):
-            init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
-            init.zeros_(module.e_score_correction_bias)
-        elif isinstance(module, GravityMoENaiveMoe):
-            init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range)
-            init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range)
-
-
-@auto_docstring
-class GravityMoEModel(GravityMoEPreTrainedModel):
-    def __init__(self, config: GravityMoEConfig):
-        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(
-            [GravityMoEDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = GravityMoERMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = GravityMoERotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @merge_with_config_defaults
-    @capture_outputs
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: torch.LongTensor | None = None,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        past_key_values: Cache | None = None,
-        inputs_embeds: torch.FloatTensor | None = None,
-        use_cache: bool | None = 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 position_ids is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
-            position_ids = position_ids.unsqueeze(0)
-
-        causal_mask = create_causal_mask(
-            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
-        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
-
-        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            hidden_states = decoder_layer(
-                hidden_states,
-                attention_mask=causal_mask,
-                position_embeddings=position_embeddings,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                use_cache=use_cache,
-                **kwargs,
-            )
-
-        hidden_states = self.norm(hidden_states)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values,
-        )
-
-
-@auto_docstring
-class GravityMoEForCausalLM(GravityMoEPreTrainedModel, GenerationMixin):
-    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
-    _tp_plan = {"lm_head": "colwise_gather_output"}
-    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = GravityMoEModel(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: torch.LongTensor | None = None,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        past_key_values: Cache | None = None,
-        inputs_embeds: torch.FloatTensor | None = None,
-        labels: torch.LongTensor | None = None,
-        use_cache: bool | None = None,
-        logits_to_keep: int | torch.Tensor = 0,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> CausalLMOutputWithPast:
-        r"""
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, GravityMoEForCausalLM
-
-        >>> model = GravityMoEForCausalLM.from_pretrained("trillion-labs/Gravity-MoE-16.2B-A3.2B")
-        >>> tokenizer = AutoTokenizer.from_pretrained("trillion-labs/Gravity-MoE-16.2B-A3.2B")
-
-        >>> 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."
-        ```"""
-        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,
-            **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,
-        )
-
 
-class GravityMoEForSequenceClassification(GenericForSequenceClassification, GravityMoEPreTrainedModel):
-    pass
+class GravityMoEModel(DeepseekV3Model):
+    config_class = GravityMoEConfig
 
 
-class GravityMoEForTokenClassification(GenericForTokenClassification, GravityMoEPreTrainedModel):
-    pass
+class GravityMoEForCausalLM(DeepseekV3ForCausalLM):
+    config_class = GravityMoEConfig
 
 
 __all__ = [
     "GravityMoEPreTrainedModel",
     "GravityMoEModel",
     "GravityMoEForCausalLM",
-    "GravityMoEForSequenceClassification",
-    "GravityMoEForTokenClassification",
 ]