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+# coding=utf-8
+# Copyright (C) 2025 THL A29 Limited, a Tencent company 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.
+"""PyTorch HunYuanMoEV1 model."""
+
+from typing import Callable, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.cache_utils import Cache
+from transformers.utils import (
+    logging,
+)
+
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs
+from ..llama.modeling_llama import (
+    LlamaAttention,
+    LlamaDecoderLayer,
+    LlamaForCausalLM,
+    LlamaForSequenceClassification,
+    LlamaMLP,
+    LlamaModel,
+    LlamaPreTrainedModel,
+    LlamaRMSNorm,
+    apply_rotary_pos_emb,
+    eager_attention_forward,
+)
+from .configuration_hunyuan_v1_moe import HunYuanMoEV1Config
+
+
+logger = logging.get_logger(__name__)
+
+
+class HunYuanMoEV1RMSNorm(LlamaRMSNorm):
+    pass
+
+
+class HunYuanMoEV1MLP(LlamaMLP):
+    def __init__(self, config: HunYuanMoEV1Config, layer_idx=None, is_shared_mlp=False):
+        super().__init__(config)
+        self.layer_idx = layer_idx
+        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)
+
+
+class HunYuanMoEV1Attention(LlamaAttention):
+    def __init__(self, config: HunYuanMoEV1Config, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.query_layernorm = HunYuanMoEV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.key_layernorm = HunYuanMoEV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    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[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+        query_states = self.query_layernorm(query_states)
+        key_states = self.key_layernorm(key_states)
+
+        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,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class HunYuanMoEV1Gate(nn.Module):
+    def __init__(self, config: HunYuanMoEV1Config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        num_experts = config.num_experts if isinstance(config.num_experts, int) else config.num_experts[layer_idx]
+        self.wg = nn.Linear(config.hidden_size, num_experts, bias=False, dtype=torch.float32)
+
+    def forward(self, hidden_states):
+        bsz, seq_len, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.reshape(-1, hidden_size)
+        if self.wg.weight.dtype == torch.float32:
+            hidden_states = hidden_states.float()
+        logits = self.wg(hidden_states)
+        return logits
+
+
+class HunYuanMoEV1Moe(nn.Module):
+    def __init__(self, config: HunYuanMoEV1Config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.num_experts = config.num_experts if isinstance(config.num_experts, int) else config.num_experts[layer_idx]
+        self.top_k = config.moe_topk if isinstance(config.moe_topk, int) else config.moe_topk[layer_idx]
+        self.gate = HunYuanMoEV1Gate(config, layer_idx=layer_idx)
+        # self.wg = nn.Linear(config.hidden_size, config.num_experts, bias=False, dtype=torch.float32)
+        self.experts = nn.ModuleList(
+            [HunYuanMoEV1MLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(self.num_experts)]
+        )
+
+        self.shared_mlp = HunYuanMoEV1MLP(config, layer_idx=layer_idx, is_shared_mlp=True)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states_mlp = self.shared_mlp(hidden_states)
+        router_logits = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # 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)
+        return final_hidden_states + hidden_states_mlp
+
+
+class HunYuanMoEV1DecoderLayer(LlamaDecoderLayer):
+    def __init__(self, config: HunYuanMoEV1Config, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.hidden_size = config.hidden_size
+        self.self_attn = HunYuanMoEV1Attention(config=config, layer_idx=layer_idx)
+        self.mlp = HunYuanMoEV1Moe(config, layer_idx=layer_idx)
+        self.input_layernorm = HunYuanMoEV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = HunYuanMoEV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.layer_idx = layer_idx
+
+
+class HunYuanMoEV1PreTrainedModel(LlamaPreTrainedModel):
+    _can_compile_fullgraph = False
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class HunYuanMoEV1RotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: HunYuanMoEV1Config, 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]
+        if self.rope_type == "dynamic" and config.rope_scaling["alpha"]:
+            # DynamicNTKAlphaRotary
+            self.dim = config.head_dim
+            base = config.rope_theta * config.rope_scaling.get("alpha") ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.attention_scaling = 1.0
+        else:
+            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)
+
+
+class HunYuanMoEV1Model(LlamaModel):
+    pass
+
+
+class HunYuanMoEV1ForCausalLM(LlamaForCausalLM):
+    pass
+
+
+class HunYuanMoEV1ForSequenceClassification(LlamaForSequenceClassification):
+    pass
+
+
+__all__ = [
+    "HunYuanMoEV1ForCausalLM",
+    "HunYuanMoEV1Model",
+    "HunYuanMoEV1PreTrainedModel",
+    "HunYuanMoEV1ForSequenceClassification",
+]