# 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 collections.abc import Callable import torch import torch.nn.functional as F from torch import nn from ... import initialization as init from ...cache_utils import Cache from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, is_grouped_mm_available, logging from ..hunyuan_v1_dense.modeling_hunyuan_v1_dense import HunYuanDenseV1RotaryEmbedding from ..llama.modeling_llama import ( LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaForSequenceClassification, LlamaMLP, LlamaModel, LlamaPreTrainedModel, LlamaRMSNorm, apply_rotary_pos_emb, eager_attention_forward, ) from ..mixtral.modeling_mixtral import MixtralExperts from .configuration_hunyuan_v1_moe import HunYuanMoEV1Config logger = logging.get_logger(__name__) class HunYuanMoEV1RMSNorm(LlamaRMSNorm): pass class HunYuanMoEV1MLP(LlamaMLP): def __init__(self, config: HunYuanMoEV1Config): super().__init__(config) 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: torch.Tensor | None, past_key_values: Cache | None = None, cache_position: torch.LongTensor | None = 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 = 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, ) 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: int | None = 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 HunYuanMoEV1Experts(MixtralExperts): pass class HunYuanMoEV1Moe(nn.Module): def __init__(self, config: HunYuanMoEV1Config, layer_idx: int | None = 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.experts = HunYuanMoEV1Experts(config) self.shared_mlp = HunYuanMoEV1MLP(config) def route_tokens_to_experts(self, hidden_states): routing_weights = F.softmax(hidden_states, 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) return selected_experts, routing_weights.to(hidden_states.dtype) 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) selected_experts, routing_weights = self.route_tokens_to_experts(router_logits) final_hidden_states = self.experts(hidden_states, selected_experts, routing_weights).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 = ( is_grouped_mm_available() ) # https://huggingface.co/docs/transformers/experts_interface#torchcompile @torch.no_grad() def _init_weights(self, module): PreTrainedModel._init_weights(self, module) if isinstance(module, HunYuanMoEV1Experts): 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) # DynamicNTKAlphaRotary - unique to this model elif "RotaryEmbedding" in module.__class__.__name__ and hasattr(module, "original_inv_freq"): if module.rope_type == "dynamic" and module.config.rope_parameters.get("alpha"): dim = module.config.head_dim rope_theta = module.config.rope_parameters["rope_theta"] alpha = module.config.rope_parameters["alpha"] base = rope_theta * alpha ** (dim / (dim - 2)) buffer_value = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) else: rope_fn = ( ROPE_INIT_FUNCTIONS[module.rope_type] if module.rope_type != "default" else module.compute_default_rope_parameters ) buffer_value, _ = rope_fn(module.config) init.copy_(module.inv_freq, buffer_value) init.copy_(module.original_inv_freq, buffer_value) class HunYuanMoEV1RotaryEmbedding(HunYuanDenseV1RotaryEmbedding): pass class HunYuanMoEV1Model(LlamaModel): pass class HunYuanMoEV1ForCausalLM(LlamaForCausalLM): pass class HunYuanMoEV1ForSequenceClassification(LlamaForSequenceClassification): pass __all__ = [ "HunYuanMoEV1ForCausalLM", "HunYuanMoEV1Model", "HunYuanMoEV1PreTrainedModel", "HunYuanMoEV1ForSequenceClassification", ]