# 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", ]