import math import torch import torch.nn as nn from typing import Optional, Tuple 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_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1): mrope_section = mrope_section * 2 cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze( unsqueeze_dim ) sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze( unsqueeze_dim ) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class Qwen2_5_VLRotaryEmbedding(nn.Module): def __init__(self, config, device=None): super().__init__() # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and config.rope_scaling is not None: 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 from transformers.modeling_rope_utils import _compute_default_rope_parameters self.rope_init_fn = _compute_default_rope_parameters 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 def _dynamic_frequency_update(self, position_ids, device): """ dynamic RoPE layers should recompute `inv_freq` in the following situations: 1 - growing beyond the cached sequence length (allow scaling) 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) """ seq_len = torch.max(position_ids) + 1 if seq_len > self.max_seq_len_cached: # growth inv_freq, self.attention_scaling = self.rope_init_fn( self.config, device, seq_len=seq_len, **self.rope_kwargs ) self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation self.max_seq_len_cached = seq_len if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) self.max_seq_len_cached = self.original_max_seq_len @torch.no_grad() def forward(self, x, position_ids): if "dynamic" in self.rope_type: self._dynamic_frequency_update(position_ids, device=x.device) # Core RoPE block. In contrast to other models, Qwen2_5_VL has different position ids for the grids # So we expand the inv_freq to shape (3, ...) inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1) position_ids_expanded = position_ids[:, :, None, :].float() # shape (3, bs, 1, positions) # Force float32 (see https://github.com/huggingface/transformers/pull/29285) device_type = x.device.type device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() sin = emb.sin() # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention cos = cos * self.attention_scaling sin = sin * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) 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) class Qwen2_5_VLAttention(nn.Module): def __init__(self, config, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.is_causal = True self.attention_dropout = config.attention_dropout self.rope_scaling = config.rope_scaling if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads})." ) self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) def forward( self, hidden_states: torch.Tensor, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_multimodal_rotary_pos_emb( query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] ) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) # Fix precision issues in Qwen2-VL float16 inference # Replace inf values with zeros in attention weights to prevent NaN propagation if query_states.dtype == torch.float16: attn_weights = torch.where(torch.isinf(attn_weights), torch.zeros_like(attn_weights), attn_weights) # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, -1) attn_output = self.o_proj(attn_output) return attn_output class Qwen2MLP(nn.Module): def __init__(self, config): super().__init__() from transformers.activations import ACT2FN self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.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 Qwen2RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Qwen2RMSNorm 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 Qwen2_5_VLDecoderLayer(nn.Module): def __init__(self, config, layer_idx): super().__init__() self.hidden_size = config.hidden_size self.self_attn = Qwen2_5_VLAttention(config, layer_idx) self.mlp = Qwen2MLP(config) self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states = self.self_attn( hidden_states=hidden_states, position_embeddings=position_embeddings, ) 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 class NexusGenImageEmbeddingMerger(nn.Module): def __init__(self, num_layers=1, out_channel=4096, expand_ratio=4, device='cpu'): super().__init__() from transformers import Qwen2_5_VLConfig from transformers.activations import ACT2FN config = Qwen2_5_VLConfig(**{ "_name_or_path": "DiffSynth-Studio/Nexus-GenV2", "architectures": [ "Qwen2_5_VLForConditionalGeneration" ], "attention_dropout": 0.0, "auto_map": { "AutoConfig": "configuration_qwen2_5_vl.Qwen2_5_VLConfig", "AutoModel": "modeling_qwen2_5_vl.Qwen2_5_VLModel", "AutoModelForCausalLM": "modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration" }, "bos_token_id": 151643, "eos_token_id": 151645, "hidden_act": "silu", "hidden_size": 3584, "image_token_id": 151655, "initializer_range": 0.02, "intermediate_size": 18944, "max_position_embeddings": 128000, "max_window_layers": 28, "model_type": "qwen2_5_vl", "num_attention_heads": 28, "num_hidden_layers": 28, "num_key_value_heads": 4, "pad_token_id": 151643, "rms_norm_eps": 1e-06, "rope_scaling": { "mrope_section": [ 16, 24, 24 ], "rope_type": "default", "type": "default" }, "rope_theta": 1000000.0, "sliding_window": 32768, "tie_word_embeddings": False, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": False, "use_sliding_window": False, "video_token_id": 151656, "vision_config": { "hidden_size": 1280, "in_chans": 3, "model_type": "qwen2_5_vl", "spatial_patch_size": 14, "tokens_per_second": 2, "torch_dtype": "bfloat16" }, "vision_end_token_id": 151653, "vision_start_token_id": 151652, "vision_token_id": 151654, "vocab_size": 152064 }) self.config = config self.num_layers = num_layers self.layers = nn.ModuleList([Qwen2_5_VLDecoderLayer(config, layer_idx) for layer_idx in range(num_layers)]) self.projector = nn.Sequential(Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps), nn.Linear(config.hidden_size, out_channel * expand_ratio), Qwen2RMSNorm(out_channel * expand_ratio, eps=config.rms_norm_eps), ACT2FN[config.hidden_act], nn.Linear(out_channel * expand_ratio, out_channel), Qwen2RMSNorm(out_channel, eps=config.rms_norm_eps)) self.base_grid = torch.tensor([[1, 72, 72]], device=device) self.rotary_emb = Qwen2_5_VLRotaryEmbedding(config=config, device=device) def get_position_ids(self, image_grid_thw): """ Generates position ids for the input embeddings grid. modified from the qwen2_vl mrope. """ batch_size = image_grid_thw.shape[0] spatial_merge_size = self.config.vision_config.spatial_merge_size t, h, w = ( image_grid_thw[0][0], image_grid_thw[0][1], image_grid_thw[0][2], ) llm_grid_t, llm_grid_h, llm_grid_w = ( t.item(), h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) scale_h = self.base_grid[0][1].item() / h.item() scale_w = self.base_grid[0][2].item() / w.item() range_tensor = torch.arange(llm_grid_t).view(-1, 1) expanded_range = range_tensor.expand(-1, llm_grid_h * llm_grid_w) time_tensor = expanded_range * self.config.vision_config.tokens_per_second t_index = time_tensor.long().flatten().to(image_grid_thw.device) h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten().to(image_grid_thw.device) * scale_h w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten().to(image_grid_thw.device) * scale_w # 3, B, L position_ids = torch.stack([t_index, h_index, w_index]).unsqueeze(0).repeat(batch_size, 1, 1).permute(1, 0, 2) return position_ids def forward(self, embeds, embeds_grid, ref_embeds=None, ref_embeds_grid=None): position_ids = self.get_position_ids(embeds_grid) hidden_states = embeds if ref_embeds is not None: position_ids_ref_embeds = self.get_position_ids(ref_embeds_grid) position_ids = torch.cat((position_ids, position_ids_ref_embeds), dim=-1) hidden_states = torch.cat((embeds, ref_embeds), dim=1) position_embeddings = self.rotary_emb(hidden_states, position_ids) for layer in self.layers: hidden_states = layer(hidden_states, position_embeddings) hidden_states = self.projector(hidden_states) return hidden_states @staticmethod def state_dict_converter(): return NexusGenMergerStateDictConverter() class NexusGenMergerStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): return state_dict def from_civitai(self, state_dict): merger_state_dict = {key.replace("embedding_merger.", ""): value for key, value in state_dict.items() if key.startswith('embedding_merger.')} return merger_state_dict class NexusGenAdapter(nn.Module): """ Adapter for Nexus-Gen generation decoder. """ def __init__(self, input_dim=3584, output_dim=4096): super(NexusGenAdapter, self).__init__() self.adapter = nn.Sequential(nn.Linear(input_dim, output_dim), nn.LayerNorm(output_dim), nn.ReLU(), nn.Linear(output_dim, output_dim), nn.LayerNorm(output_dim)) def forward(self, x): return self.adapter(x) @staticmethod def state_dict_converter(): return NexusGenAdapterStateDictConverter() class NexusGenAdapterStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): return state_dict def from_civitai(self, state_dict): adapter_state_dict = {key: value for key, value in state_dict.items() if key.startswith('adapter.')} return adapter_state_dict