# Copyright 2025 Deepseek AI and The HuggingFace 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. from collections.abc import Callable from dataclasses import dataclass import torch import torch.nn.functional as F from huggingface_hub.dataclasses import strict from torch import nn from ... import initialization as init from ...activations import ACT2FN from ...cache_utils import Cache from ...configuration_utils import PreTrainedConfig from ...generation import ClassifierFreeGuidanceLogitsProcessor, GenerationMixin, GenerationMode, LogitsProcessorList from ...generation.utils import GenerateDecoderOnlyOutput from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ModelOutput from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import ( TransformersKwargs, auto_docstring, can_return_tuple, is_vision_available, logging, torch_compilable_check, ) from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel from ..blip_2.modeling_blip_2 import Blip2VisionModel from ..chameleon.configuration_chameleon import ChameleonVQVAEConfig from ..chameleon.modeling_chameleon import ( ChameleonVQVAE, ChameleonVQVAEEncoderAttnBlock, ChameleonVQVAEEncoderConvDownsample, ChameleonVQVAEEncoderResnetBlock, ChameleonVQVAEVectorQuantizer, ) from ..idefics.modeling_idefics import IdeficsBaseModelOutputWithPast, IdeficsCausalLMOutputWithPast from ..llama.modeling_llama import eager_attention_forward from ..siglip.configuration_siglip import SiglipVisionConfig from ..siglip.modeling_siglip import SiglipEncoder, SiglipEncoderLayer, SiglipVisionEmbeddings if is_vision_available(): pass logger = logging.get_logger(__name__) # General docstring @auto_docstring(checkpoint="deepseek-community/Janus-Pro-1B") @strict class JanusVisionConfig(SiglipVisionConfig): r""" projection_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for the projection layer. num_image_tokens (`int`, *optional*, defaults to 576): Number of image tokens. """ hidden_size: int = 1024 num_hidden_layers: int = 24 num_attention_heads: int = 16 image_size: int | list[int] | tuple[int, int] = 384 hidden_act: str = "gelu" mlp_ratio: float | int = 4.0 attention_bias: bool = True hidden_dropout_rate: float | int = 0.0 projection_dim: int = 2048 projection_dropout: float | int = 0.0 use_qk_norm: bool = False initializer_range: float = 0.02 depth: int = 2 num_image_tokens: int = 576 intermediate_size = AttributeError() @auto_docstring(checkpoint="deepseek-community/Janus-Pro-1B") @strict class JanusVQVAEConfig(ChameleonVQVAEConfig): r""" base_channels (`int`, *optional*, defaults to 128): Base channel count. channel_multiplier (`list[int]`, *optional*, defaults to `[1, 1, 2, 2, 4]`): Channel multipliers for each resolution. num_res_blocks (`int`, *optional*, defaults to 2): Number of residual blocks. num_patches (`int`, *optional*, defaults to 32): Num of patches the input images can be divided into. out_channels (`int`, *optional*, defaults to 3): Number of out channels. image_token_embed_dim (`int`, *optional*, defaults to 2048): Dimension of image embeddings. It should be same as the dimensionality of text embeddings. """ embed_dim: int = 8 num_embeddings: int = 16384 double_latent: bool = False latent_channels: int = 256 num_patches: int = 32 in_channels: int = 3 out_channels: int = 3 base_channels: int = 128 channel_multiplier: list[int] | tuple[int, ...] = (1, 1, 2, 2, 4) num_res_blocks: int = 2 dropout: float | int = 0.0 initializer_range: float = 0.02 projection_dim: int = 2048 num_hidden_layers: int = 2 hidden_act: str = "gelu" image_token_embed_dim: int = 2048 resolution = AttributeError() attn_resolutions = AttributeError() attn_type = AttributeError() @auto_docstring(checkpoint="deepseek-community/Janus-Pro-1B") @strict class JanusConfig(PreTrainedConfig): r""" Example: ```python >>> from transformers import JanusForConditionalGeneration, JanusConfig, JanusVisionConfig, JanusVQVAEConfig, LlamaConfig >>> # Initializing a Janus vision config >>> vision_config = JanusVisionConfig() >>> # Initializing a Llama config >>> text_config = LlamaConfig() >>> # Initializing a VQ config >>> vq_config = JanusVQVAEConfig() >>> # Initializing a Janus Pro 1B style configuration >>> configuration = JanusConfig(vision_config=vision_config, text_config=text_config, vq_config=vq_config) >>> # Initializing a model from the Janus Pro 1B style configuration >>> model = JanusForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "janus" sub_configs = { "text_config": AutoConfig, "vision_config": JanusVisionConfig, "vq_config": JanusVQVAEConfig, } text_config: dict | PreTrainedConfig | None = None vision_config: dict | PreTrainedConfig | None = None vq_config: dict | PreTrainedConfig | None = None image_token_id: int = 100581 def __post_init__(self, **kwargs): if isinstance(self.text_config, dict): self.text_config["model_type"] = self.text_config.get("model_type", "llama") self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) elif self.text_config is None: logger.info("`text_config` is None. Initializing with default values") self.text_config = CONFIG_MAPPING["llama"]() if self.vision_config is None: logger.info("`vision_config` is None. Initializing with default JanusVisionConfig values") self.vision_config = JanusVisionConfig() elif isinstance(self.vision_config, dict): self.vision_config = JanusVisionConfig(**self.vision_config) if self.vq_config is None: logger.info("`vq_config` is None. Initializing with default JanusVQVAEConfig values") self.vq_config = JanusVQVAEConfig() elif isinstance(self.vq_config, dict): self.vq_config = JanusVQVAEConfig(**self.vq_config) # This dimension is required when decoding discrete image tokens to continuous input. self.vq_config.num_patches = self.vision_config.image_size // self.vision_config.patch_size super().__post_init__(**kwargs) @auto_docstring class JanusPreTrainedModel(PreTrainedModel): config: JanusConfig base_model_prefix = "model" input_modalities = ("image", "text") supports_gradient_checkpointing = True _no_split_modules = ["LlamaDecoderLayer", "JanusVisionEncoderLayer"] _skip_keys_device_placement = ["past_key_values", "causal_mask"] _supports_flash_attn = True _supports_sdpa = True _can_compile_fullgraph = True def _init_weights(self, module): super()._init_weights(module) if isinstance(module, JanusVisionEmbeddings): init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) @dataclass @auto_docstring( custom_intro=""" Base class for Janus VQ-VAE mode model outputs. """ ) class JanusVQVAEOutput(ModelOutput): r""" decoded_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): Reconstructed pixel values after encoding and decoding the input. embedding_loss (`torch.FloatTensor`): Embedding loss. """ decoded_pixel_values: torch.FloatTensor | None = None embedding_loss: torch.FloatTensor | None = None class JanusBaseModelOutputWithPast(IdeficsBaseModelOutputWithPast): pass class JanusCausalLMOutputWithPast(IdeficsCausalLMOutputWithPast): pass class JanusVisionEmbeddings(SiglipVisionEmbeddings): def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: _, _, height, width = pixel_values.shape target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] embeddings = patch_embeds.flatten(2).transpose(1, 2) if interpolate_pos_encoding: pos_embeds = self.interpolate_pos_encoding(embeddings, height, width) else: pos_embeds = self.position_embedding(self.position_ids) embeddings = embeddings + pos_embeds return embeddings class JanusVisionAttention(nn.Module): """Attention Class for Janus Vision Encoder""" def __init__(self, config: JanusVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout proj_dropout = config.projection_dropout qk_norm = config.use_qk_norm self.is_causal = False # Janus has no MHA, hence for `eager_attention_forward` call setting `num_key_value_groups` to 1. self.num_key_value_groups = 1 self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias) self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias) self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias) self.projection_layer = nn.Linear(self.embed_dim, self.embed_dim) self.projection_dropout = nn.Dropout(proj_dropout) if proj_dropout > 0 else nn.Identity() self.q_norm = nn.LayerNorm(self.embed_dim) if qk_norm else nn.Identity() self.k_norm = nn.LayerNorm(self.embed_dim) if qk_norm else nn.Identity() def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ): batch_size, seq_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.reshape(-1, self.num_heads, self.head_dim) query_states = self.q_norm(query_states) key_states = key_states.reshape(-1, self.num_heads, self.head_dim) key_states = self.k_norm(key_states) query_states = query_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) 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.scale, is_causal=self.is_causal, **kwargs, ) attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim) output = self.projection_layer(attn_output) output = self.projection_dropout(output) return output, attn_weights class JanusVisionMLP(nn.Module): def __init__(self, config: JanusVisionConfig): super().__init__() self.config = config self.intermediate_size = int(config.hidden_size * config.mlp_ratio) self.activation_fn = ACT2FN[config.hidden_act] # Gelu act self.fc1 = nn.Linear(config.hidden_size, self.intermediate_size) self.fc2 = nn.Linear(self.intermediate_size, config.hidden_size) self.dropout1 = nn.Dropout(config.hidden_dropout_rate) self.dropout2 = nn.Dropout(config.hidden_dropout_rate) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.dropout1(hidden_states) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout2(hidden_states) return hidden_states class JanusVisionEncoderLayer(SiglipEncoderLayer): def __init__(self, config: JanusVisionConfig): super().__init__(config) self.config = config self.embed_dim = config.hidden_size self.self_attn = JanusVisionAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = JanusVisionMLP(config) class JanusVisionEncoder(SiglipEncoder): def __init__(self, config: JanusVisionConfig): super().__init__(config) self.layers = nn.ModuleList([JanusVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]) class JanusVisionModel(Blip2VisionModel): _can_record_outputs = { "hidden_states": JanusVisionEncoderLayer, "attentions": JanusVisionAttention, } def __init__(self, config: JanusVisionConfig): super().__init__(config) self.encoder = JanusVisionEncoder(config) def forward( self, pixel_values: torch.FloatTensor | None = None, interpolate_pos_encoding: bool = False, **kwargs: Unpack[TransformersKwargs], ) -> tuple | BaseModelOutputWithPooling: if pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) encoder_outputs: BaseModelOutput = self.encoder( inputs_embeds=hidden_states, **kwargs, ) last_hidden_state = encoder_outputs.last_hidden_state last_hidden_state = self.post_layernorm(last_hidden_state) pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, ) class JanusVisionAlignerMLP(nn.Module): def __init__(self, config: JanusVisionConfig): super().__init__() self.fc1 = nn.Linear(config.hidden_size, config.projection_dim) self.hidden_layers = nn.ModuleList( [nn.Linear(config.projection_dim, config.projection_dim) for _ in range(1, config.depth)] ) self.activation_fn = ACT2FN[config.hidden_act] def forward(self, hidden_states): hidden_states = self.fc1(hidden_states) for layer in self.hidden_layers: hidden_states = self.activation_fn(hidden_states) hidden_states = layer(hidden_states) return hidden_states class JanusVQVAEVectorQuantizer(ChameleonVQVAEVectorQuantizer): def __init__(self, config: JanusVQVAEConfig): super().__init__(config) self.quant_state_dims = [config.num_patches] * 2 def get_codebook_entry(self, image_tokens: torch.LongTensor) -> torch.FloatTensor: batch_size = image_tokens.shape[0] emb_dim: int = self.embedding.weight.shape[-1] # get quantized latent vectors hidden_state_quant = self.embedding(image_tokens) # l2 normalization on the last dimension hidden_state_quant = F.normalize(hidden_state_quant, p=2, dim=-1) # reshape back to match original input shape hidden_state_quant = hidden_state_quant.view((batch_size, *self.quant_state_dims, emb_dim)) hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous() return hidden_state_quant class JanusVQVAEResnetBlock(ChameleonVQVAEEncoderResnetBlock): pass class JanusVQVAEAttnBlock(ChameleonVQVAEEncoderAttnBlock): pass class JanusVQVAEConvDownsample(ChameleonVQVAEEncoderConvDownsample): pass class JanusVQVAEConvUpsample(nn.Module): def __init__(self, in_channels): super().__init__() self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, hidden_states): hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest") hidden_states = self.conv(hidden_states) return hidden_states class JanusVQVAEMidBlock(nn.Module): def __init__(self, config: JanusVQVAEConfig, channels: int): super().__init__() self.block_1 = JanusVQVAEResnetBlock( config=config, in_channels=channels, out_channels=channels, ) self.attn_1 = JanusVQVAEAttnBlock(channels) self.block_2 = JanusVQVAEResnetBlock( config=config, in_channels=channels, out_channels=channels, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.block_1(hidden_states) hidden_states = self.attn_1(hidden_states) hidden_states = self.block_2(hidden_states) return hidden_states class JanusVQVAEEncoder(nn.Module): def __init__(self, config): super().__init__() self.num_resolutions = len(config.channel_multiplier) self.num_res_blocks = config.num_res_blocks base_channels = config.base_channels in_channels = config.in_channels double_latent = config.double_latent latent_channels = config.latent_channels channel_multiplier = config.channel_multiplier self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1) in_channel_multiplier = (1,) + tuple(channel_multiplier) self.in_channel_multiplier = in_channel_multiplier self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = base_channels * in_channel_multiplier[i_level] block_out = base_channels * channel_multiplier[i_level] for i_block in range(self.num_res_blocks): block.append( JanusVQVAEResnetBlock( config=config, in_channels=block_in, out_channels=block_out, ) ) block_in = block_out if i_level == self.num_resolutions - 1: attn.append(JanusVQVAEAttnBlock(block_in)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions - 1: down.downsample = JanusVQVAEConvDownsample(block_in) self.down.append(down) self.mid = JanusVQVAEMidBlock(config, block_in) self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) self.conv_out = torch.nn.Conv2d( block_in, 2 * latent_channels if double_latent else latent_channels, kernel_size=3, stride=1, padding=1, ) def forward(self, pixel_values: torch.LongTensor): # downsampling hidden_states = [self.conv_in(pixel_values)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): hidden_state = self.down[i_level].block[i_block]( hidden_states[-1], ) if len(self.down[i_level].attn) > 0: hidden_state = self.down[i_level].attn[i_block](hidden_state) hidden_states.append(hidden_state) if i_level != self.num_resolutions - 1: hidden_states.append(self.down[i_level].downsample(hidden_states[-1])) # middle last_hidden_state = hidden_states[-1] last_hidden_state = self.mid(last_hidden_state) # end last_hidden_state = self.norm_out(last_hidden_state) last_hidden_state *= torch.sigmoid(last_hidden_state) last_hidden_state = self.conv_out(last_hidden_state) return last_hidden_state class JanusVQVAEDecoder(nn.Module): def __init__(self, config): super().__init__() self.num_resolutions = len(config.channel_multiplier) self.num_res_blocks = config.num_res_blocks base_channels = config.base_channels latent_channels = config.latent_channels out_channels = config.out_channels # compute in_ch_mult, block_in and curr_res at lowest res block_in = base_channels * config.channel_multiplier[self.num_resolutions - 1] # z to block_in self.conv_in = torch.nn.Conv2d(latent_channels, block_in, kernel_size=3, stride=1, padding=1) # middle self.mid = JanusVQVAEMidBlock(config, block_in) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = base_channels * config.channel_multiplier[i_level] for i_block in range(self.num_res_blocks + 1): block.append( JanusVQVAEResnetBlock( config=config, in_channels=block_in, out_channels=block_out, ) ) block_in = block_out if i_level == self.num_resolutions - 1: attn.append(JanusVQVAEAttnBlock(block_in)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = JanusVQVAEConvUpsample(block_in) self.up.append(up) # end self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, hidden_state: torch.FloatTensor) -> torch.FloatTensor: hidden_state = self.conv_in(hidden_state) # middle hidden_state = self.mid(hidden_state) # upsampling for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks + 1): hidden_state = self.up[i_level].block[i_block](hidden_state) if len(self.up[i_level].attn) > 0: hidden_state = self.up[i_level].attn[i_block](hidden_state) if i_level != self.num_resolutions - 1: hidden_state = self.up[i_level].upsample(hidden_state) hidden_state = self.norm_out(hidden_state) hidden_state *= torch.sigmoid(hidden_state) hidden_state = self.conv_out(hidden_state) return hidden_state class JanusVQVAE(ChameleonVQVAE): _no_split_modules = [ "JanusVQVAEAttnBlock", "JanusVQVAEResnetBlock", "JanusVQVAEVectorQuantizer", ] _can_record_outputs = { "hidden_states": JanusVQVAEResnetBlock, "attentions": JanusVQVAEAttnBlock, } main_input_name = "pixel_values" def __init__(self, config: JanusVQVAEConfig): super().__init__(config) self.decoder = JanusVQVAEDecoder(config) self.gradient_checkpointing = False # Initialize the VQVAE model. self.post_init() def decode(self, image_tokens: torch.LongTensor) -> torch.FloatTensor: """ Decodes quantized token IDs into pixel values. Args: image_tokens (torch.LongTensor): Batch of token IDs. Returns: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): Pixel values decoded from the token IDs. """ if image_tokens.shape[1] != self.quantize.quant_state_dims[0] * self.quantize.quant_state_dims[1]: raise ValueError( f"Expected `image_tokens` to have shape `(batch_size, {self.quantize.quant_state_dims[0] * self.quantize.quant_state_dims[1]})`, " f"but got shape `{image_tokens.shape}`." ) codebook_entry = self.quantize.get_codebook_entry(image_tokens) hidden_states = self.post_quant_conv(codebook_entry) pixel_values = self.decoder(hidden_states) return pixel_values @can_return_tuple @auto_docstring def forward( self, pixel_values: torch.FloatTensor, **kwargs, ) -> tuple[torch.FloatTensor, torch.FloatTensor]: batch_size = pixel_values.shape[0] encode_outputs = self.encode(pixel_values, return_dict=True, **kwargs) decoded_pixel_values = self.decode(encode_outputs.image_tokens.view(batch_size, -1)) return JanusVQVAEOutput(decoded_pixel_values, encode_outputs.embedding_loss) class JanusVQVAEAlignerMLP(nn.Module): def __init__(self, config: JanusVQVAEConfig): super().__init__() self.fc1 = nn.Linear(config.embed_dim, config.projection_dim) self.hidden_layers = nn.ModuleList( [nn.Linear(config.projection_dim, config.projection_dim) for _ in range(1, config.num_hidden_layers)] ) self.activation_fn = ACT2FN[config.hidden_act] def forward(self, hidden_states): hidden_states = self.fc1(hidden_states) for layer in self.hidden_layers: hidden_states = self.activation_fn(hidden_states) hidden_states = layer(hidden_states) return hidden_states class JanusVQVAEHead(nn.Module): """Head used for sampling tokens in image generation, replacing the usual lm head.""" def __init__(self, config: JanusVQVAEConfig): super().__init__() self.proj_out = nn.Linear(config.image_token_embed_dim, config.projection_dim) self.activation_fn = ACT2FN[config.hidden_act] self.vision_head = nn.Linear(config.projection_dim, config.num_embeddings) def forward(self, hidden_states: torch.Tensor) -> torch.tensor: hidden_states = self.proj_out(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.vision_head(hidden_states) return hidden_states @auto_docstring( custom_intro=""" The Janus model which consists of a siglip vision backbone, a Llama language model and a VQ model. """ ) class JanusModel(JanusPreTrainedModel): def __init__(self, config: JanusConfig): super().__init__(config) self.config = config # This is necessary for backward compatibility, see SiglipModel initialization self.vision_model = JanusVisionModel._from_config(config.vision_config) self.aligner = JanusVisionAlignerMLP(self.vision_model.config) self.vqmodel = JanusVQVAE._from_config(config.vq_config) # Below generation_* modules are used for Image generation. # Embeddings used for image generation, instead of Janus vision embeddings. self.generation_embeddings = nn.Embedding(self.vqmodel.config.num_embeddings, self.vqmodel.config.embed_dim) self.generation_aligner = JanusVQVAEAlignerMLP(self.vqmodel.config) self.generation_head = JanusVQVAEHead(self.vqmodel.config) self.language_model = AutoModel.from_config(config=config.text_config) self.gradient_checkpointing = False # Initialize weights and apply final processing. self.post_init() def get_input_embeddings(self): return self.language_model.get_input_embeddings() def set_input_embeddings(self, value): self.language_model.set_input_embeddings(value) @can_return_tuple @auto_docstring def get_image_features( self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] ) -> tuple | BaseModelOutputWithPooling: vision_outputs = self.vision_model(pixel_values, return_dict=True, **kwargs) vision_outputs.pooler_output = self.aligner(vision_outputs.last_hidden_state) return vision_outputs def get_placeholder_mask( self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor ): """ Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is equal to the length of multimodal features. If the lengths are different, an error is raised. """ if input_ids is None: special_image_mask = inputs_embeds == self.get_input_embeddings()( torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) ) special_image_mask = special_image_mask.all(-1) else: special_image_mask = input_ids == self.config.image_token_id n_image_tokens = special_image_mask.sum() n_image_features = image_features.shape[0] * image_features.shape[1] special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) torch_compilable_check( inputs_embeds[special_image_mask].numel() == image_features.numel(), f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}", ) return special_image_mask @can_return_tuple @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, pixel_values: torch.FloatTensor | 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, logits_to_keep: int | torch.Tensor = 0, **kwargs, ) -> JanusBaseModelOutputWithPast: if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError( "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" ) if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(input_ids) if pixel_values is not None: image_embeds = self.get_image_features(pixel_values, return_dict=True).pooler_output image_features = image_embeds.reshape(-1, inputs_embeds.shape[-1]) image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) image_attention_mask = self.get_placeholder_mask( input_ids, inputs_embeds=inputs_embeds, image_features=image_features ) inputs_embeds = inputs_embeds.masked_scatter(image_attention_mask, image_features) lm_output = self.language_model( inputs_embeds=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, logits_to_keep=logits_to_keep, **kwargs, ) return JanusBaseModelOutputWithPast( last_hidden_state=lm_output.last_hidden_state, past_key_values=lm_output.past_key_values, hidden_states=lm_output.hidden_states, attentions=lm_output.attentions, image_hidden_states=image_embeds if pixel_values is not None else None, ) class JanusForConditionalGeneration(JanusPreTrainedModel, GenerationMixin): _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"} output_modalities = ("image", "text") _can_compile_fullgraph = True def __init__(self, config: JanusConfig): super().__init__(config) self.config = config self.model = JanusModel(config) self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) # Initialize weights and apply final processing. self.post_init() def get_input_embeddings(self): return self.model.language_model.get_input_embeddings() def set_input_embeddings(self, value): self.model.language_model.set_input_embeddings(value) def prepare_embeddings_for_image_generation(self, inputs: torch.Tensor) -> torch.Tensor: hidden_state = self.model.generation_embeddings(inputs) hidden_state = self.model.generation_aligner(hidden_state) return hidden_state @can_return_tuple @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, pixel_values: torch.FloatTensor | 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], ) -> JanusCausalLMOutputWithPast: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. """ outputs = self.model( input_ids=input_ids, pixel_values=pixel_values, 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.text_config.vocab_size, **kwargs ) return JanusCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=outputs.image_hidden_states, ) def prepare_inputs_for_generation( self, input_ids, pixel_values=None, past_key_values=None, attention_mask=None, inputs_embeds=None, logits_to_keep=None, is_first_iteration=False, **kwargs, ): # Overwritten -- extra custom processing model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask, logits_to_keep=logits_to_keep, is_first_iteration=is_first_iteration, **kwargs, ) # Pixel values are used only in the first iteration if available # In subsequent iterations, they are already merged with text and cached # NOTE: first iteration doesn't have to be prefill, it can be the first # iteration with a question and cached system prompt (continue generate from cache) if is_first_iteration or not kwargs.get("use_cache", True): model_inputs["pixel_values"] = pixel_values return model_inputs def decode_image_tokens(self, image_tokens: torch.Tensor): """ Decodes generated image tokens from language model to continuous pixel values with VQGAN module via upsampling. Args: image_tokens (`torch.LongTensor` of shape `(batch_size, num_of_tokens)`): The tensors corresponding to the input images. """ decoded_image = self.model.vqmodel.decode(image_tokens) decoded_image = decoded_image.permute(0, 2, 3, 1) return decoded_image @torch.no_grad() def generate( self, inputs: torch.Tensor | None = None, attention_mask: torch.LongTensor | None = None, logits_processor: LogitsProcessorList | None = None, **kwargs, ): # 1. Handle generation config and model kwargs # Pop generation_mode first since it's specific to Janus generation_mode = kwargs.pop("generation_mode", "text") generation_config, model_kwargs = self._prepare_generation_config( kwargs.pop("generation_config", None), **kwargs ) # Default to "text" generation if mode isn't provided if generation_mode == "text": # Set guidance_scale=None to prevent running UnbatchedCFG processor. return super().generate( inputs=inputs, attention_mask=attention_mask, generation_config=generation_config, guidance_scale=None, **model_kwargs, ) # Validate generation mode if generation_config.get_generation_mode() not in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH): raise ValueError( "Got incompatible mode for Image Generation, should be one of greedy or sampling. " "Ensure that beam search is de-activated by setting `num_beams=1`." ) # Validate the configuration and model kwargs generation_config.validate() self._validate_model_kwargs(model_kwargs.copy()) # 2. Initialize logit processors logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() # Set `use_cache=True` as we will be using input embeds for generation. model_kwargs["use_cache"] = True if generation_config.guidance_scale is None: logger.warning("`guidance_scale` is required for CFG but not provided. Setting to default value of 5.") generation_config.guidance_scale = 5 model_kwargs["guidance_scale"] = generation_config.guidance_scale # 3. Prepare model inputs input_ids, model_input_name, model_kwargs = self._prepare_model_inputs( inputs, generation_config.bos_token_id, model_kwargs ) dtype, device = input_ids.dtype, input_ids.device if len(input_ids.shape) != 2: raise ValueError( f"Expected input ids of shape (batch_size, seq_len), but got {input_ids.shape}" "Passing `inputs embeds` is not supported currently." ) # Prepare special tokens which will be used generate internally. kwargs_has_attention_mask = attention_mask is not None self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device) # 4. Add CFG processor along with user passed logit processor. if generation_config.guidance_scale and generation_config.guidance_scale > 1: logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale)) generation_config.guidance_scale = None # Reset to prevent processor duplication. # 5. Prepare logits processor logits_processor = self._get_logits_processor( generation_config=generation_config, input_ids_seq_length=input_ids.shape[1], encoder_input_ids=input_ids, prefix_allowed_tokens_fn=None, logits_processor=logits_processor, device=device, ) # 6. Expand inputs for multiple image generations per prompt. input_ids, model_kwargs = self._expand_inputs_for_generation( input_ids=input_ids, attention_mask=attention_mask, expand_size=generation_config.num_return_sequences, **model_kwargs, ) # 7. Prepare input and model caches num_image_tokens = self.model.vision_model.config.num_image_tokens batch_size, seq_len = input_ids.shape input_tokens = input_ids.repeat(2, 1) # Double batch size for conditional/unconditional logits attention_mask = model_kwargs.pop("attention_mask", None) attention_mask = attention_mask.repeat(2, 1) model_kwargs["attention_mask"] = attention_mask # Mask all the tokens that are neither BOS nor BOI with pad token in the unconditional logits. mask = (input_tokens[batch_size:, :] != generation_config.bos_token_id) & ( input_tokens[batch_size:, :] != generation_config.generation_kwargs["boi_token_id"] ) input_tokens[batch_size:, :].masked_fill_(mask, generation_config.pad_token_id) inputs_embeds = self.get_input_embeddings()(input_tokens) if model_kwargs.get("past_key_values", None) is None: # Prepare cache if not provided. model_kwargs["past_key_values"] = self._prepare_static_cache( cache_implementation=generation_config.cache_implementation or "static", # batch_size should account for both conditional/unconditional input; hence multiplied by 2. batch_size=batch_size * 2, # we should have at least a cache len of seq_len + num_image_tokens. max_cache_len=max(generation_config.max_length, num_image_tokens + seq_len), model_kwargs=model_kwargs, ) # Placeholder for generated tokens. generated_tokens = torch.zeros((batch_size, num_image_tokens), dtype=dtype, device=device) # 8. init attention / hidden states / scores tuples output_attentions = generation_config.output_attentions output_hidden_states = generation_config.output_hidden_states output_scores = generation_config.output_scores output_logits = generation_config.output_logits return_dict_in_generate = generation_config.return_dict_in_generate raw_scores = () if (return_dict_in_generate and output_scores) else None raw_logits = () if (return_dict_in_generate and output_logits) else None decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None decoder_attentions = () if (return_dict_in_generate and output_attentions) else None for i in range(num_image_tokens): # Set `is_first_iteration=True` to force using `inputs_embeds` instead of `input_ids`. # Without this, `prepare_inputs_for_generation` would use `input_ids` (the full prompt) # instead of our prepared `inputs_embeds` (1 new token). # This causes CUDA error: device-side assert triggered, seen around the call to ` self.self_attn`. # Set this to `True` is also necessary to match the expected output, see the more detailed comment # https://github.com/huggingface/transformers/pull/45044#discussion_r3020805374. model_inputs = self.prepare_inputs_for_generation( inputs_embeds=inputs_embeds, input_ids=input_tokens, is_first_iteration=True, **model_kwargs ) if "attention_mask" in model_inputs: model_inputs["attention_mask"] = model_inputs["attention_mask"].to(inputs_embeds.device) outputs = self.model.language_model( **model_inputs, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) # Update model_kwargs like attention_mask for next generation. model_kwargs = self._update_model_kwargs_for_generation(outputs, model_kwargs) hidden_state = outputs.last_hidden_state[:, -1, :].clone() # Generate scores using the generation head (Not using above defined LM Head) scores = self.model.generation_head(hidden_state) next_token_scores = logits_processor(input_ids, scores) # Sample next token. if generation_config.do_sample: probs = torch.softmax(next_token_scores, dim=-1) next_token = torch.multinomial(probs, num_samples=1).squeeze(-1) else: next_token = torch.argmax(next_token_scores, dim=-1) generated_tokens[:, i] = next_token # Prepare embeddings for the next step. next_token = torch.cat([next_token, next_token]) next_token = next_token.unsqueeze(-1) inputs_embeds = self.prepare_embeddings_for_image_generation(next_token) if return_dict_in_generate: if output_scores: raw_scores += (scores,) if output_logits: raw_logits += (hidden_state.float(),) if output_attentions: decoder_attentions += outputs.attentions if output_hidden_states: decoder_hidden_states += outputs.hidden_states if return_dict_in_generate: return GenerateDecoderOnlyOutput( sequences=generated_tokens, scores=scores, logits=raw_logits, attentions=decoder_attentions, hidden_states=decoder_hidden_states, past_key_values=outputs.past_key_values, ) else: return generated_tokens __all__ = [ "JanusPreTrainedModel", "JanusForConditionalGeneration", "JanusModel", "JanusVQVAE", "JanusVisionModel", "JanusVQVAEConfig", "JanusVisionConfig", "JanusConfig", ]