# Copyright 2025 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. import math from collections.abc import Callable from typing import Any import numpy as np import torch.nn as nn import torch.nn.functional as F from ...cache_utils import Cache from ...configuration_utils import PreTrainedConfig from ...feature_extraction_utils import BatchFeature from ...generation import GenerationMixin from ...image_utils import ImageInput from ...modeling_outputs import BaseModelOutputWithPooling from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import ImagesKwargs, ProcessorMixin, Unpack from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available, logging from ...utils.generic import merge_with_config_defaults from ...utils.output_capturing import capture_outputs from ..chameleon.modeling_chameleon import ChameleonVQVAE, ChameleonVQVAEModelOutput, ChameleonVQVAEVectorQuantizer from ..glm4v.configuration_glm4v import Glm4vTextConfig, Glm4vVisionConfig from ..glm4v.modeling_glm4v import ( Glm4vCausalLMOutputWithPast, Glm4vModel, Glm4vModelOutputWithPast, Glm4vPreTrainedModel, Glm4vTextModel, Glm4vVisionAttention, Glm4vVisionBlock, Glm4vVisionEmbeddings, Glm4vVisionModel, Glm4vVisionPatchEmbed, ) from ..glm4v_moe.modeling_glm4v_moe import Glm4vMoeTextAttention, eager_attention_forward from ..qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessor from ..qwen2_vl.image_processing_qwen2_vl_fast import Qwen2VLImageProcessorFast from ..qwen2_vl.processing_qwen2_vl import Qwen2VLProcessorKwargs from ..siglip.modeling_siglip import SiglipMLP if is_torch_available(): import torch logger = logging.get_logger(__name__) class GlmImageVQVAEConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`GlmImageVQModel`]. It is used to instantiate a `GlmImageVQModel` according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Instantiating a configuration with the defaults will yield a similar configuration to the VQModel of the [zai-org/GLM-Image](https://huggingface.co/zai-org/GLM-Image) architecture. Args: embed_dim (`int`, *optional*, defaults to 2048): Dimensionality of each embedding vector. num_embeddings (`int`, *optional*, defaults to 16384): Number of codebook embeddings. latent_channels (`int`, *optional*, defaults to 1536): Number of channels for the latent space. in_channels (`int`, *optional*, defaults to 3): Number of input channels. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. """ model_type = "glm_image_vqmodel" base_config_key = "vq_config" def __init__( self, embed_dim: int = 2048, num_embeddings: int = 16384, latent_channels: int = 1536, in_channels: int = 3, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) self.embed_dim = embed_dim self.num_embeddings = num_embeddings self.latent_channels = latent_channels self.in_channels = in_channels self.initializer_range = initializer_range class GlmImageVisionConfig(Glm4vVisionConfig): r""" This is the configuration class to store the configuration of a [`GlmImageVisionModel`]. It is used to instantiate an GlmImageVisionModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-Image [zai-org/GLM-Image](https://huggingface.co/zai-org/GLM-Image). Args: depth (`int`, *optional*, defaults to 40): Number of layers (depth) in the model. hidden_size (`int`, *optional*, defaults to 1536): Dimensionality of the encoder layers and the pooler layer. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. attention_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for attention weights. num_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer architecture. in_channels (`int`, *optional*, defaults to 3): Number of input channels. image_size (`int` or `list[int]`, *optional*, defaults to 2048): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. spatial_merge_size (`int`, *optional*, defaults to 1): The size used for merging spatial dimensions. intermediate_size (`int`, *optional*, defaults to 6144): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. """ model_type = "glm_image_vision" base_config_key = "vision_config" def __init__( self, depth=40, hidden_size=1536, hidden_act="gelu", attention_bias=True, attention_dropout=0.0, num_heads=16, in_channels=3, image_size=2048, patch_size=16, layer_norm_eps=1e-06, spatial_merge_size=1, intermediate_size=6144, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) del self.out_hidden_size del self.rms_norm_eps del self.temporal_patch_size self.layer_norm_eps = layer_norm_eps class GlmImageTextConfig(Glm4vTextConfig): r""" This is the configuration class to store the configuration of a [`GlmImageTextModel`]. It is used to instantiate a GLM-Image model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-Image [zai-org/GLM-Image](https://huggingface.co/zai-org/GLM-Image). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 168064): Vocabulary size of the GlmImage model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GlmImageModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 13696): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 40): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 2): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 131072): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. pad_token_id (`int`, *optional*, defaults to 167841): The id of the padding token. vision_vocab_size (`int`, *optional*, defaults to 16512): Vision vocabulary size of the GlmImage model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GlmImageVisionModel`] attention_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. eos_token_id (`int`, *optional*, defaults to 16385): The id of the end of sequence token. ```python >>> from transformers import GlmImageTextModel, GlmImageConfig >>> # Initializing a GlmImageConfig style configuration >>> configuration = GlmImageConfig() >>> # Initializing a model from the GlmImageConfig style configuration >>> model = GlmImageTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" def __init__( self, vocab_size: int = 168064, max_position_embeddings: int = 131072, vision_vocab_size: int = 16512, attention_bias: bool = True, pad_token_id: int = 167841, eos_token_id: int = 16385, **super_kwargs, ): super().__init__( vocab_size=vocab_size, max_position_embeddings=max_position_embeddings, pad_token_id=pad_token_id, **super_kwargs, ) self.vision_vocab_size = vision_vocab_size self.attention_bias = attention_bias self.eos_token_id = eos_token_id class GlmImageConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`GlmImageModel`]. It is used to instantiate a GLM-Image model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-Image [zai-org/GLM-Image](https://huggingface.co/zai-org/GLM-Image) architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `GlmImageTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `GlmImageVisionConfig`): The config object or dictionary of the vision backbone. vq_config (`Union[Dict, GlmImageVQVAEConfig]`, *optional*): GlmImageVQVAEConfig instance containing the configuration for the VQ-VAE model. image_token_id (`int`, *optional*, defaults to 167855): The image token index to encode the image prompt. image_start_token_id (`int`, *optional*, defaults to 16384): The image start token index to encode the start of image. image_end_token_id (`int`, *optional*, defaults to 16385): The image end token index to encode the end of image. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. ```python >>> from transformers import Glm4vForConditionalGeneration, Glm4vConfig >>> # Initializing a GLM-Image style configuration >>> configuration = Glm4vConfig() >>> # Initializing a model from the GLM-Image style configuration >>> model = Glm4vForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glm_image" sub_configs = { "vision_config": GlmImageVisionConfig, "text_config": GlmImageTextConfig, "vq_config": GlmImageVQVAEConfig, } keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, vq_config=None, image_token_id=167855, image_start_token_id=16384, image_end_token_id=16385, tie_word_embeddings: bool | None = False, **kwargs, ): if isinstance(vision_config, dict): vision_config = self.sub_configs["vision_config"](**vision_config) elif vision_config is None: vision_config = self.sub_configs["vision_config"](**kwargs) if isinstance(vq_config, dict): vq_config = self.sub_configs["vq_config"](**vq_config) elif vq_config is None: vq_config = self.sub_configs["vq_config"](**kwargs) if isinstance(text_config, dict): text_config = self.sub_configs["text_config"](**text_config) elif text_config is None: text_config = self.sub_configs["text_config"](**kwargs) self.image_token_id = image_token_id self.image_start_token_id = image_start_token_id self.image_end_token_id = image_end_token_id self.text_config = text_config self.vision_config = vision_config self.vq_config = vq_config self.tie_word_embeddings = tie_word_embeddings super().__init__(**kwargs) class GlmImageVisionMLP(SiglipMLP): pass class GlmImageVisionAttention(Glm4vVisionAttention): def __init__(self, config: GlmImageVisionConfig) -> None: super().__init__(config) self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.attention_bias) self.proj = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, **kwargs, ) -> torch.Tensor: seq_length = hidden_states.shape[0] query_states, key_states, value_states = ( self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) ) query_states = query_states.transpose(0, 1).unsqueeze(0) key_states = key_states.transpose(0, 1).unsqueeze(0) value_states = value_states.transpose(0, 1).unsqueeze(0) attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( self.config._attn_implementation, eager_attention_forward ) if "flash" in self.config._attn_implementation: # Flash Attention: Use cu_seqlens for variable length attention max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() attn_output, _ = attention_interface( self, query_states, key_states, value_states, attention_mask=None, scaling=self.scaling, dropout=0.0 if not self.training else self.attention_dropout, cu_seq_lens_q=cu_seqlens, cu_seq_lens_k=cu_seqlens, max_length_q=max_seqlen, max_length_k=max_seqlen, is_causal=False, **kwargs, ) else: # Other implementations: Process each chunk separately lengths = cu_seqlens[1:] - cu_seqlens[:-1] splits = [ torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states) ] attn_outputs = [ attention_interface( self, q, k, v, attention_mask=None, scaling=self.scaling, dropout=0.0 if not self.training else self.attention_dropout, is_causal=False, **kwargs, )[0] for q, k, v in zip(*splits) ] attn_output = torch.cat(attn_outputs, dim=1) attn_output = attn_output.reshape(seq_length, -1).contiguous() attn_output = self.proj(attn_output) return attn_output class GlmImageVisionPatchEmbed(Glm4vVisionPatchEmbed): def __init__(self, config: GlmImageVisionConfig) -> None: super().__init__(config) del self.temporal_patch_size kernel_size = [self.patch_size, self.patch_size] self.proj = nn.Conv2d(self.in_channels, self.embed_dim, kernel_size=kernel_size, stride=kernel_size) def forward(self, hidden_states): target_dtype = self.proj.weight.dtype hidden_states = hidden_states.view(-1, self.in_channels, self.patch_size, self.patch_size) hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim) return hidden_states class GlmImageVisionEmbeddings(Glm4vVisionEmbeddings): def __init__(self, config: GlmImageVisionConfig) -> None: super().__init__(config) self.interpolated_method = "bilinear" class GlmImageVisionBlock(Glm4vVisionBlock): def __init__(self, config: GlmImageVisionConfig): super().__init__(config) self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attn = GlmImageVisionAttention(config) self.mlp = GlmImageVisionMLP(config) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, **kwargs: Unpack[TransformersKwargs], ) -> torch.Tensor: r""" cu_seqlens (`torch.Tensor` of shape `(num_images_or_videos + 1,)`): The cumulative sequence lengths of each image or video feature. position_embeddings (`tuple(torch.Tensor, torch.Tensor)` of shape `(num_patches, head_dim // 2)`): The cosine and sine position embeddings for vision attention. """ residual = hidden_states hidden_states = self.norm1(hidden_states) hidden_states = self.attn( hidden_states, cu_seqlens=cu_seqlens, **kwargs, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states class GlmImageTextAttention(Glm4vMoeTextAttention): pass class GlmImagePreTrainedModel(Glm4vPreTrainedModel): config: GlmImageConfig input_modalities = ("image", "text") @torch.no_grad() def _init_weights(self, module): PreTrainedModel._init_weights(module) class GlmImageModelOutputWithPast(Glm4vModelOutputWithPast): pass class GlmImageVQVAEVectorQuantizer(ChameleonVQVAEVectorQuantizer): def __init__(self, config: GlmImageVQVAEConfig): super().__init__(config) self.num_embeddings = config.num_embeddings self.embedding_dim = config.embed_dim self.beta = getattr(config, "beta", 0.25) self.embedding = nn.Embedding(self.num_embeddings, self.embedding_dim) def forward(self, hidden_state: torch.Tensor): hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous() hidden_state_flattened = hidden_state.view(-1, self.embedding_dim) # L2 normalize hidden_state = F.normalize(hidden_state, p=2, dim=-1) hidden_state_flattened = F.normalize(hidden_state_flattened, p=2, dim=-1) embedding = F.normalize(self.embedding.weight, p=2, dim=-1) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z distances = ( torch.sum(hidden_state_flattened**2, dim=1, keepdim=True) + torch.sum(embedding**2, dim=1) - 2 * torch.einsum("bd,dn->bn", hidden_state_flattened, embedding.transpose(0, 1)) ) min_encoding_indices = torch.argmin(distances, dim=1) hidden_state_quant = embedding[min_encoding_indices].view(hidden_state.shape) # compute loss for embedding loss = torch.mean((hidden_state_quant.detach() - hidden_state) ** 2) + self.beta * torch.mean( (hidden_state_quant - hidden_state.detach()) ** 2 ) # preserve gradients hidden_state_quant = hidden_state + (hidden_state_quant - hidden_state).detach() # reshape back to match original input shape hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous() return hidden_state_quant, loss, min_encoding_indices class GlmImageVQVAEModelOutput(ChameleonVQVAEModelOutput): pass class GlmImageVQVAE(ChameleonVQVAE): _no_split_modules = [ "GlmImageVQVAEVectorQuantizer", ] _can_record_outputs = {} def __init__(self, config: GlmImageVQVAEConfig): super().__init__(config) del self.encoder def encode(self, hidden_states): conv_hidden_states = self.quant_conv(hidden_states) quantized_last_hidden_state, emb_loss, indices = self.quantize(conv_hidden_states) return GlmImageVQVAEModelOutput( last_hidden_state=hidden_states, quantized_last_hidden_state=quantized_last_hidden_state, image_tokens=indices, embedding_loss=emb_loss, ) class GlmImageVisionModel(Glm4vVisionModel): config: GlmImageVisionConfig main_input_name = "pixel_values" input_modalities = ("image",) def __init__(self, config: GlmImageVisionConfig): super().__init__(config) head_dim = config.hidden_size // config.num_heads self.head_dim = head_dim del self.merger del self.rotary_pos_emb del self.post_conv_layernorm del self.downsample del self.post_layernorm def rot_pos_emb(self, grid_thw): pos_ids = [] for t, h, w in grid_thw: hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) hpos_ids = hpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) hpos_ids = hpos_ids.permute(0, 2, 1, 3) hpos_ids = hpos_ids.flatten() wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) wpos_ids = wpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) wpos_ids = wpos_ids.permute(0, 2, 1, 3) wpos_ids = wpos_ids.flatten() pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) pos_ids = torch.cat(pos_ids, dim=0) return pos_ids @merge_with_config_defaults @capture_outputs @auto_docstring def forward( self, pixel_values: torch.Tensor, grid_thw: torch.Tensor, **kwargs: Unpack[TransformersKwargs] ) -> tuple | BaseModelOutputWithPooling: r""" pixel_values (`torch.Tensor` of shape `(total_patches, num_channels * patch_size * patch_size)`): Packed pixel values. grid_thw (`torch.Tensor` of shape `(num_images, 3)`): The temporal, height and width of feature shape of each image. Returns: `torch.Tensor` of shape `(total_patches, hidden_size)`: Hidden states. """ hidden_states = self.patch_embed(pixel_values) image_type_ids = self.rot_pos_emb(grid_thw) cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( dim=0, dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, ) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() hidden_states = self.embeddings( hidden_states, seqlens, grid_thw, image_type_ids[:, 0].to(hidden_states.device), image_type_ids[:, 1].to(hidden_states.device), ) # Transformer blocks (no position_embeddings needed, already added above) for blk in self.blocks: hidden_states = blk( hidden_states, cu_seqlens=cu_seqlens, ) return BaseModelOutputWithPooling(last_hidden_state=hidden_states) class GlmImageTextModel(Glm4vTextModel): pass class GlmImageModel(Glm4vModel): def __init__(self, config): super().__init__(config) self.visual = GlmImageVisionModel._from_config(config.vision_config) self.language_model = GlmImageTextModel._from_config(config.text_config) self.vqmodel = GlmImageVQVAE._from_config(config.vq_config) self.rope_deltas = None # cache rope_deltas here # Per-sample caches for batch processing self._cached_decode_position_ids = None # shape: [batch_size, 3, max_decode_len] self._prefill_len = None # prefill sequence length (same for all samples in batch) # Initialize weights and apply final processing self.post_init() def get_rope_index( self, input_ids: torch.LongTensor | None = None, image_grid_thw: torch.LongTensor | None = None, images_per_sample: torch.LongTensor | None = None, attention_mask: torch.LongTensor | None = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Calculate the 3D rope index for image generation task with full batch support. Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. image_grid_thw (`torch.LongTensor` of shape `(total_images_in_batch, 3)`, *optional*): The temporal, height and width of feature shape of each image. Images are packed across all samples in the batch. images_per_sample (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Number of images (including target grids) for each sample in the batch. Used to split image_grid_thw by sample. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Returns: position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`): Position IDs for temporal, height, and width dimensions. mrope_position_deltas (`torch.Tensor` of shape `(batch_size, 1)`): Position deltas for multi-modal rotary position embedding. """ batch_size, seq_len = input_ids.shape device = input_ids.device dtype = input_ids.dtype image_start_token_id = self.config.image_start_token_id image_end_token_id = self.config.image_end_token_id position_ids = torch.ones(3, batch_size, seq_len, dtype=dtype, device=device) text_positions = torch.arange(seq_len, device=device)[None, :].repeat(3, 1) # Split image_grid_thw by sample if images_per_sample is provided if image_grid_thw is not None and images_per_sample is not None: grids_per_sample = torch.split(image_grid_thw, images_per_sample.tolist()) elif image_grid_thw is not None: # Fallback: assume all grids belong to first sample (batch_size=1) grids_per_sample = [image_grid_thw] * batch_size else: grids_per_sample = [None] * batch_size # Per-sample caches for decode stage all_decode_position_ids = [] for batch_idx in range(batch_size): curr_input_ids = input_ids[batch_idx] curr_grids = grids_per_sample[batch_idx] if attention_mask is not None and attention_mask.shape[1] == seq_len: valid_mask = attention_mask[batch_idx] == 1 curr_input_ids_valid = curr_input_ids[valid_mask] else: # attention_mask may have different length during assisted decoding curr_input_ids_valid = curr_input_ids valid_mask = None # Find image boundaries in this sample image_end_positions = torch.where(curr_input_ids_valid == image_end_token_id)[0] image_start_positions = torch.where(curr_input_ids_valid == image_start_token_id)[0] + 1 num_complete_images = len(image_end_positions) current_pos = 0 prev_image_end = 0 curr_position_ids = [] # Process complete images (source images in image-to-image task) for img_idx, (start, end) in enumerate(zip(image_start_positions, image_end_positions)): if curr_grids is None or img_idx >= len(curr_grids): break grid = curr_grids[img_idx] # grid format is [temporal, height, width] _, height, width = grid.tolist() # Text tokens before this image llm_pos_length = start - prev_image_end llm_position_ids = text_positions[:, current_pos : current_pos + llm_pos_length].to(device=device) current_pos += llm_position_ids.shape[-1] # Image tokens with 2D spatial encoding # For an image with height H and width W: # - position_width cycles [0, 1, ..., W-1] for each row, repeated H times # - position_height stays constant per row, [0]*W, [1]*W, ..., [H-1]*W image_seq_length = height * width position_width = torch.arange(current_pos, current_pos + width, device=device).repeat(height) position_height = torch.arange(current_pos, current_pos + height, device=device).repeat_interleave( width ) position_temporal = torch.full((image_seq_length,), current_pos, device=device, dtype=torch.long) vision_position_ids = torch.stack([position_temporal, position_height, position_width], dim=0) current_pos += max(height, width) prev_image_end = end curr_position_ids.append(torch.cat([llm_position_ids, vision_position_ids], dim=-1)) # Remaining text tokens (including the final image_start token for generation) end_position = len(curr_input_ids_valid) - prev_image_end llm_position_ids = text_positions[:, current_pos : current_pos + end_position].to(device=device) current_pos += llm_position_ids.shape[-1] curr_position_ids.append(llm_position_ids) # Concatenate all position ids for this sample curr_position_ids = torch.cat(curr_position_ids, dim=-1) # Store in the main position_ids tensor if valid_mask is not None: position_ids[:, batch_idx, valid_mask] = curr_position_ids else: position_ids[:, batch_idx, :] = curr_position_ids # Build decode position ids for this sample if curr_grids is not None and len(curr_grids) > 0: num_decode_grids = len(curr_grids) - num_complete_images num_decode_grids = max(num_decode_grids, 0) decode_pos = current_pos decode_temporal_list = [] decode_height_list = [] decode_width_list = [] curr_grids_list = curr_grids.tolist() for i in range(1, num_decode_grids + 1): grid_idx = -i h = curr_grids_list[grid_idx][1] w = curr_grids_list[grid_idx][2] total_tokens = h * w h_indices = torch.arange(h, device=device).unsqueeze(1).expand(h, w).flatten() w_indices = torch.arange(w, device=device).unsqueeze(0).expand(h, w).flatten() decode_temporal_list.append( torch.full((total_tokens,), decode_pos, device=device, dtype=torch.long) ) decode_height_list.append(decode_pos + h_indices) decode_width_list.append(decode_pos + w_indices) decode_pos = decode_pos + max(h, w) # End marker decode_temporal_list.append(torch.tensor([decode_pos], device=device, dtype=torch.long)) decode_height_list.append(torch.tensor([decode_pos], device=device, dtype=torch.long)) decode_width_list.append(torch.tensor([decode_pos], device=device, dtype=torch.long)) sample_decode_pos_ids = torch.stack( [ torch.cat(decode_temporal_list, dim=0), torch.cat(decode_height_list, dim=0), torch.cat(decode_width_list, dim=0), ], dim=0, ) all_decode_position_ids.append(sample_decode_pos_ids) # Store prefill length (same for all samples since input_ids is padded to same length) self._prefill_len = seq_len # Pad decode position ids to same length and stack if all_decode_position_ids: max_decode_len = max(x.shape[1] for x in all_decode_position_ids) padded_decode_pos_ids = [ F.pad(pos_ids, (0, max_decode_len - pos_ids.shape[1]), mode="replicate") for pos_ids in all_decode_position_ids ] self._cached_decode_position_ids = torch.stack(padded_decode_pos_ids, dim=0) # [batch, 3, max_decode_len] else: self._cached_decode_position_ids = None mrope_position_deltas = torch.zeros([batch_size, 1], dtype=dtype, device=device) return position_ids, mrope_position_deltas def get_image_tokens( self, hidden_states: torch.FloatTensor, image_grid_thw: torch.LongTensor, ) -> torch.LongTensor: """ Tokenizes image features into discrete tokens with VQVAE module. Args: hidden_states (`torch.FloatTensor` of shape `(total_patches, hidden_size)`): The packed image features from vision encoder. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`): The temporal, height and width of feature shape of each image. Returns: image_tokens (`torch.LongTensor` of shape `(total_patches,)`): Discrete token indices from the VQVAE codebook. """ hidden_size = hidden_states.shape[-1] split_sizes = (image_grid_thw.prod(dim=-1)).tolist() hidden_states_list = torch.split(hidden_states, split_sizes, dim=0) all_image_toks = [] for i, hs in enumerate(hidden_states_list): grid_t, grid_h, grid_w = image_grid_thw[i].tolist() hs = hs.view(grid_t, grid_h, grid_w, hidden_size) hs = hs.permute(0, 3, 1, 2).contiguous() vqmodel_outputs: GlmImageVQVAEModelOutput = self.vqmodel.encode(hs) all_image_toks.append(vqmodel_outputs.image_tokens) return torch.cat(all_image_toks, dim=0) def get_video_features(self): raise AttributeError("Not needed for GlmImage") @can_return_tuple @auto_docstring def get_image_features( self, pixel_values: torch.FloatTensor, image_grid_thw: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple | BaseModelOutputWithPooling: r""" pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): The tensors corresponding to the input images. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. """ pixel_values = pixel_values.type(self.visual.dtype) vision_outputs = self.visual(pixel_values, grid_thw=image_grid_thw, return_dict=True, **kwargs) split_sizes = (image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist() image_embeds = torch.split(vision_outputs.last_hidden_state, split_sizes) vision_outputs.pooler_output = image_embeds return vision_outputs def get_placeholder_mask( self, input_ids: torch.LongTensor, image_ids: torch.LongTensor, ): """ Replace image placeholder tokens in input_ids with actual image token ids from VQVAE. Args: input_ids (`torch.LongTensor` of shape `(batch_size, seq_len)`): Input token ids with image placeholders. image_ids (`torch.LongTensor` of shape `(num_images, num_tokens_per_image)` or flattened): Discrete token indices from the VQVAE codebook. Returns: special_image_mask (`torch.LongTensor` of shape `(batch_size, seq_len)`): Mask indicating positions in input ids that will be replaced by actual image tokens. """ special_image_mask = input_ids == self.config.image_token_id n_placeholder_tokens = special_image_mask.sum() n_image_tokens = image_ids.shape[0] if n_placeholder_tokens != n_image_tokens: raise ValueError( f"Number of image placeholder tokens ({n_placeholder_tokens.item()}) does not match " f"number of image tokens from VQVAE ({n_image_tokens})" ) return special_image_mask def compute_3d_position_ids( self, input_ids: torch.Tensor | None, image_grid_thw: torch.Tensor | None, images_per_sample: torch.Tensor | None, inputs_embeds: torch.Tensor | None, attention_mask: torch.Tensor | None, past_key_values: torch.Tensor | None, cache_position: torch.Tensor | None, ) -> torch.Tensor | None: past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() can_compute_mrope = input_ids is not None and image_grid_thw is not None if can_compute_mrope and (self.rope_deltas is None or past_key_values_length == 0): position_ids, rope_deltas = self.get_rope_index( input_ids, image_grid_thw=image_grid_thw, attention_mask=attention_mask, images_per_sample=images_per_sample, ) self.rope_deltas = rope_deltas # Use pre-calculated rope-deltas to infer correct 3D position ids elif self.rope_deltas is not None: batch_size, seq_length, _ = inputs_embeds.shape if self._cached_decode_position_ids is not None: step = cache_position[0].item() - self._prefill_len position_ids = self._cached_decode_position_ids[:, :, step : step + seq_length].permute(1, 0, 2) else: position_ids = cache_position.view(1, 1, -1).repeat(3, batch_size, 1) else: # Can't build correct 3D positions. Let the model infer it from `cache_position` position_ids = None return position_ids def forward( self, input_ids: torch.LongTensor | 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, pixel_values: torch.Tensor | None = None, image_grid_thw: torch.LongTensor | None = None, images_per_sample: torch.LongTensor | None = None, rope_deltas: torch.LongTensor | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple | GlmImageModelOutputWithPast: r""" image_grid_thw (`torch.LongTensor` of shape `(total_images_in_batch, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. Images are packed across all samples in the batch. images_per_sample (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Number of images (including target grids) for each sample in the batch. rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): The rope index difference between sequence length and multimodal rope. """ if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0] if pixel_values is not None: # Process source images (image-to-image mode) # Source images are identified by counting image_end_token_id in input_ids # Note: We must exclude padding tokens since pad_token_id == image_end_token_id if images_per_sample is not None: grids_per_sample = torch.split(image_grid_thw, images_per_sample.tolist()) # Create mask for non-padding tokens (attention_mask=1 means non-padding) # Handle 4D attention mask (from static cache) by extracting diagonal if attention_mask is not None and attention_mask.ndim == 4: non_pad_mask = torch.diagonal(attention_mask[:, 0], dim1=1, dim2=2) if non_pad_mask.dtype.is_floating_point: non_pad_mask = non_pad_mask / torch.finfo(non_pad_mask.dtype).min non_pad_mask = (1.0 - non_pad_mask).int() # Only keep columns matching input_ids length non_pad_mask = non_pad_mask[:, -input_ids.shape[1] :] else: non_pad_mask = attention_mask if attention_mask is not None else torch.ones_like(input_ids) source_grids_list = [] is_image_end = input_ids == self.config.image_end_token_id is_non_pad = non_pad_mask == 1 num_source_per_sample = (is_image_end & is_non_pad).sum(dim=1).tolist() for sample_idx in range(batch_size): num_source = num_source_per_sample[sample_idx] if num_source > 0: source_grids_list.append(grids_per_sample[sample_idx][:num_source]) if len(source_grids_list) == 0: raise ValueError( "pixel_values provided but no source images found in input_ids. " "Ensure input_ids contains image_end_token_id for each source image." ) source_grids = torch.cat(source_grids_list, dim=0) else: # Fallback for batch_size=1: all but last grid are source images source_grids = image_grid_thw[:-1] image_features = self.get_image_features(pixel_values, source_grids, return_dict=True) image_embeds = torch.cat(image_features.pooler_output, dim=0) image_ids = self.get_image_tokens(image_embeds, source_grids) image_ids = image_ids.view(-1).to(input_ids.device) special_image_mask = self.get_placeholder_mask(input_ids, image_ids) input_ids = input_ids.masked_scatter(special_image_mask, image_ids) if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(input_ids) if position_ids is None: position_ids = self.compute_3d_position_ids( input_ids=input_ids, image_grid_thw=image_grid_thw, images_per_sample=images_per_sample, inputs_embeds=inputs_embeds, attention_mask=attention_mask, past_key_values=past_key_values, cache_position=cache_position, ) outputs = self.language_model( input_ids=None, position_ids=position_ids, attention_mask=attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, cache_position=cache_position, **kwargs, ) return GlmImageModelOutputWithPast( last_hidden_state=outputs.last_hidden_state, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, rope_deltas=self.rope_deltas, ) class GlmImageCausalLMOutputWithPast(Glm4vCausalLMOutputWithPast): pass class GlmImageForConditionalGeneration(GlmImagePreTrainedModel, GenerationMixin): _checkpoint_conversion_mapping = {} _tied_weights_keys = {} # Reference: fix gemma3 grad acc #37208 accepts_loss_kwargs = False base_model_prefix = "model" config: GlmImageConfig def __init__(self, config): super().__init__(config) self.model = GlmImageModel(config) self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vision_vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() @auto_docstring def get_image_features( self, pixel_values: torch.FloatTensor, image_grid_thw: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple | BaseModelOutputWithPooling: r""" pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): The tensors corresponding to the input images. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. """ return self.model.get_image_features(pixel_values, image_grid_thw, **kwargs) def get_image_tokens(self, hidden_states: torch.FloatTensor, image_grid_thw: torch.LongTensor | None = None): return self.model.get_image_tokens(hidden_states, image_grid_thw) def forward( self, input_ids: torch.LongTensor | 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, pixel_values: torch.Tensor | None = None, image_grid_thw: torch.LongTensor | None = None, images_per_sample: torch.LongTensor | None = None, cache_position: torch.LongTensor | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs: Unpack[TransformersKwargs], ) -> tuple | GlmImageCausalLMOutputWithPast: 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]`. image_grid_thw (`torch.LongTensor` of shape `(total_images_in_batch, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. Images are packed across all samples in the batch. images_per_sample (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Number of images (including target grids) for each sample in the batch. Example: ```python >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> from transformers import AutoProcessor, GlmImageForConditionalGeneration >>> model = GlmImageForConditionalGeneration.from_pretrained("zai-org/GLM-Image") >>> processor = AutoProcessor.from_pretrained("zai-org/GLM-Image") >>> messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Add a truck of this photo.28 40"}, ], }, ] >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos]) >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..." ```""" outputs = self.model( input_ids=input_ids, pixel_values=pixel_values, image_grid_thw=image_grid_thw, images_per_sample=images_per_sample, position_ids=position_ids, attention_mask=attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, cache_position=cache_position, **kwargs, ) hidden_states = outputs[0] # 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) return GlmImageCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, rope_deltas=outputs.rope_deltas, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, position_ids=None, use_cache=True, pixel_values=None, image_grid_thw=None, images_per_sample=None, is_first_iteration=False, **kwargs, ): model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, cache_position=cache_position, position_ids=position_ids, pixel_values=pixel_values, image_grid_thw=image_grid_thw, is_first_iteration=is_first_iteration, use_cache=use_cache, **kwargs, ) model_inputs["position_ids"] = None model_inputs["images_per_sample"] = images_per_sample if not is_first_iteration and use_cache: model_inputs["pixel_values"] = None return model_inputs def _get_image_nums( self, input_ids: torch.LongTensor | None, ) -> torch.Tensor: """ Get the number of images for each sample. For GLM-Image, only input_ids allow us to get the number of images. Returns: image_counts (`torch.LongTensor` of shape `(batch_size,)`) """ is_image = input_ids == self.config.image_start_token_id return is_image.sum(dim=1) def _expand_inputs_for_generation( self, expand_size: int = 1, is_encoder_decoder: bool = False, input_ids: torch.LongTensor | None = None, **model_kwargs, ) -> tuple[torch.LongTensor, dict[str, Any]]: # Overwritten -- Support for expanding tensors without a batch size dimension # e.g., pixel_values, image_grid_thw # pixel_values.shape[0] is sum(seqlen_images for samples) # image_grid_thw.shape[0] is sum(num_images for samples) if expand_size == 1: return input_ids, model_kwargs visual_keys = ["pixel_values", "image_grid_thw", "images_per_sample"] def _expand_dict_for_generation_visual(dict_to_expand): image_grid_thw = model_kwargs.get("image_grid_thw", None) if image_grid_thw is None: return dict_to_expand images_per_sample = model_kwargs.get("images_per_sample", None) # Use images_per_sample if available if images_per_sample is not None: image_nums = images_per_sample.tolist() elif input_ids is not None: # Try to infer from image_grid_thw / batch_size batch_size = input_ids.shape[0] total_grids = image_grid_thw.shape[0] if total_grids % batch_size == 0: grids_per_sample = total_grids // batch_size image_nums = [grids_per_sample] * batch_size else: # Cannot evenly distribute grids - fall back to simple repeat_interleave # This handles test cases where image_grid_thw has (batch_size + 1) rows dict_to_expand["image_grid_thw"] = image_grid_thw.repeat_interleave(expand_size, dim=0) if dict_to_expand.get("pixel_values") is not None: dict_to_expand["pixel_values"] = dict_to_expand["pixel_values"].repeat_interleave( expand_size, dim=0 ) return dict_to_expand else: image_nums = self._get_image_nums(input_ids).tolist() # Get source image counts per sample from image_end_token_id count source_image_nums = (input_ids == self.config.image_end_token_id).sum(dim=1).tolist() def _repeat_interleave_samples(x, lengths, repeat_times): samples = torch.split(x, lengths) repeat_args = [repeat_times] + [1] * (x.dim() - 1) result = torch.cat([sample.repeat(*repeat_args) for sample in samples], dim=0) return result for key in dict_to_expand: if key == "pixel_values": # Split images into samples based on source image counts if sum(source_image_nums) > 0: # Split grids by sample to compute pixel counts grids_per_sample = torch.split(image_grid_thw, image_nums) all_pixel_counts = image_grid_thw.prod(dim=1) pixel_counts_per_sample = torch.split(all_pixel_counts, image_nums) # Build source mask and compute per-sample source pixel counts in one sync source_pixel_counts = torch.zeros(len(grids_per_sample), device=image_grid_thw.device) for batch_idx in range(len(grids_per_sample)): num_source = source_image_nums[batch_idx] if num_source > 0: source_pixel_counts[batch_idx] = pixel_counts_per_sample[batch_idx][:num_source].sum() lengths = source_pixel_counts.to(torch.int64).tolist() dict_to_expand[key] = _repeat_interleave_samples( dict_to_expand[key], lengths=lengths, repeat_times=expand_size ) elif key == "image_grid_thw": # Expand all grids (source + target) per sample dict_to_expand[key] = _repeat_interleave_samples( dict_to_expand[key], lengths=image_nums, repeat_times=expand_size ) elif key == "images_per_sample": # Simply repeat the counts if dict_to_expand.get(key) is not None: dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0) return dict_to_expand def _expand_dict_for_generation(dict_to_expand): for key in dict_to_expand: if ( key != "cache_position" and dict_to_expand[key] is not None and isinstance(dict_to_expand[key], torch.Tensor) and key not in visual_keys ): dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0) return dict_to_expand model_kwargs = _expand_dict_for_generation_visual(model_kwargs) if input_ids is not None: input_ids = input_ids.repeat_interleave(expand_size, dim=0) model_kwargs = _expand_dict_for_generation(model_kwargs) if is_encoder_decoder: if model_kwargs.get("encoder_outputs") is None: raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.") model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"]) return input_ids, model_kwargs def smart_resize( height: int, width: int, factor: int = 16, min_pixels: int = 512 * 512, max_pixels: int = 2048 * 2048, ) -> tuple[int, int]: if height < factor or width < factor: raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}") elif max(height, width) / min(height, width) > 4: raise ValueError( f"absolute aspect ratio must be smaller than 4, got {max(height, width) / min(height, width)}" ) shortest_edge = int(round(math.sqrt(min_pixels))) longest_edge = int(round(math.sqrt(max_pixels))) min_side = min(height, width) max_side = max(height, width) scale = 1.0 if min_side < shortest_edge: scale = shortest_edge / min_side if max_side * scale > longest_edge: scale = longest_edge / max_side height = height // 2 width = width // 2 h_bar = max(factor, int(round(height * scale / factor)) * factor) w_bar = max(factor, int(round(width * scale / factor)) * factor) if max(h_bar, w_bar) > longest_edge: beta = max(h_bar, w_bar) / longest_edge h_bar = max(factor, int(math.floor((h_bar / beta) / factor)) * factor) w_bar = max(factor, int(math.floor((w_bar / beta) / factor)) * factor) return h_bar, w_bar class GlmImageImageProcessor(Qwen2VLImageProcessor): model_input_names = ["pixel_values", "image_grid_thw", "images_per_sample"] class GlmImageImageProcessorFast(Qwen2VLImageProcessorFast): model_input_names = ["pixel_values", "image_grid_thw", "images_per_sample"] class GlmImageImagesKwargs(ImagesKwargs, total=False): """ target_h (`int`): Height of the target image to be generated. target_w (`int`): Width of the target image to be generated. """ target_h: int target_w: int class GlmImageProcessorKwargs(Qwen2VLProcessorKwargs): images_kwargs: GlmImageImagesKwargs _defaults = { "text_kwargs": { "padding": False, "return_mm_token_type_ids": False, }, "images_kwargs": { "target_h": 1152, "target_w": 768, }, } class GlmImageProcessor(ProcessorMixin): r""" Constructs a GLM-Image processor which wraps a GLM-Image image processor and a GLM-Image tokenizer into a single processor. [`~GlmImageProcessor.__call__`] and [`~GlmImageProcessor.decode`] for more information. Args: image_processor ([`GlmImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`PreTrainedTokenizerFast`], *optional*): The tokenizer is a required input. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. """ model_input_names = ["input_ids", "attention_mask", "pixel_values", "image_grid_thw", "images_per_sample"] def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): self.image_token = tokenizer.image_token self.grid_bos_token = tokenizer.grid_bos_token self.grid_eos_token = tokenizer.grid_eos_token self.bos_token = tokenizer.bos_token self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token) super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, images: ImageInput | None = None, text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, **kwargs: Unpack[GlmImageProcessorKwargs], ) -> BatchFeature: """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode the text. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported. text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. """ output_kwargs = self._merge_kwargs( GlmImageProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) target_h = output_kwargs["images_kwargs"].pop("target_h", None) target_w = output_kwargs["images_kwargs"].pop("target_w", None) is_text_to_image = images is None if images is not None: image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) image_grid_thw = image_inputs["image_grid_thw"] else: image_inputs = {} image_grid_thw = None # Handle text=None case (image-only processing) if text is None: if images is None: raise ValueError("You must provide at least one of `text` or `images`.") return image_inputs if not isinstance(text, list): text = [text] batch_size = len(text) text = text.copy() # below lines change text in-place # Count images per sample by counting image tokens in each text images_per_sample = [] for i in range(batch_size): images_per_sample.append(text[i].count(self.image_token)) # Replace image tokens with the correct number of placeholder tokens if not is_text_to_image: index = 0 for i in range(batch_size): while self.image_token in text[i]: grid = image_grid_thw[index] num_image_tokens = int(grid[1] * grid[2]) text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) # Build prompt with target shape and combine grids in a single loop # Format: [sample0_source_grids..., sample0_target_grids, sample1_source_grids..., sample1_target_grids, ...] # Note: In i2i mode, batches are homogeneous (same number of source images per sample) num_source_images = images_per_sample[0] if images_per_sample else 0 # Validate homogeneity for i2i mode if not is_text_to_image and images_per_sample and len(set(images_per_sample)) != 1: raise ValueError( f"In image-to-image mode, all samples must have the same number of source images. " f"Got different counts: {images_per_sample}" ) all_grids = [] for i in range(batch_size): text[i], token_h, token_w, prev_h, prev_w = self._build_prompt_with_target_shape( text[i], height=target_h, width=target_w, is_text_to_image=is_text_to_image ) # Add source grids for this sample (i2i mode only) if not is_text_to_image and num_source_images > 0: start_idx = i * num_source_images all_grids.append(image_grid_thw[start_idx : start_idx + num_source_images]) # Add target grid for this sample all_grids.append( self._build_target_image_grid_thw( token_h=token_h, token_w=token_w, prev_token_h=prev_h, prev_token_w=prev_w, is_text_to_image=is_text_to_image, ) ) image_inputs["image_grid_thw"] = torch.cat(all_grids, dim=0) # Store images_per_sample for later use (add target images count) # Each sample will have: source_images + target_images (typically 2 for t2i, 1 for i2i) num_target_grids = 2 if is_text_to_image else 1 image_inputs["images_per_sample"] = torch.tensor( [num_source_images + num_target_grids] * batch_size, dtype=torch.long ) return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False) text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) self._check_special_mm_tokens(text, text_inputs, modalities=["image"]) if return_mm_token_type_ids: array_ids = np.array(text_inputs["input_ids"]) mm_token_type_ids = np.zeros_like(text_inputs["input_ids"]) mm_token_type_ids[array_ids == self.image_token_id] = 1 text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist() return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors) def _build_prompt_with_target_shape( self, prompt: str, height: int, width: int, is_text_to_image: bool, ) -> tuple[str, int, int, int, int]: factor = 32 height = (height // factor) * factor width = (width // factor) * factor token_h = height // factor token_w = width // factor ratio = token_h / token_w prev_token_h = int(math.sqrt(ratio) * (factor // 2)) prev_token_w = int(math.sqrt(1 / ratio) * (factor // 2)) if is_text_to_image: expanded_prompt = f"{prompt}{self.grid_bos_token}{token_h} {token_w}{self.grid_eos_token}{self.grid_bos_token}{prev_token_h} {prev_token_w}{self.grid_eos_token}{self.bos_token}" else: expanded_prompt = f"{prompt}{self.grid_bos_token}{token_h} {token_w}{self.grid_eos_token}{self.bos_token}" return expanded_prompt, token_h, token_w, prev_token_h, prev_token_w @staticmethod def _build_target_image_grid_thw( token_h: int, token_w: int, prev_token_h: int, prev_token_w: int, is_text_to_image: bool = True, ): if is_text_to_image: # Text-to-image: 2 target grids (large + small preview) return torch.tensor( [ [1, token_h, token_w], [1, prev_token_h, prev_token_w], ], ) else: # Image-to-image: 1 target grid only return torch.tensor( [ [1, token_h, token_w], ], ) __all__ = [ "GlmImageVQVAEConfig", "GlmImageVisionConfig", "GlmImageTextConfig", "GlmImageConfig", "GlmImagePreTrainedModel", "GlmImageVQVAE", "GlmImageVisionModel", "GlmImageTextModel", "GlmImageModel", "GlmImageForConditionalGeneration", "GlmImageImageProcessor", "GlmImageImageProcessorFast", "GlmImageProcessor", ]