# coding=utf-8 # Copyright 2025 The ZhipuAI Inc. team and HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools from typing import Callable, Optional, Union import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint from torch.nn import LayerNorm from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache from ...configuration_utils import PretrainedConfig from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput from ...masking_utils import create_causal_mask from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import BaseModelOutputWithPast from ...modeling_rope_utils import rope_config_validation from ...modeling_utils import ALL_ATTENTION_FUNCTIONS from ...processing_utils import ImagesKwargs, Unpack from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging from ...video_utils import VideoInput from ..glm4.modeling_glm4 import Glm4MLP, Glm4RMSNorm, eager_attention_forward from ..qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLConfig from ..qwen2_5_vl.modeling_qwen2_5_vl import ( Qwen2_5_VisionPatchEmbed, Qwen2_5_VisionRotaryEmbedding, Qwen2_5_VLCausalLMOutputWithPast, Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLMLP, Qwen2_5_VLModel, Qwen2_5_VLModelOutputWithPast, Qwen2_5_VLPreTrainedModel, Qwen2_5_VLRotaryEmbedding, Qwen2_5_VLTextModel, Qwen2_5_VLVisionAttention, Qwen2_5_VLVisionBlock, ) from ..qwen2_5_vl.processing_qwen2_5_vl import ( Qwen2_5_VLProcessor, Qwen2_5_VLProcessorKwargs, Qwen2_5_VLVideosProcessorKwargs, ) logger = logging.get_logger(__name__) class Glm4vVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Glm4vVisionModel`]. It is used to instantiate an Glm4vVisionModel 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-4.1V-9B-Thinking [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking). Args: hidden_size (`int`, *optional*, defaults to 1536): Dimensionality of the encoder layers and the pooler layer. depth (`int`, *optional*, defaults to 24): Number of layers (depth) in the model. attention_bias (`bool`, *optional*, defaults to `False`): Whether to add a bias to the queries, keys and values. intermediate_size (`int`, *optional*, defaults to 13696): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"selu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for attention weights. projection_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for the projection layer. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. image_size (`int` or `list[int]`, *optional*, defaults to `[336, 336]`): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to `14`): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. out_hidden_size (`int`, *optional*, defaults to 4096): The output hidden size of the vision model. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. spatial_merge_size (`int`, *optional*, defaults to 2): The size used for merging spatial dimensions. temporal_patch_size (`int`, *optional*, defaults to 2): The size used for patches along the temporal dimension. Example: ```python >>> from transformers import Glm4vVisionConfig, Glm4vVisionModel >>> # Initializing a Glm4vVisionConfig GLM-4.1V-9B style configuration >>> configuration = Glm4vVisionConfig() >>> # Initializing a model (with random weights) from the GLM-4.1V-9B configuration >>> model = Glm4vVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glm4v" base_config_key = "vision_config" def __init__( self, depth=24, hidden_size=1536, hidden_act="silu", attention_bias=False, attention_dropout=0.0, num_heads=12, in_channels=3, image_size=336, patch_size=14, rms_norm_eps=1e-05, spatial_merge_size=2, temporal_patch_size=1, out_hidden_size=4096, intermediate_size=13696, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.num_heads = num_heads self.in_channels = in_channels self.image_size = image_size self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.out_hidden_size = out_hidden_size self.intermediate_size = intermediate_size self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.attention_bias = attention_bias self.attention_dropout = attention_dropout class Glm4vTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Glm4vModel`]. It is used to instantiate a GLM-4.1V 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-4.1V-9B-Thinking [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking). 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 151552): Vocabulary size of the Glm4v model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Glm4vModel`] 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://arxiv.org/pdf/2305.13245.pdf). 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 32768): 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. image_token_id (`int`, *optional*): Token index used as placeholder for image embeddings. video_token_id (`int`, *optional*): Token index used as placeholder for video embeddings. ```python >>> from transformers import Glm4vTextModel, Glm4vConfig >>> # Initializing a GLM-4.1V style configuration >>> configuration = Glm4vConfig() >>> # Initializing a model from the GLM-4.1V style configuration >>> model = Glm4vTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glm4v_text" base_config_key = "text_config" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `Glm4v` base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_up_proj": "colwise_rep", # we need to replicate here due to the `chunk` operation "layers.*.mlp.down_proj": "rowwise_rep", # we need to replicate here due to the `chunk` operation } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=151552, hidden_size=4096, intermediate_size=13696, num_hidden_layers=40, num_attention_heads=32, num_key_value_heads=2, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, attention_dropout=0.0, rope_scaling=None, image_token_id=None, video_token_id=None, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.rope_scaling = rope_scaling # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. if self.rope_scaling is not None and "type" in self.rope_scaling: self.rope_scaling["rope_type"] = self.rope_scaling["type"] rope_config_validation(self, ignore_keys={"mrope_section"}) self.image_token_id = image_token_id self.video_token_id = video_token_id super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) class Glm4vConfig(Qwen2_5_VLConfig): r""" This is the configuration class to store the configuration of a [`Glm4vModel`]. It is used to instantiate a GLM-4.1V 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-4.1V-9B-Thinking [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking). 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 `Glm4vTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151343): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151344): The video token index to encode the image prompt. image_start_token_id (`int`, *optional*, defaults to 151339): The image start token index to encode the start of image. image_end_token_id (`int`, *optional*, defaults to 151340): The image end token index to encode the end of image. video_start_token_id (`int`, *optional*, defaults to 151341): The video start token index to encode the start of video. video_end_token_id (`int`, *optional*, defaults to 151342): The video end token index to encode the end of video. ```python >>> from transformers import Glm4vForConditionalGeneration, Glm4vConfig >>> # Initializing a GLM-4.1V style configuration >>> configuration = Glm4vConfig() >>> # Initializing a model from the GLM-4.1V style configuration >>> model = Glm4vForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" def __init__( self, text_config=None, vision_config=None, image_token_id=151343, video_token_id=151344, image_start_token_id=151339, image_end_token_id=151340, video_start_token_id=151341, video_end_token_id=151342, **kwargs, ): super().__init__() self.video_start_token_id = video_start_token_id self.video_end_token_id = video_end_token_id self.image_start_token_id = image_start_token_id self.image_end_token_id = image_end_token_id # Will be used for both Text and Vision modalities class Glm4vRMSNorm(Glm4RMSNorm): pass class Glm4VisionMlp(Qwen2_5_VLMLP): def __init__(self, config, bias: bool = False): super().__init__(config, bias) self.intermediate_size = config.out_hidden_size class Glm4vVisionPatchEmbed(Qwen2_5_VisionPatchEmbed): def __init__(self, config: Glm4vVisionConfig) -> None: Qwen2_5_VisionPatchEmbed.__init__() self.patch_size = config.patch_size self.temporal_patch_size = config.temporal_patch_size self.in_channels = config.in_channels self.embed_dim = config.hidden_size kernel_size = [self.temporal_patch_size, self.patch_size, self.patch_size] self.proj = nn.Conv3d(self.in_channels, self.embed_dim, kernel_size=kernel_size, stride=kernel_size) class Glm4vVisionRotaryEmbedding(Qwen2_5_VisionRotaryEmbedding): pass class Glm4vVisionPatchMerger(nn.Module): def __init__(self, dim: int, context_dim: int, hidden_act: str, bias: bool = False) -> None: super().__init__() self.proj = nn.Linear(dim, dim, bias=bias) self.post_projection_norm = LayerNorm(dim) self.gate_proj = nn.Linear(dim, context_dim, bias=bias) self.up_proj = nn.Linear(dim, context_dim, bias=bias) self.down_proj = nn.Linear(context_dim, dim, bias=bias) self.act1 = nn.GELU() self.act_fn = ACT2FN[hidden_act] def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: hidden_state = self.proj(hidden_state) hidden_state = self.act1(self.post_projection_norm(hidden_state)) return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) class Glm4vVisionEmbeddings(nn.Module): def __init__(self, config: Glm4vVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) def forward(self, embeddings, lengths, image_shapes, h_coords, w_coords) -> torch.Tensor: """ Forward pass with integrated position encoding adaptation using 2D interpolation. Args: embeddings: Input embeddings tensor lengths (torch.Tensor): Sequence lengths for each image in the batch. image_shapes (torch.Tensor): Tensor of shape [batch_size, 3] representing the image shapes (t, h, w). h_coords (torch.Tensor): Tensor of shape [total_seq] representing the h coordinate for each patch. w_coords (torch.Tensor): Tensor of shape [total_seq] representing the w coordinate for each patch. Returns: torch.Tensor: Embeddings with adapted position encoding added. """ # Get position embedding parameters pos_embed_weight = self.position_embedding.weight hidden_size = pos_embed_weight.shape[1] total_seq = h_coords.shape[0] device = pos_embed_weight.device # Move coordinates to correct device h_coords, w_coords = h_coords.to(device), w_coords.to(device) # Handle empty sequence case if total_seq == 0: adapted_pos_embed = torch.empty(0, hidden_size, device=device, dtype=pos_embed_weight.dtype) else: # Convert inputs to tensors if needed if isinstance(lengths, list): lengths = torch.tensor(lengths, device=device, dtype=torch.long) if not isinstance(image_shapes, torch.Tensor): image_shapes = torch.tensor(image_shapes, device=device, dtype=torch.long) # Prepare 2D position embedding orig_size_sq = pos_embed_weight.shape[0] orig_size = int(orig_size_sq**0.5) pos_embed_2d = ( pos_embed_weight.view(orig_size, orig_size, hidden_size) .permute(2, 0, 1) .unsqueeze(0) .to(device=device, dtype=torch.float32) ) # Calculate target dimensions for each patch target_h = torch.cat([image_shapes[i, 1].repeat(lengths[i]) for i in range(len(lengths))]).to( device=device, dtype=torch.float32 ) target_w = torch.cat([image_shapes[i, 2].repeat(lengths[i]) for i in range(len(lengths))]).to( device=device, dtype=torch.float32 ) # Normalize coordinates to [-1, 1] range for grid_sample h_coords = h_coords.to(device=device, dtype=torch.float32) w_coords = w_coords.to(device=device, dtype=torch.float32) norm_w = ((w_coords + 0.5) / target_w) * 2 - 1 norm_h = ((h_coords + 0.5) / target_h) * 2 - 1 # Create sampling grid grid = torch.stack((norm_w, norm_h), dim=-1).unsqueeze(0).unsqueeze(2) # Perform bicubic interpolation interpolated_embed_fp32 = F.grid_sample( pos_embed_2d, grid, mode="bicubic", align_corners=False, padding_mode="border" ) # Reshape and convert back to original dtype adapted_pos_embed_fp32 = interpolated_embed_fp32.squeeze(0).squeeze(-1).permute(1, 0) adapted_pos_embed = adapted_pos_embed_fp32.to(pos_embed_weight.dtype).to(embeddings.device) # Add adapted position encoding to embeddings embeddings = embeddings + adapted_pos_embed return embeddings class Glm4vVisionAttention(Qwen2_5_VLVisionAttention): def __init__(self, config: Glm4vVisionConfig) -> None: super().__init__() self.attention_dropout = config.attention_dropout 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=False) class Glm4vVisionBlock(Qwen2_5_VLVisionBlock): def __init__(self, config) -> None: super().__init__() self.norm1 = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.norm2 = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.attn = Glm4vVisionAttention(config) self.mlp = Glm4VisionMlp(config, bias=False) class Glm4vPreTrainedModel(Qwen2_5_VLPreTrainedModel): _no_split_modules = ["Glm4vTextDecoderLayer", "Glm4vVisionBlock"] class Glm4vVisionModel(Glm4vPreTrainedModel): config: Glm4vVisionConfig _no_split_modules = ["Glm4vVisionBlock"] def __init__(self, config) -> None: super().__init__(config) self.spatial_merge_size = config.spatial_merge_size self.patch_size = config.patch_size self.embeddings = Glm4vVisionEmbeddings(config) self.patch_embed = Glm4vVisionPatchEmbed(config) head_dim = config.hidden_size // config.num_heads self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2) self.blocks = nn.ModuleList([Glm4vVisionBlock(config) for _ in range(config.depth)]) self.merger = Glm4vVisionPatchMerger( dim=config.out_hidden_size, context_dim=config.intermediate_size, hidden_act=config.hidden_act ) self.post_conv_layernorm = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.downsample = nn.Conv2d( in_channels=config.hidden_size, out_channels=config.out_hidden_size, kernel_size=config.spatial_merge_size, stride=config.spatial_merge_size, ) self.post_layernorm = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False self.post_init() 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) max_grid_size = grid_thw[:, 1:].max() rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) return rotary_pos_emb, pos_ids def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor: """ Args: hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): The final hidden states of the model. grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`): The temporal, height and width of feature shape of each image in LLM. Returns: `torch.Tensor`: hidden_states. """ hidden_states = self.patch_embed(hidden_states) hidden_states = self.post_conv_layernorm(hidden_states) rotary_pos_emb, image_type_ids = self.rot_pos_emb(grid_thw) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) position_embeddings = (emb.cos(), emb.sin()) cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( dim=0, # Select dtype based on the following factors: # - FA2 requires that cu_seqlens_q must have dtype int32 # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw # See https://github.com/huggingface/transformers/pull/34852 for more information 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], image_type_ids[:, 1]) for blk in self.blocks: hidden_states = blk( hidden_states, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, ) hidden_states = self.post_layernorm(hidden_states) hidden_states = hidden_states.view( -1, self.spatial_merge_size, self.spatial_merge_size, hidden_states.shape[-1] ) hidden_states = hidden_states.permute(0, 3, 1, 2) hidden_states = self.downsample(hidden_states).view(-1, self.config.out_hidden_size) hidden_states = self.merger(hidden_states) return hidden_states class Glm4vTextRotaryEmbedding(Qwen2_5_VLRotaryEmbedding): pass def rotate_half_llm(x): """Rotates half the hidden dims of the input.""" x1 = x[..., 0::2] x2 = x[..., 1::2] return torch.stack((-x2, x1), dim=-1).flatten(-2) def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1): """Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors (https://qwenlm.github.io/blog/qwen2-vl/). Explanation: Multimodal 3D rotary position embedding is an extension to 1D rotary position embedding. The input embedding sequence contains vision (images / videos) embedding and text embedding or just contains text embedding. For vision embedding part, we apply rotary position embedding on temporal, height and width dimension separately. Here we split the channel dimension to 3 chunks for the temporal, height and width rotary position embedding. For text embedding part, we just apply 1D rotary position embedding. The three rotary position index (temporal, height and width) of text embedding is always the same, so the text embedding rotary position embedding has no difference with modern LLMs. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. mrope_section(`List(int)`): Multimodal rope section is for channel dimension of temporal, height and width in rope calculation. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ mrope_section = mrope_section * 2 cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze( unsqueeze_dim ) sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze( unsqueeze_dim ) # Interleave them instead of usual shape cos = cos[..., : cos.shape[-1] // 2].repeat_interleave(2, dim=-1) sin = sin[..., : sin.shape[-1] // 2].repeat_interleave(2, dim=-1) # Keep half or full tensor for later concatenation rotary_dim = cos.shape[-1] q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] # Apply rotary embeddings on the first half or full tensor q_embed = (q_rot * cos) + (rotate_half_llm(q_rot) * sin) k_embed = (k_rot * cos) + (rotate_half_llm(k_rot) * sin) # Concatenate back to full shape q_embed = torch.cat([q_embed, q_pass], dim=-1) k_embed = torch.cat([k_embed, k_pass], dim=-1) return q_embed, k_embed class Glm4vTextAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. and "Generating Long Sequences with Sparse Transformers". """ def __init__(self, config: Glm4vTextConfig, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.is_causal = True self.attention_dropout = config.attention_dropout self.rope_scaling = config.rope_scaling self.scaling = self.head_dim**-0.5 self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_multimodal_rotary_pos_emb( # diff with Llama query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] ) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights, past_key_value class Glm4vTextMLP(Glm4MLP): pass class Glm4vTextDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: Glm4vTextConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = Glm4vTextAttention(config, layer_idx) self.mlp = Glm4vTextMLP(config) self.input_layernorm = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_self_attn_layernorm = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_mlp_layernorm = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, position_embeddings=position_embeddings, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = self.post_self_attn_layernorm(hidden_states) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = self.post_mlp_layernorm(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class Glm4vModelOutputWithPast(Qwen2_5_VLModelOutputWithPast): pass class Glm4vTextModel(Qwen2_5_VLTextModel): def __init__(self, config: Glm4vTextConfig): super().__init__(config) self.layers = nn.ModuleList( [Glm4vTextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Glm4vTextRotaryEmbedding(config=config) del self._attn_implementation del self.has_sliding_layers @auto_docstring @can_return_tuple def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[list[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Union[tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # torch.jit.trace() doesn't support cache objects in the output if use_cache and past_key_values is None and not torch.jit.is_tracing(): past_key_values = DynamicCache() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) # the hard coded `3` is for temporal, height and width. if position_ids is None: position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) elif position_ids.dim() == 2: position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) causal_mask = create_causal_mask( config=self.config, input_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, position_ids=position_ids, ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, position_embeddings=position_embeddings, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, ) class Glm4vModel(Qwen2_5_VLModel): _checkpoint_conversion_mapping = {} _no_split_modules = ["Glm4vTextDecoderLayer", "Glm4vVisionBlock"] def __init__(self, config): super().__init__(config) self.visual = Glm4vVisionModel._from_config(config.vision_config) def get_rope_index( self, input_ids: Optional[torch.LongTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, ) -> tuple[torch.Tensor, torch.Tensor]: """ Calculate the 3D rope index based on image and video's temporal, height and width in LLM. Explanation: Each embedding sequence contains vision embedding and text embedding or just contains text embedding. For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs. Examples: input_ids: [T T T T T], here T is for text. temporal position_ids: [0, 1, 2, 3, 4] height position_ids: [0, 1, 2, 3, 4] width position_ids: [0, 1, 2, 3, 4] For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part and 1D rotary position embedding for text part. Examples: Temporal (Time): 3 patches, representing different segments of the video in time. Height: 2 patches, dividing each frame vertically. Width: 2 patches, dividing each frame horizontally. We also have some important parameters: fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second. tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity. temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames. interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs. input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision. vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100] vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] text temporal position_ids: [101, 102, 103, 104, 105] text height position_ids: [101, 102, 103, 104, 105] text width position_ids: [101, 102, 103, 104, 105] Here we calculate the text start position_ids as the max vision position_ids plus 1. Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. Returns: position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) """ spatial_merge_size = self.config.vision_config.spatial_merge_size image_token_id = self.config.image_token_id video_start_token_id = self.config.video_start_token_id video_end_token_id = self.config.video_end_token_id mrope_position_deltas = [] if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): total_input_ids = input_ids if attention_mask is None: attention_mask = torch.ones_like(total_input_ids) position_ids = torch.ones( 3, input_ids.shape[0], input_ids.shape[1], dtype=input_ids.dtype, device=input_ids.device, ) image_index, video_index = 0, 0 video_group_index = 0 attention_mask = attention_mask.to(total_input_ids.device) for i, input_ids in enumerate(total_input_ids): input_ids = input_ids[attention_mask[i] == 1] input_tokens = input_ids.tolist() input_token_type = [] video_check_flg = False for token in input_tokens: if token == video_start_token_id: video_check_flg = True elif token == video_end_token_id: video_check_flg = False if token == image_token_id and not video_check_flg: input_token_type.append("image") elif token == image_token_id and video_check_flg: input_token_type.append("video") else: input_token_type.append("text") input_type_group = [] for key, group in itertools.groupby(enumerate(input_token_type), lambda x: x[1]): group = list(group) start_index = group[0][0] end_index = group[-1][0] + 1 input_type_group.append((key, start_index, end_index)) llm_pos_ids_list = [] video_frame_num = 1 for modality_type, start_idx, end_idx in input_type_group: st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 if modality_type == "image": t, h, w = ( image_grid_thw[image_index][0], image_grid_thw[image_index][1], image_grid_thw[image_index][2], ) llm_grid_t, llm_grid_h, llm_grid_w = ( t.item(), h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten() h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten() llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + st_idx) image_index += 1 video_frame_num = 1 elif modality_type == "video": t, h, w = ( video_frame_num, video_grid_thw[video_index][1], video_grid_thw[video_index][2], ) llm_grid_t, llm_grid_h, llm_grid_w = ( t, h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) for t_idx in range(llm_grid_t): t_index = torch.tensor(t_idx).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten() h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(1, -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(1, llm_grid_h, -1).flatten() llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + st_idx) video_group_index += 1 if video_group_index >= video_grid_thw[video_index][0]: video_index += 1 video_group_index = 0 video_frame_num += 1 else: text_len = end_idx - start_idx llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) video_frame_num = 1 llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device) mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i])) mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1) return position_ids, mrope_position_deltas else: if attention_mask is not None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device) max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0] mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1] else: position_ids = ( torch.arange(input_ids.shape[1], device=input_ids.device) .view(1, 1, -1) .expand(3, input_ids.shape[0], -1) ) mrope_position_deltas = torch.zeros( [input_ids.shape[0], 1], device=input_ids.device, dtype=input_ids.dtype, ) return position_ids, mrope_position_deltas def get_video_features( self, pixel_values_videos: torch.FloatTensor, video_grid_thw: Optional[torch.LongTensor] = None ): """ Encodes videos into continuous embeddings that can be forwarded to the language model. Args: pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): The tensors corresponding to the input videos. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. """ pixel_values_videos = pixel_values_videos.type(self.visual.dtype) # reshape video_grid_thw -> [b, 3] -> [1, h, w] * frames temp_frames_hw = [] for t, h, w in video_grid_thw: repeated_row = torch.tensor([1, h.item(), w.item()]).unsqueeze(0).repeat(t, 1) temp_frames_hw.append(repeated_row) flattened_video_grid_thw = torch.cat(temp_frames_hw, dim=0) video_embeds = self.visual(pixel_values_videos, grid_thw=flattened_video_grid_thw) split_sizes = (video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist() video_embeds = torch.split(video_embeds, split_sizes) return video_embeds @auto_docstring @can_return_tuple def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[list[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, pixel_values: Optional[torch.Tensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, rope_deltas: Optional[torch.LongTensor] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> Union[tuple, Glm4vModelOutputWithPast]: r""" image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): The rope index difference between sequence length and multimodal rope. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") 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, image_grid_thw) image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) image_mask, _ = self.get_placeholder_mask(input_ids, inputs_embeds, image_features=image_embeds) inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) if pixel_values_videos is not None: video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw) video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) _, video_mask = self.get_placeholder_mask(input_ids, inputs_embeds, video_features=video_embeds) inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) if position_ids is None: attention_mask_tensor = ( attention_mask if not isinstance(attention_mask, dict) else attention_mask["full_attention"] ) if attention_mask_tensor is not None and attention_mask_tensor.ndim == 4: attention_mask_tensor = torch.diagonal(attention_mask_tensor[:, 0], dim1=1, dim2=2) # Only apply conversion for floating point tensors (inverted masks) if attention_mask_tensor.dtype.is_floating_point: attention_mask_tensor = attention_mask_tensor / torch.finfo(attention_mask_tensor.dtype).min attention_mask_tensor = (1.0 - attention_mask_tensor).int() # Calculate RoPE index once per generation in the pre-fill stage only. # When compiling, we can't check tensor values thus we check only input length # It is safe to assume that `length!=1` means we're in pre-fill because compiled # models currently cannot do asssisted decoding prefill_compiled_stage = is_torchdynamo_compiling() and ( (input_ids is not None and input_ids.shape[1] != 1) or (inputs_embeds is not None and inputs_embeds.shape[1] != 1) ) prefill_noncompiled_stage = not is_torchdynamo_compiling() and ( (cache_position is not None and cache_position[0] == 0) or (past_key_values is None or past_key_values.get_seq_length() == 0) ) if (prefill_compiled_stage or prefill_noncompiled_stage) or self.rope_deltas is None: position_ids, rope_deltas = self.get_rope_index( input_ids, image_grid_thw, video_grid_thw, attention_mask=attention_mask_tensor, ) self.rope_deltas = rope_deltas # then use the prev pre-calculated rope-deltas to get the correct position ids else: batch_size, seq_length, _ = inputs_embeds.shape delta = ( (cache_position[0] + self.rope_deltas).to(inputs_embeds.device) if cache_position is not None else 0 ) position_ids = torch.arange(seq_length, device=inputs_embeds.device) position_ids = position_ids.view(1, -1).expand(batch_size, -1) if cache_position is not None: # otherwise `deltas` is an int `0` delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0) position_ids = position_ids.add(delta) position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) 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, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, cache_position=cache_position, **kwargs, ) return Glm4vModelOutputWithPast( 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 Glm4vCausalLMOutputWithPast(Qwen2_5_VLCausalLMOutputWithPast): pass class Glm4vForConditionalGeneration(Qwen2_5_VLForConditionalGeneration): _checkpoint_conversion_mapping = {} def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[list[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, pixel_values: Optional[torch.Tensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, rope_deltas: Optional[torch.LongTensor] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[TransformersKwargs], ) -> Union[tuple, Glm4vCausalLMOutputWithPast]: 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 `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): The rope index difference between sequence length and multimodal rope. Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, Glm4vForConditionalGeneration >>> model = Glm4vForConditionalGeneration.from_pretrained("THUDM/GLM-4.1V-9B-Thinking") >>> processor = AutoProcessor.from_pretrained("THUDM/GLM-4.1V-9B-Thinking") >>> messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "What is shown in this image?"}, ], }, ] >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> 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 ..." ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) outputs = self.model( input_ids=input_ids, pixel_values=pixel_values, pixel_values_videos=pixel_values_videos, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, position_ids=position_ids, attention_mask=attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, 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 Glm4vCausalLMOutputWithPast( 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, pixel_values_videos=None, image_grid_thw=None, video_grid_thw=None, **kwargs, ): # Overwritten -- in specific circumstances we don't want to forward image inputs to the model 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, pixel_values_videos=pixel_values_videos, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, use_cache=use_cache, **kwargs, ) # GLM-4.1V position_ids are prepareed with rope_deltas in forward model_inputs["position_ids"] = None if cache_position[0] != 0: model_inputs["pixel_values"] = None model_inputs["pixel_values_videos"] = None return model_inputs def _get_image_nums_and_video_nums( self, input_ids: Optional[torch.LongTensor], inputs_embeds: Optional[torch.Tensor] = None, ) -> tuple[torch.Tensor, torch.Tensor]: """ Get the number of images and videos for each sample to calculate the separation length of the sample tensor. These parameters are not passed through the processor to avoid unpredictable impacts from interface modifications. Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Returns: image_nums (`torch.LongTensor` of shape `(batch_size, num_images_sample)`) video_nums (`torch.LongTensor` of shape `(batch_size, num_videos_sample)`) """ if inputs_embeds is not None: is_image = ( inputs_embeds == self.get_input_embeddings()( torch.tensor(self.config.image_start_token_id, dtype=torch.long, device=inputs_embeds.device) ) )[..., 0] is_video_start = ( inputs_embeds == self.get_input_embeddings()( torch.tensor(self.config.video_start_token_id, dtype=torch.long, device=inputs_embeds.device) ) )[..., 0] is_video_end = ( inputs_embeds == self.get_input_embeddings()( torch.tensor(self.config.video_end_token_id, dtype=torch.long, device=inputs_embeds.device) ) )[..., 0] else: is_image = input_ids == self.config.image_start_token_id is_video_start = input_ids == self.config.video_start_token_id is_video_end = input_ids == self.config.video_end_token_id # Cumulative sum to track if we're inside a video span # We'll assume well-formed video tags (i.e. matching starts and ends) video_level = torch.cumsum(is_video_start.int() - is_video_end.int(), dim=1) inside_video = video_level > 0 # shape (batch_size, seq_length) # Mask out image tokens that are inside video spans standalone_images = is_image & (~inside_video) # Count per batch image_counts = standalone_images.sum(dim=1) video_counts = is_video_start.sum(dim=1) return image_counts, video_counts class Glm4vVideosProcessorKwargs(Qwen2_5_VLVideosProcessorKwargs): pass class Glm4vImagesKwargs(ImagesKwargs): patch_size: Optional[int] temporal_patch_size: Optional[int] merge_size: Optional[int] class Glm4vProcessorKwargs(Qwen2_5_VLProcessorKwargs): images_kwargs: Glm4vImagesKwargs videos_kwargs: Glm4vVideosProcessorKwargs _defaults = { "text_kwargs": { "padding": False, "return_mm_token_type_ids": False, }, } class Glm4vProcessor(Qwen2_5_VLProcessor): r""" Constructs a GLM-4V processor which wraps a GLM-4V image processor and a GLM-4 tokenizer into a single processor. [`~Glm4vProcessor.__call__`] and [`~Glm4vProcessor.decode`] for more information. Args: image_processor ([`Glm4vProcessor`], *optional*): The image processor is a required input. tokenizer ([`PreTrainedTokenizerFast`], *optional*): The tokenizer is a required input. video_processor ([`Glm4vVideoProcessor`], *optional*): The video processor 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. """ tokenizer_class = ("PreTrainedTokenizer", "PreTrainedTokenizerFast") def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs): super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template) self.image_token = "<|image|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token self.video_token = "<|video|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token def __call__( self, images: ImageInput = None, text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None, videos: VideoInput = None, **kwargs: Unpack[Glm4vProcessorKwargs], ) -> 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). videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.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`. - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. """ output_kwargs = self._merge_kwargs( Glm4vProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) 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 if videos is not None: videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"]) timestamps = videos_inputs.pop("timestamps") video_grid_thw = videos_inputs["video_grid_thw"] else: videos_inputs = {} timestamps = [] video_grid_thw = None if not isinstance(text, list): text = [text] text = text.copy() # below lines change text in-place if image_grid_thw is not None: merge_length = self.image_processor.merge_size**2 index = 0 for i in range(len(text)): while self.image_token in text[i]: num_image_tokens = image_grid_thw[index].prod() // merge_length text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) if video_grid_thw is not None: merge_length = self.video_processor.merge_size**2 video_index = 0 for i in range(len(text)): while self.video_token in text[i]: num_frames = video_grid_thw[video_index][0] video_structure = "" if hasattr(timestamps, "tolist"): timestamps_list = timestamps.tolist()[0] else: timestamps_list = timestamps[0] if isinstance(timestamps[0], list) else timestamps unique_timestamps = [] for idx in range(0, len(timestamps_list)): unique_timestamps.append(timestamps_list[idx]) selected_timestamps = unique_timestamps[:num_frames] while len(selected_timestamps) < num_frames: selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0) for frame_idx in range(num_frames): timestamp_sec = selected_timestamps[frame_idx] frame_structure = f"<|begin_of_image|>{self.image_token}<|end_of_image|>{timestamp_sec}" video_structure += frame_structure text[i] = text[i].replace(self.video_token, video_structure, 1) num_image_tokens = ( video_grid_thw[video_index].prod() // merge_length // video_grid_thw[video_index][0] ) for frame_idx in range(num_frames): if self.image_token in text[i]: text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) video_index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) 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", "video"]) 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, **videos_inputs}, tensor_type=return_tensors) __all__ = [ "Glm4vConfig", "Glm4vTextConfig", "Glm4vForConditionalGeneration", "Glm4vModel", "Glm4vPreTrainedModel", "Glm4vProcessor", "Glm4vTextModel", ]