Support glm4.1v and glm4.5v (#8798)
Signed-off-by: Xinyuan Tong <justinning0323@outlook.com> Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com> Co-authored-by: Xinyuan Tong <justinning0323@outlook.com> Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com> Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com> Co-authored-by: zRzRzRzRzRzRzR <2448370773@qq.com> Co-authored-by: Minglei Zhu <mingleizhu1122@gmail.com> Co-authored-by: Chang Su <csu272@usc.edu>
This commit is contained in:
Binyao Jiang
589
python/sglang/srt/models/glm4v.py
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589
python/sglang/srt/models/glm4v.py
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@@ -0,0 +1,589 @@
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import logging
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from functools import lru_cache, partial
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from typing import Iterable, List, Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers.models.glm4v.configuration_glm4v import Glm4vConfig, Glm4vVisionConfig
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from sglang.srt.hf_transformers_utils import get_processor
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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MergedColumnParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.pooler import Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
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from sglang.srt.managers.schedule_batch import MultimodalDataItem
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.glm4 import Glm4Model
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from sglang.srt.models.qwen2_5_vl import (
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Qwen2_5_VisionBlock,
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Qwen2_5_VLForConditionalGeneration,
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)
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from sglang.srt.utils import add_prefix
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logger = logging.getLogger(__name__)
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cached_get_processor = lru_cache(get_processor)
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class Glm4vRMSNorm(RMSNorm):
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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original_shape = x.shape
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x_2d = x.contiguous().reshape(-1, original_shape[-1])
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x_2d = super().forward(x_2d)
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x = x_2d.reshape(original_shape)
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return x
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class Glm4vVisionMLP(nn.Module):
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def __init__(
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self,
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in_features: int,
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hidden_features: int,
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bias: bool = False,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=in_features,
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output_sizes=[hidden_features] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("gate_up_proj", prefix),
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)
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self.down_proj = RowParallelLinear(
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hidden_features,
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in_features,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("down_proj", prefix),
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)
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self.act_fn = SiluAndMul()
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def forward(self, x: torch.Tensor):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class Glm4vVisionBlock(Qwen2_5_VisionBlock):
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def __init__(
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self,
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config: Glm4vVisionConfig,
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norm_layer: Optional[nn.Module] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__(
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dim=config.hidden_size,
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intermediate_dim=config.out_hidden_size,
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num_heads=config.num_heads,
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hidden_act=config.hidden_act,
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norm_layer=norm_layer,
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quant_config=quant_config,
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prefix=prefix,
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)
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self.norm1 = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.norm2 = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.mlp = Glm4vVisionMLP(
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config.hidden_size,
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config.out_hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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class Glm4vVisionPatchEmbed(nn.Module):
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def __init__(
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self,
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patch_size: int = 14,
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temporal_patch_size: int = 2,
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in_channels: int = 3,
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hidden_size: int = 1536,
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) -> None:
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super().__init__()
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self.patch_size = patch_size
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self.temporal_patch_size = temporal_patch_size
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self.hidden_size = hidden_size
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self.in_channels = in_channels
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kernel_size = (temporal_patch_size, patch_size, patch_size)
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self.proj = nn.Conv3d(
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in_channels,
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hidden_size,
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kernel_size=kernel_size,
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stride=kernel_size,
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bias=True,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = x.view(
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-1,
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self.in_channels,
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self.temporal_patch_size,
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self.patch_size,
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self.patch_size,
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)
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x = self.proj(x).view(-1, self.hidden_size)
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return x
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class Glm4vPatchMerger(nn.Module):
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def __init__(
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self,
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d_model: int,
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context_dim: int,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = d_model
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self.proj = ColumnParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("proj", prefix),
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gather_output=True,
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)
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self.post_projection_norm = nn.LayerNorm(self.hidden_size)
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=self.hidden_size,
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output_sizes=[context_dim] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("gate_up_proj", prefix),
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)
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self.down_proj = RowParallelLinear(
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context_dim,
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self.hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("down_proj", prefix),
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)
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self.extra_activation_func = nn.GELU()
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def forward(self, x: torch.Tensor):
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x, _ = self.proj(x)
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x = self.extra_activation_func(self.post_projection_norm(x))
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gate_up, _ = self.gate_up_proj(x)
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gate, up = gate_up.chunk(2, dim=-1)
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x = F.silu(gate) * up
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x, _ = self.down_proj(x)
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return x
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class Glm4vVisionEmbeddings(nn.Module):
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def __init__(self, config: Glm4vVisionConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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self.num_patches = (self.image_size // self.patch_size) ** 2
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self.num_positions = self.num_patches
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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self.register_buffer(
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"position_ids",
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torch.arange(self.num_positions).expand((1, -1)),
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persistent=False,
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)
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def forward(
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self, embeddings, lengths, image_shapes, h_coords, w_coords
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) -> torch.Tensor:
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pos_embed_weight = self.position_embedding.weight
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hidden_size = pos_embed_weight.shape[1]
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total_seq = h_coords.shape[0]
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device = pos_embed_weight.device
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# Move coordinates to correct device
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h_coords, w_coords = h_coords.to(device), w_coords.to(device)
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# Handle empty sequence case
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if total_seq == 0:
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adapted_pos_embed = torch.empty(
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0, hidden_size, device=device, dtype=pos_embed_weight.dtype
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)
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else:
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# Convert inputs to tensors if needed
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if isinstance(lengths, list):
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lengths = torch.tensor(lengths, device=device, dtype=torch.long)
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if not isinstance(image_shapes, torch.Tensor):
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image_shapes = torch.tensor(
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image_shapes, device=device, dtype=torch.long
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)
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# Prepare 2D position embedding
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orig_size_sq = pos_embed_weight.shape[0]
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orig_size = int(orig_size_sq**0.5)
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pos_embed_2d = (
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pos_embed_weight.view(orig_size, orig_size, hidden_size)
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.permute(2, 0, 1)
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.unsqueeze(0)
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.to(device=device, dtype=torch.float32)
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)
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# Calculate target dimensions for each patch
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target_h = torch.cat(
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[image_shapes[i, 1].repeat(lengths[i]) for i in range(len(lengths))]
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).to(device=device, dtype=torch.float32)
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target_w = torch.cat(
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[image_shapes[i, 2].repeat(lengths[i]) for i in range(len(lengths))]
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).to(device=device, dtype=torch.float32)
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# Normalize coordinates to [-1, 1] range for grid_sample
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h_coords = h_coords.to(device=device, dtype=torch.float32)
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w_coords = w_coords.to(device=device, dtype=torch.float32)
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norm_w = ((w_coords + 0.5) / target_w) * 2 - 1
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norm_h = ((h_coords + 0.5) / target_h) * 2 - 1
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# Create sampling grid
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grid = torch.stack((norm_w, norm_h), dim=-1).unsqueeze(0).unsqueeze(2)
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# Perform bicubic interpolation
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interpolated_embed_fp32 = F.grid_sample(
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pos_embed_2d,
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grid,
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mode="bicubic",
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align_corners=False,
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padding_mode="border",
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)
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# Reshape and convert back to original dtype
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adapted_pos_embed_fp32 = (
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interpolated_embed_fp32.squeeze(0).squeeze(-1).permute(1, 0)
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)
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adapted_pos_embed = adapted_pos_embed_fp32.to(pos_embed_weight.dtype).to(
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embeddings.device
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)
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# Add adapted position encoding to embeddings
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embeddings = embeddings + adapted_pos_embed
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return embeddings
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class Glm4vVisionRotaryEmbedding(nn.Module):
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def __init__(self, dim: int, theta: float = 10000.0) -> None:
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super().__init__()
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self.dim = dim
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self.theta = theta
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inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self._seq_len_cached = 0
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self._freqs_cached = None
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def update_freqs_cache(self, seqlen: int) -> None:
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if seqlen > self._seq_len_cached:
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seqlen *= 2
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self._seq_len_cached = seqlen
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self.inv_freq = 1.0 / (
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self.theta
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** (
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torch.arange(
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0,
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self.dim,
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2,
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dtype=torch.float,
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device=self.inv_freq.device,
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)
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/ self.dim
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)
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)
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seq = torch.arange(
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seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
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)
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freqs = torch.outer(seq, self.inv_freq)
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self._freqs_cached = freqs
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def forward(self, seqlen: int) -> torch.Tensor:
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self.update_freqs_cache(seqlen)
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return self._freqs_cached[:seqlen]
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class Glm4vVisionModel(nn.Module):
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def __init__(
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self,
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vision_config: Glm4vVisionConfig,
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norm_eps: float = 1e-6,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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patch_size = vision_config.patch_size
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temporal_patch_size = vision_config.temporal_patch_size
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in_channels = vision_config.in_channels
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depth = vision_config.depth
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self.hidden_size = vision_config.hidden_size
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self.num_heads = vision_config.num_heads
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self.patch_size = vision_config.patch_size
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self.spatial_merge_size = vision_config.spatial_merge_size
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self.out_hidden_size = vision_config.out_hidden_size
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self.patch_embed = Glm4vVisionPatchEmbed(
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patch_size=patch_size,
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temporal_patch_size=temporal_patch_size,
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in_channels=in_channels,
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hidden_size=self.hidden_size,
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)
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norm_layer = partial(Glm4vRMSNorm, eps=norm_eps)
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head_dim = self.hidden_size // self.num_heads
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self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2)
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self.blocks = nn.ModuleList(
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[
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Glm4vVisionBlock(
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config=vision_config,
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norm_layer=norm_layer,
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quant_config=quant_config,
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prefix=add_prefix(f"blocks.{layer_idx}", prefix),
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)
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for layer_idx in range(depth)
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]
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)
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self.merger = Glm4vPatchMerger(
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d_model=vision_config.out_hidden_size,
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context_dim=vision_config.intermediate_size,
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quant_config=quant_config,
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bias=False,
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prefix=add_prefix("merger", prefix),
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)
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self.embeddings = Glm4vVisionEmbeddings(vision_config)
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self.post_conv_layernorm = Glm4vRMSNorm(
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vision_config.hidden_size, eps=vision_config.rms_norm_eps
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)
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self.downsample = nn.Conv2d(
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in_channels=vision_config.hidden_size,
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out_channels=vision_config.out_hidden_size,
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kernel_size=vision_config.spatial_merge_size,
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stride=vision_config.spatial_merge_size,
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)
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self.post_layernorm = Glm4vRMSNorm(
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vision_config.hidden_size, eps=vision_config.rms_norm_eps
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)
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@property
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def dtype(self) -> torch.dtype:
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return self.patch_embed.proj.weight.dtype
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@property
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def device(self) -> torch.device:
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return self.patch_embed.proj.weight.device
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def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
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pos_ids = []
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for t, h, w in grid_thw:
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hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
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wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
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hpos_ids = (
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hpos_ids.reshape(
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h // self.spatial_merge_size,
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self.spatial_merge_size,
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w // self.spatial_merge_size,
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self.spatial_merge_size,
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)
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.permute(0, 2, 1, 3)
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.flatten()
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)
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wpos_ids = (
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wpos_ids.reshape(
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h // self.spatial_merge_size,
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self.spatial_merge_size,
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w // self.spatial_merge_size,
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self.spatial_merge_size,
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)
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.permute(0, 2, 1, 3)
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.flatten()
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)
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pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
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pos_ids = torch.cat(pos_ids, dim=0)
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max_grid_size = grid_thw[:, 1:].max()
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rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
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rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
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return rotary_pos_emb, pos_ids
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def forward(self, x: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
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# patchify
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x = x.to(device=self.device, dtype=self.dtype)
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x = self.patch_embed(x)
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x = self.post_conv_layernorm(x)
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# compute position embedding
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rotary_pos_emb, image_type_ids = self.rot_pos_emb(grid_thw)
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# compute cu_seqlens
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cu_seqlens = torch.repeat_interleave(
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grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
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).cumsum(dim=0, dtype=torch.int32)
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cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
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seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
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x = self.embeddings(
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x, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1]
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)
|
||||
|
||||
emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
|
||||
rotary_pos_emb_tuple = (emb.cos(), emb.sin())
|
||||
|
||||
# x.shape: (s, b, d) where b=1 for vision processing
|
||||
# transformers
|
||||
x = x.unsqueeze(1)
|
||||
for blk in self.blocks:
|
||||
x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=rotary_pos_emb_tuple)
|
||||
|
||||
# adapter
|
||||
x = self.post_layernorm(x)
|
||||
x = x.view(-1, self.spatial_merge_size, self.spatial_merge_size, x.shape[-1])
|
||||
x = x.permute(0, 3, 1, 2)
|
||||
x = self.downsample(x).view(-1, self.out_hidden_size)
|
||||
x = self.merger(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Glm4vForConditionalGeneration(Qwen2_5_VLForConditionalGeneration):
|
||||
def __init__(
|
||||
self,
|
||||
config: Glm4vConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
nn.Module.__init__(self)
|
||||
|
||||
self.config = config
|
||||
|
||||
self.model = Glm4Model(
|
||||
config,
|
||||
quant_config,
|
||||
prefix=add_prefix("model", prefix),
|
||||
)
|
||||
self.visual = Glm4vVisionModel(
|
||||
config.vision_config,
|
||||
norm_eps=getattr(config, "rms_norm_eps", 1e-5),
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("visual", prefix),
|
||||
)
|
||||
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head = self.model.embed_tokens
|
||||
else:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("lm_head", prefix),
|
||||
)
|
||||
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
|
||||
self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
|
||||
|
||||
def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
|
||||
pixel_values = torch.cat(
|
||||
[item.feature.squeeze(0) for item in items], dim=0
|
||||
).type(self.visual.dtype)
|
||||
image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
|
||||
# For multi-image, pixel_values is [num_of_images, L, C] shape
|
||||
# assert pixel_values.dim() == 2, pixel_values.dim()
|
||||
assert image_grid_thw.dim() == 2, image_grid_thw.dim()
|
||||
image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
|
||||
split_sizes = (
|
||||
image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2
|
||||
).tolist()
|
||||
image_embeds = torch.split(image_embeds, split_sizes)
|
||||
return torch.cat(image_embeds)
|
||||
|
||||
def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
|
||||
pixel_values_videos = torch.cat(
|
||||
[item.feature.squeeze(0) for item in items], dim=0
|
||||
).type(self.visual.dtype)
|
||||
video_grid_thw = torch.concat([item.video_grid_thw for item in items], dim=0)
|
||||
# For multi-video, pixel_values_videos is [num_of_videos, L, C] shape
|
||||
# assert pixel_values_videos.dim() == 2, pixel_values_videos.dim()
|
||||
assert video_grid_thw.dim() == 2, video_grid_thw.dim()
|
||||
|
||||
# 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 torch.cat(video_embeds)
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
(".qkv_proj", ".q_proj", "q"),
|
||||
(".qkv_proj", ".k_proj", "k"),
|
||||
(".qkv_proj", ".v_proj", "v"),
|
||||
(".gate_up_proj", ".up_proj", 1),
|
||||
(".gate_up_proj", ".gate_proj", 0),
|
||||
]
|
||||
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
||||
for name, loaded_weight in weights:
|
||||
if "language_model." in name:
|
||||
name = name.replace("language_model.", "")
|
||||
if "model.visual." in name:
|
||||
name = name.replace("model.visual.", "visual.")
|
||||
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
if "visual" in name:
|
||||
# adapt to VisionAttention
|
||||
name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
|
||||
|
||||
try:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
except KeyError:
|
||||
print(params_dict.keys())
|
||||
raise
|
||||
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
EntryClass = [Glm4vForConditionalGeneration]
|
||||
Reference in New Issue
Block a user