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llama.cpp
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model: support GLM4V vision encoder (#18042) 

* convert ok

* no deepstack

* less new tensors

* cgraph ok

* add mrope for text model

* faster patch merger

* add GGML_ROPE_TYPE_MRNORM

* add support for metal

* move glm4v do dedicated graph

* convert: add norm_embd

* clip: add debugging fn

* working correctly

* fix style

* use bicubic

* fix mrope metal

* improve cpu

* convert to neox ordering on conversion

* revert backend changes

* force stop if using old weight

* support moe variant

* fix conversion

* fix convert (2)

* Update tools/mtmd/clip-graph.h

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* process mrope_section on TextModel base class

* resolve conflict merge

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Xuan-Son Nguyen 2025-12-16 11:25:26 +01:00 committed by GitHub
parent 9963b81f63
commit 3d86c6c2b5
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17 changed files with 412 additions and 79 deletions
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111
convert_hf_to_gguf.py
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@ -862,6 +862,14 @@ class TextModel(ModelBase):
logger.warning(f"Unknown RoPE type: {rope_type}")
logger.info(f"gguf: rope scaling type = {rope_gguf_type.name}")
if "mrope_section" in self.rope_parameters:
mrope_section = self.rope_parameters["mrope_section"]
# Pad to 4 dimensions [time, height, width, extra]
while len(mrope_section) < 4:
mrope_section.append(0)
self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
logger.info(f"gguf: mrope sections: {mrope_section[:4]}")
if (rope_theta := rope_params.get("rope_theta")) is not None:
self.gguf_writer.add_rope_freq_base(rope_theta)
logger.info(f"gguf: rope theta = {rope_theta}")
@ -3739,9 +3747,6 @@ class Qwen2VLModel(TextModel):
def set_gguf_parameters(self):
super().set_gguf_parameters()
mrope_section = self.hparams["rope_scaling"]["mrope_section"]
mrope_section += [0] * max(0, 4 - len(mrope_section))
self.gguf_writer.add_rope_dimension_sections(mrope_section)
def set_vocab(self):
try:
@ -4377,6 +4382,30 @@ class Qwen3VLVisionModel(MmprojModel):
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Glm4vForConditionalGeneration", "Glm4vMoeForConditionalGeneration")
class Glm4VVisionModel(Qwen3VLVisionModel):
def set_gguf_parameters(self):
MmprojModel.set_gguf_parameters(self) # skip Qwen3VLVisionModel parameters
assert self.hparams_vision is not None
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLM4V)
hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
if hidden_act == "gelu":
self.gguf_writer.add_vision_use_gelu(True)
elif hidden_act == "silu":
self.gguf_writer.add_vision_use_silu(True)
rms_norm_eps = self.hparams_vision.get("rms_norm_eps", 1e-5)
self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.startswith("model.visual."):
name = name.replace("model.visual.", "visual.")
if name.startswith("visual.merger."):
return [(self.map_tensor_name(name), data_torch)]
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Qwen3VLForConditionalGeneration")
class Qwen3VLTextModel(Qwen3Model):
model_arch = gguf.MODEL_ARCH.QWEN3VL
@ -4385,20 +4414,6 @@ class Qwen3VLTextModel(Qwen3Model):
super().set_gguf_parameters()
# Handle MRoPE (Multi-axis Rotary Position Embedding) for Qwen3-VL
text_config = self.hparams.get("text_config", {})
# rope_scaling is deprecated in V5, use rope_parameters instead
rope_scaling = text_config.get("rope_scaling") or text_config.get("rope_parameters") or {}
if rope_scaling.get("mrope_section"):
# mrope_section contains [time, height, width] dimensions
mrope_section = rope_scaling["mrope_section"]
# Pad to 4 dimensions [time, height, width, extra]
while len(mrope_section) < 4:
mrope_section.append(0)
self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
logger.info(f"MRoPE sections: {mrope_section[:4]}")
vision_config = self.hparams.get("vision_config", {})
deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
@ -4417,22 +4432,6 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
def set_gguf_parameters(self):
super().set_gguf_parameters()
# Handle MRoPE (Multi-axis Rotary Position Embedding) for Qwen3-VL
text_config = self.hparams.get("text_config", {})
# rope_scaling is deprecated in V5, use rope_parameters instead
rope_scaling = text_config.get("rope_scaling") or text_config.get("rope_parameters") or {}
if rope_scaling.get("mrope_section"):
# mrope_section contains [time, height, width] dimensions
mrope_section = rope_scaling["mrope_section"]
# Pad to 4 dimensions [time, height, width, extra]
while len(mrope_section) < 4:
mrope_section.append(0)
self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
logger.info(f"MRoPE sections: {mrope_section[:4]}")
vision_config = self.hparams.get("vision_config", {})
deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
@ -7795,6 +7794,15 @@ class JaisModel(TextModel):
@ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration")
class Glm4Model(TextModel):
model_arch = gguf.MODEL_ARCH.GLM4
use_mrope = False
partial_rotary_factor = 0.5
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 0.5)
if "mrope_section" in self.rope_parameters:
self.use_mrope = True
logger.info("Q/K weight will need to be permuted for M-RoPE")
def set_vocab(self):
from transformers import AutoTokenizer
@ -7816,17 +7824,49 @@ class Glm4Model(TextModel):
super().set_gguf_parameters()
if (rope_dim := self.hparams.get("head_dim")) is None:
rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.partial_rotary_factor))
@staticmethod
def normal_to_neox(weights: Tensor, n_head: int, n_head_kv: int, head_dim: int, partial_rotary_factor: float) -> Tensor:
orig_shape = weights.shape
if len(orig_shape) == 1:
weights = weights.unsqueeze(1) # [out_dim, 1]
if len(weights.shape) != 2:
raise ValueError("Only 1D and 2D tensors are supported.")
n_effective_heads = weights.shape[0] // head_dim
if n_head_kv is not None and n_effective_heads != n_head:
if n_effective_heads != n_head_kv:
raise AssertionError(f"Mismatch in effective heads: computed {n_effective_heads}, expected {n_head} or {n_head_kv}")
rotary_dim = int(head_dim * partial_rotary_factor)
if rotary_dim % 2 != 0:
raise ValueError("rotary_dim must be even.")
reshaped = weights.reshape(n_effective_heads, head_dim, -1)
rot_part = reshaped[:, :rotary_dim, :]
non_rot_part = reshaped[:, rotary_dim:, :]
permuted_rot = torch.cat((rot_part[:, ::2, :], rot_part[:, 1::2, :]), dim=1)
combined = torch.cat((permuted_rot, non_rot_part), dim=1)
result = combined.reshape(weights.shape)
return result if len(orig_shape) != 1 else result.squeeze(1)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.startswith("model.visual."): # ignore visual part of Glm4v
return []
elif name.startswith("model.language_model."):
name = name.replace("language_model.", "") # for Glm4v
if self.use_mrope:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams["num_key_value_heads"]
n_embd = self.hparams["hidden_size"]
head_dim = n_embd // n_head
# because llama.cpp M-RoPE kernel only supports Neox ordering, we have to permute the weights here
if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_head, head_dim, self.partial_rotary_factor)
if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_kv_head, head_dim, self.partial_rotary_factor)
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Glm4MoeForCausalLM")
@ModelBase.register("Glm4MoeForCausalLM", "Glm4vMoeForConditionalGeneration")
class Glm4MoeModel(TextModel):
model_arch = gguf.MODEL_ARCH.GLM4_MOE
@ -7893,6 +7933,7 @@ class Glm4MoeModel(TextModel):
_experts: list[dict[str, Tensor]] | None = None
# note: unlike GLM4V non-MoE, we don't need to permute Q/K here since GLM4V_MOE uses Neox ordering already
def modify_tensors(
self, data_torch: Tensor, name: str, bid: int | None
) -> Iterable[tuple[str, Tensor]]:

7
gguf-py/gguf/constants.py
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@ -643,6 +643,7 @@ class MODEL_TENSOR(IntEnum):
V_MMPROJ_PEG = auto()
V_ENC_EMBD_CLS = auto()
V_ENC_EMBD_PATCH = auto()
V_ENC_EMBD_NORM = auto()
V_ENC_EMBD_POS = auto()
V_ENC_INPUT_NORM = auto()
V_ENC_ATTN_QKV = auto()
@ -661,6 +662,7 @@ class MODEL_TENSOR(IntEnum):
V_LAYER_SCALE_2 = auto()
V_PRE_NORM = auto()
V_POST_NORM = auto()
V_MM_POST_NORM = auto()
V_MM_INP_NORM = auto()
V_MM_INP_PROJ = auto() # gemma3
V_MM_SOFT_EMB_NORM = auto() # gemma3
@ -1016,6 +1018,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.V_MMPROJ_PEG: "mm.model.peg.{bid}",
MODEL_TENSOR.V_ENC_EMBD_CLS: "v.class_embd",
MODEL_TENSOR.V_ENC_EMBD_PATCH: "v.patch_embd",
MODEL_TENSOR.V_ENC_EMBD_NORM: "v.norm_embd",
MODEL_TENSOR.V_ENC_EMBD_POS: "v.position_embd",
MODEL_TENSOR.V_ENC_ATTN_QKV: "v.blk.{bid}.attn_qkv",
MODEL_TENSOR.V_ENC_ATTN_Q: "v.blk.{bid}.attn_q",
@ -1034,6 +1037,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.V_LAYER_SCALE_2: "v.blk.{bid}.ls2",
MODEL_TENSOR.V_PRE_NORM: "v.pre_ln",
MODEL_TENSOR.V_POST_NORM: "v.post_ln",
MODEL_TENSOR.V_MM_POST_NORM: "mm.post_norm",
MODEL_TENSOR.V_MM_INP_PROJ: "mm.input_projection",
MODEL_TENSOR.V_MM_INP_NORM: "mm.input_norm",
MODEL_TENSOR.V_MM_SOFT_EMB_NORM: "mm.soft_emb_norm",
@ -1094,6 +1098,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.V_MMPROJ_PEG,
MODEL_TENSOR.V_ENC_EMBD_CLS,
MODEL_TENSOR.V_ENC_EMBD_PATCH,
MODEL_TENSOR.V_ENC_EMBD_NORM,
MODEL_TENSOR.V_ENC_EMBD_POS,
MODEL_TENSOR.V_ENC_INPUT_NORM,
MODEL_TENSOR.V_ENC_ATTN_QKV,
@ -1112,6 +1117,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.V_LAYER_SCALE_2,
MODEL_TENSOR.V_PRE_NORM,
MODEL_TENSOR.V_POST_NORM,
MODEL_TENSOR.V_MM_POST_NORM,
MODEL_TENSOR.V_MM_INP_PROJ,
MODEL_TENSOR.V_MM_INP_NORM,
MODEL_TENSOR.V_MM_SOFT_EMB_NORM,
@ -3357,6 +3363,7 @@ class VisionProjectorType:
LIGHTONOCR = "lightonocr"
COGVLM = "cogvlm"
JANUS_PRO = "janus_pro"
GLM4V = "glm4v"
# Items here are (block size, type size)

15
gguf-py/gguf/tensor_mapping.py
View File

@ -1212,6 +1212,7 @@ class TensorNameMap:
MODEL_TENSOR.V_MMPROJ_FC: (
"model.connector.modality_projection.proj", # SmolVLM
"model.vision.linear_proj.linear_proj", # cogvlm
"visual.merger.proj", # glm4v
),
MODEL_TENSOR.V_MMPROJ_MLP: (
@ -1245,6 +1246,10 @@ class TensorNameMap:
"model.vision.patch_embedding.proj", # cogvlm
),
MODEL_TENSOR.V_ENC_EMBD_NORM: (
"visual.post_conv_layernorm", # glm4v
),
MODEL_TENSOR.V_ENC_EMBD_POS: (
"vision_tower.vision_model.embeddings.position_embedding",
"model.vision_tower.embeddings.position_embeddings", # Intern-S1
@ -1254,6 +1259,7 @@ class TensorNameMap:
"vision_tower.patch_embed.pos_emb", # kimi-vl
"visual.pos_embed", # qwen3vl
"model.vision.patch_embedding.position_embedding", # cogvlm
"visual.embeddings.position_embedding", # glm4v
),
MODEL_TENSOR.V_ENC_ATTN_QKV: (
@ -1409,6 +1415,11 @@ class TensorNameMap:
"vision_model.layernorm_post", # llama4
"visual.merger.ln_q", # qwen2vl
"vision_tower.encoder.final_layernorm", # kimi-vl
"visual.post_layernorm", # glm4v
),
MODEL_TENSOR.V_MM_POST_NORM: (
"visual.merger.post_projection_norm", # glm4v
),
MODEL_TENSOR.V_MM_INP_PROJ: (
@ -1478,6 +1489,7 @@ class TensorNameMap:
MODEL_TENSOR.V_MM_PATCH_MERGER: (
"multi_modal_projector.patch_merger.merging_layer", # mistral small 3.1 - hf
"patch_merger.merging_layer", # mistral
"visual.downsample", # glm4v
),
MODEL_TENSOR.V_DS_NORM: (
@ -1498,14 +1510,17 @@ class TensorNameMap:
MODEL_TENSOR.V_MM_UP: (
"model.vision.linear_proj.dense_h_to_4h", # cogvlm
"visual.merger.up_proj", # glm4v
),
MODEL_TENSOR.V_MM_DOWN: (
"model.vision.linear_proj.dense_4h_to_h", # cogvlm
"visual.merger.down_proj", # glm4v
),
MODEL_TENSOR.V_MM_GATE: (
"model.vision.linear_proj.gate_proj", # cogvlm
"visual.merger.gate_proj", # glm4v
),
MODEL_TENSOR.V_TOK_BOI: (

4
src/llama-hparams.cpp
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@ -231,3 +231,7 @@ bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama
return false;
}
bool llama_hparams::use_mrope() const {
return rope_sections[0] > 0 && rope_sections[1] > 0;
}

2
src/llama-hparams.h
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@ -270,6 +270,8 @@ struct llama_hparams {
// TODO: think of a better place for this function
// TODO: pack the SWA params in a struct?
static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1);
bool use_mrope() const;
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");

9
src/llama-model.cpp
View File

@ -1690,6 +1690,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case LLM_ARCH_GLM4:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
switch (hparams.n_layer) {
case 40: type = LLM_TYPE_9B; break;
case 61: type = LLM_TYPE_32B; break;
@ -1700,6 +1701,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
{
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
// MoE parameters
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
@ -7792,7 +7794,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_PLM:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GLM4:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_GRANITE_HYBRID:
@ -7854,7 +7855,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
case LLM_ARCH_SMALLTHINKER:
case LLM_ARCH_GLM4_MOE:
case LLM_ARCH_SEED_OSS:
case LLM_ARCH_GROVEMOE:
case LLM_ARCH_APERTUS:
@ -7871,6 +7871,11 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_QWEN3VLMOE:
return LLAMA_ROPE_TYPE_IMROPE;
case LLM_ARCH_GLM4:
return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NORM;
case LLM_ARCH_GLM4_MOE:
return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NEOX;
// all model arches should be listed explicitly here
case LLM_ARCH_UNKNOWN:
GGML_ABORT("unknown architecture");

37
src/models/glm4-moe.cpp
View File

@ -5,11 +5,20 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
int sections[4];
std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
bool use_mrope = hparams.use_mrope();
if (ubatch.embd && !use_mrope) {
// unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
}
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
@ -60,17 +69,25 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);
}
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
if (use_mrope) {
Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
} else {
// Normal RoPE
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
}
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);

27
src/models/glm4.cpp
View File

@ -8,11 +8,20 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
int sections[4];
std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
bool use_mrope = hparams.use_mrope();
if (ubatch.embd && !use_mrope) {
// unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
}
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
@ -63,11 +72,25 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params
Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
}
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
if (use_mrope) {
Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
} else {
// Normal RoPE
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
}
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);

1
tools/mtmd/CMakeLists.txt
View File

@ -15,6 +15,7 @@ add_library(mtmd
clip-graph.h
models/models.h
models/cogvlm.cpp
models/glm4v.cpp
models/internvl.cpp
models/kimivl.cpp
models/llama4.cpp

4
tools/mtmd/clip-graph.h
View File

@ -9,6 +9,8 @@
#include <vector>
#include <functional>
#define DEFAULT_INTERPOLATION_MODE (GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS)
struct clip_graph {
const clip_model & model;
const clip_hparams & hparams;
@ -49,7 +51,7 @@ struct clip_graph {
void cb(ggml_tensor * cur0, const char * name, int il) const;
// siglip2 naflex
ggml_tensor * resize_position_embeddings();
ggml_tensor * resize_position_embeddings(uint32_t interpolation_mode = DEFAULT_INTERPOLATION_MODE);
// build vision transformer (ViT) cgraph
// this function should cover most of the models

11
tools/mtmd/clip-impl.h
View File

@ -68,6 +68,7 @@
#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backwrad compat
#define TN_PATCH_EMBD_1 "v.patch_embd.weight.1"
#define TN_PATCH_BIAS "v.patch_embd.bias"
#define TN_NORM_EMBD "v.norm_embd.%s"
#define TN_ATTN_QKV "%s.blk.%d.attn_qkv.%s"
#define TN_ATTN_K "%s.blk.%d.attn_k.%s"
#define TN_ATTN_Q "%s.blk.%d.attn_q.%s"
@ -86,6 +87,10 @@
#define TN_LN_PRE "%s.pre_ln.%s"
#define TN_LN_POST "%s.post_ln.%s"
#define TN_LLAVA_PROJ "mm.%d.%s"
#define TN_MM_UP "mm.up.%s"
#define TN_MM_GATE "mm.gate.%s"
#define TN_MM_DOWN "mm.down.%s"
#define TN_MM_POST_NORM "mm.post_norm.%s"
#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s"
#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s"
@ -95,7 +100,7 @@
#define TN_MM_INP_PROJ "mm.input_projection.weight" // gemma3
#define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3
#define TN_MM_PROJECTOR "mm.model.fc.weight" // idefics3
#define TN_MM_PATCH_MERGER "mm.patch_merger.weight" // mistral small 3.1
#define TN_MM_PATCH_MERGER "mm.patch_merger.%s" // mistral small 3.1, glm4v
#define TN_TOK_IMG_BREAK "v.token_embd.img_break" // pixtral
#define TN_TOK_GLM_BOI "adapter.boi" // glm-edge (these embeddings are not in text model)
#define TN_TOK_GLM_EOI "adapter.eoi" // glm-edge (these embeddings are not in text model)
@ -165,6 +170,7 @@ enum projector_type {
PROJECTOR_TYPE_LIGHTONOCR,
PROJECTOR_TYPE_COGVLM,
PROJECTOR_TYPE_JANUS_PRO,
PROJECTOR_TYPE_GLM4V,
PROJECTOR_TYPE_UNKNOWN,
};
@ -192,6 +198,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
{ PROJECTOR_TYPE_LIGHTONOCR,"lightonocr"},
{ PROJECTOR_TYPE_COGVLM, "cogvlm"},
{ PROJECTOR_TYPE_JANUS_PRO, "janus_pro"},
{ PROJECTOR_TYPE_GLM4V, "glm4v"},
};
static projector_type clip_projector_type_from_string(const std::string & str) {
@ -495,6 +502,8 @@ static void print_tensor_data(ggml_tensor * t, uint8_t * data, int64_t n) {
}
}
void clip_debug_encode(clip_ctx * ctx, int h, int w, float fill_value);
//
// API used internally with mtmd
//

13
tools/mtmd/clip-model.h
View File

@ -158,6 +158,8 @@ struct clip_model {
ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
ggml_tensor * patch_bias = nullptr;
ggml_tensor * position_embeddings = nullptr;
ggml_tensor * norm_embd_w = nullptr;
ggml_tensor * norm_embd_b = nullptr;
ggml_tensor * pre_ln_w = nullptr;
ggml_tensor * pre_ln_b = nullptr;
@ -172,6 +174,14 @@ struct clip_model {
ggml_tensor * projection; // TODO: rename it to fc (fully connected layer)
ggml_tensor * mm_fc_w;
ggml_tensor * mm_fc_b;
ggml_tensor * mm_ffn_up_w = nullptr;
ggml_tensor * mm_ffn_up_b = nullptr;
ggml_tensor * mm_ffn_gate_w = nullptr;
ggml_tensor * mm_ffn_gate_b = nullptr;
ggml_tensor * mm_ffn_down_w = nullptr;
ggml_tensor * mm_ffn_down_b = nullptr;
ggml_tensor * mm_post_norm_w = nullptr;
ggml_tensor * mm_post_norm_b = nullptr;
// LLaVA projection
ggml_tensor * mm_input_norm_w = nullptr;
@ -253,9 +263,10 @@ struct clip_model {
ggml_tensor * mm_input_proj_w = nullptr;
ggml_tensor * mm_soft_emb_norm_w = nullptr;
// pixtral
// pixtral, glm4v
ggml_tensor * token_embd_img_break = nullptr;
ggml_tensor * mm_patch_merger_w = nullptr;
ggml_tensor * mm_patch_merger_b = nullptr;
// ultravox / whisper encoder
ggml_tensor * conv1d_1_w = nullptr;

99
tools/mtmd/clip.cpp
View File

@ -264,11 +264,11 @@ void clip_graph::cb(ggml_tensor * cur0, const char * name, int il) const {
}
// siglip2 naflex
ggml_tensor * clip_graph::resize_position_embeddings() {
ggml_tensor * clip_graph::resize_position_embeddings(uint32_t interpolation_mode) {
ggml_tensor * pos_embd = model.position_embeddings;
const int height = img.ny / patch_size;
const int width = img.nx / patch_size;
const uint32_t mode = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS;
const uint32_t mode = interpolation_mode;
const int n_per_side = (int)std::sqrt(pos_embd->ne[1]);
GGML_ASSERT(pos_embd);
@ -485,19 +485,14 @@ ggml_tensor * clip_graph::build_norm(
? ggml_rms_norm(ctx0, cur, norm_eps)
: ggml_norm(ctx0, cur, norm_eps);
if (mw || mb) {
cb(cur, "norm", il);
}
if (mw) {
cur = ggml_mul(ctx0, cur, mw);
if (mb) {
cb(cur, "norm_w", il);
}
}
if (mb) {
cur = ggml_add(ctx0, cur, mb);
cb(cur, "norm_b", il);
}
return cur;
@ -842,6 +837,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
{
builder = std::make_unique<clip_graph_llava>(ctx, img);
} break;
case PROJECTOR_TYPE_GLM4V:
{
builder = std::make_unique<clip_graph_glm4v>(ctx, img);
} break;
default:
GGML_ABORT("missing cgraph builder");
}
@ -1155,6 +1154,14 @@ struct clip_model_loader {
LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__);
}
} break;
case PROJECTOR_TYPE_GLM4V:
{
hparams.rope_theta = 10000.0f;
hparams.n_merge = 2; // default value for GLM4-V
get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
hparams.set_limit_image_tokens(8, 4096);
hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup
} break;
case PROJECTOR_TYPE_LLAMA4:
{
hparams.rope_theta = 10000.0f;
@ -1282,6 +1289,9 @@ struct clip_model_loader {
model.patch_embeddings_0 = get_tensor(TN_PATCH_EMBD, false);
model.patch_embeddings_1 = get_tensor(TN_PATCH_EMBD_1, false);
model.norm_embd_w = get_tensor(string_format(TN_NORM_EMBD, "weight"), false);
model.norm_embd_b = get_tensor(string_format(TN_NORM_EMBD, "bias"), false);
model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, prefix), false);
// layers
@ -1470,6 +1480,20 @@ struct clip_model_loader {
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
} break;
case PROJECTOR_TYPE_GLM4V:
{
model.projection = get_tensor(TN_MM_PROJECTOR);
model.mm_ffn_up_w = get_tensor(string_format(TN_MM_UP, "weight"));
model.mm_ffn_up_b = get_tensor(string_format(TN_MM_UP, "bias"), false);
model.mm_ffn_gate_w = get_tensor(string_format(TN_MM_GATE, "weight"));
model.mm_ffn_gate_b = get_tensor(string_format(TN_MM_GATE, "bias"), false);
model.mm_ffn_down_w = get_tensor(string_format(TN_MM_DOWN, "weight"));
model.mm_ffn_down_b = get_tensor(string_format(TN_MM_DOWN, "bias"), false);
model.mm_post_norm_w = get_tensor(string_format(TN_MM_POST_NORM, "weight"));
model.mm_post_norm_b = get_tensor(string_format(TN_MM_POST_NORM, "bias"), false);
model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"));
model.mm_patch_merger_b = get_tensor(string_format(TN_MM_PATCH_MERGER, "bias"));
} break;
case PROJECTOR_TYPE_GEMMA3:
{
model.mm_input_proj_w = get_tensor(TN_MM_INP_PROJ);
@ -1499,7 +1523,7 @@ struct clip_model_loader {
model.token_embd_img_break = get_tensor(TN_TOK_IMG_BREAK);
// for mistral small 3.1
model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM, false);
model.mm_patch_merger_w = get_tensor(TN_MM_PATCH_MERGER, false);
model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"), false);
} break;
case PROJECTOR_TYPE_LIGHTONOCR:
{
@ -1508,7 +1532,7 @@ struct clip_model_loader {
model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false);
model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM, false);
model.mm_patch_merger_w = get_tensor(TN_MM_PATCH_MERGER, false);
model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"), false);
} break;
case PROJECTOR_TYPE_ULTRAVOX:
{
@ -1873,6 +1897,8 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
if (ctx_params.warmup) {
loader.warmup(*ctx_vision);
}
// clip_debug_encode(ctx_vision, 24*14, 24*14, 0.5f);
}
if (loader.has_audio) {
@ -2582,6 +2608,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
case PROJECTOR_TYPE_GLM4V:
{
GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0);
clip_image_u8 resized;
@ -2824,16 +2851,30 @@ const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
const auto & params = ctx->model.hparams;
const int n_total = clip_n_output_tokens(ctx, img);
if (ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL) {
return img->nx / (params.patch_size * 2);
const auto & proj = ctx->proj_type();
switch (proj) {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
case PROJECTOR_TYPE_GLM4V:
return (img->nx / params.patch_size) / 2;
default:
break;
}
return n_total;
}
int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
const auto & params = ctx->model.hparams;
if (ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL) {
return img->ny / (params.patch_size * 2);
const auto & proj = ctx->proj_type();
switch (proj) {
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
case PROJECTOR_TYPE_GLM4V:
return (img->ny / params.patch_size) / 2;
default:
break;
}
return 1;
}
@ -2890,6 +2931,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
case PROJECTOR_TYPE_GLM4V:
{
// dynamic size (2 conv, so double patch size)
int x_patch = img->nx / (params.patch_size * 2);
@ -3137,6 +3179,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
} break;
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN3VL:
case PROJECTOR_TYPE_GLM4V:
{
const int merge_ratio = hparams.n_merge;
const int pw = image_size_width / patch_size;
@ -3363,7 +3406,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
}
// copy the embeddings to the location passed by the user
if (vec != nullptr) {
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
}
return true;
}
@ -3411,6 +3456,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
return ctx->model.mm_2_w->ne[1];
case PROJECTOR_TYPE_COGVLM:
return ctx->model.mm_4h_to_h_w->ne[1];
case PROJECTOR_TYPE_GLM4V:
return ctx->model.mm_ffn_down_w->ne[1];
default:
GGML_ABORT("Unknown projector type");
}
@ -3427,10 +3474,11 @@ bool clip_is_glm(const struct clip_ctx * ctx) {
return ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE;
}
bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
bool clip_is_mrope(const struct clip_ctx * ctx) {
return ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL
|| ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL
|| ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL;
|| ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL
|| ctx->proj_type() == PROJECTOR_TYPE_GLM4V;
}
bool clip_is_llava(const struct clip_ctx * ctx) {
@ -3491,3 +3539,22 @@ void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel
const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx) {
return &ctx->model.hparams;
}
//
// API for debugging
//
void clip_debug_encode(clip_ctx * ctx, int h, int w, float fill_value) {
clip_image_f32 img;
img.nx = w;
img.ny = h;
img.buf.resize(h * w * 3);
for (int i = 0; i < h * w * 3; i++) {
img.buf[i] = static_cast<float>(fill_value);
}
bool cur_debug_graph = ctx->debug_graph;
ctx->debug_graph = true;
clip_image_encode(ctx, 1, &img, nullptr);
ctx->debug_graph = cur_debug_graph;
GGML_ASSERT(img.buf.empty() && "expected, always stop here");
}

2
tools/mtmd/clip.h
View File

@ -104,7 +104,7 @@ bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct
int clip_is_minicpmv(const struct clip_ctx * ctx);
bool clip_is_glm(const struct clip_ctx * ctx);
bool clip_is_qwen2vl(const struct clip_ctx * ctx);
bool clip_is_mrope(const struct clip_ctx * ctx);
bool clip_is_llava(const struct clip_ctx * ctx);
bool clip_is_gemma3(const struct clip_ctx * ctx);

120
tools/mtmd/models/glm4v.cpp Normal file
View File

@ -0,0 +1,120 @@
#include "models.h"
ggml_cgraph * clip_graph_glm4v::build() {
GGML_ASSERT(model.patch_bias != nullptr);
GGML_ASSERT(model.position_embeddings != nullptr);
GGML_ASSERT(model.class_embedding == nullptr);
const int batch_size = 1;
norm_type norm_t = NORM_TYPE_RMS;
ggml_tensor * inp_raw = build_inp_raw();
ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches * 4);
ggml_set_name(positions, "positions");
ggml_set_input(positions);
GGML_ASSERT(img.nx % (patch_size * 2) == 0);
GGML_ASSERT(img.ny % (patch_size * 2) == 0);
// second conv dimension
{
auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
inp = ggml_add(ctx0, inp, inp_1);
inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b]
inp = ggml_cont_4d(
ctx0, inp,
n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
inp = ggml_reshape_4d(
ctx0, inp,
n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
inp = ggml_cont_3d(
ctx0, inp,
n_embd, n_patches_x * n_patches_y, batch_size);
}
// add patch bias
inp = ggml_add(ctx0, inp, model.patch_bias);
cb(inp, "patch_bias", -1);
// pos-conv norm
inp = build_norm(inp, model.norm_embd_w, model.norm_embd_b, norm_t, eps, -1);
// calculate absolute position embedding and apply
ggml_tensor * learned_pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BICUBIC);
learned_pos_embd = ggml_cont_4d(
ctx0, learned_pos_embd,
n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
learned_pos_embd = ggml_reshape_4d(
ctx0, learned_pos_embd,
n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3);
learned_pos_embd = ggml_cont_3d(
ctx0, learned_pos_embd,
n_embd, n_patches_x * n_patches_y, batch_size);
cb(learned_pos_embd, "learned_pos_embd", -1);
auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
return ggml_rope_multi(
ctx0, cur, positions, nullptr,
d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION,
32768, hparams.rope_theta, 1, 0, 1, 32, 1);
};
ggml_tensor * cur = build_vit(
inp, n_patches,
norm_t,
hparams.ffn_op,
learned_pos_embd,
add_pos);
cb(cur, "vit_out", -1);
// cb(ggml_sum(ctx0, cur), "vit_out_sum", -1);
// GLM4V projector
// ref: https://github.com/huggingface/transformers/blob/40dc11cd3eb4126652aa41ef8272525affd4a636/src/transformers/models/glm4v/modeling_glm4v.py#L116-L130
// patch merger (downsample)
{
int n_merge = hparams.n_merge;
GGML_ASSERT(n_merge > 0);
int n_token_out = n_patches / n_merge / n_merge;
cur = ggml_reshape_4d(ctx0, cur, n_embd, n_merge, n_merge, n_token_out);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 2, 0, 1, 3)); // [n_merge, n_merge, n_embd, n_token_out]
cur = ggml_conv_2d(ctx0, model.mm_patch_merger_w, cur, n_merge, n_merge, 0, 0, 1, 1);
cur = ggml_reshape_2d(ctx0, cur, cur->ne[2], n_token_out); // [n_embd_out, n_token_out]
cur = ggml_add(ctx0, cur, model.mm_patch_merger_b);
}
// FC projector
{
cur = ggml_mul_mat(ctx0, model.projection, cur);
// default LayerNorm (post_projection_norm)
cur = build_norm(cur, model.mm_post_norm_w, model.mm_post_norm_b, NORM_TYPE_NORMAL, 1e-5, -1);
cur = ggml_gelu_erf(ctx0, cur);
cb(cur, "after_fc_proj", -1);
}
// FFN projector
{
cur = build_ffn(cur,
model.mm_ffn_up_w, model.mm_ffn_up_b,
model.mm_ffn_gate_w, model.mm_ffn_gate_b,
model.mm_ffn_down_w, model.mm_ffn_down_b,
hparams.ffn_op, -1);
cb(cur, "after_ffn_proj", -1);
// cb(ggml_sum(ctx0, cur), "merged_sum", -1);
}
// build the graph
ggml_build_forward_expand(gf, cur);
return gf;
}

5
tools/mtmd/models/models.h
View File

@ -56,3 +56,8 @@ struct clip_graph_whisper_enc : clip_graph {
clip_graph_whisper_enc(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_glm4v : clip_graph {
clip_graph_glm4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};

6
tools/mtmd/mtmd.cpp
View File

@ -217,7 +217,7 @@ struct mtmd_context {
void init_vision() {
GGML_ASSERT(ctx_v != nullptr);
use_mrope = clip_is_qwen2vl(ctx_v);
use_mrope = clip_is_mrope(ctx_v);
projector_type proj = clip_get_projector_type(ctx_v);
int minicpmv_version = clip_is_minicpmv(ctx_v);
@ -309,6 +309,10 @@ struct mtmd_context {
img_beg = "<|image_start|>";
img_end = "<|image_end|>";
} else if (proj == PROJECTOR_TYPE_GLM4V) {
img_beg = "<|begin_of_image|>";
img_end = "<|end_of_image|>";
}
}