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Merged
Thireus merged 2 commits into from
Oct 29, 2025
Merged

qwen3vl - code clean + use fused qkv in clip #32

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0b37fff
code clean
JJJYmmm Oct 29, 2025
2be9279
use fused qkv in clip
JJJYmmm Oct 29, 2025
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18 changes: 0 additions & 18 deletions convert_hf_to_gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4147,24 +4147,6 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
return [(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".bias", data_torch)]

if name.startswith("visual."):
if ".qkv." in name:
if data_torch.ndim == 2:
c3, _ = data_torch.shape
else:
c3 = data_torch.shape[0]
if c3 % 3 != 0:
raise ValueError(f"Unexpected QKV shape for {name}: {data_torch.shape}")
c = c3 // 3
wq = data_torch[:c]
wk = data_torch[c: c * 2]
wv = data_torch[c * 2:]
base = name.replace("qkv", "{placeholder}")
return [
(self.map_tensor_name(base.format(placeholder="q")), wq),
(self.map_tensor_name(base.format(placeholder="k")), wk),
(self.map_tensor_name(base.format(placeholder="v")), wv),
]

return [(self.map_tensor_name(name), data_torch)]

# Fall back to parent class for other tensors
Expand Down
9 changes: 6 additions & 3 deletions gguf-py/gguf/constants.py
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Expand Up @@ -278,7 +278,7 @@ class ClipVision:
USE_GELU = "clip.use_gelu"
USE_SILU = "clip.use_silu"
N_WA_PATTERN = "clip.vision.n_wa_pattern" # used by qwen2.5vl
IS_DEEPSTACK_LAYERS = "clip.vision.is_deepstack_layers"
IS_DEEPSTACK_LAYERS = "clip.vision.is_deepstack_layers"

class Attention:
HEAD_COUNT = "clip.vision.attention.head_count"
Expand Down Expand Up @@ -614,6 +614,7 @@ class MODEL_TENSOR(IntEnum):
V_ENC_EMBD_PATCH = auto()
V_ENC_EMBD_POS = auto()
V_ENC_INPUT_NORM = auto()
V_ENC_ATTN_QKV = auto()
V_ENC_ATTN_Q = auto()
V_ENC_ATTN_Q_NORM = auto()
V_ENC_ATTN_K = auto()
Expand Down Expand Up @@ -646,8 +647,8 @@ class MODEL_TENSOR(IntEnum):
V_TOK_EMBD_IMG_BREAK = auto() # pixtral
V_MM_PATCH_MERGER = auto() # mistral small 3.1
V_DS_NORM = auto() # qwen3vl
V_DS_FC1 = auto() # qwen3vl
V_DS_FC2 = auto() # qwen3vl
V_DS_FC1 = auto() # qwen3vl
V_DS_FC2 = auto() # qwen3vl
# audio (mtmd)
A_ENC_EMBD_POS = auto()
A_ENC_CONV1D = auto()
Expand Down Expand Up @@ -964,6 +965,7 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.V_ENC_EMBD_CLS: "v.class_embd",
MODEL_TENSOR.V_ENC_EMBD_PATCH: "v.patch_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",
MODEL_TENSOR.V_ENC_ATTN_Q_NORM: "v.blk.{bid}.attn_q_norm",
MODEL_TENSOR.V_ENC_ATTN_K: "v.blk.{bid}.attn_k",
Expand Down Expand Up @@ -1036,6 +1038,7 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.V_ENC_EMBD_PATCH,
MODEL_TENSOR.V_ENC_EMBD_POS,
MODEL_TENSOR.V_ENC_INPUT_NORM,
MODEL_TENSOR.V_ENC_ATTN_QKV,
MODEL_TENSOR.V_ENC_ATTN_Q,
MODEL_TENSOR.V_ENC_ATTN_Q_NORM,
MODEL_TENSOR.V_ENC_ATTN_K,
Expand Down
4 changes: 4 additions & 0 deletions gguf-py/gguf/tensor_mapping.py
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Expand Up @@ -1188,6 +1188,10 @@ class TensorNameMap:
"visual.pos_embed", # qwen3vl
),

MODEL_TENSOR.V_ENC_ATTN_QKV: (
"visual.blocks.{bid}.attn.qkv", # qwen3vl
),

MODEL_TENSOR.V_ENC_ATTN_Q: (
"vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj",
"model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1
Expand Down
34 changes: 17 additions & 17 deletions src/llama-model.cpp
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Expand Up @@ -1027,7 +1027,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
} break;
case LLM_ARCH_QWEN3VL:
{
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, 0);
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
Expand All @@ -1036,8 +1036,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case 64: type = LLM_TYPE_32B; break;
default: type = LLM_TYPE_UNKNOWN;
}
// for deepstack patch, we consider the embd to be [main_embd, deepstack_embd_1, deepstack_embd_2, ...]
hparams.n_embd = hparams.n_embd * (hparams.n_deepstack_layers + 1);
// since vision model stacks deepstack features along feature dim
// we also create a fake "n_embd" for text model to be the main embd + deepstack embds
hparams.n_embd *= hparams.n_deepstack_layers + 1;
} break;
case LLM_ARCH_QWEN3MOE:
{
Expand All @@ -1052,17 +1053,18 @@ void llama_model::load_hparams(llama_model_loader & ml) {
} break;
case LLM_ARCH_QWEN3VLMOE:
{
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, 0);
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 48: type = LLM_TYPE_30B_A3B; break;
case 94: type = LLM_TYPE_235B_A22B; break;
default: type = LLM_TYPE_UNKNOWN;
}
// for deepstack patch, we consider the embd to be [main_embd, deepstack_embd_1, deepstack_embd_2, ...]
hparams.n_embd = hparams.n_embd * (hparams.n_deepstack_layers + 1);
// since vision model stacks deepstack features along feature dim
// we also create a fake "n_embd" for text model to be the main embd + deepstack embds
hparams.n_embd *= hparams.n_deepstack_layers + 1;
} break;
case LLM_ARCH_PHI2:
{
Expand Down Expand Up @@ -3307,11 +3309,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_QWEN3:
case LLM_ARCH_QWEN3VL:
{
int64_t n_embd = hparams.n_embd;
// for deepstack features, we consider the embd to be [main_embd, deepstack_embd_1, deepstack_embd_2, ...]
if (arch == LLM_ARCH_QWEN3VL) {
n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
}
// for model loading, the weights only have the main embd
// so we need to divide by the number of deepstack layers + 1
// n_embd is const int so we declare a new variable
int64_t n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

// output
Expand Down Expand Up @@ -3347,11 +3348,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_QWEN3MOE:
case LLM_ARCH_QWEN3VLMOE:
{
// for deepstack features, we consider the embd to be [main_embd, deepstack_embd_1, deepstack_embd_2, ...]
int64_t n_embd = hparams.n_embd;
if (arch == LLM_ARCH_QWEN3VLMOE) {
n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
}
// for model loading, the weights only have the main embd
// so we need to divide by the number of deepstack layers + 1
// n_embd is const int so we declare a new variable
int64_t n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

// output
Expand Down
9 changes: 5 additions & 4 deletions tools/mtmd/clip-impl.h
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Expand Up @@ -39,7 +39,7 @@
#define KEY_FEATURE_LAYER "clip.vision.feature_layer"
#define KEY_PROJ_SCALE_FACTOR "clip.vision.projector.scale_factor"
#define KEY_SPATIAL_MERGE_SIZE "clip.vision.spatial_merge_size"
#define KEY_IS_DEEPSTACK_LAYERS "clip.vision.is_deepstack_layers"
#define KEY_IS_DEEPSTACK_LAYERS "clip.vision.is_deepstack_layers"

#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
Expand All @@ -64,6 +64,7 @@
#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_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"
#define TN_ATTN_V "%s.blk.%d.attn_v.%s"
Expand Down Expand Up @@ -94,9 +95,9 @@
#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)
#define TN_DEEPSTACK_NORM "v.deepstack.%d.norm.%s" // qwen3vl deepstack
#define TN_DEEPSTACK_FC1 "v.deepstack.%d.fc1.%s" // qwen3vl deepstack
#define TN_DEEPSTACK_FC2 "v.deepstack.%d.fc2.%s" // qwen3vl deepstack
#define TN_DEEPSTACK_NORM "v.deepstack.%d.norm.%s" // qwen3vl deepstack
#define TN_DEEPSTACK_FC1 "v.deepstack.%d.fc1.%s" // qwen3vl deepstack
#define TN_DEEPSTACK_FC2 "v.deepstack.%d.fc2.%s" // qwen3vl deepstack

// mimicpmv
#define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k"
Expand Down
27 changes: 15 additions & 12 deletions tools/mtmd/clip.cpp
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Expand Up @@ -216,6 +216,8 @@ struct clip_layer {
ggml_tensor * q_b = nullptr;
ggml_tensor * v_w = nullptr;
ggml_tensor * v_b = nullptr;
ggml_tensor * qkv_w = nullptr;
ggml_tensor * qkv_b = nullptr;

ggml_tensor * o_w = nullptr;
ggml_tensor * o_b = nullptr;
Expand Down Expand Up @@ -927,16 +929,15 @@ struct clip_graph {

// self-attention
{
ggml_tensor * Qcur = ggml_add(ctx0,
ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b);
ggml_tensor * Kcur = ggml_add(ctx0,
ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b);
ggml_tensor * Vcur = ggml_add(ctx0,
ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b);
cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
cur = ggml_add(ctx0, cur, layer.qkv_b);

Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches);
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches);
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches);
ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
cur->nb[1], 0);
ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
cur->nb[1], n_embd * sizeof(float));
ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
cur->nb[1], 2 * n_embd * sizeof(float));

cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
Expand Down Expand Up @@ -2758,10 +2759,11 @@ struct clip_model_loader {
model.layers.resize(hparams.n_layer);
for (int il = 0; il < hparams.n_layer; ++il) {
auto & layer = model.layers[il];
layer.k_w = get_tensor(string_format(TN_ATTN_K, prefix, il, "weight"));
layer.q_w = get_tensor(string_format(TN_ATTN_Q, prefix, il, "weight"));
layer.v_w = get_tensor(string_format(TN_ATTN_V, prefix, il, "weight"));
layer.k_w = get_tensor(string_format(TN_ATTN_K, prefix, il, "weight"), false);
layer.q_w = get_tensor(string_format(TN_ATTN_Q, prefix, il, "weight"), false);
layer.v_w = get_tensor(string_format(TN_ATTN_V, prefix, il, "weight"), false);
layer.o_w = get_tensor(string_format(TN_ATTN_OUTPUT, prefix, il, "weight"));
layer.qkv_w = get_tensor(string_format(TN_ATTN_QKV, prefix, il, "weight"), false);
layer.k_norm = get_tensor(string_format(TN_ATTN_K_NORM, prefix, il, "weight"), false);
layer.q_norm = get_tensor(string_format(TN_ATTN_Q_NORM, prefix, il, "weight"), false);
layer.ln_1_w = get_tensor(string_format(TN_LN_1, prefix, il, "weight"), false);
Expand All @@ -2773,6 +2775,7 @@ struct clip_model_loader {
layer.q_b = get_tensor(string_format(TN_ATTN_Q, prefix, il, "bias"), false);
layer.v_b = get_tensor(string_format(TN_ATTN_V, prefix, il, "bias"), false);
layer.o_b = get_tensor(string_format(TN_ATTN_OUTPUT, prefix, il, "bias"), false);
layer.qkv_b = get_tensor(string_format(TN_ATTN_QKV, prefix, il, "bias"), false);
layer.ln_1_b = get_tensor(string_format(TN_LN_1, prefix, il, "bias"), false);
layer.ln_2_b = get_tensor(string_format(TN_LN_2, prefix, il, "bias"), false);

Expand Down
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