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mtmd: add minicpmv46 downsample (#25993)

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* add minicpmv46 downsample

Signed-off-by: tc-mb <tianchi_cai@icloud.com>

* put downsample mode inside gguf.

Signed-off-by: tc-mb <tianchi_cai@icloud.com>

* build mtmd_image_preprocessor_llava_uhd

Signed-off-by: tc-mb <tianchi_cai@icloud.com>

* fix code

Signed-off-by: tc-mb <tianchi_cai@icloud.com>

* add convert

Signed-off-by: tc-mb <tianchi_cai@icloud.com>

* add 4x ignore vit merger

Signed-off-by: tc-mb <tianchi_cai@icloud.com>

---------

Signed-off-by: tc-mb <tianchi_cai@icloud.com>
This commit is contained in:
tc-mb
2026-08-01 19:38:36 +08:00
committed by GitHub
parent ddd4ec1428
commit de699957b9
6 changed files with 251 additions and 252 deletions
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13
conversion/minicpm.py
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@@ -137,6 +137,15 @@ class MiniCPMV4_6TextModel(Qwen3_5TextModel):
class MiniCPMV4_6VisionModel(MmprojModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.downsample_mode = self.preprocessor_config.get("downsample_mode", "16x")
if self.downsample_mode not in {"4x", "16x"}:
raise ValueError(f"Unsupported downsample mode: {self.downsample_mode}")
if self.downsample_mode == "4x":
self.model_tensors = {
name: tensor for name, tensor in self.model_tensors.items()
if ".vit_merger." not in name
}
if self.hparams_vision is not None:
# In MiniCPM-V 4.6 `vision_config.image_size` (980) describes the SigLIP
# positional embedding bucket grid (70 x 70), while the per-slice processing
@@ -156,8 +165,8 @@ class MiniCPMV4_6VisionModel(MmprojModel):
# (mapped to PROJECTOR_TYPE_MINICPMV4_6).
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINICPMV4_6)
# ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension; used for slice alignment
self.gguf_writer.add_vision_projector_scale_factor(4)
self.gguf_writer.add_vision_projector_scale_factor(
2 if self.downsample_mode == "4x" else 4)
# borrow wa_layer_indexes for vit_merger insertion point
insert_layer_id = int(self.global_config.get(

143
tools/mtmd/clip.cpp
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@@ -1337,6 +1337,7 @@ struct clip_model_loader {
// ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension
hparams.n_merge = 4;
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
GGML_ASSERT(hparams.n_merge == 2 || hparams.n_merge == 4);
// borrow wa_layer_indexes for vit_merger insertion point
std::vector<int> wa_layer_indexes_vec;
@@ -2143,24 +2144,29 @@ struct clip_model_loader {
} break;
case PROJECTOR_TYPE_MINICPMV4_6:
{
const bool merger_required = hparams.n_merge == 4;
auto get_merger_tensor = [&](const std::string & name, bool required = true) {
return get_tensor(name, merger_required && required);
};
// ViT merger: window self-attention
model.vit_merger_ln1_w = get_tensor(string_format(TN_VIT_MERGER_LN1, "weight"));
model.vit_merger_ln1_b = get_tensor(string_format(TN_VIT_MERGER_LN1, "bias"));
model.vit_merger_attn_q_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "weight"));
model.vit_merger_attn_q_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "bias"), false);
model.vit_merger_attn_k_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_K, "weight"));
model.vit_merger_attn_k_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_K, "bias"), false);
model.vit_merger_attn_v_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_V, "weight"));
model.vit_merger_attn_v_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_V, "bias"), false);
model.vit_merger_attn_o_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_O, "weight"));
model.vit_merger_attn_o_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_O, "bias"), false);
model.vit_merger_ln1_w = get_merger_tensor(string_format(TN_VIT_MERGER_LN1, "weight"));
model.vit_merger_ln1_b = get_merger_tensor(string_format(TN_VIT_MERGER_LN1, "bias"));
model.vit_merger_attn_q_w = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "weight"));
model.vit_merger_attn_q_b = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "bias"), false);
model.vit_merger_attn_k_w = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_K, "weight"));
model.vit_merger_attn_k_b = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_K, "bias"), false);
model.vit_merger_attn_v_w = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_V, "weight"));
model.vit_merger_attn_v_b = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_V, "bias"), false);
model.vit_merger_attn_o_w = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_O, "weight"));
model.vit_merger_attn_o_b = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_O, "bias"), false);
// ViT merger: MLP downsample
model.vit_merger_ds_ln_w = get_tensor(string_format(TN_VIT_MERGER_DS_LN, "weight"));
model.vit_merger_ds_ln_b = get_tensor(string_format(TN_VIT_MERGER_DS_LN, "bias"));
model.vit_merger_ds_up_w = get_tensor(string_format(TN_VIT_MERGER_DS_UP, "weight"));
model.vit_merger_ds_up_b = get_tensor(string_format(TN_VIT_MERGER_DS_UP, "bias"), false);
model.vit_merger_ds_down_w = get_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "weight"));
model.vit_merger_ds_down_b = get_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "bias"), false);
model.vit_merger_ds_ln_w = get_merger_tensor(string_format(TN_VIT_MERGER_DS_LN, "weight"));
model.vit_merger_ds_ln_b = get_merger_tensor(string_format(TN_VIT_MERGER_DS_LN, "bias"));
model.vit_merger_ds_up_w = get_merger_tensor(string_format(TN_VIT_MERGER_DS_UP, "weight"));
model.vit_merger_ds_up_b = get_merger_tensor(string_format(TN_VIT_MERGER_DS_UP, "bias"), false);
model.vit_merger_ds_down_w = get_merger_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "weight"));
model.vit_merger_ds_down_b = get_merger_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "bias"), false);
// Final Merger (DownsampleMLP)
model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM);
model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B, false);
@@ -3591,8 +3597,7 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
} break;
case PROJECTOR_TYPE_MINICPMV4_6:
{
// ViT merger 4x + final merger 4x = 16x total spatial downsample
n_patches = n_patches / 16;
n_patches /= params.n_merge * params.n_merge;
} break;
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
@@ -3974,6 +3979,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32
} break;
case PROJECTOR_TYPE_MINICPMV4_6:
{
const bool is_4x = hparams.n_merge == 2;
// SigLIP position buckets (same as resampler path)
std::vector<int32_t> positions(pos_h * pos_w);
int bucket_coords_h[1024];
@@ -3994,40 +4001,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32
const int half_h = pos_h / 2;
const int half_w = pos_w / 2;
// window reorder indices for 2x2 windows
std::vector<int32_t> window_idx(n_pos);
std::vector<int32_t> inv_window_idx(n_pos);
{
int k = 0;
for (int wi = 0; wi < half_h; wi++) {
for (int wj = 0; wj < half_w; wj++) {
window_idx[k++] = (2*wi ) * pos_w + (2*wj );
window_idx[k++] = (2*wi ) * pos_w + (2*wj + 1);
window_idx[k++] = (2*wi + 1) * pos_w + (2*wj );
window_idx[k++] = (2*wi + 1) * pos_w + (2*wj + 1);
}
}
for (int i = 0; i < n_pos; i++) {
inv_window_idx[window_idx[i]] = i;
}
}
set_input_i32("vit_merger_window_idx", window_idx);
set_input_i32("vit_merger_inv_window_idx", inv_window_idx);
// block-diagonal attention mask: tokens in the same 4-token
// window attend to each other (mask = 0), all other positions
// are masked out (-inf). matches the window-major reorder above.
std::vector<float> window_mask_data(n_pos * n_pos, std::numeric_limits<float>::lowest());
for (int wi = 0; wi < n_pos / 4; wi++) {
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++) {
window_mask_data[(wi*4 + i) * n_pos + (wi*4 + j)] = 0.0f;
}
}
}
set_input_f32("vit_merger_window_mask", window_mask_data);
// ViT merger 2x2 downsample indices
auto make_ds_idx = [](int off_r, int off_c, int ds_h, int ds_w, int stride_w) {
std::vector<int32_t> idx(ds_h * ds_w);
for (int i = 0; i < ds_h; i++) {
@@ -4037,22 +4010,58 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32
}
return idx;
};
auto vit_merger_ds_0 = make_ds_idx(0, 0, half_h, half_w, pos_w);
auto vit_merger_ds_1 = make_ds_idx(0, 1, half_h, half_w, pos_w);
auto vit_merger_ds_2 = make_ds_idx(1, 0, half_h, half_w, pos_w);
auto vit_merger_ds_3 = make_ds_idx(1, 1, half_h, half_w, pos_w);
set_input_i32("vit_merger_ds_idx_0", vit_merger_ds_0);
set_input_i32("vit_merger_ds_idx_1", vit_merger_ds_1);
set_input_i32("vit_merger_ds_idx_2", vit_merger_ds_2);
set_input_i32("vit_merger_ds_idx_3", vit_merger_ds_3);
// final merger 2x2 downsample indices (operates on half_h x half_w grid)
const int qh = half_h / 2;
const int qw = half_w / 2;
auto m_ds_0 = make_ds_idx(0, 0, qh, qw, half_w);
auto m_ds_1 = make_ds_idx(0, 1, qh, qw, half_w);
auto m_ds_2 = make_ds_idx(1, 0, qh, qw, half_w);
auto m_ds_3 = make_ds_idx(1, 1, qh, qw, half_w);
if (!is_4x) {
// window reorder indices for 2x2 windows
std::vector<int32_t> window_idx(n_pos);
std::vector<int32_t> inv_window_idx(n_pos);
{
int k = 0;
for (int wi = 0; wi < half_h; wi++) {
for (int wj = 0; wj < half_w; wj++) {
window_idx[k++] = (2*wi ) * pos_w + (2*wj );
window_idx[k++] = (2*wi ) * pos_w + (2*wj + 1);
window_idx[k++] = (2*wi + 1) * pos_w + (2*wj );
window_idx[k++] = (2*wi + 1) * pos_w + (2*wj + 1);
}
}
for (int i = 0; i < n_pos; i++) {
inv_window_idx[window_idx[i]] = i;
}
}
set_input_i32("vit_merger_window_idx", window_idx);
set_input_i32("vit_merger_inv_window_idx", inv_window_idx);
// block-diagonal attention mask: tokens in the same 4-token
// window attend to each other (mask = 0), all other positions
// are masked out (-inf). matches the window-major reorder above.
std::vector<float> window_mask_data(n_pos * n_pos, std::numeric_limits<float>::lowest());
for (int wi = 0; wi < n_pos / 4; wi++) {
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++) {
window_mask_data[(wi*4 + i) * n_pos + (wi*4 + j)] = 0.0f;
}
}
}
set_input_f32("vit_merger_window_mask", window_mask_data);
// ViT merger 2x2 downsample indices
auto vit_merger_ds_0 = make_ds_idx(0, 0, half_h, half_w, pos_w);
auto vit_merger_ds_1 = make_ds_idx(0, 1, half_h, half_w, pos_w);
auto vit_merger_ds_2 = make_ds_idx(1, 0, half_h, half_w, pos_w);
auto vit_merger_ds_3 = make_ds_idx(1, 1, half_h, half_w, pos_w);
set_input_i32("vit_merger_ds_idx_0", vit_merger_ds_0);
set_input_i32("vit_merger_ds_idx_1", vit_merger_ds_1);
set_input_i32("vit_merger_ds_idx_2", vit_merger_ds_2);
set_input_i32("vit_merger_ds_idx_3", vit_merger_ds_3);
}
const int merger_h = is_4x ? pos_h : half_h;
const int merger_w = is_4x ? pos_w : half_w;
auto m_ds_0 = make_ds_idx(0, 0, merger_h / 2, merger_w / 2, merger_w);
auto m_ds_1 = make_ds_idx(0, 1, merger_h / 2, merger_w / 2, merger_w);
auto m_ds_2 = make_ds_idx(1, 0, merger_h / 2, merger_w / 2, merger_w);
auto m_ds_3 = make_ds_idx(1, 1, merger_h / 2, merger_w / 2, merger_w);
set_input_i32("merger_ds_idx_0", m_ds_0);
set_input_i32("merger_ds_idx_1", m_ds_1);
set_input_i32("merger_ds_idx_2", m_ds_2);

315
tools/mtmd/models/minicpmv.cpp
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@@ -114,14 +114,12 @@ ggml_cgraph * clip_graph_minicpmv::build() {
}
ggml_cgraph * clip_graph_minicpmv4_6::build() {
const int insert_lid = hparams.insert_layer_id;
const int n_pos = n_patches;
const int half_h = n_patches_y / 2;
const int half_w = n_patches_x / 2;
const int n_ds = half_h * half_w; // after ViT merger 2x2 downsample
const int qh = half_h / 2;
const int qw = half_w / 2;
const int n_ds2 = qh * qw; // after final merger 2x2 downsample
const bool is_4x = hparams.n_merge == 2;
const int n_pos = n_patches;
const int half_h = n_patches_y / 2;
const int half_w = n_patches_x / 2;
const int n_ds = half_h * half_w;
const int n_out = is_4x ? n_ds : (half_h / 2) * (half_w / 2);
auto add_i32_input = [&](const char * name, int n) {
ggml_tensor * t = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n);
@@ -134,29 +132,39 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() {
ggml_tensor * positions = add_i32_input("positions", n_pos);
ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);
// ViT merger window reorder indices + block-diagonal mask
// (mask layout follows qwen2vl: -inf except for 4x4 blocks on the diagonal,
// so each window-major group of 4 tokens only attends to itself)
ggml_tensor * vit_merger_window_idx = add_i32_input("vit_merger_window_idx", n_pos);
ggml_tensor * vit_merger_inv_window_idx = add_i32_input("vit_merger_inv_window_idx", n_pos);
ggml_tensor * vit_merger_window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
ggml_set_name(vit_merger_window_mask, "vit_merger_window_mask");
ggml_set_input(vit_merger_window_mask);
if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
vit_merger_window_mask = ggml_cast(ctx0, vit_merger_window_mask, GGML_TYPE_F16);
ggml_tensor * vit_merger_window_idx = nullptr;
ggml_tensor * vit_merger_inv_window_idx = nullptr;
ggml_tensor * vit_merger_window_mask = nullptr;
ggml_tensor * vit_merger_ds_idx_0 = nullptr;
ggml_tensor * vit_merger_ds_idx_1 = nullptr;
ggml_tensor * vit_merger_ds_idx_2 = nullptr;
ggml_tensor * vit_merger_ds_idx_3 = nullptr;
if (!is_4x) {
// ViT merger window reorder indices + block-diagonal mask
// (mask layout follows qwen2vl: -inf except for 4x4 blocks on the diagonal,
// so each window-major group of 4 tokens only attends to itself)
vit_merger_window_idx = add_i32_input("vit_merger_window_idx", n_pos);
vit_merger_inv_window_idx = add_i32_input("vit_merger_inv_window_idx", n_pos);
vit_merger_window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
ggml_set_name(vit_merger_window_mask, "vit_merger_window_mask");
ggml_set_input(vit_merger_window_mask);
if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
vit_merger_window_mask = ggml_cast(ctx0, vit_merger_window_mask, GGML_TYPE_F16);
}
// ViT merger 2x2 downsample gather indices
vit_merger_ds_idx_0 = add_i32_input("vit_merger_ds_idx_0", n_ds);
vit_merger_ds_idx_1 = add_i32_input("vit_merger_ds_idx_1", n_ds);
vit_merger_ds_idx_2 = add_i32_input("vit_merger_ds_idx_2", n_ds);
vit_merger_ds_idx_3 = add_i32_input("vit_merger_ds_idx_3", n_ds);
}
// ViT merger 2x2 downsample gather indices
ggml_tensor * vit_merger_ds_idx_0 = add_i32_input("vit_merger_ds_idx_0", n_ds);
ggml_tensor * vit_merger_ds_idx_1 = add_i32_input("vit_merger_ds_idx_1", n_ds);
ggml_tensor * vit_merger_ds_idx_2 = add_i32_input("vit_merger_ds_idx_2", n_ds);
ggml_tensor * vit_merger_ds_idx_3 = add_i32_input("vit_merger_ds_idx_3", n_ds);
// final merger 2x2 downsample gather indices
ggml_tensor * merger_ds_idx_0 = add_i32_input("merger_ds_idx_0", n_ds2);
ggml_tensor * merger_ds_idx_1 = add_i32_input("merger_ds_idx_1", n_ds2);
ggml_tensor * merger_ds_idx_2 = add_i32_input("merger_ds_idx_2", n_ds2);
ggml_tensor * merger_ds_idx_3 = add_i32_input("merger_ds_idx_3", n_ds2);
ggml_tensor * merger_ds_idx_0 = add_i32_input("merger_ds_idx_0", n_out);
ggml_tensor * merger_ds_idx_1 = add_i32_input("merger_ds_idx_1", n_out);
ggml_tensor * merger_ds_idx_2 = add_i32_input("merger_ds_idx_2", n_out);
ggml_tensor * merger_ds_idx_3 = add_i32_input("merger_ds_idx_3", n_out);
// patch embedding + positional embedding
ggml_tensor * inp = build_inp();
@@ -169,150 +177,10 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() {
cb(inpL, "pre_ln", -1);
}
// ViT layers 0..insert_layer_id (inclusive)
// Mirrors the separate-qkv path of clip_graph::build_vit so the two manually
// unrolled segments around the ViT merger read like build_vit() expansions.
for (int il = 0; il <= insert_lid; il++) {
auto & layer = model.layers[il];
ggml_tensor * cur = inpL;
cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
cb(cur, "layer_inp_normed", il);
{
ggml_tensor * Qcur = build_mm(layer.q_w, cur);
if (layer.q_b) {
Qcur = ggml_add(ctx0, Qcur, layer.q_b);
}
ggml_tensor * Kcur = build_mm(layer.k_w, cur);
if (layer.k_b) {
Kcur = ggml_add(ctx0, Kcur, layer.k_b);
}
ggml_tensor * Vcur = build_mm(layer.v_w, cur);
if (layer.v_b) {
Vcur = ggml_add(ctx0, Vcur, layer.v_b);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
}
if (layer.ls_1_w) {
cur = ggml_mul(ctx0, cur, layer.ls_1_w);
cb(cur, "attn_out_scaled", il);
}
cur = ggml_add(ctx0, cur, inpL);
inpL = cur;
cb(cur, "ffn_inp", il);
cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
cb(cur, "ffn_inp_normed", il);
cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, layer.ff_gate_w, layer.ff_gate_b,
layer.ff_down_w, layer.ff_down_b, hparams.ffn_op, il);
cb(cur, "ffn_out", il);
if (layer.ls_2_w) {
cur = ggml_mul(ctx0, cur, layer.ls_2_w);
cb(cur, "ffn_out_scaled", il);
}
cur = ggml_add(ctx0, inpL, cur);
cb(cur, "layer_out", il);
inpL = cur;
}
// ViT merger: window self-attention
// Tokens are reordered to window-major (4 tokens per window are contiguous),
// and a block-diagonal mask restricts attention to within each window. This
// mirrors the qwen2vl windowed-attention pattern so build_attn() can pick the
// flash-attention path when available.
{
ggml_tensor * residual = inpL;
ggml_tensor * cur = build_norm(inpL,
model.vit_merger_ln1_w, model.vit_merger_ln1_b,
NORM_TYPE_NORMAL, eps, -1);
cb(cur, "vit_merger_attn_inp_normed", -1);
cur = ggml_get_rows(ctx0, cur, vit_merger_window_idx);
cb(cur, "vit_merger_window_reorder", -1);
ggml_tensor * Qcur = build_mm(model.vit_merger_attn_q_w, cur);
if (model.vit_merger_attn_q_b) {
Qcur = ggml_add(ctx0, Qcur, model.vit_merger_attn_q_b);
}
ggml_tensor * Kcur = build_mm(model.vit_merger_attn_k_w, cur);
if (model.vit_merger_attn_k_b) {
Kcur = ggml_add(ctx0, Kcur, model.vit_merger_attn_k_b);
}
ggml_tensor * Vcur = build_mm(model.vit_merger_attn_v_w, cur);
if (model.vit_merger_attn_v_b) {
Vcur = ggml_add(ctx0, Vcur, model.vit_merger_attn_v_b);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
cb(Qcur, "vit_merger_Qcur", -1);
cb(Kcur, "vit_merger_Kcur", -1);
cb(Vcur, "vit_merger_Vcur", -1);
cur = build_attn(model.vit_merger_attn_o_w, model.vit_merger_attn_o_b,
Qcur, Kcur, Vcur, vit_merger_window_mask, kq_scale, -1);
cb(cur, "vit_merger_attn_out", -1);
cur = ggml_get_rows(ctx0, cur, vit_merger_inv_window_idx);
inpL = ggml_add(ctx0, cur, residual);
cb(inpL, "vit_merger_attn_residual", -1);
}
// ViT merger: 2x2 spatial downsample + MLP (4 tokens -> 1)
{
ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_0);
ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_1);
ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_2);
ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_3);
ggml_tensor * mean_res = ggml_add(ctx0, p0, p1);
mean_res = ggml_add(ctx0, mean_res, p2);
mean_res = ggml_add(ctx0, mean_res, p3);
mean_res = ggml_scale(ctx0, mean_res, 0.25f);
cb(mean_res, "vit_merger_ds_mean_res", -1);
ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0);
cat = ggml_concat(ctx0, cat, p2, 0);
cat = ggml_concat(ctx0, cat, p3, 0);
ggml_tensor * cur = build_norm(cat,
model.vit_merger_ds_ln_w, model.vit_merger_ds_ln_b,
NORM_TYPE_NORMAL, eps, -1);
cb(cur, "vit_merger_ds_normed", -1);
// ViTWindowAttentionMerger downsample MLP uses gelu_pytorch_tanh (FFN_GELU)
cur = build_ffn(cur,
model.vit_merger_ds_up_w, model.vit_merger_ds_up_b,
nullptr, nullptr,
model.vit_merger_ds_down_w, model.vit_merger_ds_down_b,
FFN_GELU, -1);
cb(cur, "vit_merger_ds_mlp_out", -1);
inpL = ggml_add(ctx0, cur, mean_res);
cb(inpL, "vit_merger_ds_out", -1);
}
// ViT layers (insert_layer_id+1)..n_layer-1, operating on the downsampled tokens
{
const int64_t n_pos_ds = n_ds;
for (int il = insert_lid + 1; il < n_layer; il++) {
auto build_vit_layers = [&](ggml_tensor * input, int il_begin, int il_end, int64_t n_pos_layer) {
for (int il = il_begin; il < il_end; il++) {
auto & layer = model.layers[il];
ggml_tensor * cur = inpL;
ggml_tensor * cur = input;
cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
cb(cur, "layer_inp_normed", il);
@@ -331,9 +199,9 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() {
Vcur = ggml_add(ctx0, Vcur, layer.v_b);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos_ds);
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos_ds);
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos_ds);
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos_layer);
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos_layer);
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos_layer);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
@@ -346,8 +214,8 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() {
cur = ggml_mul(ctx0, cur, layer.ls_1_w);
cb(cur, "attn_out_scaled", il);
}
cur = ggml_add(ctx0, cur, inpL);
inpL = cur;
cur = ggml_add(ctx0, cur, input);
input = cur;
cb(cur, "ffn_inp", il);
cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
@@ -361,11 +229,98 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() {
cur = ggml_mul(ctx0, cur, layer.ls_2_w);
cb(cur, "ffn_out_scaled", il);
}
cur = ggml_add(ctx0, inpL, cur);
cb(cur, "layer_out", il);
inpL = cur;
input = ggml_add(ctx0, input, cur);
cb(input, "layer_out", il);
}
return input;
};
if (!is_4x) {
const int insert_lid = hparams.insert_layer_id;
inpL = build_vit_layers(inpL, 0, insert_lid + 1, n_pos);
// ViT merger: window self-attention
// Tokens are reordered to window-major (4 tokens per window are contiguous),
// and a block-diagonal mask restricts attention to within each window. This
// mirrors the qwen2vl windowed-attention pattern so build_attn() can pick the
// flash-attention path when available.
{
ggml_tensor * residual = inpL;
ggml_tensor * cur = build_norm(inpL,
model.vit_merger_ln1_w, model.vit_merger_ln1_b,
NORM_TYPE_NORMAL, eps, -1);
cb(cur, "vit_merger_attn_inp_normed", -1);
cur = ggml_get_rows(ctx0, cur, vit_merger_window_idx);
cb(cur, "vit_merger_window_reorder", -1);
ggml_tensor * Qcur = build_mm(model.vit_merger_attn_q_w, cur);
if (model.vit_merger_attn_q_b) {
Qcur = ggml_add(ctx0, Qcur, model.vit_merger_attn_q_b);
}
ggml_tensor * Kcur = build_mm(model.vit_merger_attn_k_w, cur);
if (model.vit_merger_attn_k_b) {
Kcur = ggml_add(ctx0, Kcur, model.vit_merger_attn_k_b);
}
ggml_tensor * Vcur = build_mm(model.vit_merger_attn_v_w, cur);
if (model.vit_merger_attn_v_b) {
Vcur = ggml_add(ctx0, Vcur, model.vit_merger_attn_v_b);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
cb(Qcur, "vit_merger_Qcur", -1);
cb(Kcur, "vit_merger_Kcur", -1);
cb(Vcur, "vit_merger_Vcur", -1);
cur = build_attn(model.vit_merger_attn_o_w, model.vit_merger_attn_o_b,
Qcur, Kcur, Vcur, vit_merger_window_mask, kq_scale, -1);
cb(cur, "vit_merger_attn_out", -1);
cur = ggml_get_rows(ctx0, cur, vit_merger_inv_window_idx);
inpL = ggml_add(ctx0, cur, residual);
cb(inpL, "vit_merger_attn_residual", -1);
}
// ViT merger: 2x2 spatial downsample + MLP (4 tokens -> 1)
{
ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_0);
ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_1);
ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_2);
ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_3);
ggml_tensor * mean_res = ggml_add(ctx0, p0, p1);
mean_res = ggml_add(ctx0, mean_res, p2);
mean_res = ggml_add(ctx0, mean_res, p3);
mean_res = ggml_scale(ctx0, mean_res, 0.25f);
cb(mean_res, "vit_merger_ds_mean_res", -1);
ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0);
cat = ggml_concat(ctx0, cat, p2, 0);
cat = ggml_concat(ctx0, cat, p3, 0);
ggml_tensor * cur = build_norm(cat,
model.vit_merger_ds_ln_w, model.vit_merger_ds_ln_b,
NORM_TYPE_NORMAL, eps, -1);
cb(cur, "vit_merger_ds_normed", -1);
// ViTWindowAttentionMerger downsample MLP uses gelu_pytorch_tanh (FFN_GELU)
cur = build_ffn(cur,
model.vit_merger_ds_up_w, model.vit_merger_ds_up_b,
nullptr, nullptr,
model.vit_merger_ds_down_w, model.vit_merger_ds_down_b,
FFN_GELU, -1);
cb(cur, "vit_merger_ds_mlp_out", -1);
inpL = ggml_add(ctx0, cur, mean_res);
cb(inpL, "vit_merger_ds_out", -1);
}
inpL = build_vit_layers(inpL, insert_lid + 1, n_layer, n_ds);
} else {
inpL = build_vit_layers(inpL, 0, n_layer, n_pos);
}
if (model.post_ln_w) {

20
tools/mtmd/mtmd-image.cpp
View File

@@ -972,6 +972,26 @@ mtmd_image_preproc_out mtmd_image_preprocessor_longest_edge::preprocess(const cl
return output;
}
//
// mtmd_image_preprocessor_minicpmv
//
mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_minicpmv::get_slice_instructions(const clip_image_size & original_size) {
if (hparams.n_merge == 2) {
const int slice_size = hparams.image_size;
const float ratio = (float)original_size.width * original_size.height / (slice_size * slice_size);
if (ratio <= 1.0f) {
mtmd_image_preprocessor_llava_uhd::slice_instructions inst;
const int patch_size = hparams.patch_size * hparams.n_merge;
inst.overview_size = get_best_resize(original_size, slice_size, patch_size, true);
inst.refined_size = clip_image_size{0, 0};
inst.grid_size = clip_image_size{0, 0};
return inst;
}
}
return mtmd_image_preprocessor_llava_uhd::get_slice_instructions(original_size);
}
//
// mtmd_image_preprocessor_lfm2
//

10
tools/mtmd/mtmd-image.h
View File

@@ -74,7 +74,6 @@ struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
std::vector<slice_coordinates> slices;
};
// LFM2 override this function to implement its custom slicing logic
virtual slice_instructions get_slice_instructions(const clip_image_size & original_size);
struct slice_output {
@@ -83,9 +82,10 @@ struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
};
slice_output slice_image(const clip_image_u8 & img, const slice_instructions & inst);
private:
protected:
clip_image_size get_best_resize(const clip_image_size & original_size, int scale_resolution, int patch_size, bool allow_upscale = false);
private:
clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max);
/**
@@ -129,6 +129,12 @@ struct mtmd_image_preprocessor_longest_edge : mtmd_image_preprocessor {
mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
};
// custom llava-uhd slicing logic for MiniCPM-V
struct mtmd_image_preprocessor_minicpmv : mtmd_image_preprocessor_llava_uhd {
using mtmd_image_preprocessor_llava_uhd::mtmd_image_preprocessor_llava_uhd;
slice_instructions get_slice_instructions(const clip_image_size & original_size) override;
};
// custom llava-uhd slicing logic for LFM2
// ref: https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/lfm2_vl/image_processing_lfm2_vl_fast.py
struct mtmd_image_preprocessor_lfm2 : mtmd_image_preprocessor_llava_uhd {

2
tools/mtmd/mtmd.cpp
View File

@@ -451,7 +451,7 @@ struct mtmd_context {
tok_row_end = {lookup_token("\n")};
tok_row_end_trail = false; // no trailing end-of-row token
ov_img_first = true;
image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
image_preproc = std::make_unique<mtmd_image_preprocessor_minicpmv>(ctx_v);
} break;
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL: