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mtmd : support InternVL 2.5 and 3 (#13422)

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* convert : internvl support

* InternVL3-1B working

* fix regression

* rm mobilevlm from test

* fix conversion

* add test for internvl

* add to list of pre-quant

* restore boi/eoi check

* add clarify comment for norm eps
This commit is contained in:
Xuan-Son Nguyen
2025-05-10 16:26:42 +02:00
committed by GitHub
parent d8919424f1
commit 053367d149
9 changed files with 243 additions and 25 deletions
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76
convert_hf_to_gguf.py
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@ -426,7 +426,11 @@ class ModelBase:
logger.warning(f"Failed to load model config from {dir_model}: {e}")
logger.warning("Trying to load config.json instead")
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
return json.load(f)
config = json.load(f)
if "llm_config" in config:
# rename for InternVL
config["text_config"] = config["llm_config"]
return config
@classmethod
def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
@ -2606,6 +2610,11 @@ class Qwen2Model(TextModel):
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if self.hf_arch == "Qwen2Model":
name = f"model.{name}" # map to Qwen2ForCausalLM tensors
if "language_model." in name:
name = name.replace("language_model.", "") # for InternVL
if name.startswith("mlp") or name.startswith("vision_model"):
# skip visual tensors
return []
yield from super().modify_tensors(data_torch, name, bid)
@ -2709,6 +2718,62 @@ class Qwen2VLVisionModel(VisionModel):
return [] # skip other tensors
@ModelBase.register("InternVisionModel")
class InternVisionModel(VisionModel):
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.INTERNVL)
self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
# hidden_act
if hparams["hidden_act"] == "silu":
self.gguf_writer.add_vision_use_silu(True)
elif hparams["hidden_act"] == "gelu":
self.gguf_writer.add_vision_use_gelu(True)
else:
raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
# downsample_ratio
downsample_ratio = self.global_config.get("downsample_ratio")
assert downsample_ratio is not None
self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
def tensor_force_quant(self, name, new_name, bid, n_dims):
del bid, name, n_dims # unused
if ".patch_embd." in new_name:
return gguf.GGMLQuantizationType.F16
if ".position_embd." in new_name:
return gguf.GGMLQuantizationType.F32
return False
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
if name.startswith("vision_model") or name.startswith("mlp"):
# process visual tensors
# correct name
if name.startswith("vision_model"):
name = "vision_tower." + name
if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"):
name += ".weight"
# split QKV tensors if needed
if ".qkv." in name:
if data_torch.ndim == 2: # weight
c3, _ = data_torch.shape
else: # bias
c3 = data_torch.shape[0]
assert c3 % 3 == 0
c = c3 // 3
wq = data_torch[:c]
wk = data_torch[c: c * 2]
wv = data_torch[c * 2:]
return [
(self.map_tensor_name(name.replace("attn.qkv", "self_attn.q_proj")), wq),
(self.map_tensor_name(name.replace("attn.qkv", "self_attn.k_proj")), wk),
(self.map_tensor_name(name.replace("attn.qkv", "self_attn.v_proj")), wv),
]
return [(self.map_tensor_name(name), data_torch)]
return [] # skip other tensors
@ModelBase.register("WavTokenizerDec")
class WavTokenizerDecModel(TextModel):
model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC
@ -3360,6 +3425,11 @@ class InternLM2Model(TextModel):
head_dim = n_embd // num_heads
num_groups = num_heads // q_per_kv
name = name.replace("language_model.", "") # InternVL
if name.startswith("mlp") or name.startswith("vision_model"):
# skip visual tensors
return []
if bid is not None and f"model.layers.{bid}.attention.wqkv" in name:
qkv = data_torch
@ -3433,6 +3503,10 @@ class InternLM3Model(TextModel):
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
name = name.replace("language_model.", "") # InternVL
if name.startswith("mlp") or name.startswith("vision_model"):
# skip visual tensors
return []
if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith(("k_proj.weight", "k_proj.bias")):

8
docs/multimodal.md
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@ -66,4 +66,12 @@ NOTE: some models may require large context window, for example: `-c 8192`
# Mistral Small 3.1 24B (IQ2_M quantization)
(tool_name) -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF
# InternVL 2.5 and 3
(tool_name) -hf ggml-org/InternVL2_5-1B-GGUF
(tool_name) -hf ggml-org/InternVL2_5-2B-GGUF
(tool_name) -hf ggml-org/InternVL3-1B-Instruct-GGUF
(tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF
(tool_name) -hf ggml-org/InternVL3-4B-Instruct-GGUF
(tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF
```

7
gguf-py/gguf/constants.py
View File

@ -491,6 +491,8 @@ class MODEL_TENSOR(IntEnum):
V_ENC_FFN_UP = auto()
V_ENC_FFN_GATE = auto()
V_ENC_FFN_DOWN = auto()
V_LAYER_SCALE_1 = auto()
V_LAYER_SCALE_2 = auto()
V_PRE_NORM = auto()
V_POST_NORM = auto()
V_MM_INP_NORM = auto()
@ -748,6 +750,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.V_ENC_FFN_UP: "v.blk.{bid}.ffn_up",
MODEL_TENSOR.V_ENC_FFN_GATE: "v.blk.{bid}.ffn_gate",
MODEL_TENSOR.V_ENC_FFN_DOWN: "v.blk.{bid}.ffn_down",
MODEL_TENSOR.V_LAYER_SCALE_1: "v.blk.{bid}.ls1",
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_INP_PROJ: "mm.input_projection",
@ -786,6 +790,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.V_ENC_FFN_UP,
MODEL_TENSOR.V_ENC_FFN_GATE,
MODEL_TENSOR.V_ENC_FFN_DOWN,
MODEL_TENSOR.V_LAYER_SCALE_1,
MODEL_TENSOR.V_LAYER_SCALE_2,
MODEL_TENSOR.V_PRE_NORM,
MODEL_TENSOR.V_POST_NORM,
MODEL_TENSOR.V_MM_INP_PROJ,
@ -2167,6 +2173,7 @@ class VisionProjectorType:
PIXTRAL = "pixtral"
QWEN2VL = "qwen2vl_merger"
QWEN25VL = "qwen2.5vl_merger"
INTERNVL = "internvl"
# Items here are (block size, type size)

12
gguf-py/gguf/tensor_mapping.py
View File

@ -905,6 +905,7 @@ class TensorNameMap:
MODEL_TENSOR.V_MMPROJ_MLP: (
"model.mm_projector.mlp.mlp.{bid}",
"mlp1.{bid}", # InternVL
),
MODEL_TENSOR.V_MMPROJ_PEG: (
@ -955,6 +956,7 @@ class TensorNameMap:
MODEL_TENSOR.V_ENC_INPUT_NORM: (
"vision_tower.vision_model.encoder.layers.{bid}.layer_norm1",
"vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL
"vpm.encoder.layers.{bid}.layer_norm1",
"model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM
"vision_tower.transformer.layers.{bid}.attention_norm", # pixtral
@ -963,6 +965,7 @@ class TensorNameMap:
MODEL_TENSOR.V_ENC_OUTPUT: (
"vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
"vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL
"vpm.encoder.layers.{bid}.self_attn.out_proj",
"model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM
"vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral
@ -971,6 +974,7 @@ class TensorNameMap:
MODEL_TENSOR.V_ENC_OUTPUT_NORM: (
"vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
"vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL
"vpm.encoder.layers.{bid}.layer_norm2",
"model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM
"vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral
@ -1000,6 +1004,14 @@ class TensorNameMap:
"visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl
),
MODEL_TENSOR.V_LAYER_SCALE_1: (
"vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL
),
MODEL_TENSOR.V_LAYER_SCALE_2: (
"vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL
),
MODEL_TENSOR.V_PRE_NORM: (
"vision_tower.vision_model.pre_layrnorm",
"vision_tower.ln_pre", # pixtral

1
tools/mtmd/README.md
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@ -48,6 +48,7 @@ For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` fla
- [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint
- Qwen 2 VL and Qwen 2.5 VL (from [Qwen](https://huggingface.co/Qwen))
- [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)
- InternVL 2.5 and InternVL 3 from [OpenGVLab](https://huggingface.co/OpenGVLab) (note: we don't support conversion of `InternVL3-*-hf` model, only non-HF version is supported ; `InternLM2Model` **text** model is not supported)
For older models, please refer to the relevant guide for instructions on how to obtain or create them:

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

@ -33,9 +33,6 @@
#define KEY_PROJ_TYPE "clip.projector_type"
#define KEY_SPATIAL_MERGE_SIZE "clip.vision.spatial_merge_size"
#define KEY_USE_GLU_MLP "clip.use_glu_mlp" // for qwen2.5vl
#define KEY_USE_RMS_NORM "clip.use_rms_norm" // for qwen2.5vl
#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
@ -60,8 +57,10 @@
#define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s"
#define TN_FFN_UP "%s.blk.%d.ffn_up.%s"
#define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s"
#define TN_LN_1 "%s.blk.%d.ln1.%s"
#define TN_LN_2 "%s.blk.%d.ln2.%s"
#define TN_LN_1 "%s.blk.%d.ln1.%s" // layer norm
#define TN_LN_2 "%s.blk.%d.ln2.%s" // layer norm
#define TN_LS_1 "%s.blk.%d.ls1.%s" // layer scale
#define TN_LS_2 "%s.blk.%d.ls2.%s" // layer scale
#define TN_LN_PRE "%s.pre_ln.%s"
#define TN_LN_POST "%s.post_ln.%s"
#define TN_LLAVA_PROJ "mm.%d.%s"
@ -105,6 +104,7 @@ enum projector_type {
PROJECTOR_TYPE_IDEFICS3,
PROJECTOR_TYPE_PIXTRAL,
PROJECTOR_TYPE_QWEN25VL,
PROJECTOR_TYPE_INTERNVL,
PROJECTOR_TYPE_UNKNOWN,
};
@ -119,6 +119,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
{ PROJECTOR_TYPE_GEMMA3, "gemma3"},
{ PROJECTOR_TYPE_IDEFICS3, "idefics3"},
{ PROJECTOR_TYPE_PIXTRAL, "pixtral"},
{ PROJECTOR_TYPE_INTERNVL, "internvl"},
};
static projector_type clip_projector_type_from_string(const std::string & str) {

140
tools/mtmd/clip.cpp
View File

@ -215,6 +215,10 @@ struct clip_layer {
// layernorm 2
ggml_tensor * ln_2_w = nullptr;
ggml_tensor * ln_2_b = nullptr;
// layer scale (no bias)
ggml_tensor * ls_1_w = nullptr;
ggml_tensor * ls_2_w = nullptr;
};
struct clip_vision_model {
@ -589,6 +593,9 @@ struct clip_graph {
// Qwen2VL and Qwen2.5VL use M-RoPE
ggml_cgraph * build_qwen2vl() {
GGML_ASSERT(model.patch_bias == nullptr);
GGML_ASSERT(model.class_embedding == nullptr);
const int batch_size = 1;
const bool use_window_attn = hparams.n_wa_pattern > 0;
const int n_wa_pattern = hparams.n_wa_pattern;
@ -625,10 +632,6 @@ struct clip_graph {
n_embd, n_patches_x * n_patches_y, batch_size);
}
if (model.patch_bias) {
inp = ggml_add(ctx0, inp, model.patch_bias);
}
ggml_tensor * inpL = inp;
ggml_tensor * window_mask = nullptr;
ggml_tensor * window_idx = nullptr;
@ -859,6 +862,67 @@ struct clip_graph {
return gf;
}
ggml_cgraph * build_internvl() {
GGML_ASSERT(model.class_embedding != nullptr);
GGML_ASSERT(model.position_embeddings != nullptr);
const int n_pos = n_patches + 1;
ggml_tensor * inp = build_inp();
// add CLS token
inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
ggml_tensor * cur = build_vit(
inp, n_pos,
NORM_TYPE_NORMAL,
hparams.ffn_op,
model.position_embeddings,
nullptr);
// remove CLS token
cur = ggml_view_2d(ctx0, cur,
n_embd, n_patches,
ggml_row_size(cur->type, n_embd), 0);
// pixel shuffle
{
const int scale_factor = model.hparams.proj_scale_factor;
const int bsz = 1; // batch size, always 1 for now since we don't support batching
const int height = n_patches_y;
const int width = n_patches_x;
GGML_ASSERT(scale_factor > 0);
cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz);
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur),
n_embd * scale_factor * scale_factor,
height / scale_factor,
width / scale_factor,
bsz);
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
// flatten to 2D
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, cur),
n_embd * scale_factor * scale_factor,
cur->ne[1] * cur->ne[2]);
}
// projector (always using GELU activation)
{
// projector LayerNorm uses pytorch's default eps = 1e-5
// ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79
cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1);
cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
cur = ggml_add(ctx0, cur, model.mm_1_b);
cur = ggml_gelu(ctx0, cur);
cur = ggml_mul_mat(ctx0, model.mm_3_w, cur);
cur = ggml_add(ctx0, cur, model.mm_3_b);
}
// build the graph
ggml_build_forward_expand(gf, cur);
return gf;
}
// this graph is used by llava, granite and glm
// due to having embedding_stack (used by granite), we cannot reuse build_vit
ggml_cgraph * build_llava() {
@ -890,10 +954,6 @@ struct clip_graph {
ggml_tensor * inp = build_inp();
if (model.patch_bias) {
inp = ggml_add(ctx0, inp, model.patch_bias);
}
// concat class_embeddings and patch_embeddings
if (model.class_embedding) {
inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
@ -1260,11 +1320,6 @@ private:
ggml_tensor * learned_pos_embd,
std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos
) {
if (model.patch_bias) {
inp = ggml_add(ctx0, inp, model.patch_bias);
cb(inp, "patch_bias", -1);
}
if (learned_pos_embd) {
inp = ggml_add(ctx0, inp, learned_pos_embd);
cb(inp, "pos_embed", -1);
@ -1324,6 +1379,11 @@ private:
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);
}
// re-add the layer input, e.g., residual
cur = ggml_add(ctx0, cur, inpL);
@ -1344,6 +1404,11 @@ private:
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);
}
// residual 2
cur = ggml_add(ctx0, inpL, cur);
cb(cur, "layer_out", il);
@ -1365,6 +1430,10 @@ private:
ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd);
inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
if (model.patch_bias) {
inp = ggml_add(ctx0, inp, model.patch_bias);
cb(inp, "patch_bias", -1);
}
return inp;
}
@ -1627,6 +1696,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
{
res = graph.build_minicpmv();
} break;
case PROJECTOR_TYPE_INTERNVL:
{
res = graph.build_internvl();
} break;
default:
{
res = graph.build_llava();
@ -1790,6 +1863,7 @@ struct clip_model_loader {
}
} break;
case PROJECTOR_TYPE_IDEFICS3:
case PROJECTOR_TYPE_INTERNVL:
{
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false);
} break;
@ -1897,6 +1971,9 @@ struct clip_model_loader {
layer.o_w = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "weight"));
layer.ln_1_w = get_tensor(string_format(TN_LN_1, "v", il, "weight"), false);
layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false);
layer.ls_1_w = get_tensor(string_format(TN_LS_1, "v", il, "weight"), false); // no bias
layer.ls_2_w = get_tensor(string_format(TN_LS_2, "v", il, "weight"), false); // no bias
layer.k_b = get_tensor(string_format(TN_ATTN_K, "v", il, "bias"), false);
layer.q_b = get_tensor(string_format(TN_ATTN_Q, "v", il, "bias"), false);
layer.v_b = get_tensor(string_format(TN_ATTN_V, "v", il, "bias"), false);
@ -1904,7 +1981,7 @@ struct clip_model_loader {
layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), false);
layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), false);
// new naming
// ffn
layer.ff_up_w = get_tensor(string_format(TN_FFN_UP, "v", il, "weight"));
layer.ff_up_b = get_tensor(string_format(TN_FFN_UP, "v", il, "bias"), false);
layer.ff_gate_w = get_tensor(string_format(TN_FFN_GATE, "v", il, "weight"), false);
@ -2052,6 +2129,15 @@ struct clip_model_loader {
vision_model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM, false);
vision_model.mm_patch_merger_w = get_tensor(TN_MM_PATCH_MERGER, false);
} break;
case PROJECTOR_TYPE_INTERNVL:
{
vision_model.mm_0_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight"));
vision_model.mm_0_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "bias"));
vision_model.mm_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight"));
vision_model.mm_1_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "bias"));
vision_model.mm_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight"));
vision_model.mm_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias"));
} break;
default:
GGML_ASSERT(false && "unknown projector type");
}
@ -2838,7 +2924,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
}
else if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE
|| ctx->proj_type == PROJECTOR_TYPE_GEMMA3
|| ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) {
|| ctx->proj_type == PROJECTOR_TYPE_IDEFICS3
|| ctx->proj_type == PROJECTOR_TYPE_INTERNVL // TODO @ngxson : support dynamic resolution
) {
clip_image_u8 resized_image;
int sz = params.image_size;
image_manipulation::resize_and_pad_image(*img, resized_image, {sz, sz});
@ -2988,9 +3076,13 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
if (ctx->proj_type == PROJECTOR_TYPE_LDP
|| ctx->proj_type == PROJECTOR_TYPE_LDPV2
|| ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
n_patches /= 4;
n_patches += 2; // for BOI and EOI token embeddings
if (ctx->vision_model.mm_glm_tok_boi) {
n_patches += 2; // for BOI and EOI token embeddings
}
} else if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) {
if (ctx->minicpmv_version == 2) {
n_patches = 96;
@ -3013,7 +3105,8 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
int n_per_side = params.image_size / params.patch_size;
int n_per_side_2d_pool = n_per_side / params.proj_scale_factor;
n_patches = n_per_side_2d_pool * n_per_side_2d_pool;
} else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) {
} else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3 || ctx->proj_type == PROJECTOR_TYPE_INTERNVL) {
// both W and H are divided by proj_scale_factor
n_patches /= (params.proj_scale_factor * params.proj_scale_factor);
} else if (ctx->proj_type == PROJECTOR_TYPE_PIXTRAL) {
int n_merge = params.spatial_merge_size;
@ -3408,6 +3501,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
} break;
case PROJECTOR_TYPE_GEMMA3:
case PROJECTOR_TYPE_IDEFICS3:
case PROJECTOR_TYPE_INTERNVL:
{
// do nothing
} break;
@ -3434,6 +3528,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
// the last node is the embedding tensor
ggml_tensor * embeddings = ggml_graph_node(gf, -1);
// sanity check (only support batch size of 1 for now)
const int n_tokens_out = embeddings->ne[1];
const int expected_n_tokens_out = clip_n_output_tokens(ctx, imgs.entries[0].get());
if (n_tokens_out != expected_n_tokens_out) {
LOG_ERR("%s: expected %d tokens, got %d\n", __func__, expected_n_tokens_out, n_tokens_out);
GGML_ABORT("Invalid number of output tokens");
}
// copy the embeddings to the location passed by the user
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
@ -3604,6 +3706,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
return ctx->vision_model.mm_input_proj_w->ne[0];
case PROJECTOR_TYPE_IDEFICS3:
return ctx->vision_model.projection->ne[1];
case PROJECTOR_TYPE_INTERNVL:
return ctx->vision_model.mm_3_w->ne[1];
default:
GGML_ABORT("Unknown projector type");
}

7
tools/mtmd/mtmd.cpp
View File

@ -252,6 +252,13 @@ int32_t mtmd_tokenize(mtmd_context * ctx,
}
else if (proj_type == PROJECTOR_TYPE_INTERNVL) {
// <img> ... (image embeddings) ... </img>
marker_modified = "<img>" + ctx->image_marker + "</img>";
string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
}
// llava-1.5, llava-1.6, Yi-VL, Yi-34B, granite: don't need to add prefix and suffix
// for glm-edge, BOI and EOI token's embeddings are not present in the text model

6
tools/mtmd/tests.sh
View File

@ -40,7 +40,6 @@ add_test "llama-mtmd-cli" "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0"
add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0"
add_test "llama-mtmd-cli" "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek"
add_test "llama-mtmd-cli" "THUDM/glm-edge-v-5b-gguf:Q4_K_M"
add_test "llama-mtmd-cli" "second-state/Llava-v1.5-7B-GGUF:Q2_K" "vicuna"
add_test "llama-mtmd-cli" "cjpais/llava-1.6-mistral-7b-gguf:Q3_K" "vicuna"
@ -50,6 +49,8 @@ add_test "llama-mtmd-cli" "openbmb/MiniCPM-V-2_6-gguf:Q2_K"
add_test "llama-mtmd-cli" "openbmb/MiniCPM-o-2_6-gguf:Q4_0"
add_test "llama-mtmd-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/InternVL2_5-1B-GGUF:Q8_0"
add_test "llama-mtmd-cli" "ggml-org/InternVL3-1B-Instruct-GGUF:Q8_0"
# to test the big models, run: ./tests.sh big
if [ "$RUN_BIG_TESTS" = true ]; then
@ -59,6 +60,8 @@ if [ "$RUN_BIG_TESTS" = true ]; then
add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/InternVL3-8B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M"
# add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
# add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M" # too big
fi
@ -70,6 +73,7 @@ fi
# this model has broken chat template, not usable
# add_test "llama-mtmd-cli" "cmp-nct/Yi-VL-6B-GGUF:Q5_K"
# add_test "llama-mtmd-cli" "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek"
###############