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implment vision model architecture, gguf convertor

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HimariO 2025-02-02 18:02:07 +08:00
parent e391d3ee8d
commit 9c7cc6de9c
3 changed files with 220 additions and 85 deletions
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5
examples/llava/clip-impl.h
View file

@ -22,6 +22,8 @@
#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger" #define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger"
#define KEY_USE_GELU "clip.use_gelu" #define KEY_USE_GELU "clip.use_gelu"
#define KEY_USE_SILU "clip.use_silu" #define KEY_USE_SILU "clip.use_silu"
#define KEY_USE_GLU_MLP "clip.use_glu_mlp"
#define KEY_USE_RMS_NORM "clip.use_rms_norm"
#define KEY_N_EMBD "clip.%s.embedding_length" #define KEY_N_EMBD "clip.%s.embedding_length"
#define KEY_N_FF "clip.%s.feed_forward_length" #define KEY_N_FF "clip.%s.feed_forward_length"
#define KEY_N_BLOCK "clip.%s.block_count" #define KEY_N_BLOCK "clip.%s.block_count"
@ -40,6 +42,8 @@
#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type" #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_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution" #define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
#define KEY_FULLATTN_BLK_IDX "clip.vision.fullatt_block_indexes"
#define KEY_ATTN_WINDOW_SIZE "clip.vision.window_size"
// //
@ -58,6 +62,7 @@
#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s" #define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s"
#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" #define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s"
#define TN_FFN_UP "%s.blk.%d.ffn_up.%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_1 "%s.blk.%d.ln1.%s"
#define TN_LN_2 "%s.blk.%d.ln2.%s" #define TN_LN_2 "%s.blk.%d.ln2.%s"
#define TN_LN_PRE "%s.pre_ln.%s" #define TN_LN_PRE "%s.pre_ln.%s"

134
examples/llava/clip.cpp
View file

@ -183,6 +183,7 @@ struct clip_hparams {
std::vector<int32_t> image_grid_pinpoints; std::vector<int32_t> image_grid_pinpoints;
int32_t image_crop_resolution; int32_t image_crop_resolution;
std::unordered_set<int32_t> vision_feature_layer; std::unordered_set<int32_t> vision_feature_layer;
std::vector<int32_t> full_attn_layers;
}; };
struct clip_layer { struct clip_layer {
@ -208,6 +209,9 @@ struct clip_layer {
struct ggml_tensor * ff_o_w = nullptr; struct ggml_tensor * ff_o_w = nullptr;
struct ggml_tensor * ff_o_b = nullptr; struct ggml_tensor * ff_o_b = nullptr;
struct ggml_tensor * ff_g_w = NULL;
struct ggml_tensor * ff_g_b = NULL;
// layernorm 2 // layernorm 2
struct ggml_tensor * ln_2_w = nullptr; struct ggml_tensor * ln_2_w = nullptr;
struct ggml_tensor * ln_2_b = nullptr; struct ggml_tensor * ln_2_b = nullptr;
@ -331,6 +335,8 @@ struct clip_ctx {
float image_std[3]; float image_std[3];
bool use_gelu = false; bool use_gelu = false;
bool use_silu = false; bool use_silu = false;
bool use_glu_mlp = false;
bool use_rms_norm = false;
int32_t ftype = 1; int32_t ftype = 1;
struct gguf_context * ctx_gguf = nullptr; struct gguf_context * ctx_gguf = nullptr;
@ -576,6 +582,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
const int n_head = hparams.n_head; const int n_head = hparams.n_head;
const int d_head = hidden_size / n_head; const int d_head = hidden_size / n_head;
const float eps = hparams.eps; const float eps = hparams.eps;
const bool use_window_attn = hparams.full_attn_layers.size() > 0;
int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
const int batch_size = imgs->size; const int batch_size = imgs->size;
@ -626,8 +633,10 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
// inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
inp = ggml_add(ctx0, inp, model.patch_bias); inp = ggml_add(ctx0, inp, model.patch_bias);
} }
struct ggml_tensor * embeddings = inp; struct ggml_tensor * embeddings = inp;
struct ggml_tensor * pos_embed = nullptr; struct ggml_tensor * pos_embed = nullptr;
struct ggml_tensor * window_mask = nullptr;
struct ggml_tensor * window_idx = nullptr;
if (ctx->has_llava_projector) { if (ctx->has_llava_projector) {
// concat class_embeddings and patch_embeddings // concat class_embeddings and patch_embeddings
@ -679,6 +688,28 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
const auto & vision_feature_layer = hparams.vision_feature_layer; const auto & vision_feature_layer = hparams.vision_feature_layer;
// loop over layers // loop over layers
if (use_window_attn) {
window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
ggml_set_name(window_idx, "window_idx");
ggml_set_input(window_idx);
// mask for window attention
window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, num_positions, num_positions);
ggml_set_name(window_mask, "window_mask");
ggml_set_input(window_mask);
// embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
GGML_ASSERT(batch_size == 1);
embeddings = ggml_reshape_2d(ctx0, embeddings, hidden_size * 4, patches_w * patches_h * batch_size / 4);
embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size);
positions = ggml_reshape_2d(ctx0, positions, 16, num_position_ids / 4 / 4);
positions = ggml_get_rows(ctx0, positions, window_idx);
positions = ggml_reshape_1d(ctx0, positions, num_position_ids);
}
for (int il = 0; il < ctx->max_feature_layer; il++) { for (int il = 0; il < ctx->max_feature_layer; il++) {
struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
@ -691,9 +722,12 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
//const size_t nb_q_w = model.layers[il].q_w->nb[0]; //const size_t nb_q_w = model.layers[il].q_w->nb[0];
// layernorm1 // layernorm1
{ if (ctx->use_rms_norm) {
cur = ggml_rms_norm(ctx0, cur, eps);
cur = ggml_mul(ctx0, cur, model.layers[il].ln_1_w);
}
else {
cur = ggml_norm(ctx0, cur, eps); cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w), cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w),
model.layers[il].ln_1_b); model.layers[il].ln_1_b);
} }
@ -733,7 +767,14 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f); const bool inlist = std::find(hparams.full_attn_layers.begin(), hparams.full_attn_layers.end(), il) != hparams.full_attn_layers.end();
const bool full_attn = use_window_attn ? inlist : true;
if (full_attn) {
KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
} else {
KQ = ggml_soft_max_ext(ctx0, KQ, window_mask, 1.0f, 0.0f);
}
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size); KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
@ -750,25 +791,50 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
embeddings = cur; // embeddings = residual, cur = hidden_states embeddings = cur; // embeddings = residual, cur = hidden_states
// layernorm2 // layernorm2
{ if (ctx->use_rms_norm) {
cur = ggml_rms_norm(ctx0, cur, eps);
cur = ggml_mul(ctx0, cur, model.layers[il].ln_2_w);
} else {
cur = ggml_norm(ctx0, cur, eps); cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b); cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
} }
cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur); // mlp
cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b); if (ctx->use_glu_mlp) {
// ffn_up
auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_o_b);
if (ctx->use_gelu) { auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_g_w, cur);
cur = ggml_gelu_inplace(ctx0, cur); cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_g_b);
} else if (ctx->use_silu) { if (ctx->use_gelu) {
cur = ggml_silu_inplace(ctx0, cur); cur_gate = ggml_gelu_inplace(ctx0, cur_gate);
} else { } else if (ctx->use_silu) {
cur = ggml_gelu_quick_inplace(ctx0, cur); cur_gate = ggml_silu_inplace(ctx0, cur_gate);
} else {
cur_gate = ggml_gelu_quick_inplace(ctx0, cur_gate);
}
cur = ggml_mul(ctx0, cur_gate, cur_up);
// ffn_down
cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
} }
else {
cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur); if (ctx->use_gelu) {
cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b); cur = ggml_gelu_inplace(ctx0, cur);
} else if (ctx->use_silu) {
cur = ggml_silu_inplace(ctx0, cur);
} else {
cur = ggml_gelu_quick_inplace(ctx0, cur);
}
cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
}
// residual 2 // residual 2
cur = ggml_add(ctx0, embeddings, cur); cur = ggml_add(ctx0, embeddings, cur);
@ -778,10 +844,17 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
// post-layernorm // post-layernorm
if (model.post_ln_w) { if (model.post_ln_w) {
embeddings = ggml_norm(ctx0, embeddings, eps); if (ctx->use_rms_norm) {
ggml_set_name(embeddings, "post_ln"); embeddings = ggml_rms_norm(ctx0, embeddings, eps);
ggml_set_name(embeddings, "post_ln");
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w);
} else {
embeddings = ggml_norm(ctx0, embeddings, eps);
ggml_set_name(embeddings, "post_ln");
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
}
} }
// final layer is a vision feature layer // final layer is a vision feature layer
@ -1095,6 +1168,18 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
} }
if (use_window_attn) {
struct ggml_tensor * inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
ggml_set_name(inv_window_idx, "inv_window_idx");
ggml_set_input(inv_window_idx);
// embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
GGML_ASSERT(batch_size == 1);
embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4);
embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx);
embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4, batch_size);
}
// build the graph // build the graph
ggml_build_forward_expand(gf, embeddings); ggml_build_forward_expand(gf, embeddings);
@ -1203,6 +1288,8 @@ struct clip_model_loader {
get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false); get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false);
get_bool(KEY_USE_SILU, ctx_clip.use_silu, false); get_bool(KEY_USE_SILU, ctx_clip.use_silu, false);
get_bool(KEY_USE_GLU_MLP, ctx_clip.use_glu_mlp, false);
get_bool(KEY_USE_RMS_NORM, ctx_clip.use_rms_norm, false);
auto & hparams = ctx_clip.vision_model.hparams; auto & hparams = ctx_clip.vision_model.hparams;
get_u32(string_format(KEY_N_EMBD, "vision"), hparams.hidden_size); get_u32(string_format(KEY_N_EMBD, "vision"), hparams.hidden_size);
@ -1215,6 +1302,7 @@ struct clip_model_loader {
get_u32(KEY_PATCH_SIZE, hparams.patch_size); get_u32(KEY_PATCH_SIZE, hparams.patch_size);
get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false); get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false); get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false);
get_arr_int(KEY_FULLATTN_BLK_IDX, hparams.full_attn_layers, false);
{ {
std::string mm_patch_merge_type; std::string mm_patch_merge_type;
@ -1330,14 +1418,16 @@ struct clip_model_loader {
layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false); layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false);
layer.ff_i_w = get_tensor(string_format(TN_FFN_DOWN, "v", il, "weight")); layer.ff_i_w = get_tensor(string_format(TN_FFN_DOWN, "v", il, "weight"));
layer.ff_o_w = get_tensor(string_format(TN_FFN_UP, "v", il, "weight")); layer.ff_o_w = get_tensor(string_format(TN_FFN_UP, "v", il, "weight"));
layer.ff_g_w = get_tensor(string_format(TN_FFN_GATE, "v", il, "weight"), ctx_clip.use_glu_mlp);
layer.k_b = get_tensor(string_format(TN_ATTN_K, "v", il, "bias"), false); 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.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); layer.v_b = get_tensor(string_format(TN_ATTN_V, "v", il, "bias"), false);
layer.o_b = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "bias"), false); layer.o_b = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "bias"), false);
layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), false); layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), !ctx_clip.use_rms_norm);
layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), false); layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), !ctx_clip.use_rms_norm);
layer.ff_i_b = get_tensor(string_format(TN_FFN_DOWN, "v", il, "bias"), false); layer.ff_i_b = get_tensor(string_format(TN_FFN_DOWN, "v", il, "bias"), false);
layer.ff_o_b = get_tensor(string_format(TN_FFN_UP, "v", il, "bias"), false); layer.ff_o_b = get_tensor(string_format(TN_FFN_UP, "v", il, "bias"), false);
layer.ff_g_b = get_tensor(string_format(TN_FFN_GATE, "v", il, "bias"), ctx_clip.use_glu_mlp);
} }
switch (ctx_clip.proj_type) { switch (ctx_clip.proj_type) {

166
examples/llava/qwen2_vl_surgery.py
View file

@ -5,10 +5,12 @@ import torch
import numpy as np import numpy as np
from gguf import * from gguf import *
from transformers import ( from transformers import (
Qwen2VLForConditionalGeneration,
Qwen2VLProcessor,
AutoProcessor, AutoProcessor,
Qwen2VLConfig Qwen2VLForConditionalGeneration,
Qwen2_5_VLForConditionalGeneration,
Qwen2VLProcessor,
Qwen2VLConfig,
Qwen2_5_VLConfig,
) )
@ -18,62 +20,80 @@ VISION = "clip.vision"
def k(raw_key: str, arch: str) -> str: def k(raw_key: str, arch: str) -> str:
return raw_key.format(arch=arch) return raw_key.format(arch=arch)
class VL2:
def to_gguf_name(name: str) -> str: @staticmethod
og = name def to_gguf_name(name: str) -> str:
name = name.replace("text_model", "t").replace("vision_model", "v") og = name
name = name.replace("blocks", "blk").replace("embeddings.", "") name = name.replace("text_model", "t").replace("vision_model", "v")
name = name.replace("attn.", "attn_") name = name.replace("blocks", "blk").replace("embeddings.", "")
name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.") name = name.replace("attn.", "attn_")
# name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln") name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.")
name = name.replace("norm1", "ln1").replace("norm2", "ln2") # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln")
name = name.replace("merger.mlp", 'mm') name = name.replace("norm1", "ln1").replace("norm2", "ln2")
print(f"[to_gguf_name] {og} --> {name}") name = name.replace("merger.mlp", 'mm')
return name print(f"[to_gguf_name] {og} --> {name}")
return name
@classmethod
def find_vision_tensors(qwen2vl, dtype) -> Dict[str, np.ndarray]: def find_vision_tensors(cls, qwen2vl, dtype) -> Dict[str, np.ndarray]:
vision_model = qwen2vl.visual vision_model = qwen2vl.visual
tensor_map = {} tensor_map = {}
for name, ten in vision_model.state_dict().items(): for name, ten in vision_model.state_dict().items():
ten = ten.numpy() ten = ten.numpy()
if 'qkv' in name: if 'qkv' in name:
if ten.ndim == 2: # weight if ten.ndim == 2: # weight
c3, _ = ten.shape c3, _ = ten.shape
else: # bias else: # bias
c3 = ten.shape[0] c3 = ten.shape[0]
assert c3 % 3 == 0 assert c3 % 3 == 0
c = c3 // 3 c = c3 // 3
wq = ten[:c] wq = ten[:c]
wk = ten[c: c * 2] wk = ten[c: c * 2]
wv = ten[c * 2:] wv = ten[c * 2:]
tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq
tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk
tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv
elif 'merger' in name: elif 'merger' in name:
if name.endswith("ln_q.weight"): if name.endswith("ln_q.weight"):
tensor_map['v.post_ln.weight'] = ten tensor_map['v.post_ln.weight'] = ten
elif name.endswith("ln_q.bias"): elif name.endswith("ln_q.bias"):
tensor_map['v.post_ln.bias'] = ten tensor_map['v.post_ln.bias'] = ten
else:
# "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias"
tensor_map[cls.to_gguf_name(name)] = ten
elif 'patch_embed.proj.weight' in name:
# NOTE: split Conv3D into Conv2Ds
c1, c2, kt, kh, kw = ten.shape
assert kt == 2, "Current implmentation only support temporal_patch_size of 2"
tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...]
tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...]
else: else:
# "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias" tensor_map[cls.to_gguf_name(f"vision_model.{name}")] = ten
tensor_map[to_gguf_name(name)] = ten
elif 'patch_embed.proj.weight' in name:
# NOTE: split Conv3D into Conv2Ds
c1, c2, kt, kh, kw = ten.shape
assert kt == 2, "Current implmentation only support temporal_patch_size of 2"
tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...]
tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...]
else:
tensor_map[to_gguf_name(f"vision_model.{name}")] = ten
for new_name, ten in tensor_map.items(): for new_name, ten in tensor_map.items():
if ten.ndim <= 1 or new_name.endswith("_norm.weight"): if ten.ndim <= 1 or new_name.endswith("_norm.weight"):
tensor_map[new_name] = ten.astype(np.float32) tensor_map[new_name] = ten.astype(np.float32)
else: else:
tensor_map[new_name] = ten.astype(dtype) tensor_map[new_name] = ten.astype(dtype)
tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32) # dummy tensor, just here as a placeholder tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32) # dummy tensor, just here as a placeholder
return tensor_map return tensor_map
class VL25(VL2):
@staticmethod
def to_gguf_name(name: str) -> str:
og = name
name = name.replace("text_model", "t").replace("vision_model", "v")
name = name.replace("blocks", "blk").replace("embeddings.", "")
name = name.replace("attn.", "attn_")
name = name.replace("mlp.down_proj", "ffn_down").replace("mlp.up_proj", "ffn_up")
name = name.replace("mlp.gate_proj", "ffn_gate").replace("proj.", "out.")
name = name.replace("norm1", "ln1").replace("norm2", "ln2")
name = name.replace("merger.mlp", 'mm')
print(f"[vl25][to_gguf_name] {og} --> {name}")
return name
def main(args): def main(args):
@ -92,11 +112,18 @@ def main(args):
model_path = "" model_path = ""
model_name = args.model_name model_name = args.model_name
print("model_name: ", model_name) print("model_name: ", model_name)
qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained( if args.model_type == "qwen2vl":
model_name, torch_dtype=dtype, device_map="cpu" qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained(
) model_name, torch_dtype=dtype, device_map="cpu"
cfg: Qwen2VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] )
vcfg = cfg.vision_config cfg: Qwen2VLConfig = qwen2vl.config # type: ignore[reportAssignmentType]
vcfg = cfg.vision_config
else:
qwen2vl = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_name, torch_dtype=dtype, device_map="cpu"
)
cfg: Qwen2_5_VLConfig = qwen2vl.config # type: ignore[reportAssignmentType]
vcfg = cfg.vision_config
if os.path.isdir(model_name): if os.path.isdir(model_name):
local_model = True local_model = True
@ -125,14 +152,26 @@ def main(args):
else: else:
raise ValueError() raise ValueError()
tensor_map = find_vision_tensors(qwen2vl, np_dtype) if args.model_type == "qwen2.5vl":
fout.add_bool("clip.use_glu_mlp", True) # gate linear unit MLP layer in vision model
fout.add_bool("clip.use_rms_norm", True)
fout.add_array("clip.vision.fullatt_block_indexes", vcfg.fullatt_block_indexes)
fout.add_uint32("clip.vision.window_size", vcfg.window_size)
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.hidden_size)
fout.add_uint32("clip.vision.projection_dim", vcfg.out_hidden_size)
else:
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim)
fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size)
if args.model_type == "qwen2.5vl":
tensor_map = VL25.find_vision_tensors(qwen2vl, np_dtype)
else:
tensor_map = VL2.find_vision_tensors(qwen2vl, np_dtype)
for name, data in tensor_map.items(): for name, data in tensor_map.items():
fout.add_tensor(name, data) fout.add_tensor(name, data)
fout.add_uint32("clip.vision.patch_size", vcfg.patch_size) fout.add_uint32("clip.vision.patch_size", vcfg.patch_size)
fout.add_uint32("clip.vision.image_size", 14 * 40) # some reasonable size that is divable by (14*2) fout.add_uint32("clip.vision.image_size", 14 * 40) # some reasonable size that is divable by (14*2)
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim)
fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size)
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads) fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads)
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth) fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth)
@ -160,6 +199,7 @@ def main(args):
if __name__ == "__main__": if __name__ == "__main__":
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct") parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct")
parser.add_argument("--model_type", nargs='?', choices=['qwen2vl', 'qwen2.5vl'], default="qwen2vl")
parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32") parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32")
args = parser.parse_args() args = parser.parse_args()
main(args) main(args)