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llama.cpp
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mtmd: debug CLIP-L & first working DeepSeek-OCR model

This commit is contained in:
bluebread 2025-11-29 16:40:50 +00:00
parent ccb2f2385e
commit 841a4a88df
1 changed files with 34 additions and 41 deletions
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75
tools/mtmd/clip.cpp
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@ -837,34 +837,32 @@ struct clip_graph {
ggml_cgraph * build_deepseek_ocr() {
//patch embedding
ggml_tensor * inp_raw = build_inp_raw();
ggml_tensor * global_features_1 = build_sam_enc(inp_raw, std::max(img.nx, img.ny));
ggml_tensor * global_features_2 = build_dp_ocr_clip(global_features_1);
// FIXME remove n_patches is hardcoded
// torch global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1)
global_features_1 = ggml_cont(ctx0,ggml_permute(ctx0, global_features_1,2,1,0,3));
global_features_1 = ggml_cont(ctx0,ggml_permute(ctx0, global_features_1, 1, 2, 0, 3));
int clip_n_patches = global_features_1->ne[1] * global_features_1->ne[2];
// flatten 2nd and 3rd dims
global_features_1 = ggml_reshape_2d(ctx0, global_features_1, global_features_1->ne[0], clip_n_patches);
// remove CLS token
global_features_2 = ggml_view_2d(ctx0, global_features_2,
n_embd, clip_n_patches,
ggml_row_size(global_features_2->type, n_embd), 0);
ggml_tensor * global_features = ggml_concat(ctx0, global_features_2, global_features_1, 1);
global_features_2 = ggml_view_2d(ctx0, global_features_2, n_embd, clip_n_patches,
global_features_2->nb[1], global_features_2->nb[1]);
ggml_tensor * global_features = ggml_concat(ctx0, global_features_2, global_features_1, 0);
global_features = ggml_reshape_2d(ctx0, global_features, 2* n_embd,clip_n_patches);
global_features = ggml_cont(ctx0, global_features);
global_features = ggml_mul_mat(ctx0, model.fc_w, global_features);
global_features = ggml_add(ctx0, global_features, model.fc_b);
global_features = build_global_local_features(ctx0,global_features);
global_features = ggml_cont(ctx0, ggml_permute(ctx0, global_features, 1, 0, 2, 3));
cb(global_features, "dsocr_output", -1);
ggml_build_forward_expand(gf, global_features);
return gf;
}
@ -878,30 +876,23 @@ struct clip_graph {
GGML_ASSERT(model.image_newline != nullptr);
GGML_ASSERT(model.view_seperator != nullptr);
// 1) global_features: [n_dim, h*w] -> [n_dim, w, h] -> [h, w, n_dim]
const auto h = static_cast<int>(std::sqrt(static_cast<float>(global_features->ne[1])));
const auto w = h;
const auto n_dim = global_features->ne[0];
ggml_tensor * t = ggml_reshape_4d(ctx0, global_features, n_dim, h, w, 1); // (n_dim, w, h)
t = ggml_cont(ctx0, ggml_permute(ctx0, t, 2, 1, 0, 3)); // (h, w, n_dim)
ggml_tensor * nl = ggml_cont(ctx0,ggml_permute(ctx0, model.image_newline, 2, 1, 0, 3));
nl = ggml_repeat_4d(ctx0, nl, h, 1, n_dim, 1); // n_pos rows
ggml_tensor * cur;
ggml_tensor * imgnl;
ggml_tensor * vs;
// 2) image_newline: [n_dim] -> [1, 1, n_dim] -> repeat to [h, 1, n_dim]
t = ggml_concat(ctx0, t, nl, 1); // (h, w+1, n_dim)
cur = ggml_reshape_3d(ctx0, global_features, n_dim, w, h);
imgnl = ggml_repeat_4d(ctx0, model.image_newline, n_dim, 1, h, 1);
cur = ggml_reshape_2d(ctx0, ggml_concat(ctx0, cur, imgnl, 1), n_dim, (w+1)*h);
cb(cur, "insert_imgnl", -1);
vs = ggml_reshape_2d(ctx0, model.view_seperator, n_dim, 1); // (n_dim, 1)
cur = ggml_concat(ctx0, cur, vs, 1); // (n_dim, h*(w+1) + 1)
cb(cur, "insert_vs", -1);
t = ggml_reshape_2d(ctx0, t, n_dim, h* (h + 1)); // (n_dim, h*(w+1))
// 5) append view_separator as an extra "token":
// view_separator: [n_dim] -> [n_dim, 1]
ggml_tensor * vs = ggml_reshape_2d(ctx0, model.view_seperator, n_dim, 1); // (n_dim, 1)
// concat along token dimension (dim=1):
t = ggml_concat(ctx0, t, vs, 1); // (n_dim, h*(w+1) + 1)
return t;
return cur;
}
@ -1596,8 +1587,8 @@ struct clip_graph {
ggml_tensor * positions = ggml_cast(ctx0, ggml_arange(ctx0, 0, n_pos, 1), GGML_TYPE_I32);
ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, new_pos_embd, positions);
ggml_tensor * cur = build_vit(inp, n_pos, norm_t, hparams.ffn_op, learned_pos_embd,
nullptr); // shape [1024, 16, 16]
ggml_tensor * cur = build_vit(inp, n_pos, norm_t, ffn_op_type::FFN_GELU_QUICK,
learned_pos_embd, nullptr); // shape [1024, 16, 16]
ggml_build_forward_expand(gf, cur);
@ -2394,7 +2385,7 @@ private:
// pre-layernorm
if (model.pre_ln_w) {
inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
cb(inpL, "vit_pre_ln", -1);
cb(inpL, "pre_ln", -1);
}
// loop over layers
@ -5411,12 +5402,15 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
} break;
case PROJECTOR_TYPE_DEEPSEEKOCR:
{
int x_patch = img->nx / (params.patch_size);
n_patches += x_patch + 1;
n_patches = 1280;
// SAM encoder applies two stride-2 convolutions (net_2 and net_3)
// which reduces spatial dimensions by 4x in each direction (16x total)
// E.g., 64x64 -> 16x16 patches
n_patches /= 16;
// build_global_local_features adds image newlines and view separator
// Formula: h*(w+1) + 1 where h = w = sqrt(n_patches)
int h = static_cast<int>(std::sqrt(static_cast<float>(n_patches)));
n_patches = h * (h + 1) + 1;
} break;
default:
GGML_ABORT("unsupported projector type");
@ -5807,7 +5801,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
bool is_stored = false;
std::vector<std::string> patterns = {
/* Add tensor names here to dump (e.g. "sam_output") */
"vit_pre_ln"
};
for (auto & p : patterns) {