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implment vision model architecture, gguf convertor
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HimariO
parent
e391d3ee8d
commit
9c7cc6de9c
3 changed files with 220 additions and 85 deletions
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@ -183,6 +183,7 @@ struct clip_hparams {
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std::vector<int32_t> image_grid_pinpoints;
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int32_t image_crop_resolution;
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std::unordered_set<int32_t> vision_feature_layer;
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std::vector<int32_t> full_attn_layers;
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};
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struct clip_layer {
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@ -208,6 +209,9 @@ struct clip_layer {
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struct ggml_tensor * ff_o_w = nullptr;
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struct ggml_tensor * ff_o_b = nullptr;
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struct ggml_tensor * ff_g_w = NULL;
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struct ggml_tensor * ff_g_b = NULL;
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// layernorm 2
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struct ggml_tensor * ln_2_w = nullptr;
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struct ggml_tensor * ln_2_b = nullptr;
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@ -331,6 +335,8 @@ struct clip_ctx {
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float image_std[3];
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bool use_gelu = false;
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bool use_silu = false;
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bool use_glu_mlp = false;
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bool use_rms_norm = false;
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int32_t ftype = 1;
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struct gguf_context * ctx_gguf = nullptr;
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@ -576,6 +582,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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const int n_head = hparams.n_head;
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const int d_head = hidden_size / n_head;
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const float eps = hparams.eps;
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const bool use_window_attn = hparams.full_attn_layers.size() > 0;
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int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
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const int batch_size = imgs->size;
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@ -626,8 +633,10 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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// inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
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inp = ggml_add(ctx0, inp, model.patch_bias);
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}
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struct ggml_tensor * embeddings = inp;
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struct ggml_tensor * pos_embed = nullptr;
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struct ggml_tensor * embeddings = inp;
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struct ggml_tensor * pos_embed = nullptr;
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struct ggml_tensor * window_mask = nullptr;
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struct ggml_tensor * window_idx = nullptr;
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if (ctx->has_llava_projector) {
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// concat class_embeddings and patch_embeddings
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@ -679,6 +688,28 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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const auto & vision_feature_layer = hparams.vision_feature_layer;
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// loop over layers
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if (use_window_attn) {
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window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
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ggml_set_name(window_idx, "window_idx");
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ggml_set_input(window_idx);
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// mask for window attention
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window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, num_positions, num_positions);
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ggml_set_name(window_mask, "window_mask");
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ggml_set_input(window_mask);
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// embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
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GGML_ASSERT(batch_size == 1);
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embeddings = ggml_reshape_2d(ctx0, embeddings, hidden_size * 4, patches_w * patches_h * batch_size / 4);
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embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
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embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size);
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positions = ggml_reshape_2d(ctx0, positions, 16, num_position_ids / 4 / 4);
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positions = ggml_get_rows(ctx0, positions, window_idx);
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positions = ggml_reshape_1d(ctx0, positions, num_position_ids);
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}
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for (int il = 0; il < ctx->max_feature_layer; il++) {
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struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
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@ -691,9 +722,12 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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//const size_t nb_q_w = model.layers[il].q_w->nb[0];
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// layernorm1
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{
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if (ctx->use_rms_norm) {
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cur = ggml_rms_norm(ctx0, cur, eps);
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cur = ggml_mul(ctx0, cur, model.layers[il].ln_1_w);
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}
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else {
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cur = ggml_norm(ctx0, cur, eps);
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cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w),
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model.layers[il].ln_1_b);
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}
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@ -733,7 +767,14 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
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const bool inlist = std::find(hparams.full_attn_layers.begin(), hparams.full_attn_layers.end(), il) != hparams.full_attn_layers.end();
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const bool full_attn = use_window_attn ? inlist : true;
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if (full_attn) {
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KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
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} else {
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KQ = ggml_soft_max_ext(ctx0, KQ, window_mask, 1.0f, 0.0f);
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}
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
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KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
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KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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@ -750,25 +791,50 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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embeddings = cur; // embeddings = residual, cur = hidden_states
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// layernorm2
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{
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if (ctx->use_rms_norm) {
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cur = ggml_rms_norm(ctx0, cur, eps);
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cur = ggml_mul(ctx0, cur, model.layers[il].ln_2_w);
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} else {
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cur = ggml_norm(ctx0, cur, eps);
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cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
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}
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
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cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
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// mlp
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if (ctx->use_glu_mlp) {
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// ffn_up
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auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
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cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_o_b);
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if (ctx->use_gelu) {
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cur = ggml_gelu_inplace(ctx0, cur);
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} else if (ctx->use_silu) {
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cur = ggml_silu_inplace(ctx0, cur);
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} else {
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cur = ggml_gelu_quick_inplace(ctx0, cur);
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auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_g_w, cur);
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cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_g_b);
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if (ctx->use_gelu) {
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cur_gate = ggml_gelu_inplace(ctx0, cur_gate);
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} else if (ctx->use_silu) {
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cur_gate = ggml_silu_inplace(ctx0, cur_gate);
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} else {
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cur_gate = ggml_gelu_quick_inplace(ctx0, cur_gate);
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}
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cur = ggml_mul(ctx0, cur_gate, cur_up);
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// ffn_down
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
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cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
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}
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else {
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
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cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
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cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
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if (ctx->use_gelu) {
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cur = ggml_gelu_inplace(ctx0, cur);
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} else if (ctx->use_silu) {
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cur = ggml_silu_inplace(ctx0, cur);
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} else {
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cur = ggml_gelu_quick_inplace(ctx0, cur);
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}
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cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
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cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
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}
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// residual 2
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cur = ggml_add(ctx0, embeddings, cur);
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@ -778,10 +844,17 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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// post-layernorm
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if (model.post_ln_w) {
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embeddings = ggml_norm(ctx0, embeddings, eps);
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ggml_set_name(embeddings, "post_ln");
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if (ctx->use_rms_norm) {
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embeddings = ggml_rms_norm(ctx0, embeddings, eps);
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ggml_set_name(embeddings, "post_ln");
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embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
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embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w);
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} else {
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embeddings = ggml_norm(ctx0, embeddings, eps);
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ggml_set_name(embeddings, "post_ln");
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embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
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}
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}
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// final layer is a vision feature layer
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@ -1095,6 +1168,18 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
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}
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if (use_window_attn) {
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struct ggml_tensor * inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
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ggml_set_name(inv_window_idx, "inv_window_idx");
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ggml_set_input(inv_window_idx);
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// embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
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GGML_ASSERT(batch_size == 1);
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embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4);
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embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx);
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embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4, batch_size);
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}
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// build the graph
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ggml_build_forward_expand(gf, embeddings);
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@ -1203,6 +1288,8 @@ struct clip_model_loader {
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get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false);
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get_bool(KEY_USE_SILU, ctx_clip.use_silu, false);
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get_bool(KEY_USE_GLU_MLP, ctx_clip.use_glu_mlp, false);
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get_bool(KEY_USE_RMS_NORM, ctx_clip.use_rms_norm, false);
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auto & hparams = ctx_clip.vision_model.hparams;
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get_u32(string_format(KEY_N_EMBD, "vision"), hparams.hidden_size);
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@ -1215,6 +1302,7 @@ struct clip_model_loader {
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get_u32(KEY_PATCH_SIZE, hparams.patch_size);
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get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
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get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false);
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get_arr_int(KEY_FULLATTN_BLK_IDX, hparams.full_attn_layers, false);
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{
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std::string mm_patch_merge_type;
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@ -1330,14 +1418,16 @@ struct clip_model_loader {
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layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false);
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layer.ff_i_w = get_tensor(string_format(TN_FFN_DOWN, "v", il, "weight"));
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layer.ff_o_w = get_tensor(string_format(TN_FFN_UP, "v", il, "weight"));
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layer.ff_g_w = get_tensor(string_format(TN_FFN_GATE, "v", il, "weight"), ctx_clip.use_glu_mlp);
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layer.k_b = get_tensor(string_format(TN_ATTN_K, "v", il, "bias"), false);
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layer.q_b = get_tensor(string_format(TN_ATTN_Q, "v", il, "bias"), false);
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layer.v_b = get_tensor(string_format(TN_ATTN_V, "v", il, "bias"), false);
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layer.o_b = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "bias"), false);
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layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), false);
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layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), false);
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layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), !ctx_clip.use_rms_norm);
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layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), !ctx_clip.use_rms_norm);
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layer.ff_i_b = get_tensor(string_format(TN_FFN_DOWN, "v", il, "bias"), false);
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layer.ff_o_b = get_tensor(string_format(TN_FFN_UP, "v", il, "bias"), false);
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layer.ff_g_b = get_tensor(string_format(TN_FFN_GATE, "v", il, "bias"), ctx_clip.use_glu_mlp);
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}
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switch (ctx_clip.proj_type) {
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