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https://github.com/LostRuins/koboldcpp.git
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remove KEY_USE_GLU_MLP, KEY_USE_RMS_NORM
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HimariO
parent
caa7e57ec5
commit
f69e9fa04d
2 changed files with 29 additions and 133 deletions
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@ -330,8 +330,6 @@ 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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gguf_context_ptr ctx_gguf;
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@ -847,7 +845,6 @@ static ggml_cgraph * clip_image_build_graph_qwen2_5_vl(clip_ctx * ctx, const cli
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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 * window_mask = nullptr;
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struct ggml_tensor * window_idx = nullptr;
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struct ggml_tensor * inv_window_idx = nullptr;
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@ -858,17 +855,10 @@ static ggml_cgraph * clip_image_build_graph_qwen2_5_vl(clip_ctx * ctx, const cli
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// pre-layernorm
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if (model.pre_ln_w) {
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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, "pre_ln");
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embeddings = ggml_mul(ctx0, embeddings, model.pre_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, "pre_ln");
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embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
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}
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}
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std::vector<struct ggml_tensor *> embedding_stack;
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@ -991,17 +981,10 @@ static ggml_cgraph * clip_image_build_graph_qwen2_5_vl(clip_ctx * ctx, const cli
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// post-layernorm
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if (model.post_ln_w) {
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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_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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@ -1086,7 +1069,6 @@ 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.entries.size();
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@ -1118,7 +1100,6 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
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inp = ggml_add(ctx0, inp, inp_1);
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inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b]
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inp = ggml_reshape_4d(
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ctx0, inp,
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@ -1142,9 +1123,6 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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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 * window_mask = nullptr;
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struct ggml_tensor * window_idx = nullptr;
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struct ggml_tensor * inv_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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@ -1186,40 +1164,16 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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// pre-layernorm
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if (model.pre_ln_w) {
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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, "pre_ln");
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embeddings = ggml_mul(ctx0, embeddings, model.pre_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, "pre_ln");
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embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
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}
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}
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std::vector<struct ggml_tensor *> embedding_stack;
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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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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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// 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, inv_window_idx);
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embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size);
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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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@ -1232,12 +1186,9 @@ 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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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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{
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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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@ -1277,14 +1228,7 @@ 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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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 / sqrtf((float)d_head), 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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@ -1301,36 +1245,12 @@ 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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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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{
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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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// 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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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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@ -1344,7 +1264,6 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
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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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@ -1354,18 +1273,11 @@ 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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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_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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if (vision_feature_layer.find(ctx->max_feature_layer) != vision_feature_layer.end()) {
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@ -1678,18 +1590,6 @@ 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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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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// 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, 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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@ -1810,8 +1710,6 @@ 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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get_u32(string_format(KEY_N_EMBD, "vision"), hparams.hidden_size);
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get_u32(string_format(KEY_N_HEAD, "vision"), hparams.n_head);
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@ -152,8 +152,6 @@ def main(args):
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raise ValueError()
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if args.model_type == "qwen2.5vl":
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fout.add_bool("clip.use_glu_mlp", True) # gate linear unit MLP layer in vision model
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fout.add_bool("clip.use_rms_norm", True)
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fout.add_array("clip.vision.fullatt_block_indexes", vcfg.fullatt_block_indexes)
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fout.add_uint32("clip.vision.window_size", vcfg.window_size)
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fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.hidden_size)
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