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llava : Add Granite Vision Support (#11794)
* Add super wip scripts for multimodal granite gguf Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Add example for converting mmgranite to gguf Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * remove hardcoded path Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Add vision feature layer to gguf params Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Clean up llava surgery and remove name substitution hacks Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Add transformers llava next tensor name mapping Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Make siglip / openclip mutuall exclusive Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Fix projector linear substitution Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Fix linear 2 substitution index Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Increase max flattened gridpoints to 64 Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Fix hardcoded concat for multiple feature layers Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Pull vision feature layers out of gguf keys Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * fix num gridpoints and use all layers Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Avoid dropping last image encoder layer in llava models Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Use 10 for max number of patches Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Standardize vision feature layers Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Cleanup logs Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Update comment for vision feature layer init Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Update notes for alternative to legacy llm conversion script Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Fix notes rendering Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Add v prefix to vision feature layer log Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Use current defaults for feature layer Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Use constant for max gridpoints / feat layers, style fixes Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * clarify non-negative feature layers Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Remove CLIP_API from func signature Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * USE MAX_IMAGE_FEATURE_LAYERS const in layer calc Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Clarify feature layers are non negative ints and not uint Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Fix condition for reading feature layers Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * pop last llava layer when feature layers are unset Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Fix unset vision layer 0 Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Update examples/llava/clip.cpp Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com> * Reenable assertion for out of bounds get_rows Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Use std vector for gridpoints and feature layers Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Caculate max feature layer at load time Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Include base patch for granite vision allocation Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Fix trailing whitespace Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Add max num patches = 10 back for minicpmv Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Use unordered set to store feature layers Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com> Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Use max feature layer for postnorm Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> * Apply suggestions from code review --------- Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com> Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
This commit is contained in:
Alex Brooks
GitHub
@ -40,6 +40,7 @@
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#include <map>
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#include <regex>
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#include <stdexcept>
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#include <unordered_set>
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#include <vector>
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#include <sstream>
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#include <cinttypes>
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@ -120,6 +121,7 @@ static std::string format(const char * fmt, ...) {
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#define KEY_IMAGE_MEAN "clip.vision.image_mean"
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#define KEY_IMAGE_STD "clip.vision.image_std"
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#define KEY_PROJ_TYPE "clip.projector_type"
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#define KEY_FEATURE_LAYER "clip.vision.feature_layer"
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#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
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#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
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@ -444,8 +446,9 @@ struct clip_hparams {
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char mm_patch_merge_type[32] = "flat"; // spatial_unpad or flat (default)
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int32_t image_grid_pinpoints[32];
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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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};
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struct clip_layer {
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@ -585,6 +588,7 @@ struct clip_ctx {
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struct clip_vision_model vision_model;
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projector_type proj_type = PROJECTOR_TYPE_MLP;
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int32_t max_feature_layer;
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float image_mean[3];
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float image_std[3];
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bool use_gelu = false;
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@ -651,7 +655,6 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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const int hidden_size = hparams.hidden_size;
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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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int n_layer = hparams.n_layer;
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const float eps = hparams.eps;
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int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
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@ -752,13 +755,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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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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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 (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) {
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n_layer += 1;
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}
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for (int il = 0; il < n_layer - 1; il++) {
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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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// If this is an embedding feature layer, save the output.
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// NOTE: 0 index here refers to the input to the encoder.
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if (vision_feature_layer.find(il) != vision_feature_layer.end()) {
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embedding_stack.push_back(embeddings);
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}
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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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@ -846,7 +855,6 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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cur = ggml_add(ctx0, embeddings, cur);
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embeddings = cur;
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}
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// post-layernorm
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@ -857,6 +865,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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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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// 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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embedding_stack.push_back(embeddings);
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}
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// If feature layers are explicitly set, stack them (if we have multiple)
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if (!embedding_stack.empty()) {
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embeddings = embedding_stack[0];
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for (size_t i = 1; i < embedding_stack.size(); i++) {
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embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0);
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}
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}
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// llava projector
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if (ctx->has_llava_projector) {
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embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
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@ -1443,14 +1464,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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int idx = get_key_idx(ctx, KEY_IMAGE_GRID_PINPOINTS);
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int n = gguf_get_arr_n(ctx, idx);
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const int32_t * pinpoints = (const int32_t *)gguf_get_arr_data(ctx, idx);
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for (int i = 0; i < 32 && i < n && pinpoints[i] != 0; ++i) {
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hparams.image_grid_pinpoints[i] = pinpoints[i];
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for (int i = 0; i < n; ++i) {
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hparams.image_grid_pinpoints.push_back(pinpoints[i]);
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}
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if (n < 32)
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hparams.image_grid_pinpoints[n] = 0;
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} catch (std::runtime_error & /*e*/) {
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hparams.image_grid_pinpoints[0]=0;
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}
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} catch (std::runtime_error & /*e*/) { }
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// Load the vision feature layer indices if they are explicitly provided;
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// if multiple vision feature layers are present, the values will be concatenated
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// to form the final visual features.
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// NOTE: gguf conversions should standardize the values of the vision feature layer to
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// be non-negative, since we use -1 to mark values as unset here.
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try {
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int idx = get_key_idx(ctx, KEY_FEATURE_LAYER);
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int n = gguf_get_arr_n(ctx, idx);
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const int32_t * vision_feature_layer = (const int32_t *)gguf_get_arr_data(ctx, idx);
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for (int i = 0; i < n; ++i) {
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hparams.vision_feature_layer.insert(vision_feature_layer[i]);
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}
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} catch (std::runtime_error & /*e*/) { }
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try {
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int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
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@ -1476,6 +1509,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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new_clip->image_std[i] = std_data[i];
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}
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// Calculate the deepest feature layer based on hparams and projector type
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new_clip->max_feature_layer = get_deepest_feature_layer(new_clip);
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if (verbosity >= 2) {
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LOG_INF("\n%s: vision model hparams\n", __func__);
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LOG_INF("image_size %d\n", hparams.image_size);
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@ -1489,8 +1525,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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LOG_INF("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
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LOG_INF("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
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LOG_INF("v_image_grid_pinpoints: ");
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for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
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LOG_INF("%d ", hparams.image_grid_pinpoints[i]);
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for (const auto & pp : hparams.image_grid_pinpoints) {
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LOG_INF("%d ", pp);
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}
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LOG_INF("\n");
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LOG_INF("v_vision_feature_layer: ");
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for (const auto & feature_layer: hparams.vision_feature_layer) {
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LOG_INF("%d ", feature_layer);
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}
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LOG_INF("\n");
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LOG_INF("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
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@ -2235,10 +2276,10 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
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}
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}
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} else {
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if (params.image_grid_pinpoints[0] != 0) {
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if (!params.image_grid_pinpoints.empty()) {
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// "spatial_unpad" with "anyres" processing for llava-1.6
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std::vector<std::pair<int, int>> possible_resolutions;
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for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
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for (size_t i = 0; i < params.image_grid_pinpoints.size(); i+=2) {
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possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
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}
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std::pair<int, int> best_resolution = select_best_resolution({img->nx, img->ny}, possible_resolutions);
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@ -2404,7 +2445,14 @@ const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
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}
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const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
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return ctx->vision_model.hparams.image_grid_pinpoints;
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if (ctx->vision_model.hparams.image_grid_pinpoints.size()) {
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return &ctx->vision_model.hparams.image_grid_pinpoints.front();
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}
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return nullptr;
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}
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size_t get_clip_image_grid_size(const struct clip_ctx * ctx) {
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return ctx->vision_model.hparams.image_grid_pinpoints.size();
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}
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int clip_n_patches(const struct clip_ctx * ctx) {
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@ -2929,6 +2977,28 @@ bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
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return ctx->has_qwen2vl_merger;
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}
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// Determine the number of encoder layers to iterate over
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int get_deepest_feature_layer(const struct clip_ctx * ctx) {
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// Get the index of the second to last layer; this is the
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// default for models that have a llava projector
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const auto & hparams = ctx->vision_model.hparams;
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int n_layer = hparams.n_layer - 1;
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int deepest_feature_layer = -1;
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// Handle other projectors; incrementing here indicates that we
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// should use the last encoder layer for the vision features.
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if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) {
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n_layer += 1;
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}
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// If we set explicit vision feature layers, only go up to the deepest one
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for (const auto & feature_layer : hparams.vision_feature_layer) {
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if (feature_layer > deepest_feature_layer) {
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deepest_feature_layer = feature_layer;
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}
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}
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return deepest_feature_layer < 0 ? n_layer : deepest_feature_layer;
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}
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bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) {
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clip_image_f32 clip_img;
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