mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
clip : refactor + bug fixes (#4696)
* clip : refactor + bug fixes ggml-ci * server : add log message
This commit is contained in:
Georgi Gerganov
GitHub
parent
39d8bc71ed
commit
9fbda719de
@ -146,6 +146,27 @@ static std::string get_ftype(int ftype) {
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}
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}
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//
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// image data
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//
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// RGB uint8 image
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struct clip_image_u8 {
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int nx;
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int ny;
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std::vector<uint8_t> buf;
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};
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// RGB float32 image (NHWC)
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// Memory layout: RGBRGBRGB...
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struct clip_image_f32 {
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int nx;
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int ny;
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std::vector<float> buf;
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};
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//
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// clip layers
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//
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@ -207,13 +228,18 @@ struct clip_ctx {
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bool has_text_encoder = false;
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bool has_vision_encoder = false;
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bool has_llava_projector = false;
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struct clip_vision_model vision_model;
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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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int32_t ftype = 1;
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struct ggml_context * ctx;
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struct gguf_context * ctx_gguf;
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struct ggml_context * ctx_data;
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std::vector<uint8_t> buf_compute_meta;
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// memory buffers to evaluate the model
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ggml_backend_buffer_t params_buffer = NULL;
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@ -222,7 +248,7 @@ struct clip_ctx {
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ggml_allocr * compute_alloc = NULL;
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};
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static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_image_f32_batch * imgs) {
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static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
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if (!ctx->has_vision_encoder) {
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printf("This gguf file seems to have no vision encoder\n");
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return nullptr;
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@ -243,12 +269,13 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
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//const int projection_dim = hparams.projection_dim;
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const float eps = hparams.eps;
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int batch_size = imgs->size;
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if(ctx->has_llava_projector) {
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if (ctx->has_llava_projector) {
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GGML_ASSERT(batch_size == 1);
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}
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struct ggml_init_params params = {
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/*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead(),
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/*.mem_buffer =*/ NULL,
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/*.mem_size =*/ ctx->buf_compute_meta.size(),
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/*.mem_buffer =*/ ctx->buf_compute_meta.data(),
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/*.no_alloc =*/ true,
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};
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@ -272,7 +299,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
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for (int k = 0; k < 3; k++) {
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for (int y = 0; y < ny; y++) {
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for (int x = 0; x < nx; x++) {
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data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].data[3 * (y * nx + x) + k];
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data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
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}
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}
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}
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@ -413,7 +440,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima
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ggml_allocr_alloc(ctx->compute_alloc, patches);
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if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
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int* patches_data = (int*)malloc(ggml_nbytes(patches));
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for (int i = 0; i < num_positions; i++) {
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for (int i = 0; i < num_patches; i++) {
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patches_data[i] = i + 1;
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}
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ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
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@ -561,8 +588,8 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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/*.no_alloc =*/ true,
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};
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new_clip->ctx = ggml_init(params);
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if (!new_clip->ctx) {
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new_clip->ctx_data = ggml_init(params);
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if (!new_clip->ctx_data) {
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fprintf(stderr, "%s: ggml_init() failed\n", __func__);
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clip_free(new_clip);
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return nullptr;
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@ -579,7 +606,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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for (int i = 0; i < n_tensors; ++i) {
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const char * name = gguf_get_tensor_name(ctx, i);
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struct ggml_tensor * t = ggml_get_tensor(meta, name);
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struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx, t);
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struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx_data, t);
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ggml_set_name(cur, name);
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}
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@ -588,7 +615,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer);
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for (int i = 0; i < n_tensors; ++i) {
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const char * name = gguf_get_tensor_name(ctx, i);
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struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx, name);
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struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name);
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ggml_allocr_alloc(alloc, cur);
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const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
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fin.seekg(offset, std::ios::beg);
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@ -644,35 +671,35 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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printf("v_n_layer %d\n", hparams.n_layer);
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}
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vision_model.patch_embeddings = get_tensor(new_clip->ctx, TN_PATCH_EMBD);
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vision_model.class_embedding = get_tensor(new_clip->ctx, TN_CLASS_EMBD);
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vision_model.position_embeddings = get_tensor(new_clip->ctx, format(TN_POS_EMBD, "v"));
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vision_model.pre_ln_w = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "weight"));
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vision_model.pre_ln_b = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "bias"));
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vision_model.mm_0_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "weight"));
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vision_model.mm_0_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "bias"));
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vision_model.mm_2_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "weight"));
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vision_model.mm_2_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "bias"));
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vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
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vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
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vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
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vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
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vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
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vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
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vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
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vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
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vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
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vision_model.layers.resize(hparams.n_layer);
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for (int il = 0; il < hparams.n_layer; ++il) {
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auto & layer = vision_model.layers[il];
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layer.k_w = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "weight"));
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layer.q_w = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "weight"));
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layer.v_w = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "weight"));
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layer.o_w = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "weight"));
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layer.ln_1_w = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "weight"));
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layer.ln_2_w = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "weight"));
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layer.ff_i_w = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "weight"));
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layer.ff_o_w = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "weight"));
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layer.k_b = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "bias"));
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layer.q_b = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "bias"));
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layer.v_b = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "bias"));
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layer.o_b = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "bias"));
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layer.ln_1_b = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "bias"));
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layer.ln_2_b = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "bias"));
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layer.ff_i_b = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "bias"));
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layer.ff_o_b = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "bias"));
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layer.k_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "weight"));
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layer.q_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "weight"));
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layer.v_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "weight"));
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layer.o_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "weight"));
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layer.ln_1_w = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "weight"));
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layer.ln_2_w = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "weight"));
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layer.ff_i_w = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "weight"));
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layer.ff_o_w = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "weight"));
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layer.k_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "bias"));
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layer.q_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "bias"));
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layer.v_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "bias"));
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layer.o_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "bias"));
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layer.ln_1_b = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "bias"));
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layer.ln_2_b = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "bias"));
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layer.ff_i_b = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "bias"));
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layer.ff_o_b = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "bias"));
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}
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}
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@ -680,8 +707,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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new_clip->ctx_gguf = ctx;
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// measure mem requirement and allocate
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// measure mem requirement and allocate
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{
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new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
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new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend);
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clip_image_f32_batch batch;
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batch.size = 1;
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@ -697,26 +725,27 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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return new_clip;
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}
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clip_image_u8 * make_clip_image_u8() {
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auto img = new clip_image_u8();
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return img;
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struct clip_image_u8 * clip_image_u8_init() {
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return new clip_image_u8();
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}
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clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); }
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void clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; }
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void clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; }
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struct clip_image_f32 * clip_image_f32_init() {
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return new clip_image_f32();
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}
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void clip_image_u8_free (struct clip_image_u8 * img) { delete img; }
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void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
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static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) {
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img->nx = nx;
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img->ny = ny;
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img->size = nx * ny * 3;
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img->data = new uint8_t[img->size]();
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memcpy(img->data, data, img->size);
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img->buf.resize(3 * nx * ny);
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memcpy(img->buf.data(), data, img->buf.size());
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}
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bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
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int nx, ny, nc;
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auto data = stbi_load(fname, &nx, &ny, &nc, 3);
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auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
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if (!data) {
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fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
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return false;
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@ -728,7 +757,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
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bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) {
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int nx, ny, nc;
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auto data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
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auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
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if (!data) {
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fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
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return false;
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@ -740,7 +769,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
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// normalize: x = (x - mean) / std
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// TODO: implement bicubic interpolation instead of linear.
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bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
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bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
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if (!ctx->has_vision_encoder) {
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printf("This gguf file seems to have no vision encoder\n");
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return false;
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@ -749,18 +778,17 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
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// the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
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// see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
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clip_image_u8 * temp = make_clip_image_u8(); // we will keep the input image data here temporarily
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clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily
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if (pad2square && img->nx != img->ny) {
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int longer_side = std::max(img->nx, img->ny);
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temp->nx = longer_side;
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temp->ny = longer_side;
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temp->size = 3 * longer_side * longer_side;
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temp->data = new uint8_t[temp->size]();
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uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
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temp->buf.resize(3 * longer_side * longer_side);
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const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
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// fill with background color
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for (size_t i = 0; i < temp->size; i++) {
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temp->data[i] = bc[i % 3];
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for (size_t i = 0; i < temp->buf.size(); i++) {
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temp->buf[i] = bc[i % 3];
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}
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// copy from the input image
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@ -768,17 +796,16 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
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for (int x = 0; x < img->nx; x++) {
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const int i = 3 * (y * img->nx + x);
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const int j = 3 * (y * temp->nx + x);
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temp->data[j] = img->data[i];
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temp->data[j+1] = img->data[i+1];
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temp->data[j+2] = img->data[i+2];
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temp->buf[j] = img->buf[i];
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temp->buf[j+1] = img->buf[i+1];
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temp->buf[j+2] = img->buf[i+2];
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}
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}
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} else {
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temp->nx = img->nx;
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temp->ny = img->ny;
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temp->size = img->size;
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temp->data = new uint8_t[temp->size]();
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memcpy(&temp->data[0], &img->data[0], temp->size); // copy
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temp->buf.resize(img->buf.size());
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memcpy(temp->buf.data(), img->buf.data(), temp->buf.size());
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}
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const int nx = temp->nx;
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@ -789,8 +816,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
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res->nx = nx2;
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res->ny = ny2;
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res->size = 3 * nx2 * ny2;
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res->data = new float[res->size]();
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res->buf.resize(3 * nx2 * ny2);
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const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size;
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@ -821,10 +847,10 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
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const int j10 = 3 * (y1 * nx + x0) + c;
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const int j11 = 3 * (y1 * nx + x1) + c;
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const float v00 = temp->data[j00];
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const float v01 = temp->data[j01];
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const float v10 = temp->data[j10];
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const float v11 = temp->data[j11];
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const float v00 = temp->buf[j00];
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const float v01 = temp->buf[j01];
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const float v10 = temp->buf[j10];
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const float v11 = temp->buf[j11];
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const float v0 = v00 * (1.0f - dx) + v01 * dx;
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const float v1 = v10 * (1.0f - dx) + v11 * dx;
|
||||
@ -835,7 +861,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
|
||||
|
||||
const int i = 3 * (y * nx3 + x) + c;
|
||||
|
||||
res->data[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
|
||||
res->buf[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -845,12 +871,13 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
|
||||
}
|
||||
|
||||
void clip_free(clip_ctx * ctx) {
|
||||
ggml_free(ctx->ctx);
|
||||
ggml_free(ctx->ctx_data);
|
||||
gguf_free(ctx->ctx_gguf);
|
||||
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
|
||||
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
return false;
|
||||
@ -862,8 +889,7 @@ bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32
|
||||
return clip_image_batch_encode(ctx, n_threads, &imgs, vec);
|
||||
}
|
||||
|
||||
bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
|
||||
|
||||
bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
return false;
|
||||
@ -926,11 +952,12 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
||||
return false;
|
||||
};
|
||||
|
||||
auto ctx_clip = clip_model_load(fname_inp, 2);
|
||||
const auto & ctx_src = ctx_clip->ctx_gguf;
|
||||
const auto & ctx_data = ctx_clip->ctx;
|
||||
auto * ctx_clip = clip_model_load(fname_inp, 2);
|
||||
|
||||
auto ctx_out = gguf_init_empty();
|
||||
const auto & ctx_src = ctx_clip->ctx_gguf;
|
||||
const auto & ctx_data = ctx_clip->ctx_data;
|
||||
|
||||
auto * ctx_out = gguf_init_empty();
|
||||
gguf_set_kv(ctx_out, ctx_src);
|
||||
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
|
||||
gguf_set_val_u32(ctx_out, "general.file_type", itype);
|
||||
|
||||
@ -35,31 +35,14 @@ struct clip_vision_hparams {
|
||||
float eps;
|
||||
};
|
||||
|
||||
/** load mmproj model */
|
||||
CLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity);
|
||||
/** free mmproj model */
|
||||
CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
|
||||
|
||||
CLIP_API void clip_free(struct clip_ctx * ctx);
|
||||
|
||||
size_t clip_embd_nbytes(const struct clip_ctx * ctx);
|
||||
int clip_n_patches(const struct clip_ctx * ctx);
|
||||
int clip_n_mmproj_embd(const struct clip_ctx * ctx);
|
||||
CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
|
||||
|
||||
// RGB uint8 image
|
||||
struct clip_image_u8 {
|
||||
int nx;
|
||||
int ny;
|
||||
uint8_t * data = NULL;
|
||||
size_t size;
|
||||
};
|
||||
|
||||
// RGB float32 image (NHWC)
|
||||
// Memory layout: RGBRGBRGB...
|
||||
struct clip_image_f32 {
|
||||
int nx;
|
||||
int ny;
|
||||
float * data = NULL;
|
||||
size_t size;
|
||||
};
|
||||
CLIP_API int clip_n_patches (const struct clip_ctx * ctx);
|
||||
CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
|
||||
|
||||
struct clip_image_u8_batch {
|
||||
struct clip_image_u8 * data;
|
||||
@ -71,21 +54,22 @@ struct clip_image_f32_batch {
|
||||
size_t size;
|
||||
};
|
||||
|
||||
struct clip_image_u8 * make_clip_image_u8();
|
||||
struct clip_image_f32 * make_clip_image_f32();
|
||||
CLIP_API void clip_image_u8_free(clip_image_u8 * img);
|
||||
CLIP_API void clip_image_f32_free(clip_image_f32 * img);
|
||||
CLIP_API struct clip_image_u8 * clip_image_u8_init ();
|
||||
CLIP_API struct clip_image_f32 * clip_image_f32_init();
|
||||
|
||||
CLIP_API void clip_image_u8_free (struct clip_image_u8 * img);
|
||||
CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
|
||||
|
||||
CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
|
||||
|
||||
/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
|
||||
CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
|
||||
|
||||
bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square);
|
||||
bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec);
|
||||
CLIP_API bool clip_image_preprocess (struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, bool pad2square);
|
||||
CLIP_API bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
|
||||
CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
|
||||
|
||||
bool clip_image_batch_encode(const struct clip_ctx * ctx, const int n_threads, const struct clip_image_f32_batch * imgs,
|
||||
float * vec);
|
||||
|
||||
bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype);
|
||||
CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
@ -10,7 +10,7 @@
|
||||
#include "base64.hpp"
|
||||
|
||||
static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
|
||||
clip_image_f32 * img_res = make_clip_image_f32();
|
||||
clip_image_f32 * img_res = clip_image_f32_init();
|
||||
if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) {
|
||||
fprintf(stderr, "%s: unable to preprocess image\n", __func__);
|
||||
clip_image_f32_free(img_res);
|
||||
@ -86,7 +86,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
|
||||
}
|
||||
|
||||
LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
|
||||
clip_image_u8 * img = make_clip_image_u8();
|
||||
clip_image_u8 * img = clip_image_u8_init();
|
||||
if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
|
||||
clip_image_u8_free(img);
|
||||
fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
|
||||
|
||||
@ -82,7 +82,7 @@ static inline bool is_base64(uint8_t c)
|
||||
return (isalnum(c) || (c == '+') || (c == '/'));
|
||||
}
|
||||
|
||||
static std::vector<uint8_t> base64_decode(std::string const &encoded_string)
|
||||
static std::vector<uint8_t> base64_decode(const std::string & encoded_string)
|
||||
{
|
||||
int i = 0;
|
||||
int j = 0;
|
||||
@ -209,10 +209,10 @@ struct slot_image
|
||||
int32_t id;
|
||||
|
||||
bool request_encode_image = false;
|
||||
float* image_embedding = nullptr;
|
||||
float * image_embedding = nullptr;
|
||||
int32_t image_tokens = 0;
|
||||
|
||||
clip_image_u8 img_data;
|
||||
clip_image_u8 * img_data;
|
||||
|
||||
std::string prefix_prompt; // before of this image
|
||||
};
|
||||
@ -434,10 +434,12 @@ struct llama_client_slot
|
||||
|
||||
generated_token_probs.clear();
|
||||
|
||||
for (slot_image &img : images)
|
||||
for (slot_image & img : images)
|
||||
{
|
||||
free(img.image_embedding);
|
||||
delete[] img.img_data.data;
|
||||
if (img.img_data) {
|
||||
clip_image_u8_free(img.img_data);
|
||||
}
|
||||
img.prefix_prompt = "";
|
||||
}
|
||||
|
||||
@ -851,24 +853,17 @@ struct llama_server_context
|
||||
{
|
||||
for (const auto &img : *images_data)
|
||||
{
|
||||
std::string data_b64 = img["data"].get<std::string>();
|
||||
const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
|
||||
|
||||
slot_image img_sl;
|
||||
img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
|
||||
int width, height, channels;
|
||||
std::vector<uint8_t> image_buffer = base64_decode(data_b64);
|
||||
data_b64.clear();
|
||||
auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3);
|
||||
if (!data) {
|
||||
img_sl.img_data = clip_image_u8_init();
|
||||
if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
|
||||
{
|
||||
LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
|
||||
return false;
|
||||
}
|
||||
LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height);
|
||||
img_sl.img_data.nx = width;
|
||||
img_sl.img_data.ny = height;
|
||||
img_sl.img_data.size = width * height * 3;
|
||||
img_sl.img_data.data = new uint8_t[width * height * 3]();
|
||||
memcpy(img_sl.img_data.data, data, width * height * 3);
|
||||
stbi_image_free(data);
|
||||
LOG_TEE("slot %i - loaded image\n", slot->id);
|
||||
img_sl.request_encode_image = true;
|
||||
slot->images.push_back(img_sl);
|
||||
}
|
||||
@ -1143,8 +1138,8 @@ struct llama_server_context
|
||||
{
|
||||
continue;
|
||||
}
|
||||
clip_image_f32 img_res;
|
||||
if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true))
|
||||
clip_image_f32 * img_res = clip_image_f32_init();
|
||||
if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
|
||||
{
|
||||
LOG_TEE("Error processing the given image");
|
||||
clip_free(clp_ctx);
|
||||
@ -1159,11 +1154,12 @@ struct llama_server_context
|
||||
return false;
|
||||
}
|
||||
LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
|
||||
if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding))
|
||||
if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
|
||||
{
|
||||
LOG_TEE("Unable to encode image\n");
|
||||
return false;
|
||||
}
|
||||
clip_image_f32_free(img_res);
|
||||
img.request_encode_image = false;
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user