diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 9c0d351e..019a147c 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -718,10 +718,12 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - embeddings = ggml_gelu(ctx0, embeddings); - embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); - + if (model.mm_2_w) + { + embeddings = ggml_gelu(ctx0, embeddings); + embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + } } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); @@ -2102,6 +2104,10 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->vision_model.mm_model_peg_0_b->ne[0]; } if (ctx->proj_type == PROJECTOR_TYPE_MLP) { + if (ctx->vision_model.mm_2_b == nullptr) + { + return ctx->vision_model.mm_0_b->ne[0]; + } return ctx->vision_model.mm_2_b->ne[0]; } if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { diff --git a/include/llama.h b/include/llama.h index 6072e76e..4c572a74 100644 --- a/include/llama.h +++ b/include/llama.h @@ -444,6 +444,12 @@ extern "C" { // Frees all allocated memory LLAMA_API void llama_free(struct llama_context * ctx); + // Sets image embeddings + LLAMA_API void set_image_embeds(struct llama_context *ctx, float *data); + + // Get architecture + LLAMA_API int llama_get_architecture(struct llama_model *model); + LLAMA_API int64_t llama_time_us(void); LLAMA_API size_t llama_max_devices(void); diff --git a/src/llama.cpp b/src/llama.cpp index d883ed19..322b4b59 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -2710,6 +2710,8 @@ struct llama_context { bool logits_all = false; + float *image_embeds = nullptr; + // embeddings output (2-dimensional array: [n_outputs][n_embd]) // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE size_t embd_size = 0; // capacity (of floats) for embeddings @@ -11591,6 +11593,15 @@ struct llm_build_context { inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + if (lctx.image_embeds) + { + struct ggml_tensor *image_embeds = ggml_dup_tensor(ctx0, inpL); + image_embeds->data = lctx.image_embeds; + image_embeds->ne[1] = 256; + inpL = ggml_set_2d_inplace(ctx0, inpL, image_embeds, inpL->nb[1], 0); + lctx.image_embeds = NULL; + } + inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); @@ -14468,6 +14479,7 @@ static int llama_decode_internal( const int64_t n_embd = hparams.n_embd; const int64_t n_vocab = hparams.n_vocab; + const bool has_image_embeds = lctx.image_embeds; uint32_t n_outputs = 0; uint32_t n_outputs_prev = 0; @@ -14581,7 +14593,8 @@ static int llama_decode_internal( } // non-causal masks do not use the KV cache - if (hparams.causal_attn) { + if (hparams.causal_attn || lctx.image_embeds) + { llama_kv_cache_update(&lctx); // if we have enough unused cells before the current head -> @@ -16455,6 +16468,16 @@ void llama_free_model(struct llama_model * model) { delete model; } +void set_image_embeds(llama_context *ctx, float *data) +{ + ctx->image_embeds = data; +} + +int llama_get_architecture(llama_model *model) +{ + return model->arch; +} + struct llama_context * llama_new_context_with_model( struct llama_model * model, struct llama_context_params params) {