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llava/mtmd : fixes to fully support dl backends #13303

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Merged
merged 1 commit into from
May 4, 2025
Merged

llava/mtmd : fixes to fully support dl backends #13303

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2 changes: 1 addition & 1 deletion tools/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -27,11 +27,11 @@ else()
add_subdirectory(run)
add_subdirectory(tokenize)
add_subdirectory(tts)
add_subdirectory(llava)
if (NOT GGML_BACKEND_DL)
# these examples use the backends directly and cannot be built with dynamic loading
add_subdirectory(cvector-generator)
add_subdirectory(export-lora)
add_subdirectory(llava)
if (GGML_RPC)
add_subdirectory(rpc)
endif()
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10 changes: 9 additions & 1 deletion tools/llava/clip.cpp
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Expand Up @@ -3382,7 +3382,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
GGML_ABORT("Unknown projector type");
}

ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);
// ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);
ggml_backend_dev_t dev = ggml_backend_get_device(ctx->backend_cpu);
ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr;
if (reg) {
auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
if (ggml_backend_set_n_threads_fn) {
ggml_backend_set_n_threads_fn(ctx->backend_cpu, n_threads);
}
}

auto status = ggml_backend_sched_graph_compute(ctx->sched.get(), gf);
if (status != GGML_STATUS_SUCCESS) {
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6 changes: 5 additions & 1 deletion tools/llava/llava.cpp
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Expand Up @@ -2,6 +2,7 @@
#include "llava.h"

#include "llama.h"
#include "ggml-cpp.h"

#include <algorithm>
#include <cerrno>
Expand Down Expand Up @@ -209,7 +210,10 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side, size_ele * clip_n_mmproj_embd(ctx_clip), 0);
// ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
ggml_build_forward_expand(gf, flatten);
ggml_graph_compute_with_ctx(model.ctx, gf, 1);

ggml_backend_ptr backend { ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr) };
ggml_backend_graph_compute(backend.get(), gf);

struct ggml_tensor* result = ggml_graph_node(gf, -1);

memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
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