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ggml : hide ggml_object, ggml_cgraph, ggml_hash_set

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ggml-ci
This commit is contained in:
Georgi Gerganov
2024-09-10 16:38:06 +03:00
parent 00ba2ff781
commit c8a3f291fe
13 changed files with 131 additions and 92 deletions
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6
examples/benchmark/benchmark-matmult.cpp
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@@ -183,7 +183,7 @@ int main(int argc, char ** argv) {
ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads);
TENSOR_DUMP(gf->nodes[0]);
TENSOR_DUMP(ggml_graph_node(gf, 0));
printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype));
@@ -224,7 +224,7 @@ int main(int argc, char ** argv) {
// Let's use the F32 result from above as a reference for the quantized multiplication
float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]);
float sum_of_F32_reference = tensor_sum_elements(ggml_graph_node(gf, 0));
printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n");
printf("=====================================================================================\n");
@@ -252,7 +252,7 @@ int main(int argc, char ** argv) {
// Check that the matrix multiplication result is in the right ballpark
// We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different
float sum_of_Q4_result = tensor_sum_elements(gf31->nodes[0]);
float sum_of_Q4_result = tensor_sum_elements(ggml_graph_node(gf31, 0));
float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference);
float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6

4
examples/cvector-generator/pca.hpp
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@@ -226,8 +226,8 @@ static ggml_status compute_piter(
result.eigenvectors.resize(params.n_batch);
result.distances.resize(params.n_batch);
// get output nodes
for (int i = 0; i < gf->n_nodes; ++i) {
auto node = gf->nodes[i];
for (int i = 0; i < ggml_graph_n_nodes(gf); ++i) {
auto node = ggml_graph_node(gf, i);
int iter = -1;
// find b_tensor (without copying data from device)
if ((iter = extract_i("b_tensor_norm_", node->name)) > -1) {

2
examples/export-lora/export-lora.cpp
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@@ -370,7 +370,7 @@ struct lora_merge_ctx {
// write data to output file
{
auto result = gf->nodes[gf->n_nodes - 1];
auto * result = ggml_graph_node(gf, -1);
size_t len = ggml_nbytes(result);
if (read_buf.size() < len) {
read_buf.resize(len);

2
examples/llava/clip.cpp
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@@ -2449,7 +2449,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
ggml_backend_graph_compute(ctx->backend, gf);
// the last node is the embedding tensor
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1];
struct ggml_tensor * embeddings = ggml_graph_node(gf, -1);
// copy the embeddings to the location passed by the user
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));

2
examples/llava/llava.cpp
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@@ -184,7 +184,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
// ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
ggml_build_forward_expand(gf, flatten);
ggml_graph_compute_with_ctx(model.ctx, gf, 1);
struct ggml_tensor* result = gf->nodes[gf->n_nodes - 1];
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
// append without newline tokens (default behavior in llava_arch when not using unpad ):