vbatts/llama.cpp
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ggml : hide ggml_object, ggml_cgraph, ggml_hash_set (#9408)

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* ggml : hide ggml_object, ggml_cgraph, ggml_hash_set

ggml-ci

* ggml : add ggml-impl.h to backends

* ggml : fix compiler warnings

ggml-ci

* ggml : add assert upon adding nodes
This commit is contained in:
Georgi Gerganov 2024-09-12 14:23:49 +03:00 • committed by GitHub
parent c9c8575a1a
commit d6a04f872d
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GPG key ID: B5690EEEBB952194
18 changed files with 170 additions and 129 deletions
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22
src/llama.cpp
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@ -9877,8 +9877,8 @@ struct llm_build_context {
struct ggml_cgraph * append_pooling(struct ggml_cgraph * gf) {
// find result_norm tensor for input
struct ggml_tensor * inp = nullptr;
for (int i = gf->n_nodes - 1; i >= 0; --i) {
inp = gf->nodes[i];
for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
inp = ggml_graph_node(gf, i);
if (strcmp(inp->name, "result_norm") == 0 || strcmp(inp->name, "result_embd") == 0) {
break;
} else {
@ -16207,8 +16207,8 @@ static int llama_decode_internal(
ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false);
// the output is always the last tensor in the graph
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
struct ggml_tensor * res = ggml_graph_node(gf, -1);
struct ggml_tensor * embd = ggml_graph_node(gf, -2);
if (lctx.n_outputs == 0) {
// no output
@ -16217,9 +16217,9 @@ static int llama_decode_internal(
} else if (cparams.embeddings) {
res = nullptr; // do not extract logits for embedding case
embd = nullptr;
for (int i = gf->n_nodes - 1; i >= 0; --i) {
if (strcmp(gf->nodes[i]->name, "result_embd_pooled") == 0) {
embd = gf->nodes[i];
for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
if (strcmp(ggml_graph_node(gf, i)->name, "result_embd_pooled") == 0) {
embd = ggml_graph_node(gf, i);
break;
}
}
@ -16436,15 +16436,15 @@ static int llama_encode_internal(
// there are two cases here
if (llama_model_has_decoder(&lctx.model)) {
// first case is an encoder-decoder T5 model where embeddings are passed to decoder
embd = gf->nodes[gf->n_nodes - 1];
embd = ggml_graph_node(gf, -1);
GGML_ASSERT(strcmp(embd->name, "result_norm") == 0 && "missing result_output tensor");
} else {
// second case is an encoder-only T5 model
if (cparams.embeddings) {
// only output embeddings if required
embd = gf->nodes[gf->n_nodes - 1];
embd = ggml_graph_node(gf, -1);
if (strcmp(embd->name, "result_embd_pooled") != 0) {
embd = gf->nodes[gf->n_nodes - 2];
embd = ggml_graph_node(gf, -2);
}
GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
}
@ -18492,7 +18492,7 @@ struct llama_context * llama_new_context_with_model(
// note: the number of splits during measure is higher than during inference due to the kv shift
int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, gf->n_nodes);
LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, ggml_graph_n_nodes(gf));
LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits);
}
}