tqcq/llama.cpp
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cont : use returend tensors from the graph build

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ggml-ci
This commit is contained in:
Georgi Gerganov
2025-02-18 14:24:17 +02:00
parent 172f61690c
commit bc6f187e9c
1 changed files with 13 additions and 47 deletions
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60
src/llama-context.cpp
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@ -1855,7 +1855,7 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
auto ctx = graph_init();
auto res = graph_build(ctx, ubatch, false);
auto & gf = res.gf;
auto * gf = res.gf;
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
@ -1863,29 +1863,6 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
input_set(ubatch);
// the output is always the last tensor in the graph
struct ggml_tensor * t_logits = ggml_graph_node(gf, -1);
struct ggml_tensor * t_embd = ggml_graph_node(gf, -2);
if (n_outputs == 0) {
// no output
t_logits = nullptr;
t_embd = nullptr;
} else if (cparams.embeddings) {
t_logits = nullptr; // do not extract logits for embedding case
t_embd = nullptr;
for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
if (strcmp(ggml_graph_node(gf, i)->name, "result_embd_pooled") == 0) {
t_embd = ggml_graph_node(gf, i);
break;
}
}
GGML_ASSERT(t_embd != nullptr && "missing embeddings tensor");
} else {
t_embd = nullptr; // do not extract embeddings when not needed
GGML_ASSERT(strcmp(t_logits->name, "result_output") == 0 && "missing result_output tensor");
}
const auto compute_status = graph_compute(gf, ubatch.n_tokens > 1);
if (compute_status != GGML_STATUS_SUCCESS) {
switch (compute_status) {
@ -1914,8 +1891,15 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
//}
auto * t_logits = cparams.embeddings ? nullptr : res.t_logits;
auto * t_embd = cparams.embeddings ? res.t_embd : nullptr;
if (t_embd && res.t_embd_pooled) {
t_embd = res.t_embd_pooled;
}
// extract logits
if (t_logits) {
if (t_logits && n_outputs > 0) {
ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
GGML_ASSERT(backend_res != nullptr);
GGML_ASSERT(logits != nullptr);
@ -1930,7 +1914,7 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
}
// extract embeddings
if (t_embd) {
if (t_embd && n_outputs > 0) {
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
GGML_ASSERT(backend_embd != nullptr);
@ -2103,32 +2087,12 @@ int llama_context_kv_self::encode(llama_batch & inp_batch) {
auto ctx = graph_init();
auto res = graph_build(ctx, ubatch, false);
auto & gf = res.gf;
auto * gf = res.gf;
ggml_backend_sched_alloc_graph(sched.get(), gf);
input_set(ubatch);
// the output embeddings after the final encoder normalization
struct ggml_tensor * t_embd = nullptr;
// there are two cases here
if (llama_model_has_decoder(&model)) {
// first case is an encoder-decoder T5 model where embeddings are passed to decoder
t_embd = ggml_graph_node(gf, -1);
GGML_ASSERT(strcmp(t_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
t_embd = ggml_graph_node(gf, -1);
if (strcmp(t_embd->name, "result_embd_pooled") != 0) {
t_embd = ggml_graph_node(gf, -2);
}
GGML_ASSERT(strcmp(t_embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
}
}
const auto compute_status = graph_compute(gf, n_tokens > 1);
switch (compute_status) {
case GGML_STATUS_SUCCESS:
@ -2142,6 +2106,8 @@ int llama_context_kv_self::encode(llama_batch & inp_batch) {
return -3;
}
auto * t_embd = res.t_embd_pooled ? res.t_embd_pooled : res.t_embd;
// extract embeddings
if (t_embd) {
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);