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chore: update mllama to use ollama engine (#10637)

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This commit is contained in:
Michael Yang
2025-05-13 17:36:02 -07:00
committed by GitHub
parent 0478d440f0
commit 23125648b8
67 changed files with 785 additions and 4354 deletions
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23
llama/llama.cpp/src/llama-context.cpp vendored
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@@ -514,7 +514,7 @@ float * llama_context::get_logits_ith(int32_t i) {
throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, n_outputs));
}
return logits + j*model.hparams.n_vocab;
return logits + j*model.vocab.n_tokens();
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
#ifndef NDEBUG
@@ -632,10 +632,6 @@ void llama_context::set_warmup(bool value) {
cparams.warmup = value;
}
void llama_context::set_cross_attn(bool value) {
cparams.cross_attn = value;
}
void llama_context::set_adapter_lora(
llama_adapter_lora * adapter,
float scale) {
@@ -713,7 +709,7 @@ int llama_context::encode(llama_batch & inp_batch) {
const int64_t n_embd = hparams.n_embd;
llama_sbatch sbatch = llama_sbatch(batch, batch.n_embd, /* simple_split */ true, /* logits_all */ true);
llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
const llama_ubatch ubatch = sbatch.split_simple(n_tokens);
@@ -867,9 +863,10 @@ int llama_context::decode(llama_batch & inp_batch) {
const llama_batch & batch = batch_allocr.batch;
const auto & vocab = model.vocab;
const auto & hparams = model.hparams;
const int32_t n_vocab = hparams.n_vocab;
const int32_t n_vocab = vocab.n_tokens();
const int64_t n_tokens_all = batch.n_tokens;
const int64_t n_embd = hparams.n_embd;
@@ -1093,7 +1090,7 @@ int llama_context::decode(llama_batch & inp_batch) {
// make the outputs have the same order they had in the user-provided batch
// note: this is mostly relevant for recurrent models atm
if (!sorted_output) {
const uint32_t n_vocab = model.hparams.n_vocab;
const uint32_t n_vocab = model.vocab.n_tokens();
const uint32_t n_embd = model.hparams.n_embd;
GGML_ASSERT((size_t) n_outputs == out_ids.size());
@@ -1148,11 +1145,12 @@ int llama_context::decode(llama_batch & inp_batch) {
int32_t llama_context::output_reserve(int32_t n_outputs) {
const auto & hparams = model.hparams;
const auto & vocab = model.vocab;
const int64_t n_outputs_max = std::max<int64_t>(n_outputs, n_seq_max());
const auto n_batch = cparams.n_batch;
const auto n_vocab = hparams.n_vocab;
const auto n_vocab = vocab.n_tokens();
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
@@ -1687,7 +1685,7 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
{
LLAMA_LOG_DEBUG("%s: - writing logits\n", __func__);
const uint64_t logits_size = std::min((uint64_t) this->logits_size, (uint64_t) n_outputs * model.hparams.n_vocab);
const uint64_t logits_size = std::min((uint64_t) this->logits_size, (uint64_t) n_outputs * model.vocab.n_tokens());
io.write(&logits_size, sizeof(logits_size));
@@ -2099,7 +2097,6 @@ llama_context_params llama_context_default_params() {
/*.flash_attn =*/ false,
/*.no_perf =*/ true,
/*.op_offload =*/ true,
/*.cross_attn =*/ false,
};
return result;
@@ -2225,10 +2222,6 @@ void llama_set_warmup(llama_context * ctx, bool warmup) {
ctx->set_warmup(warmup);
}
void llama_set_cross_attention(struct llama_context * ctx, bool cross_attention) {
ctx->set_cross_attn(cross_attention);
}
void llama_synchronize(llama_context * ctx) {
ctx->synchronize();
}