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Merge commit '0b14b87d7c20cb753b94b96854dd7b45306fc696' into concedo_experimental

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# Conflicts:
#	.github/workflows/build-cpu.yml
#	.github/workflows/release.yml
#	AGENTS.md
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-sycl/common.hpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl
#	src/models/qwen3next.cpp
#	tests/test-backend-ops.cpp
#	tests/test-chat-peg-parser.cpp
#	tests/test-chat.cpp
#	tests/test-llama-archs.cpp
#	tests/test-recurrent-state-rollback.cpp
This commit is contained in:
Concedo
2026-08-07 18:04:33 +08:00
parent 569532f7e4 0b14b87d7c
commit 7098c9c53f
67 changed files with 4134 additions and 807 deletions
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src/llama-context.cpp
+14 -10
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@@ -123,8 +123,9 @@ llama_context::llama_context(
cparams.no_perf = params.no_perf;
cparams.warmup = false;
cparams.embeddings_layer_inp.resize(hparams.n_layer(), false);
embd_layer_inp.resize(hparams.n_layer());
// +1: id n_layer() taps the output of the last layer ("input" of the head)
cparams.embeddings_layer_inp.resize(hparams.n_layer() + 1, false);
embd_layer_inp.resize(hparams.n_layer() + 1);
cparams.ctx_type = params.ctx_type;
cparams.pooling_type = params.pooling_type;
@@ -1174,7 +1175,7 @@ void llama_context::set_embeddings_nextn(bool value, bool masked) {
void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
LLAMA_LOG_DEBUG("%s: lid = %d, enable = %d\n", __func__, lid, enable);
GGML_ASSERT(lid < model.hparams.n_layer());
GGML_ASSERT(lid <= model.hparams.n_layer());
cparams.embeddings_layer_inp[lid] = enable;
@@ -1726,7 +1727,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
const auto & hparams = model.hparams;
const int64_t n_vocab = vocab.n_tokens();
const int64_t n_embd = hparams.n_embd_inp();
const bool mtp_embd = cparams.ctx_type == LLAMA_CONTEXT_TYPE_MTP && batch_inp.embd;
const int64_t n_embd = mtp_embd ? hparams.n_embd_out() : hparams.n_embd_inp();
// when computing embeddings, all tokens are output
const bool output_all = cparams.embeddings;
@@ -2285,8 +2287,9 @@ void llama_context::extract_layer_inputs(const llm_graph_result * res, size_t to
}
void llama_context::output_reorder() {
const uint64_t n_vocab = model.vocab.n_tokens();
const uint64_t n_embd = model.hparams.n_embd;
const uint64_t n_vocab = model.vocab.n_tokens();
const uint64_t n_embd = model.hparams.n_embd;
const uint64_t n_embd_out = model.hparams.n_embd_out();
for (size_t s = 0; s < output_swaps.size(); ++s) {
const uint64_t i0 = output_swaps[s].i0;
@@ -2299,14 +2302,14 @@ void llama_context::output_reorder() {
}
if (embd.size > 0) {
for (uint64_t k = 0; k < n_embd; k++) {
std::swap(embd.data[i0*n_embd + k], embd.data[i1*n_embd + k]);
for (uint64_t k = 0; k < n_embd_out; k++) {
std::swap(embd.data[i0*n_embd_out + k], embd.data[i1*n_embd_out + k]);
}
}
if (embd_nextn.size > 0) {
for (uint64_t k = 0; k < n_embd; k++) {
std::swap(embd_nextn.data[i0*n_embd + k], embd_nextn.data[i1*n_embd + k]);
for (uint64_t k = 0; k < n_embd_out; k++) {
std::swap(embd_nextn.data[i0*n_embd_out + k], embd_nextn.data[i1*n_embd_out + k]);
}
}
@@ -2361,6 +2364,7 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
model.arch == LLM_ARCH_QWEN35 ||
model.arch == LLM_ARCH_QWEN35MOE ||
model.arch == LLM_ARCH_DEEPSEEK4 ||
(model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) ||
model.arch == LLM_ARCH_NANBEIGE ||
model.arch == LLM_ARCH_MINIMAX_M3) {
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());