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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.devops/nix/package.nix
#	.github/workflows/server.yml
#	docs/development/HOWTO-add-model.md
#	ggml/src/ggml-hexagon/ggml-hexagon.cpp
#	ggml/src/ggml-hexagon/htp/CMakeLists.txt
#	ggml/src/ggml-hexagon/htp/argsort-ops.c
#	ggml/src/ggml-hexagon/htp/concat-ops.c
#	ggml/src/ggml-hexagon/htp/flash-attn-ops.c
#	ggml/src/ggml-hexagon/htp/gated-delta-net-ops.c
#	ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
#	ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
#	ggml/src/ggml-hexagon/htp/hmx-ops.h
#	ggml/src/ggml-hexagon/htp/htp-ctx.h
#	ggml/src/ggml-hexagon/htp/hvx-utils.h
#	ggml/src/ggml-hexagon/htp/main.c
#	ggml/src/ggml-hexagon/htp/matmul-ops.c
#	ggml/src/ggml-hexagon/htp/pad-ops.c
#	ggml/src/ggml-hexagon/htp/unary-ops.c
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-opencl/kernels/cvt.cl
#	ggml/src/ggml-webgpu/wgsl-shaders/cpy.wgsl
#	scripts/sync_vendor.py
#	src/llama-context.cpp
#	tests/test-backend-sampler.cpp
#	tools/server/README.md
#	tools/ui/tests/stories/ChatScreenForm.a11y.stories.svelte
This commit is contained in:
Concedo
2026-06-02 19:28:39 +08:00
parent 153214c66a d5ab0834ab
commit 13eaa04269
78 changed files with 1801 additions and 477 deletions
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src/llama-context.cpp
+15 -7
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@@ -185,6 +185,8 @@ llama_context::llama_context(
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
cparams.n_outputs_max = params.n_outputs_max == 0 ? cparams.n_batch : params.n_outputs_max;
cparams.op_offload = params.op_offload;
cparams.kv_unified = params.kv_unified;
@@ -230,6 +232,7 @@ llama_context::llama_context(
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: n_rs_seq = %u\n", __func__, cparams.n_rs_seq);
LLAMA_LOG_INFO("%s: n_outputs_max = %u\n", __func__, cparams.n_outputs_max);
if (cparams.n_ctx_seq < hparams.n_ctx_train) {
LLAMA_LOG_WARN("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
@@ -540,7 +543,7 @@ void llama_context::sched_reserve() {
// note: n_outputs must match n_tokens for embedding models with mean/rank pooling,
// because build_pooling creates inp_mean with shape [n_tokens, n_seqs] and multiplies
// it with t_embd which is reduced to [n_outputs, ...] via out_ids. if n_outputs != n_tokens,
// the ggml_mul_mat assertion fails. this matches the pp reservation below (line ~553).
// the ggml_mul_mat assertion fails.
const uint32_t n_tokens_ch = 16*n_seqs;
auto * gf = graph_reserve(n_tokens_ch, n_seqs, n_tokens_ch, mctx.get(), true);
if (!gf) {
@@ -586,16 +589,18 @@ void llama_context::sched_reserve() {
int n_splits_tg = -1;
int n_nodes_tg = -1;
const uint32_t n_outputs_pp = std::min(n_tokens, cparams.n_outputs_max);
// reserve pp (prompt processing) graph first so that buffers are only allocated once
{
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(),
auto * gf = graph_reserve(n_tokens, n_seqs, n_outputs_pp, mctx.get(),
model.hparams.no_alloc, model.hparams.no_alloc ? backend_buf_exp_size.data() : nullptr);
if (!gf) {
if (cparams.pipeline_parallel) {
LLAMA_LOG_WARN("%s: compute buffer allocation failed, retrying without pipeline parallelism\n", __func__);
cparams.pipeline_parallel = false;
sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, false, cparams.op_offload));
gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
gf = graph_reserve(n_tokens, n_seqs, n_outputs_pp, mctx.get());
}
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
@@ -623,7 +628,7 @@ void llama_context::sched_reserve() {
//
// auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get());
//
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), model.hparams.no_alloc);
auto * gf = graph_reserve(n_tokens, n_seqs, n_outputs_pp, mctx.get(), model.hparams.no_alloc);
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
@@ -784,7 +789,9 @@ bool llama_context::memory_update(bool optimize) {
const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
const uint32_t n_outputs_max = std::min(n_tokens, cparams.n_outputs_max);
auto * gf = graph_reserve(n_tokens, n_seqs, n_outputs_max, mctx.get());
if (!gf) {
LLAMA_LOG_ERROR("%s: failed to reserve graph after the memory update\n", __func__);
}
@@ -2150,6 +2157,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
this->n_outputs = 0;
GGML_ASSERT(n_outputs_max <= cparams.n_outputs_max);
return n_outputs_max;
}
@@ -2236,8 +2245,6 @@ ggml_cgraph * llama_context::graph_reserve(
if (n_tokens % n_seqs != 0) {
n_tokens = ((n_tokens + (n_seqs - 1)) / n_seqs) * n_seqs; // round to next multiple of n_seqs
n_outputs = std::max(n_outputs, n_tokens);
//LLAMA_LOG_DEBUG("%s: making n_tokens a multiple of n_seqs - n_tokens = %u, n_seqs = %u, n_outputs = %u\n", __func__, n_tokens, n_seqs, n_outputs);
}
@@ -3347,6 +3354,7 @@ llama_context_params llama_context_default_params() {
/*.n_ubatch =*/ 512,
/*.n_seq_max =*/ 1,
/*.n_rs_seq =*/ 0,
/*.n_outputs_max =*/ 0,
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
/*.ctx_type =*/ LLAMA_CONTEXT_TYPE_DEFAULT,