Skip to content

UPSTREAM PR #16490: graph : reuse SSM graphs #1083

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
loci-dev wants to merge 7 commits into from
Closed

UPSTREAM PR #16490: graph : reuse SSM graphs #1083

Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
cc10fab
graph : reuse hybrid graphs
ggerganov Oct 9, 2025
4f71c75
graph : reuse recurrent graphs
ggerganov Oct 9, 2025
36a95e6
graph : fix reuse check for recurrent inputs
ggerganov Oct 10, 2025
3aa4e3c
memory : move the recurrent state into the memory context
ggerganov Oct 10, 2025
d24eb42
Revert "memory : move the recurrent state into the memory context"
ggerganov Oct 10, 2025
454ab90
cont : fix build
ggerganov Dec 15, 2025
b3492b6
Merge branch 'main' into loci/pr-16490-gg-graph-mamba-reuse
DajanaV Jan 31, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
107 changes: 0 additions & 107 deletions src/llama-graph.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -533,113 +533,6 @@ bool llm_graph_input_mem_hybrid::can_reuse(const llm_graph_params & params) {
return res;
}

void llm_graph_input_mem_hybrid_iswa::set_input(const llama_ubatch * ubatch) {
const auto * attn_ctx = mctx->get_attn();

// base tensors may not be allocated if there are no non-SWA attention layers
if (inp_attn->self_k_idxs && inp_attn->self_k_idxs->buffer) {
attn_ctx->get_base()->set_input_k_idxs(inp_attn->self_k_idxs, ubatch);
attn_ctx->get_base()->set_input_v_idxs(inp_attn->self_v_idxs, ubatch);

attn_ctx->get_base()->set_input_kq_mask(inp_attn->self_kq_mask, ubatch, cparams.causal_attn);
}

// swa tensors may not be allocated if there are no SWA attention layers
if (inp_attn->self_k_idxs_swa && inp_attn->self_k_idxs_swa->buffer) {
attn_ctx->get_swa()->set_input_k_idxs(inp_attn->self_k_idxs_swa, ubatch);
attn_ctx->get_swa()->set_input_v_idxs(inp_attn->self_v_idxs_swa, ubatch);

attn_ctx->get_swa()->set_input_kq_mask(inp_attn->self_kq_mask_swa, ubatch, cparams.causal_attn);
}

const int64_t n_rs = mctx->get_recr()->get_n_rs();

if (inp_rs->s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(inp_rs->s_copy->buffer));
int32_t * data = (int32_t *) inp_rs->s_copy->data;

// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_rs; ++i) {
data[i] = mctx->get_recr()->s_copy(i);
}
}
}

bool llm_graph_input_mem_hybrid_iswa::can_reuse(const llm_graph_params & params) {
const auto * mctx = static_cast<const llama_memory_hybrid_iswa_context *>(params.mctx);

this->mctx = mctx;

bool res = true;

const auto * attn_ctx = mctx->get_attn();

// base tensors may not be allocated if there are no non-SWA attention layers
if (inp_attn->self_k_idxs && inp_attn->self_k_idxs->buffer) {
res &= inp_attn->self_k_idxs->ne[0] == params.ubatch.n_tokens;
//res &= inp_attn->self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there

res &= inp_attn->self_kq_mask->ne[0] == attn_ctx->get_base()->get_n_kv();
res &= inp_attn->self_kq_mask->ne[1] == params.ubatch.n_tokens;
}

// swa tensors may not be allocated if there are no SWA attention layers
if (inp_attn->self_k_idxs_swa && inp_attn->self_k_idxs_swa->buffer) {
res &= inp_attn->self_k_idxs_swa->ne[0] == params.ubatch.n_tokens;
//res &= inp_attn->self_v_idxs_swa->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there

res &= inp_attn->self_kq_mask_swa->ne[0] == attn_ctx->get_swa()->get_n_kv();
res &= inp_attn->self_kq_mask_swa->ne[1] == params.ubatch.n_tokens;
}

res &= inp_rs->s_copy->ne[0] == mctx->get_recr()->get_n_rs();

res &= inp_rs->s_copy_main->ne[0] == params.ubatch.n_seqs;
res &= inp_rs->s_copy_extra->ne[0] == mctx->get_recr()->get_n_rs() - params.ubatch.n_seqs;

res &= inp_rs->head == mctx->get_recr()->get_head();
res &= inp_rs->rs_z == mctx->get_recr()->get_rs_z();

return res;
}

void llm_graph_input_sampling::set_input(const llama_ubatch * ubatch) {
// set the inputs only for the active samplers in the current ubatch
std::unordered_set<llama_seq_id> active_samplers;
for (uint32_t i = 0; i < ubatch->n_tokens; i++) {
if (ubatch->output[i]) {
llama_seq_id seq_id = ubatch->seq_id[i][0];
active_samplers.insert(seq_id);
}
}

for (auto seq_id : active_samplers) {
if (samplers.find(seq_id) == samplers.end()) {
continue;
}

auto & sampler = samplers[seq_id];

if (sampler->iface->backend_set_input) {
sampler->iface->backend_set_input(sampler);
}
}
}

bool llm_graph_input_sampling::can_reuse(const llm_graph_params & params) {
if (samplers.size() != params.samplers.size()) {
return false;
}

for (const auto & [seq_id, sampler] : params.samplers) {
if (samplers[seq_id] != sampler) {
return false;
}
}

return true;
}

//
// llm_graph_result
//
Expand Down
Loading