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server : improve context checkpoint logic #16440

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Merged
merged 1 commit into from
Oct 8, 2025
+56 −35
Merged

server : improve context checkpoint logic #16440

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5 changes: 4 additions & 1 deletion src/llama-memory-recurrent.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -861,9 +861,12 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id) {
if (dest_seq_id != -1) {
// single sequence

seq_rm(dest_seq_id, -1, -1);

if (cell_count == 0) {
return true;
}

llama_batch_allocr balloc(hparams.n_pos_per_embd());

llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1);
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86 changes: 52 additions & 34 deletions tools/server/server.cpp
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Expand Up @@ -3676,6 +3676,20 @@ struct server_context {
alora_disabled_id = enabled_loras[0];
}

bool do_checkpoint = params_base.n_ctx_checkpoints > 0;

// make a checkpoint of the parts of the memory that cannot be rolled back.
// checkpoints are created only if:
// - the model uses SWA and we are not using `swa_full`
// - the model architecture is marked as recurrent or hybrid
//
// TODO: try to make this conditional on the context or the memory module, instead of the model type
do_checkpoint = do_checkpoint && (
llama_model_is_recurrent(model) ||
llama_model_is_hybrid(model) ||
(llama_model_n_swa(model) > 0 && !params_base.swa_full)
);

// add prompt tokens for processing in the current batch
while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
// get next token to process
Expand All @@ -3700,6 +3714,11 @@ struct server_context {

slot.n_prompt_tokens_processed++;
slot.n_past++;

// process the last few tokens of the prompt separately in order to allow for a checkpoint to be created.
if (do_checkpoint && slot.n_prompt_tokens - slot.n_past == 64) {
break;
}
}

// SLT_INF(slot, "new cache_tokens: %s\n", slot.cache_tokens.str().c_str());
Expand Down Expand Up @@ -3730,6 +3749,39 @@ struct server_context {
slot.i_batch = batch.n_tokens - 1;

SLT_INF(slot, "prompt done, n_past = %d, n_tokens = %d\n", slot.n_past, batch.n_tokens);

const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id);

// no need for empty or small checkpoints
do_checkpoint = do_checkpoint && (pos_min >= 0 && pos_max >= 64);

// no need to create checkpoints that are too close together
do_checkpoint = do_checkpoint && (slot.ctx_checkpoints.empty() || pos_max > slot.ctx_checkpoints.back().pos_max + 64);

if (do_checkpoint) {
while (slot.ctx_checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) {
// make room for the new checkpoint, if needed
const auto & cur = slot.ctx_checkpoints.front();
SLT_WRN(slot, "erasing old context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n",
cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);

slot.ctx_checkpoints.erase(slot.ctx_checkpoints.begin());
}

const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);

auto & cur = slot.ctx_checkpoints.emplace_back(ctx_checkpoint{
/*.pos_min = */ pos_min,
/*.pos_max = */ pos_max,
/*.data = */ std::vector<uint8_t>(checkpoint_size),
});

llama_state_seq_get_data_ext(ctx, cur.data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);

SLT_WRN(slot, "saved context checkpoint %d of %d (pos_min = %d, pos_max = %d, size = %.3f MiB)\n",
(int) slot.ctx_checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);
}
}
}

Expand Down Expand Up @@ -3853,40 +3905,6 @@ struct server_context {

// prompt evaluated for next-token prediction
slot.state = SLOT_STATE_GENERATING;

// make a checkpoint of the parts of the memory that cannot be rolled back.
// checkpoints are created only if:
// - the model uses SWA and we are not using `swa_full`
// - the model architecture is marked as recurrent or hybrid
//
// TODO: try to make this conditional on the context or the memory module, instead of the model type
const bool do_checkpoint =
(llama_model_is_recurrent(model) || llama_model_is_hybrid(model)) ||
(llama_model_n_swa(model) > 0 && !params_base.swa_full);

if (do_checkpoint && params_base.n_ctx_checkpoints > 0) {
while (slot.ctx_checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) {
// make room for the new checkpoint, if needed
const auto & cur = slot.ctx_checkpoints.front();
SLT_WRN(slot, "erasing old context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n",
cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);

slot.ctx_checkpoints.erase(slot.ctx_checkpoints.begin());
}

const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);

auto & cur = slot.ctx_checkpoints.emplace_back(ctx_checkpoint{
/*.pos_min = */ llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id),
/*.pos_max = */ llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id),
/*.data = */ std::vector<uint8_t>(checkpoint_size),
});

llama_state_seq_get_data_ext(ctx, cur.data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);

SLT_WRN(slot, "saved context checkpoint %d of %d (pos_min = %d, pos_max = %d, size = %.3f MiB)\n",
(int) slot.ctx_checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);
}
} else if (slot.state != SLOT_STATE_GENERATING) {
continue; // continue loop of slots
}
Expand Down
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