#include "arg.h"
#include "common.h"
#include "llama.h"
#include <algorithm>
#include <clocale>
#include <cmath>
#include <cstdio>
#include <vector>
static llama_context * make_ctx(const common_params & params, llama_model * model) {
auto cparams = common_context_params_to_llama(params);
cparams.n_seq_max = 1;
cparams.n_rs_seq = 8;
cparams.n_batch = std::max(cparams.n_batch, (uint32_t) (cparams.n_rs_seq + 1));
cparams.n_ubatch = std::max(cparams.n_ubatch, (uint32_t) (cparams.n_rs_seq + 1));
return llama_init_from_model(model, cparams);
}
static bool decode_tokens(llama_context * ctx, const std::vector<llama_token> & tokens, uint32_t count) {
llama_batch batch = llama_batch_init(count, 0, 1);
for (uint32_t pos = 0; pos < count; ++pos) {
common_batch_add(batch, tokens[pos], pos, { 0 }, pos + 1 == count);
}
const bool ok = llama_decode(ctx, batch) == 0;
llama_batch_free(batch);
return ok;
}
static bool decode_one(llama_context * ctx, llama_token tok, llama_pos pos) {
llama_batch batch = llama_batch_init(1, 0, 1);
common_batch_add(batch, tok, pos, { 0 }, true);
const bool ok = llama_decode(ctx, batch) == 0;
llama_batch_free(batch);
return ok;
}
// Roll back multiple sequences, then replay them in a single batch whose
// per-seq token count exceeds n_ubatch: each seq's replay spans several
// ubatches while its rollback restore is still pending. Compared against a
// reference context that never advanced past the rollback point and decodes
// the identical replay batch.
static bool test_multi_seq_split_replay(const common_params & params, llama_model * model, const int n_vocab) {
constexpr uint32_t n_seqs = 2;
constexpr uint32_t n_ubatch = 16;
constexpr uint32_t n_prompt = 19;
constexpr uint32_t n_rollback = 3;
constexpr uint32_t n_replay = 40; // > n_ubatch so each seq spans multiple ubatches
constexpr llama_pos p0 = n_prompt - n_rollback;
const auto make_ctx_multi = [&]() {
auto cparams = common_context_params_to_llama(params);
cparams.n_seq_max = n_seqs;
cparams.n_rs_seq = 8;
cparams.n_ctx = 256;
cparams.n_batch = 256;
cparams.n_ubatch = n_ubatch;
cparams.kv_unified = false;
return llama_init_from_model(model, cparams);
};
llama_context * ctx_roll = make_ctx_multi();
llama_context * ctx_ref = make_ctx_multi();
if (ctx_roll == nullptr || ctx_ref == nullptr) {
fprintf(stderr, "%s : failed to init multi-seq contexts\n", __func__);
return false;
}
const auto cleanup = [&]() {
llama_free(ctx_roll);
llama_free(ctx_ref);
};
if (llama_n_rs_seq(ctx_roll) < n_rollback) {
fprintf(stderr, "%s : skipping because n_rs_seq is too small\n", __func__);
cleanup();
return true;
}
const auto tok = [&](uint32_t seq, llama_pos pos) {
return (llama_token) ((7*(uint32_t) pos + 31*seq + 1) % (uint32_t) n_vocab);
};
bool ok = true;
// both contexts decode the identical [0, p0) prefill; only ctx_roll decodes
// the tail, which is then rolled back so its restore is pending at replay
for (uint32_t s = 0; s < n_seqs && ok; ++s) {
llama_batch batch = llama_batch_init(n_prompt, 0, 1);
for (llama_pos pos = 0; pos < (llama_pos) p0; ++pos) {
common_batch_add(batch, tok(s, pos), pos, { (llama_seq_id) s }, false);
}
ok = ok && llama_decode(ctx_roll, batch) == 0;
ok = ok && llama_decode(ctx_ref, batch) == 0;
common_batch_clear(batch);
for (llama_pos pos = p0; pos < (llama_pos) n_prompt; ++pos) {
common_batch_add(batch, tok(s, pos), pos, { (llama_seq_id) s }, false);
}
ok = ok && llama_decode(ctx_roll, batch) == 0;
llama_batch_free(batch);
ok = ok && llama_memory_seq_rm(llama_get_memory(ctx_roll), (llama_seq_id) s, p0, -1);
// a second partial removal while one is pending must be refused
ok = ok && !llama_memory_seq_rm(llama_get_memory(ctx_roll), (llama_seq_id) s, p0 - 1, -1);
}
if (!ok) {
fprintf(stderr, "%s : multi-seq prefill/rollback failed\n", __func__);
cleanup();
return false;
}
llama_batch batch = llama_batch_init(n_seqs*n_replay, 0, 1);
for (uint32_t s = 0; s < n_seqs; ++s) {
for (uint32_t i = 0; i < n_replay; ++i) {
const llama_pos pos = p0 + (llama_pos) i;
common_batch_add(batch, tok(s, pos), pos, { (llama_seq_id) s }, true);
}
}
ok = llama_decode(ctx_roll, batch) == 0;
ok = ok && llama_decode(ctx_ref, batch) == 0;
llama_batch_free(batch);
if (!ok) {
fprintf(stderr, "%s : multi-seq replay decode failed\n", __func__);
cleanup();
return false;
}
// identical ubatch shapes from bit-exact states: a correct implementation
// matches bitwise, so eps only allows backend scheduling noise
constexpr float eps = 1e-7f;
float diff_max = 0.0f;
uint32_t seq_first = 0;
int32_t pos_first = -1;
for (uint32_t i = 0; i < n_seqs*n_replay; ++i) {
const float * l_roll = llama_get_logits_ith(ctx_roll, i);
const float * l_ref = llama_get_logits_ith(ctx_ref, i);
if (l_roll == nullptr || l_ref == nullptr) {
fprintf(stderr, "%s : missing multi-seq logits at index %u\n", __func__, i);
cleanup();
return false;
}
for (int t = 0; t < n_vocab; ++t) {
const float diff = std::fabs(l_roll[t] - l_ref[t]);
if (diff > eps && pos_first < 0) {
seq_first = i/n_replay;
pos_first = p0 + (int32_t) (i%n_replay);
}
diff_max = std::max(diff_max, diff);
}
}
if (diff_max > eps) {
fprintf(stderr, "%s : multi-seq split replay logits mismatch (max diff %g, first at seq %u pos %d)\n",
__func__, (double) diff_max, seq_first, pos_first);
cleanup();
return false;
}
fprintf(stderr, "%s : multi-seq split replay matched (max diff %g)\n", __func__, (double) diff_max);
// seq-1-only decodes must be independent of seq 0's content: diverge seq 0
// in ctx_ref only, then compare identical seq-1-only continuations bitwise
constexpr uint32_t n_tail = 4;
{
llama_batch batch_tail = llama_batch_init(n_tail, 0, 1);
for (uint32_t i = 0; i < n_tail; ++i) {
const llama_pos pos = p0 + (llama_pos) (n_replay + i);
common_batch_add(batch_tail, tok(0, pos + 7), pos, { 0 }, false);
}
ok = llama_decode(ctx_ref, batch_tail) == 0;
llama_batch_free(batch_tail);
}
float diff_tail = 0.0f;
for (uint32_t i = 0; i < n_tail && ok; ++i) {
const llama_pos pos = p0 + (llama_pos) (n_replay + i);
llama_batch batch_one = llama_batch_init(1, 0, 1);
common_batch_add(batch_one, tok(1, pos), pos, { 1 }, true);
ok = llama_decode(ctx_roll, batch_one) == 0;
ok = ok && llama_decode(ctx_ref, batch_one) == 0;
llama_batch_free(batch_one);
if (!ok) {
break;
}
const float * l_roll = llama_get_logits_ith(ctx_roll, 0);
const float * l_ref = llama_get_logits_ith(ctx_ref, 0);
ok = l_roll != nullptr && l_ref != nullptr;
for (int t = 0; ok && t < n_vocab; ++t) {
diff_tail = std::max(diff_tail, std::fabs(l_roll[t] - l_ref[t]));
}
}
if (!ok || diff_tail > eps) {
fprintf(stderr, "%s : seq-1-only decode leaked seq 0 state (ok=%d, max diff %g)\n",
__func__, ok ? 1 : 0, (double) diff_tail);
cleanup();
return false;
}
fprintf(stderr, "%s : seq-1-only decode independent of seq 0 (max diff %g)\n", __func__, (double) diff_tail);
cleanup();
return true;
}
int main(int argc, char ** argv) {
std::setlocale(LC_NUMERIC, "C");
common_params params;
params.sampling.seed = 1234;
params.n_predict = 1;
common_init();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
ggml_backend_load_all();
common_init_result_ptr llama_init = common_init_from_params(params);
llama_model * model = llama_init->model();
if (model == nullptr) {
fprintf(stderr, "%s : failed to init model\n", __func__);
return 1;
}
if (!llama_model_is_recurrent(model) && !llama_model_is_hybrid(model)) {
fprintf(stderr, "%s : skipping for non-recurrent model\n", __func__);
return 0;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
const int n_vocab = llama_vocab_n_tokens(vocab);
llama_context * ctx_src = make_ctx(params, model);
llama_context * ctx_dst = make_ctx(params, model);
if (ctx_src == nullptr || ctx_dst == nullptr) {
fprintf(stderr, "%s : failed to init contexts\n", __func__);
return 1;
}
if (llama_n_rs_seq(ctx_src) == 0) {
fprintf(stderr, "%s : skipping because n_rs_seq is disabled\n", __func__);
llama_free(ctx_src);
llama_free(ctx_dst);
return 0;
}
std::vector<llama_token> tokens;
if (llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_NONE) {
tokens = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
} else {
tokens = common_tokenize(ctx_src, "The quick brown fox jumps over the lazy dog", true);
}
const uint32_t n_rs_seq = llama_n_rs_seq(ctx_src);
constexpr uint32_t n_rollback = 3;
if (n_rs_seq < n_rollback) {
fprintf(stderr, "%s : skipping because n_rs_seq is too small\n", __func__);
llama_free(ctx_src);
llama_free(ctx_dst);
return 0;
}
if (tokens.empty()) {
fprintf(stderr, "%s : not enough prompt tokens\n", __func__);
return 1;
}
tokens.resize(n_rs_seq + 1, tokens.back());
const uint32_t n_tokens = tokens.size();
const llama_pos rollback_pos = (llama_pos) n_tokens - n_rollback;
// Decode the full prompt on the source, then roll back three positions.
// Replaying them crosses DSV4's ratio-4 compressor boundary.
// Rollback leaves the recurrent memory in a snapshot state (rs_idx != 0).
if (!decode_tokens(ctx_src, tokens, n_tokens)) {
fprintf(stderr, "%s : failed to decode prompt\n", __func__);
return 1;
}
if (!llama_memory_seq_rm(llama_get_memory(ctx_src), 0, rollback_pos, -1)) {
fprintf(stderr, "%s : rollback failed\n", __func__);
return 1;
}
// Save the rolled-back state and restore it into a fresh context.
common_prompt_checkpoint ckpt;
ckpt.update_tgt(ctx_src, 0, 0);
ckpt.load_tgt(ctx_dst, 0, 0);
constexpr float eps = 1e-5f;
std::vector<std::vector<float>> logits_src_replay(n_rollback);
const auto replay_and_compare = [&](const char * mode) {
for (uint32_t i = 0; i < n_rollback; ++i) {
const llama_pos pos = rollback_pos + i;
if (!decode_one(ctx_src, tokens[pos], pos) ||
!decode_one(ctx_dst, tokens[pos], pos)) {
fprintf(stderr, "%s : %s replay failed at position %d\n", __func__, mode, pos);
return false;
}
const float * logits_src = llama_get_logits_ith(ctx_src, 0);
const float * logits_dst = llama_get_logits_ith(ctx_dst, 0);
if (logits_src == nullptr || logits_dst == nullptr) {
fprintf(stderr, "%s : missing %s logits at position %d\n", __func__, mode, pos);
return false;
}
logits_src_replay[i].assign(logits_src, logits_src + n_vocab);
for (int token = 0; token < n_vocab; ++token) {
if (std::fabs(logits_src[token] - logits_dst[token]) > eps) {
fprintf(stderr, "%s : %s logits mismatch at position %d, token %d (%g != %g)\n",
__func__, mode, pos, token, (double) logits_src[token], (double) logits_dst[token]);
return false;
}
}
}
return true;
};
if (!replay_and_compare("full")) {
return 1;
}
if (!llama_memory_seq_rm(llama_get_memory(ctx_src), 0, rollback_pos, -1) ||
!llama_memory_seq_rm(llama_get_memory(ctx_dst), 0, rollback_pos, -1)) {
fprintf(stderr, "%s : partial rollback failed\n", __func__);
return 1;
}
constexpr llama_state_seq_flags partial_flags = LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY;
common_prompt_checkpoint ckpt_partial;
ckpt_partial.update_tgt(ctx_src, 0, partial_flags);
ckpt_partial.load_tgt(ctx_dst, 0, partial_flags);
if (!replay_and_compare("partial")) {
return 1;
}
// Repeat the load into a context that already has its own rollback state:
// groups 1..n_rs_seq hold a different prompt's history, and rs_idx[0] is
// non-zero at load time. The restore must wipe that state and still match.
llama_context * ctx_dirty = make_ctx(params, model);
if (ctx_dirty == nullptr) {
fprintf(stderr, "%s : failed to init dirty ctx\n", __func__);
return 1;
}
std::vector<llama_token> noise = tokens;
for (auto & t : noise) {
t = (t + 1) % n_vocab;
if (t < 0) {
t = 0;
}
}
if (!decode_tokens(ctx_dirty, noise, n_tokens)) {
fprintf(stderr, "%s : dirty prompt decode failed\n", __func__);
return 1;
}
if (!llama_memory_seq_rm(llama_get_memory(ctx_dirty), 0, rollback_pos, -1)) {
fprintf(stderr, "%s : dirty rollback failed\n", __func__);
return 1;
}
ckpt.load_tgt(ctx_dirty, 0, 0);
for (uint32_t i = 0; i < n_rollback; ++i) {
const llama_pos pos = rollback_pos + i;
if (!decode_one(ctx_dirty, tokens[pos], pos)) {
fprintf(stderr, "%s : dirty replay failed at position %d\n", __func__, pos);
return 1;
}
const float * logits_dirty = llama_get_logits_ith(ctx_dirty, 0);
if (logits_dirty == nullptr) {
fprintf(stderr, "%s : missing dirty logits at position %d\n", __func__, pos);
return 1;
}
for (int token = 0; token < n_vocab; ++token) {
if (std::fabs(logits_src_replay[i][token] - logits_dirty[token]) > eps) {
fprintf(stderr, "%s : dirty-ctx logits mismatch at position %d, token %d (%g != %g)\n",
__func__, pos, token, (double) logits_src_replay[i][token], (double) logits_dirty[token]);
return 1;
}
}
}
fprintf(stderr, "%s : recurrent rollback checkpoint restored successfully\n", __func__);
llama_free(ctx_src);
llama_free(ctx_dst);
llama_free(ctx_dirty);
if (!test_multi_seq_split_replay(params, model, n_vocab)) {
return 1;
}
return 0;
}