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ubatch : new splitting logic (#14217)

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ggml-ci
This commit is contained in:
Georgi Gerganov
2025-06-20 10:14:14 +03:00
committed by GitHub
parent 9eaa51e7f0
commit 4c9fdfbe15
19 changed files with 992 additions and 915 deletions
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136
src/llama-kv-cache-unified.cpp
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@@ -308,17 +308,23 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
}
llama_memory_state_ptr llama_kv_cache_unified::init_batch(
const llama_batch & batch,
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) {
GGML_UNUSED(embd_all);
do {
auto sbatch = llama_sbatch(batch, hparams.n_embd, true);
balloc.split_reset();
std::vector<llama_ubatch> ubatches;
while (sbatch.n_tokens > 0) {
ubatches.push_back(sbatch.split_simple(n_ubatch));
while (true) {
auto ubatch = balloc.split_simple(n_ubatch);
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(std::move(ubatch)); // NOLINT
}
auto heads = prepare(ubatches);
@@ -327,7 +333,7 @@ llama_memory_state_ptr llama_kv_cache_unified::init_batch(
}
return std::make_unique<llama_kv_cache_unified_state>(
this, std::move(sbatch), std::move(heads), std::move(ubatches));
this, std::move(heads), std::move(ubatches));
} while (false);
return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
@@ -644,12 +650,6 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
}
void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch) {
if (debug > 0) {
LLAMA_LOG_DEBUG("%s: ubatch info:\n", __func__);
LLAMA_LOG_DEBUG("%s: n_tokens = %d, equal_seqs = %d\n", __func__, ubatch.n_tokens, ubatch.equal_seqs);
LLAMA_LOG_DEBUG("%s: n_seq_tokens = %d, n_seqs = %d\n", __func__, ubatch.n_seq_tokens, ubatch.n_seqs);
}
// keep track of the max sequence position that we would overwrite with this ubatch
// for non-SWA cache, this would be always empty
llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ];
@@ -657,27 +657,22 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
seq_pos_max_rm[s] = -1;
}
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
for (uint32_t j = 0; j < ubatch.n_seq_tokens; ++j) {
const uint32_t idx = s*ubatch.n_seq_tokens + j;
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
if (!cells.is_empty(head_cur + i)) {
assert(cells.seq_count(head_cur + i) == 1);
if (!cells.is_empty(head_cur + idx)) {
assert(cells.seq_count(head_cur + idx) == 1);
const llama_seq_id seq_id = cells.seq_get(head_cur + i);
const llama_pos pos = cells.pos_get(head_cur + i);
const llama_seq_id seq_id = cells.seq_get(head_cur + idx);
const llama_pos pos = cells.pos_get(head_cur + idx);
seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos);
seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos);
cells.rm(head_cur + i);
}
cells.rm(head_cur + idx);
}
cells.pos_set(head_cur + i, ubatch.pos[i]);
cells.pos_set(head_cur + idx, ubatch.pos[idx]);
// TODO: fix indexing [UBATCH_IDX]
for (int32_t i = 0; i < ubatch.n_seq_id[s]; i++) {
cells.seq_add(head_cur + idx, ubatch.seq_id[s][i]);
}
for (int32_t s = 0; s < ubatch.n_seq_id[i]; s++) {
cells.seq_add(head_cur + i, ubatch.seq_id[i][s]);
}
}
@@ -696,6 +691,7 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
seq_rm(s, cells.seq_pos_min(s), seq_pos_max_rm[s] + 1);
}
}
// move the head at the end of the slot
head = head_cur + ubatch.n_tokens;
}
@@ -792,9 +788,7 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
}
void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
const uint32_t n_tokens = ubatch->n_tokens;
const uint32_t n_seq_tokens = ubatch->n_seq_tokens;
const uint32_t n_seqs = ubatch->n_seqs;
const uint32_t n_tokens = ubatch->n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
float * data = (float *) dst->data;
@@ -814,52 +808,48 @@ void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ub
// xxxxx-----
// To visualize the mask, see https://github.com/ggml-org/llama.cpp/pull/12615
for (uint32_t h = 0; h < 1; ++h) {
for (uint32_t s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s][0];
for (uint32_t i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = ubatch->seq_id[i][0];
for (uint32_t j = 0; j < n_seq_tokens; ++j) {
const uint32_t idx = s*n_seq_tokens + j;
const llama_pos p1 = ubatch->pos[i];
const llama_pos p1 = ubatch->pos[idx];
for (uint32_t j = 0; j < n_kv; ++j) {
float f = 0.0f;
for (uint32_t i = 0; i < n_kv; ++i) {
float f = 0.0f;
bool masked = false;
bool masked = false;
if (cells.is_empty(j)) {
masked = true;
} else {
const llama_pos p0 = cells.pos_get(j);
if (cells.is_empty(i)) {
masked = true;
} else {
const llama_pos p0 = cells.pos_get(i);
// mask the token if not the same sequence
masked = masked || (!cells.seq_has(j, seq_id));
// mask the token if not the same sequence
masked = masked || (!cells.seq_has(i, seq_id));
// mask future tokens
masked = masked || (causal_attn && p0 > p1);
// mask future tokens
masked = masked || (causal_attn && p0 > p1);
// apply SWA if any
masked = masked || (is_masked_swa(p0, p1));
// apply SWA if any
masked = masked || (is_masked_swa(p0, p1));
if (!masked && hparams.use_alibi) {
f = -std::abs(p0 - p1);
}
if (!masked && hparams.use_alibi) {
f = -std::abs(p0 - p1);
}
if (masked) {
f = -INFINITY;
}
data[h*(n_kv*n_tokens) + idx*n_kv + i] = f;
}
if (masked) {
f = -INFINITY;
}
data[h*(n_kv*n_tokens) + i*n_kv + j] = f;
}
}
// mask padded tokens
if (data) {
for (uint32_t j = n_tokens; j < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++j) {
for (uint32_t i = 0; i < n_kv; ++i) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
for (uint32_t i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (uint32_t j = 0; j < n_kv; ++j) {
data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
}
}
}
@@ -887,12 +877,12 @@ void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama
const int32_t n_kv = dst->ne[0];
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_kv; ++i) {
for (int i = 0; i < n_tokens; ++i) {
for (int j = 0; j < n_kv; ++j) {
// the position when the cells is empty is irrelevant - it will be masked out later in the attention
const llama_pos p0 = cells.is_empty(i) ? -1 : cells.pos_get(i);
const llama_pos p0 = cells.is_empty(j) ? -1 : cells.pos_get(j);
data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(p0, ubatch->pos[j], hparams.n_rel_attn_bkts, false);
data[h*(n_kv*n_tokens) + i*n_kv + j] = llama_relative_position_bucket(p0, ubatch->pos[i], hparams.n_rel_attn_bkts, false);
}
}
}
@@ -1509,12 +1499,9 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
seq_rm(dest_seq_id, -1, -1);
llama_sbatch sbatch;
llama_ubatch ubatch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
llama_batch_allocr balloc(hparams.n_pos_per_embd());
ubatch.n_tokens = cell_count;
ubatch.n_seq_tokens = cell_count;
ubatch.n_seqs = 1;
llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1);
for (uint32_t i = 0; i < cell_count; ++i) {
llama_pos pos;
@@ -1746,9 +1733,8 @@ llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_kv_cache_unified * kv,
llama_sbatch sbatch,
llama_kv_cache_unified::ubatch_heads heads,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), sbatch(std::move(sbatch)), heads(std::move(heads)), ubatches(std::move(ubatches)) {
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), heads(std::move(heads)), ubatches(std::move(ubatches)) {
}
llama_kv_cache_unified_state::~llama_kv_cache_unified_state() = default;
@@ -1781,12 +1767,6 @@ bool llama_kv_cache_unified_state::apply() {
return true;
}
std::vector<int64_t> & llama_kv_cache_unified_state::out_ids() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return sbatch.out_ids;
}
llama_memory_status llama_kv_cache_unified_state::get_status() const {
return status;
}