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main : add self-extend support (#4815)

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* examples : add passkey test

* passkey : better prints

* passkey : select pass key pos from CLI

* passkey : simplify n_past logic

* llama : "self-extend"-like context extension

* passkey : add comment

* main : add Self-Extend support

* llama : add comment about llama_kv_cache_seq_div
This commit is contained in:
Georgi Gerganov 2024-01-08 11:18:32 +02:00 committed by GitHub
parent b0034d93ce
commit 52531fdff8
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4 changed files with 87 additions and 24 deletions
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18
common/common.cpp
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@ -220,6 +220,20 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
params.n_ctx = std::stoi(argv[i]);
} else if (arg == "--grp-attn-n" || arg == "-gan") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.grp_attn_n = std::stoi(argv[i]);
} else if (arg == "--grp-attn-w" || arg == "-gaw") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.grp_attn_w = std::stoi(argv[i]);
} else if (arg == "--rope-freq-base") {
if (++i >= argc) {
invalid_param = true;
@ -904,6 +918,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" Not recommended since this is both slower and uses more VRAM.\n");
#endif // GGML_USE_CUBLAS
#endif
printf(" -gan N, --grp-attn-n N\n");
printf(" group-attention factor (default: %d)\n", params.grp_attn_n);
printf(" -gat N, --grp-attn-w N\n");
printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w);
printf(" --verbose-prompt print prompt before generation\n");
printf(" -dkvc, --dump-kv-cache\n");
printf(" verbose print of the KV cache\n");

2
common/common.h
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@ -62,6 +62,8 @@ struct gpt_params {
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
int32_t n_beams = 0; // if non-zero then use beam search of given width.
int32_t grp_attn_n = 1; // group-attention factor
int32_t grp_attn_w = 512; // group-attention width
float rope_freq_base = 0.0f; // RoPE base frequency
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor

41
examples/main/main.cpp
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@ -439,6 +439,21 @@ int main(int argc, char ** argv) {
LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
// group-attention state
// number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
int ga_i = 0;
const int ga_n = params.grp_attn_n;
const int ga_w = params.grp_attn_w;
if (ga_n != 1) {
GGML_ASSERT(ga_n > 0 && "grp_attn_n must be positive"); // NOLINT
GGML_ASSERT(ga_w % ga_n == 0 && "grp_attn_w must be a multiple of grp_attn_n"); // NOLINT
//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of grp_attn_w"); // NOLINT
//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
}
LOG_TEE("\n\n");
if (params.interactive) {
@ -500,7 +515,8 @@ int main(int argc, char ** argv) {
fflush(stdout);
}
// infinite text generation via context swapping
if (ga_n == 1) {
// infinite text generation via context shifting
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
@ -532,6 +548,29 @@ int main(int argc, char ** argv) {
LOG("clear session path\n");
path_session.clear();
}
} else {
// context extension via Self-Extend
while (n_past >= ga_i + ga_w) {
const int ib = (ga_n*ga_i)/ga_w;
const int bd = (ga_w/ga_n)*(ga_n - 1);
const int dd = (ga_w/ga_n) - ib*bd - ga_w;
LOG("\n");
LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd);
LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
llama_kv_cache_seq_shift(ctx, 0, ga_i, n_past, ib*bd);
llama_kv_cache_seq_div (ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n);
llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd);
n_past -= bd;
ga_i += ga_w/ga_n;
LOG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i);
}
}
// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
if (n_session_consumed < (int) session_tokens.size()) {

4
llama.h
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@ -484,6 +484,10 @@ extern "C" {
llama_pos p1,
llama_pos delta);
// Integer division of the positions by factor of `d > 1`
// If the KV cache is RoPEd, the KV data is updated accordingly
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_div(
struct llama_context * ctx,
llama_seq_id seq_id,