diff --git a/common/common.cpp b/common/common.cpp index c08f01b42..2bc0b8800 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -955,7 +955,7 @@ struct common_init_result common_init_from_params(common_params & params) { } if (llama_model_has_encoder(model)) { - llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0)); + llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size())); llama_token decoder_start_token_id = llama_model_decoder_start_token(model); if (decoder_start_token_id == -1) { decoder_start_token_id = bos; @@ -964,7 +964,7 @@ struct common_init_result common_init_from_params(common_params & params) { tmp.push_back(decoder_start_token_id); } if (llama_model_has_decoder(model)) { - llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch))); } llama_kv_cache_clear(lctx); llama_synchronize(lctx); diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 81c3220ad..a3b21ad6b 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -74,7 +74,6 @@ int main(int argc, char ** argv) { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp index 69e141ecb..d1731bba6 100644 --- a/examples/cvector-generator/cvector-generator.cpp +++ b/examples/cvector-generator/cvector-generator.cpp @@ -339,7 +339,7 @@ static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) { static bool get_hidden_layers(llama_context * ctx, std::vector & tokens) { llama_kv_cache_clear(ctx); - if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { fprintf(stderr, "%s : failed to eval\n", __func__); return false; } diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index fb52db4e1..c08e3e5f6 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -131,7 +131,7 @@ static bool run(llama_context * ctx, const common_params & params) { std::vector tokens = common_tokenize(ctx, params.prompt, add_bos); - if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { LOG_ERR("%s : failed to eval\n", __func__); return false; } diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index d1ff3e8bc..70ff47768 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -496,6 +496,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -508,9 +510,14 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); } - // TODO: use batch.logits to save computations instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return false; } @@ -523,6 +530,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index f82c614f5..f18362c91 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -396,7 +396,7 @@ int main(int argc, char ** argv) { LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 60a7aef5b..4a8ea9676 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -1428,7 +1428,7 @@ struct sql_printer : public printer { } }; -static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) { +static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); const llama_model * model = llama_get_model(ctx); @@ -1444,14 +1444,14 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat for (int i = 1; i < n_tokens; i++) { tokens[i] = std::rand() % n_vocab; } - llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0)); + llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens)); n_processed += n_tokens; } llama_synchronize(ctx); } -static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) { +static void test_gen(llama_context * ctx, int n_gen, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); const llama_model * model = llama_get_model(ctx); @@ -1460,7 +1460,7 @@ static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; for (int i = 0; i < n_gen; i++) { - llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0)); + llama_decode(ctx, llama_batch_get_one(&token, 1)); llama_synchronize(ctx); token = std::rand() % n_vocab; } @@ -1596,13 +1596,13 @@ int main(int argc, char ** argv) { fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count); } //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads); - test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); + test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count); } - test_gen(ctx, 1, 0, t.n_threads); + test_gen(ctx, 1, t.n_threads); } for (int i = 0; i < params.reps; i++) { @@ -1614,13 +1614,13 @@ int main(int argc, char ** argv) { if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps); } - test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); + test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps); } - test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads); + test_gen(ctx, t.n_gen, t.n_threads); } uint64_t t_ns = get_time_ns() - t_start; diff --git a/examples/llama.android/llama/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp index f5ffd063f..b3858ddfb 100644 --- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -283,9 +283,6 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, nullptr, nullptr, nullptr, - 0, - 0, - 0, }; if (embd) { diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 5f9abe2b6..161098585 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -20,7 +20,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector n_batch) { n_eval = n_batch; } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) { LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); return false; } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 2c96973c8..be6988540 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -401,6 +401,39 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co return true; } +struct llava_embd_batch { + std::vector pos; + std::vector n_seq_id; + std::vector seq_id_0; + std::vector seq_ids; + std::vector logits; + llama_batch batch; + llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) { + pos .resize(n_tokens); + n_seq_id.resize(n_tokens); + seq_ids .resize(n_tokens + 1); + logits .resize(n_tokens); + seq_id_0.resize(1); + seq_id_0[0] = seq_id; + seq_ids [n_tokens] = nullptr; + batch = { + /*n_tokens =*/ n_tokens, + /*tokens =*/ nullptr, + /*embd =*/ embd, + /*pos =*/ pos.data(), + /*n_seq_id =*/ n_seq_id.data(), + /*seq_id =*/ seq_ids.data(), + /*logits =*/ logits.data(), + }; + for (int i = 0; i < n_tokens; i++) { + batch.pos [i] = pos_0 + i; + batch.n_seq_id[i] = 1; + batch.seq_id [i] = seq_id_0.data(); + batch.logits [i] = false; + } + } +}; + bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) { int n_embd = llama_n_embd(llama_get_model(ctx_llama)); @@ -409,8 +442,9 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_ if (n_eval > n_batch) { n_eval = n_batch; } - llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; - if (llama_decode(ctx_llama, batch)) { + float * embd = image_embed->embed+i*n_embd; + llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0); + if (llama_decode(ctx_llama, llava_batch.batch)) { LOG_ERR("%s : failed to eval\n", __func__); return false; } diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp index 6b666de1b..cbecec343 100644 --- a/examples/llava/minicpmv-cli.cpp +++ b/examples/llava/minicpmv-cli.cpp @@ -97,7 +97,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector n_batch) { n_eval = n_batch; } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) { LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); return false; } diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index f9e4aba81..3c0ccfea2 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -89,8 +89,8 @@ int main(int argc, char ** argv) { const auto t_enc_start = ggml_time_us(); // eval the prompt - llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1)); for (int s = 1; s < W + G + 1; ++s) { llama_kv_cache_seq_cp(ctx, 0, s, -1, -1); diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 82fc7d466..a04728b18 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -89,8 +89,8 @@ int main(int argc, char ** argv){ const auto t_enc_start = ggml_time_us(); - llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1)); const auto t_enc_end = ggml_time_us(); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 65483c45f..374ed47ad 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -528,7 +528,7 @@ int main(int argc, char ** argv) { int enc_input_size = embd_inp.size(); llama_token * enc_input_buf = embd_inp.data(); - if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) { + if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } @@ -648,7 +648,7 @@ int main(int argc, char ** argv) { LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 20274c147..43c8f3ed5 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -308,7 +308,6 @@ int main(int argc, char ** argv) { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index efb41b80a..e803ff143 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -408,14 +408,21 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + //LOG_DBG(" Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); - // TODO: use llama_batch.logits instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + if (llama_decode(ctx, batch)) { //LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return {tokens, -1, logit_history, prob_history}; } @@ -435,6 +442,8 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { @@ -704,7 +713,6 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector< batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); @@ -1791,6 +1799,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -1803,9 +1813,14 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); } - // TODO: use llama_batch.logits instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return; } @@ -1818,6 +1833,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 3866cfa27..5f60a86cb 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -48,9 +48,16 @@ int main(int argc, char ** argv) { // tokenize prompt auto tokens = common_tokenize(ctx, params.prompt, true); + // prepare the batch + llama_batch batch = llama_batch_init(tokens.size(), 0, 1); + for (size_t i = 0; i < tokens.size(); i++) { + common_batch_add(batch, tokens[i], i, {0}, false); + } + batch.logits[batch.n_tokens - 1] = true; // generate next token + // evaluate prompt - llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0)); - n_past += tokens.size(); + llama_decode(ctx, batch); + n_past += batch.n_tokens; // save state (rng, logits, embedding and kv_cache) to file { @@ -77,8 +84,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result0 += next_token_str; - if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {0}, true); + + if (llama_decode(ctx, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx); llama_free_model(model); return 1; @@ -133,8 +144,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result1 += next_token_str; - if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {0}, true); + + if (llama_decode(ctx2, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx2); llama_free_model(model); return 1; @@ -221,8 +236,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result2 += next_token_str; - if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {1}, true); + + if (llama_decode(ctx3, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx3); llama_free_model(model); return 1; @@ -236,6 +255,7 @@ int main(int argc, char ** argv) { llama_sampler_free(smpl2); llama_sampler_free(smpl3); + llama_batch_free(batch); llama_free(ctx3); llama_free_model(model); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8fd443878..3992108e7 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2326,7 +2326,6 @@ struct server_context { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index be91b2891..59760fe95 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -138,7 +138,7 @@ int main(int argc, char ** argv) { // prepare a batch for the prompt - llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size(), 0, 0); + llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size()); // main loop @@ -175,7 +175,7 @@ int main(int argc, char ** argv) { fflush(stdout); // prepare the next batch with the sampled token - batch = llama_batch_get_one(&new_token_id, 1, n_pos, 0); + batch = llama_batch_get_one(&new_token_id, 1); n_decode += 1; } diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 5a7b3084f..b201bd714 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -155,9 +155,9 @@ int main(int argc, char ** argv) { const auto t_enc_start = ggml_time_us(); // eval the prompt with both models - llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); - llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input, 0, 0)); + llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1)); + llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input)); const auto t_enc_end = ggml_time_us(); diff --git a/include/llama.h b/include/llama.h index 1a13360c2..2558e9267 100644 --- a/include/llama.h +++ b/include/llama.h @@ -232,8 +232,11 @@ extern "C" { // - token : the token ids of the input (used when embd is NULL) // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) // - pos : the positions of the respective token in the sequence + // (if set to NULL, the token position will be tracked automatically by llama_decode) // - seq_id : the sequence to which the respective token belongs + // (if set to NULL, the sequence ID will be assumed to be 0) // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output + // (if set to NULL, only the logits for last token will be returned) // typedef struct llama_batch { int32_t n_tokens; @@ -244,15 +247,6 @@ extern "C" { int32_t * n_seq_id; llama_seq_id ** seq_id; int8_t * logits; // TODO: rename this to "output" - - // NOTE: helpers for smooth API transition - can be deprecated in the future - // for future-proof code, use the above fields instead and ignore everything below - // - // pos[i] = all_pos_0 + i*all_pos_1 - // - llama_pos all_pos_0; // used if pos == NULL - llama_pos all_pos_1; // used if pos == NULL - llama_seq_id all_seq_id; // used if seq_id == NULL } llama_batch; enum llama_model_kv_override_type { @@ -776,15 +770,15 @@ extern "C" { // Decoding // - // Return batch for single sequence of tokens starting at pos_0 + // Return batch for single sequence of tokens + // The sequence ID will be fixed to 0 + // The position of the tokens will be tracked automatically by llama_decode // // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it // LLAMA_API struct llama_batch llama_batch_get_one( llama_token * tokens, - int32_t n_tokens, - llama_pos pos_0, - llama_seq_id seq_id); + int32_t n_tokens); // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens // Each token can be assigned up to n_seq_max sequence ids diff --git a/src/llama.cpp b/src/llama.cpp index 10c975bf4..1813dd29b 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -2949,9 +2949,6 @@ struct llama_sbatch_seq { llama_seq_id * seq_id; size_t offset; size_t length; - - // helper for smoother batch API transition -- can be deprecated in the future - llama_seq_id all_seq_id; // used if seq_id == NULL }; // sequence-length-aware batch splitting @@ -3046,30 +3043,18 @@ struct llama_sbatch { } else { ubatch.embd = nullptr; } - // from here on, the else branches are deprecated; - // they are helpers for smoother batch API transition - if (batch->pos) { - if (ubatch.equal_seqs) { - for (size_t i = 0; i < length; ++i) { - ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]]; - } - } else { - // simple split - ubatch.pos = batch->pos + seq.offset; + if (ubatch.equal_seqs) { + for (size_t i = 0; i < length; ++i) { + ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]]; } } else { - for (size_t i = 0; i < length; ++i) { - llama_pos bi = ids[seq.offset + i]; - ubatch.pos[ubatch.n_tokens + i] = batch->all_pos_0 + (bi * batch->all_pos_1); - } + // simple split + ubatch.pos = batch->pos + seq.offset; } if (ubatch.equal_seqs) { ubatch.n_seq_id[ubatch.n_seqs] = seq.n_seq_id; if (seq.seq_id) { ubatch.seq_id[ubatch.n_seqs] = seq.seq_id; - } else { - GGML_ASSERT(seq.n_seq_id == 1); - ubatch.seq_id[ubatch.n_seqs] = &seq.all_seq_id; } } else { // simple split @@ -3082,10 +3067,6 @@ struct llama_sbatch { } if (batch->seq_id) { ubatch.seq_id = batch->seq_id + seq.offset; - } else { - for (size_t i = 0; i < length; ++i) { - ubatch.seq_id[ubatch.n_seqs + i] = &seq.all_seq_id; - } } } if (logits_all) { @@ -3204,7 +3185,6 @@ struct llama_sbatch { s.seq_id = nullptr; s.offset = 0; s.length = n_tokens; - s.all_seq_id = batch.all_seq_id; return; } std::sort(ids.begin(), ids.end(), @@ -3227,7 +3207,7 @@ struct llama_sbatch { if (batch.pos) { return batch.pos[a] < batch.pos[b]; } - // no pos, sort by id (assuming batch.all_pos_1 is positive) + // no pos, sort by id return a < b; } // shared prompts go first @@ -3237,30 +3217,25 @@ struct llama_sbatch { // init seq llama_sbatch_seq * last_seq = nullptr; - if (batch.n_seq_id != nullptr && batch.seq_id != nullptr) { - for (size_t i = 0; i < n_tokens; ++i) { - const size_t bi = ids[i]; - const int32_t n_seqs = batch.n_seq_id[bi]; - llama_seq_id * seq_ids = batch.seq_id[bi]; - if (last_seq != nullptr) { - bool same = n_seqs == last_seq->n_seq_id; - for (int32_t j = 0; same && j < n_seqs; ++j) { - if (seq_ids[j] != last_seq->seq_id[j]) { - same = false; - } - } - if (same) { - last_seq->length += 1; - continue; + for (size_t i = 0; i < n_tokens; ++i) { + const size_t bi = ids[i]; + const int32_t n_seqs = batch.n_seq_id[bi]; + llama_seq_id * seq_ids = batch.seq_id[bi]; + if (last_seq != nullptr) { + bool same = n_seqs == last_seq->n_seq_id; + for (int32_t j = 0; same && j < n_seqs; ++j) { + if (seq_ids[j] != last_seq->seq_id[j]) { + same = false; } } - llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1, batch.all_seq_id}; - seq.push_back(new_seq); - last_seq = &seq.back(); + if (same) { + last_seq->length += 1; + continue; + } } - } else { - llama_sbatch_seq new_seq = {1, nullptr, 0, n_tokens, batch.all_seq_id}; + llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1}; seq.push_back(new_seq); + last_seq = &seq.back(); } // keep shared prompts first at the end, then sort by length descending. std::sort(seq.begin(), seq.end(), @@ -21096,9 +21071,7 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) { struct llama_batch llama_batch_get_one( llama_token * tokens, - int32_t n_tokens, - llama_pos pos_0, - llama_seq_id seq_id) { + int32_t n_tokens) { return { /*n_tokens =*/ n_tokens, /*tokens =*/ tokens, @@ -21107,9 +21080,6 @@ struct llama_batch llama_batch_get_one( /*n_seq_id =*/ nullptr, /*seq_id =*/ nullptr, /*logits =*/ nullptr, - /*all_pos_0 =*/ pos_0, - /*all_pos_1 =*/ 1, - /*all_seq_id =*/ seq_id, }; } @@ -21122,9 +21092,6 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_ /*n_seq_id =*/ nullptr, /*seq_id =*/ nullptr, /*logits =*/ nullptr, - /*all_pos_0 =*/ 0, - /*all_pos_1 =*/ 0, - /*all_seq_id =*/ 0, }; if (embd) { @@ -21160,11 +21127,62 @@ void llama_batch_free(struct llama_batch batch) { if (batch.logits) free(batch.logits); } +// temporary allocate memory for the input batch if needed +static const llama_seq_id batch_default_seq_id = 0; +struct llama_batch_allocr { + std::array seq_id_0 = {batch_default_seq_id}; + std::vector pos; + std::vector n_seq_id; + std::vector seq_id; + std::vector logits; + struct llama_batch batch; + // optionally fulfill the batch returned by llama_batch_get_one + llama_batch_allocr(struct llama_context * ctx, struct llama_batch in_batch) { + batch = in_batch; + if (!batch.pos) { + // determine the last position in KV cache + llama_pos last_pos = -1; + for (const auto & cell : ctx->kv_self.cells) { + if (cell.has_seq_id(batch_default_seq_id)) { + last_pos = std::max(last_pos, cell.pos); + } + } + last_pos++; // next position + pos.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + pos[i] = i+last_pos; + } + batch.pos = pos.data(); + } + if (!batch.n_seq_id) { + n_seq_id.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + n_seq_id[i] = seq_id_0.size(); + } + batch.n_seq_id = n_seq_id.data(); + } + if (!batch.seq_id) { + seq_id.resize(batch.n_tokens + 1); + seq_id[batch.n_tokens] = NULL; + for (int32_t i = 0; i < batch.n_tokens; i++) { + seq_id[i] = seq_id_0.data(); + } + batch.seq_id = seq_id.data(); + } + if (!batch.logits) { + logits.resize(batch.n_tokens); + logits[logits.size() - 1] = true; + batch.logits = logits.data(); + } + } +}; + int32_t llama_encode( struct llama_context * ctx, struct llama_batch batch) { - const int ret = llama_encode_internal(*ctx, batch); - if (ret < 0) { + llama_batch_allocr batch_allocr(ctx, batch); + const int ret = llama_encode_internal(*ctx, batch_allocr.batch); + if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret); } @@ -21174,8 +21192,9 @@ int32_t llama_encode( int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch) { - const int ret = llama_decode_internal(*ctx, batch); - if (ret < 0) { + llama_batch_allocr batch_allocr(ctx, batch); + const int ret = llama_decode_internal(*ctx, batch_allocr.batch); + if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret); }