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3 files changed, 310 insertions, 0 deletions

diff --git a/llama.cpp/examples/batched/CMakeLists.txt b/llama.cpp/examples/batched/CMakeLists.txt
new file mode 100644
index 0000000..0d439f4
--- /dev/null
+++ b/llama.cpp/examples/batched/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET llama-batched)
+add_executable(${TARGET} batched.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/llama.cpp/examples/batched/README.md b/llama.cpp/examples/batched/README.md
new file mode 100644
index 0000000..8cde35d
--- /dev/null
+++ b/llama.cpp/examples/batched/README.md
@@ -0,0 +1,44 @@
+# llama.cpp/example/batched
+
+The example demonstrates batched generation from a given prompt
+
+```bash
+./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4 --kv-unified
+
+...
+
+main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113
+
+ Hello my name is
+
+main: generating 4 sequences ...
+
+main: stream 0 finished
+main: stream 1 finished
+main: stream 2 finished
+main: stream 3 finished
+
+sequence 0:
+
+Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b
+
+sequence 1:
+
+Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between
+
+sequence 2:
+
+Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am
+
+sequence 3:
+
+Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and
+
+main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s
+
+llama_print_timings:        load time =   587.00 ms
+llama_print_timings:      sample time =     2.56 ms /   112 runs   (    0.02 ms per token, 43664.72 tokens per second)
+llama_print_timings: prompt eval time =  4089.11 ms /   118 tokens (   34.65 ms per token,    28.86 tokens per second)
+llama_print_timings:        eval time =     0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
+llama_print_timings:       total time =  4156.04 ms
+```
diff --git a/llama.cpp/examples/batched/batched.cpp b/llama.cpp/examples/batched/batched.cpp
new file mode 100644
index 0000000..a8c19a6
--- /dev/null
+++ b/llama.cpp/examples/batched/batched.cpp
@@ -0,0 +1,261 @@
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+#include "llama.h"
+#include "sampling.h"
+
+#include <algorithm>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+static void print_usage(int, char ** argv) {
+    LOG("\nexample usage:\n");
+    LOG("\n    %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]);
+    LOG("\n");
+}
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    params.prompt = "Hello my name is";
+    params.n_predict = 32;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BATCHED, print_usage)) {
+        return 1;
+    }
+
+    common_init();
+
+    // number of parallel batches
+    int n_parallel = params.n_parallel;
+
+    // total length of the sequences including the prompt
+    int n_predict = params.n_predict;
+
+    // init LLM
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    // initialize the model
+
+    llama_model_params model_params = common_model_params_to_llama(params);
+
+    llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params);
+
+    if (model == NULL) {
+        LOG_ERR("%s: error: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    // tokenize the prompt
+
+    std::vector<llama_token> tokens_list;
+    tokens_list = common_tokenize(vocab, params.prompt, true);
+
+    const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
+
+    // initialize the context
+
+    llama_context_params ctx_params = common_context_params_to_llama(params);
+
+    ctx_params.n_ctx   = n_kv_req;
+    ctx_params.n_batch = std::max(n_predict, n_parallel);
+
+    auto sparams = llama_sampler_chain_default_params();
+    sparams.no_perf = false;
+
+    std::vector<llama_sampler_seq_config> sampler_configs;
+
+    for (int32_t i = 0; i < n_parallel; ++i) {
+        llama_sampler * smpl = llama_sampler_chain_init(sparams);
+
+        llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
+        llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
+        llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
+        llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
+
+        sampler_configs.push_back({ i, smpl });
+    }
+
+    if (params.sampling.backend_sampling) {
+        ctx_params.samplers   = sampler_configs.data();
+        ctx_params.n_samplers = sampler_configs.size();
+    }
+
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
+
+    if (ctx == NULL) {
+        LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    const int n_ctx = llama_n_ctx(ctx);
+
+    LOG_INF("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
+
+    // make sure the KV cache is big enough to hold all the prompt and generated tokens
+    if (n_kv_req > n_ctx) {
+        LOG_ERR("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__,  n_kv_req);
+        LOG_ERR("%s:        either reduce n_parallel or increase n_ctx\n", __func__);
+        return 1;
+    }
+
+    // print the prompt token-by-token
+
+    LOG("\n");
+
+    for (auto id : tokens_list) {
+        LOG("%s", common_token_to_piece(ctx, id).c_str());
+    }
+
+    // create a llama_batch
+    // we use this object to submit token data for decoding
+    llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel);
+
+    std::vector<llama_seq_id> seq_ids(n_parallel, 0);
+    for (int32_t i = 0; i < n_parallel; ++i) {
+        seq_ids[i] = i;
+    }
+
+    // evaluate the initial prompt
+    for (size_t i = 0; i < tokens_list.size(); ++i) {
+        common_batch_add(batch, tokens_list[i], i, seq_ids, false);
+    }
+    GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
+
+    if (llama_model_has_encoder(model)) {
+        if (llama_encode(ctx, batch)) {
+            LOG_ERR("%s : failed to eval\n", __func__);
+            return 1;
+        }
+
+        llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+        if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
+            decoder_start_token_id = llama_vocab_bos(vocab);
+        }
+
+        common_batch_clear(batch);
+        common_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
+    }
+
+    // llama_decode will output logits only for the last token of the prompt
+    batch.logits[batch.n_tokens - 1] = true;
+
+    if (llama_decode(ctx, batch) != 0) {
+        LOG_ERR("%s: llama_decode() failed\n", __func__);
+        return 1;
+    }
+
+    //// assign the system KV cache to all parallel sequences
+    //// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
+    //for (int32_t i = 1; i < n_parallel; ++i) {
+    //    llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+    //}
+
+    if (n_parallel > 1) {
+        LOG("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
+    }
+
+    // main loop
+
+    // we will store the parallel decoded sequences in this vector
+    std::vector<std::string> streams(n_parallel);
+
+    // remember the batch index of the last token for each parallel sequence
+    // we need this to determine which logits to sample from
+    std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
+
+    int n_cur    = batch.n_tokens;
+    int n_decode = 0;
+
+    const auto t_main_start = ggml_time_us();
+
+    while (n_cur <= n_predict) {
+        // prepare the next batch
+        common_batch_clear(batch);
+
+        // sample the next token for each parallel sequence / stream
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            if (i_batch[i] < 0) {
+                // the stream has already finished
+                continue;
+            }
+
+            const llama_token new_token_id = llama_sampler_sample(sampler_configs[i].sampler, ctx, i_batch[i]);
+
+            // is it an end of generation? -> mark the stream as finished
+            if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
+                i_batch[i] = -1;
+                LOG("\n");
+                if (n_parallel > 1) {
+                    LOG_INF("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
+                }
+
+                continue;
+            }
+
+            // if there is only one stream, we print immediately to stdout
+            if (n_parallel == 1) {
+                LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
+            }
+
+            streams[i] += common_token_to_piece(ctx, new_token_id);
+
+            i_batch[i] = batch.n_tokens;
+
+            // push this new token for next evaluation
+            common_batch_add(batch, new_token_id, n_cur, { i }, true);
+
+            n_decode += 1;
+        }
+
+        // all streams are finished
+        if (batch.n_tokens == 0) {
+            break;
+        }
+
+        n_cur += 1;
+
+        // evaluate the current batch with the transformer model
+        if (llama_decode(ctx, batch)) {
+            LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
+            return 1;
+        }
+    }
+
+    if (n_parallel > 1) {
+        LOG("\n");
+
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            LOG("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
+        }
+    }
+
+    const auto t_main_end = ggml_time_us();
+
+    LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
+            __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
+
+    LOG("\n");
+    llama_perf_sampler_print(sampler_configs[0].sampler);
+    llama_perf_context_print(ctx);
+
+    fprintf(stderr, "\n");
+
+    llama_batch_free(batch);
+
+    for (auto & sampler_config : sampler_configs) {
+        llama_sampler_free(sampler_config.sampler);
+    }
+
+    llama_free(ctx);
+    llama_model_free(model);
+
+    llama_backend_free();
+
+    return 0;
+}