simple : add parallel decoding support

Author: ggerganov

common/common.cpp

@@ -956,19 +956,19 @@ llama_token llama_sample_token(
         if (mirostat == 1) {
             static float mirostat_mu = 2.0f * mirostat_tau;
             const int mirostat_m = 100;
-            llama_sample_temperature(ctx, &cur_p, temp);
+            llama_sample_temp(ctx, &cur_p, temp);
             id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
         } else if (mirostat == 2) {
             static float mirostat_mu = 2.0f * mirostat_tau;
-            llama_sample_temperature(ctx, &cur_p, temp);
+            llama_sample_temp(ctx, &cur_p, temp);
             id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
         } else {
             // Temperature sampling
             llama_sample_top_k      (ctx, &cur_p, top_k, 1);
             llama_sample_tail_free  (ctx, &cur_p, tfs_z, 1);
             llama_sample_typical    (ctx, &cur_p, typical_p, 1);
             llama_sample_top_p      (ctx, &cur_p, top_p, 1);
-            llama_sample_temperature(ctx, &cur_p, temp);
+            llama_sample_temp(ctx, &cur_p, temp);
 
             {
                 const int n_top = 10;

examples/embd-input/embd-input-lib.cpp

@@ -79,7 +79,7 @@ bool eval_float(void * model, float * input, int N){
         if (n_eval > n_batch) {
             n_eval = n_batch;
         }
-        llama_batch batch = { uint32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, n_past, 1, 0, };
+        llama_batch batch = {  int32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, n_past, 1, 0, };
         if (llama_decode(ctx, batch, params.n_threads)) {
             fprintf(stderr, "%s : failed to eval\n", __func__);
             return false;
@@ -183,19 +183,19 @@ llama_token sampling_id(struct MyModel* mymodel) {
             if (mirostat == 1) {
                 static float mirostat_mu = 2.0f * mirostat_tau;
                 const int mirostat_m = 100;
-                llama_sample_temperature(ctx, &candidates_p, temp);
+                llama_sample_temp(ctx, &candidates_p, temp);
                 id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
             } else if (mirostat == 2) {
                 static float mirostat_mu = 2.0f * mirostat_tau;
-                llama_sample_temperature(ctx, &candidates_p, temp);
+                llama_sample_temp(ctx, &candidates_p, temp);
                 id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
             } else {
                 // Temperature sampling
                 llama_sample_top_k(ctx, &candidates_p, top_k, 1);
                 llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
                 llama_sample_typical(ctx, &candidates_p, typical_p, 1);
                 llama_sample_top_p(ctx, &candidates_p, top_p, 1);
-                llama_sample_temperature(ctx, &candidates_p, temp);
+                llama_sample_temp(ctx, &candidates_p, temp);
                 id = llama_sample_token(ctx, &candidates_p);
             }
         }

examples/parallel/parallel.cpp

@@ -123,7 +123,7 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> tokens_system;
     tokens_system = ::llama_tokenize(ctx, k_system, true);
-    const uint32_t n_tokens_system = tokens_system.size();
+    const int32_t n_tokens_system = tokens_system.size();
 
     llama_seq_id g_seq_id = 0;
 
@@ -144,7 +144,7 @@ int main(int argc, char ** argv) {
 
         batch.n_tokens = n_tokens_system;
 
-        for (uint32_t i = 0; i < batch.n_tokens; ++i) {
+        for (int32_t i = 0; i < batch.n_tokens; ++i) {
             batch.token[i]  = tokens_system[i];
             batch.pos[i]    = i;
             batch.seq_id[i] = 0;
@@ -156,7 +156,7 @@ int main(int argc, char ** argv) {
             return 1;
         }
 
-        // assign the system KV cachce to all parallel sequences
+        // assign the system KV cache to all parallel sequences
         for (int32_t i = 1; i < n_clients; ++i) {
             llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system);
         }
@@ -248,7 +248,7 @@ int main(int argc, char ** argv) {
         int32_t n_batch = params.n_batch;
 
         for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
-            const uint32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
+            const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
 
             llama_batch batch_view = {
                 n_tokens,

examples/server/server.cpp

@@ -523,13 +523,13 @@ struct llama_server_context
                 {
                     static float mirostat_mu = 2.0f * mirostat_tau;
                     const int mirostat_m = 100;
-                    llama_sample_temperature(ctx, &candidates_p, temp);
+                    llama_sample_temp(ctx, &candidates_p, temp);
                     result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
                 }
                 else if (mirostat == 2)
                 {
                     static float mirostat_mu = 2.0f * mirostat_tau;
-                    llama_sample_temperature(ctx, &candidates_p, temp);
+                    llama_sample_temp(ctx, &candidates_p, temp);
                     result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
                 }
                 else
@@ -540,7 +540,7 @@ struct llama_server_context
                     llama_sample_tail_free(ctx, &candidates_p, tfs_z, min_keep);
                     llama_sample_typical(ctx, &candidates_p, typical_p, min_keep);
                     llama_sample_top_p(ctx, &candidates_p, top_p, min_keep);
-                    llama_sample_temperature(ctx, &candidates_p, temp);
+                    llama_sample_temp(ctx, &candidates_p, temp);
                     result.tok = llama_sample_token(ctx, &candidates_p);
                 }
             }

examples/simple/simple.cpp

@@ -32,12 +32,18 @@ int main(int argc, char ** argv) {
         params.prompt = "Hello my name is";
     }
 
+    // total length of the sequences including the prompt
+    const int n_len = 32;
+
     // init LLM
 
     llama_backend_init(params.numa);
 
     llama_context_params ctx_params = llama_context_default_params();
 
+    ctx_params.seed  = 1234;
+    ctx_params.n_ctx = 2048;
+
     llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
 
     if (model == NULL) {
@@ -47,87 +53,187 @@ int main(int argc, char ** argv) {
 
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
+    if (ctx == NULL) {
+        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
     // tokenize the prompt
 
     std::vector<llama_token> tokens_list;
     tokens_list = ::llama_tokenize(ctx, params.prompt, true);
 
-    const int max_context_size     = llama_n_ctx(ctx);
-    const int max_tokens_list_size = max_context_size - 4;
+    const int n_ctx    = llama_n_ctx(ctx);
+    const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
 
-    if ((int) tokens_list.size() > max_tokens_list_size) {
-        fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) tokens_list.size(), max_tokens_list_size);
+    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_parallel, n_kv_req);
+
+    // make sure wi
+    if (n_kv_req > n_ctx) {
+        LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
+        LOG_TEE("%s:        either reduce n_parallel or increase n_ctx\n", __func__);
         return 1;
     }
 
-    fprintf(stderr, "\n\n");
+    fprintf(stderr, "\n");
 
     for (auto id : tokens_list) {
         fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
     }
 
     fflush(stderr);
 
+    // create a llama_batch with size 512
+    // we use this object to submit token data for decoding
+
+    llama_batch batch = llama_batch_init(512, 0);
+
+    // evaluate the initial prompt
+    batch.n_tokens = tokens_list.size();
+
+    for (int32_t i = 0; i < batch.n_tokens; i++) {
+        batch.token[i]  = tokens_list[i];
+        batch.pos[i]    = i;
+        batch.seq_id[i] = 0;
+        batch.logits[i] = 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, params.n_threads) != 0) {
+        LOG_TEE("%s: llama_decode() failed\n", __func__);
+        return 1;
+    }
+
+    // assign the system KV cache to all parallel sequences
+    for (int32_t i = 1; i < n_parallel; ++i) {
+        llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
+    }
+
+    if (n_parallel > 1) {
+        LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
+    }
+
     // main loop
 
-    // The LLM keeps a contextual cache memory of previous token evaluation.
-    // Usually, once this cache is full, it is required to recompute a compressed context based on previous
-    // tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
-    // example, we will just stop the loop once this cache is full or once an end of stream is detected.
+    // we will store the parallel decoded sequences in this vector
+    std::vector<std::string> streams(n_parallel);
 
-    const int n_gen = std::min(32, max_context_size);
+    // remember the batch index of the last tokenn for each parallel sequence
+    // we will use this to know which logits to sample from
+    std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
 
-    int n_cur = 0;
+    int n_cur    = batch.n_tokens;
+    int n_decode = 0;
 
-    while (n_cur < n_gen) {
-        // evaluate the transformer
+    const auto t_main_start = ggml_time_us();
 
-        if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), int(tokens_list.size()), n_cur, 0), params.n_threads)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
+    while (n_cur <= n_len) {
+        // evaluate the current batch with the transformer model
+        if (llama_decode(ctx, batch, params.n_threads)) {
+            fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
             return 1;
         }
 
-        n_cur += tokens_list.size();
-        tokens_list.clear();
+        // prepare the next batch
+        batch.n_tokens = 0;
 
-        // sample the next token
+        // 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;
+            }
 
-        llama_token new_token_id = 0;
+            auto n_vocab = llama_n_vocab(ctx);
+            auto logits  = llama_get_logits(ctx) + i_batch[i] * n_vocab;
 
-        auto logits  = llama_get_logits(ctx);
-        auto n_vocab = llama_n_vocab(ctx);
+            std::vector<llama_token_data> candidates;
+            candidates.reserve(n_vocab);
 
-        std::vector<llama_token_data> candidates;
-        candidates.reserve(n_vocab);
+            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+                candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
+            }
 
-        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-            candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
-        }
+            llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
 
-        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+            const int   top_k = 40;
+            const float top_p = 0.9f;
+            const float temp  = 0.4f;
 
-        new_token_id = llama_sample_token_greedy(ctx , &candidates_p);
+            llama_sample_top_k(ctx, &candidates_p, top_k, 1);
+            llama_sample_top_p(ctx, &candidates_p, top_p, 1);
+            llama_sample_temp (ctx, &candidates_p, temp);
 
-        // is it an end of stream ?
-        if (new_token_id == llama_token_eos(ctx)) {
-            fprintf(stderr, " [end of text]\n");
+            const llama_token new_token_id = llama_sample_token(ctx, &candidates_p);
+
+            //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
+
+            // is it an end of stream ?
+            // mark this stream as finished
+            if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
+                i_batch[i] = -1;
+                LOG_TEE("\n");
+                if (n_parallel > 1) {
+                    LOG_TEE("%s: stream %d finished", __func__, i);
+                }
+
+                continue;
+            }
+
+            if (n_parallel == 1) {
+                // print the new token :
+                LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
+                fflush(stdout);
+            }
+
+            streams[i] += llama_token_to_piece(ctx, new_token_id);
+
+            // push this new token for next evaluation
+            batch.token [batch.n_tokens] = new_token_id;
+            batch.pos   [batch.n_tokens] = n_cur;
+            batch.seq_id[batch.n_tokens] = i;
+            batch.logits[batch.n_tokens] = true;
+
+            i_batch[i] = batch.n_tokens;
+
+            batch.n_tokens += 1;
+
+            n_decode += 1;
+        }
+
+        if (batch.n_tokens == 0) {
+            // all streams are finished
             break;
         }
 
-        // print the new token :
-        printf("%s", llama_token_to_piece(ctx, new_token_id).c_str());
-        fflush(stdout);
+        n_cur += 1;
+    }
+
+    LOG_TEE("\n");
 
-        // push this new token for next evaluation
-        tokens_list.push_back(new_token_id);
+    if (n_parallel > 1) {
+        LOG_TEE("\n");
+
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            LOG_TEE("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_TEE("%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));
+
+    llama_print_timings(ctx);
+
+    fprintf(stderr, "\n");
+
     llama_free(ctx);
     llama_free_model(model);
 
     llama_backend_free();
 
-    fprintf(stderr, "\n\n");
-
     return 0;
 }