kv-cache : revert the (n_swa + n_ubatch) change (for next PR)

ggml-ci

Author: ggerganov

src/llama-kv-cache.cpp

@@ -1721,14 +1721,14 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
                      bool   swa_full,
                  uint32_t   kv_size,
                  uint32_t   n_seq_max,
-                 uint32_t   n_ubatch,
+                 uint32_t   n_batch,
                  uint32_t   n_pad) : hparams(model.hparams) {
     llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
     llama_kv_cache_unified::layer_filter_cb filter_swa  = [&](int32_t il) { return  model.hparams.is_swa(il); };
 
     const uint32_t size_base = kv_size;
 
-    uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_ubatch, n_pad));
+    uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_batch, n_pad));
 
     // when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size
     if (swa_full) {

src/llama-kv-cache.h

@@ -252,7 +252,7 @@ class llama_kv_cache_unified_iswa : public llama_kv_cache {
                          bool   swa_full,
                      uint32_t   kv_size,
                      uint32_t   n_seq_max,
-                     uint32_t   n_ubatch,
+                     uint32_t   n_batch,
                      uint32_t   n_pad);
 
     ~llama_kv_cache_unified_iswa() = default;

src/llama-model.cpp

@@ -13228,7 +13228,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                             params.swa_full,
                             cparams.n_ctx,
                             cparams.n_seq_max,
-                            cparams.n_ubatch,
+                            cparams.n_batch,
                             padding);
                 } else {
                     GGML_ASSERT(!hparams.is_swa_any());

tools/server/server.cpp

@@ -2019,6 +2019,11 @@ struct server_context {
                 params_base.n_cache_reuse = 0;
                 SRV_WRN("%s\n", "cache_reuse is not supported by this context, it will be disabled");
             }
+
+            if (!params_base.speculative.model.path.empty()) {
+                SRV_ERR("%s\n", "err: speculative decode is not supported by this context");
+                return false;
+            }
         }
 
         return true;
@@ -3211,7 +3216,7 @@ struct server_context {
                                 slot.cache_tokens.clear(); // TODO: not needed, will be cleared later via "keep_first()"
                             }
 
-                            if (slot.n_past > 0 && slot.n_past + 32 < (int) slot.cache_tokens.size()) {
+                            if (slot.n_past > 0 && slot.n_past < (int) slot.cache_tokens.size()) {
                                 const auto pos_min = llama_kv_self_seq_pos_min(ctx, slot.id);
                                 if (pos_min > 0) {
                                     SLT_WRN(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d\n", slot.n_past, (int) slot.cache_tokens.size(), slot.id, pos_min);
@@ -3425,10 +3430,11 @@ struct server_context {
 
                 // retry with half the batch size to try to find a free slot in the KV cache
                 n_batch /= 2;
-                i -= n_batch;
 
                 SRV_WRN("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation, i = %d, n_batch = %d, ret = %d\n", i, n_batch, ret);
 
+                i -= n_batch;
+
                 continue; // continue loop of n_batch
             }