context : simplify sbatch logic

ggml-ci

Author: ggerganov

src/llama-batch.cpp

@@ -189,7 +189,7 @@ llama_ubatch llama_sbatch::split_seq(size_t n_ubatch) {
     return ubatch;
 }
 
-void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) {
+llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) {
     GGML_ASSERT(batch.n_tokens >= 0);
     this->batch = &batch;
     this->n_embd = n_embd;
@@ -203,6 +203,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim
     for (size_t i = 0; i < n_tokens; ++i) {
         ids[i] = i;
     }
+
     if (simple_split) {
         seq.resize(1);
         llama_sbatch_seq & s = seq[0];
@@ -212,6 +213,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim
         s.length = n_tokens;
         return;
     }
+
     std::sort(ids.begin(), ids.end(),
             [&batch](size_t a, size_t b) {
                 int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1;
@@ -239,6 +241,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim
                 return n_seq_a > n_seq_b;
             }
     );
+
     // init seq
     llama_sbatch_seq * last_seq = nullptr;
 
@@ -262,6 +265,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim
         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(),
             [](llama_sbatch_seq & a, llama_sbatch_seq & b) {

src/llama-batch.h

@@ -70,7 +70,8 @@ struct llama_sbatch {
     // sequence-wise split
     llama_ubatch split_seq(size_t n_ubatch);
 
-    void from_batch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false);
+    llama_sbatch() = default;
+    llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false);
 };
 
 // temporary allocate memory for the input batch if needed

src/llama-context.cpp

@@ -810,9 +810,6 @@ enum llama_pooling_type llama_context::pooling_type() const {
 }
 
 float * llama_context::get_logits() {
-    // reorder logits for backward compatibility
-    output_reorder();
-
     return logits;
 }
 
@@ -855,9 +852,6 @@ float * llama_context::get_logits_ith(int32_t i) {
 }
 
 float * llama_context::get_embeddings() {
-    // reorder embeddings for backward compatibility
-    output_reorder();
-
     return embd;
 }
 
@@ -1039,7 +1033,7 @@ int llama_context::encode(llama_batch & inp_batch) {
 
     const int64_t n_embd = hparams.n_embd;
 
-    sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
+    llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
 
     const llama_ubatch ubatch = sbatch.split_simple(n_tokens);
 
@@ -1230,13 +1224,7 @@ int llama_context::decode(llama_batch & inp_batch) {
         n_outputs_all = 1;
     }
 
-    const bool logits_all = n_outputs_all == n_tokens_all;
-
-    const bool is_recurrent = llama_model_is_recurrent(&model);
-
-    sbatch.from_batch(batch, n_embd,
-            /* simple_split */ !is_recurrent,
-            /* logits_all   */ logits_all);
+    llama_sbatch sbatch = kv_self->sbatch_init(batch, /* logits_all */ n_outputs_all == n_tokens_all);
 
     // reserve output buffer
     if (output_reserve(n_outputs_all) < n_outputs_all) {
@@ -1393,18 +1381,52 @@ int llama_context::decode(llama_batch & inp_batch) {
     {
         bool sorted_output = true;
 
-        GGML_ASSERT(sbatch.out_ids.size() == (size_t) n_outputs_all);
+        auto & out_ids = sbatch.out_ids;
+
+        GGML_ASSERT(out_ids.size() == (size_t) n_outputs_all);
 
         for (int64_t i = 0; i < n_outputs_all; ++i) {
-            int64_t out_id = sbatch.out_ids[i];
+            int64_t out_id = out_ids[i];
             output_ids[out_id] = i;
             if (out_id != i) {
                 sorted_output = false;
             }
         }
 
-        if (sorted_output) {
-            sbatch.out_ids.clear();
+        // make the outputs have the same order they had in the user-provided batch
+        // note: this is mostly relevant for recurrent models atm
+        if (!sorted_output) {
+            const uint32_t n_vocab = model.vocab.n_tokens();
+            const uint32_t n_embd  = model.hparams.n_embd;
+
+            GGML_ASSERT((size_t) n_outputs == out_ids.size());
+
+            // TODO: is there something more efficient which also minimizes swaps?
+            // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
+            for (int32_t i = 0; i < n_outputs - 1; ++i) {
+                int32_t j_min = i;
+                for (int32_t j = i + 1; j < n_outputs; ++j) {
+                    if (out_ids[j] < out_ids[j_min]) {
+                        j_min = j;
+                    }
+                }
+                if (j_min == i) { continue; }
+                std::swap(out_ids[i], out_ids[j_min]);
+                if (logits_size > 0) {
+                    for (uint32_t k = 0; k < n_vocab; k++) {
+                        std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]);
+                    }
+                }
+                if (embd_size > 0) {
+                    for (uint32_t k = 0; k < n_embd; k++) {
+                        std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]);
+                    }
+                }
+            }
+            std::fill(output_ids.begin(), output_ids.end(), -1);
+            for (int32_t i = 0; i < n_outputs; ++i) {
+                output_ids[out_ids[i]] = i;
+            }
         }
     }
 
@@ -1515,44 +1537,6 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
     return n_outputs_max;
 }
 
-void llama_context::output_reorder() {
-    auto & out_ids = sbatch.out_ids;
-    if (!out_ids.empty()) {
-        const uint32_t n_vocab = model.vocab.n_tokens();
-        const uint32_t n_embd  = model.hparams.n_embd;
-
-        GGML_ASSERT((size_t) n_outputs == out_ids.size());
-
-        // TODO: is there something more efficient which also minimizes swaps?
-        // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
-        for (int32_t i = 0; i < n_outputs - 1; ++i) {
-            int32_t j_min = i;
-            for (int32_t j = i + 1; j < n_outputs; ++j) {
-                if (out_ids[j] < out_ids[j_min]) {
-                    j_min = j;
-                }
-            }
-            if (j_min == i) { continue; }
-            std::swap(out_ids[i], out_ids[j_min]);
-            if (logits_size > 0) {
-                for (uint32_t k = 0; k < n_vocab; k++) {
-                    std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]);
-                }
-            }
-            if (embd_size > 0) {
-                for (uint32_t k = 0; k < n_embd; k++) {
-                    std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]);
-                }
-            }
-        }
-        std::fill(output_ids.begin(), output_ids.end(), -1);
-        for (int32_t i = 0; i < n_outputs; ++i) {
-            output_ids[out_ids[i]] = i;
-        }
-        out_ids.clear();
-    }
-}
-
 //
 // graph
 //
@@ -1993,8 +1977,6 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
     {
         LLAMA_LOG_DEBUG("%s: - writing output ids\n", __func__);
 
-        output_reorder();
-
         const auto n_outputs    = this->n_outputs;
         const auto & output_ids = this->output_ids;
 

src/llama-context.h

@@ -137,10 +137,6 @@ struct llama_context {
     // Returns max number of outputs for which space was reserved.
     int32_t output_reserve(int32_t n_outputs);
 
-    // make the outputs have the same order they had in the user-provided batch
-    // TODO: maybe remove this
-    void output_reorder();
-
     //
     // graph
     //
@@ -197,7 +193,6 @@ struct llama_context {
     llama_cparams       cparams;
     llama_adapter_cvec  cvec;
     llama_adapter_loras loras;
-    llama_sbatch        sbatch;
 
     llama_cross cross; // TODO: tmp for handling cross-attention - need something better probably
 

src/llama-kv-cache.cpp

@@ -476,6 +476,12 @@ bool llama_kv_cache_unified::find_slot(
     return true;
 }
 
+llama_sbatch llama_kv_cache_unified::sbatch_init(
+        const llama_batch & batch,
+        bool logits_all) {
+    return llama_sbatch(batch, hparams.n_embd, true, logits_all);
+}
+
 llama_ubatch llama_kv_cache_unified::ubatch_next(
         llama_sbatch & sbatch,
         uint32_t n_ubatch,
@@ -1547,6 +1553,12 @@ bool llama_kv_cache_recurrent::find_slot(
     return n >= n_seqs;
 }
 
+llama_sbatch llama_kv_cache_recurrent::sbatch_init(
+        const llama_batch & batch,
+        bool logits_all) {
+    return llama_sbatch(batch, hparams.n_embd, false, logits_all);
+}
+
 llama_ubatch llama_kv_cache_recurrent::ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const {
     if (embd_pooled) {
         // Pooled embeddings cannot be split across ubatches (yet)

src/llama-kv-cache.h

@@ -45,6 +45,8 @@ struct llama_kv_cache : public llama_memory_i {
 
     virtual bool find_slot(const llama_ubatch & batch) = 0;
 
+    virtual llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) = 0;
+
     // different KV caches require different batch splitting strategies
     virtual llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const = 0;
 
@@ -143,6 +145,8 @@ class llama_kv_cache_unified : public llama_kv_cache {
     // to the first cell of the slot.
     bool find_slot(const llama_ubatch & batch) override;
 
+    llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
+
     llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
 
     static uint32_t get_padding(const llama_cparams & cparams);
@@ -269,6 +273,8 @@ class llama_kv_cache_recurrent : public llama_kv_cache {
     // to the first cell of the slot.
     bool find_slot(const llama_ubatch & batch) override;
 
+    llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
+
     llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
 
     // find how many cells are currently in use