feat: First pass at llama_kv_cache_hybrid_recurrent

This follows the pattern in iswa where the two child caches are held
explicitly to support the case where a model requires a single attention
cache and a single recurrent cache where each layer uses exactly one of the
caches.

This is a rewrite of the more generic approach in the original hybrid cache
PR: ggml-org#13276

Branch: HybridRecurrentCache

Signed-off-by: Gabe Goodhart <[email protected]>

Author: gabe-l-hart

src/llama-kv-cache.cpp

@@ -1740,8 +1740,8 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
                  uint32_t   n_seq_max,
                  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); };
+    llama_kv_cache::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
+    llama_kv_cache::layer_filter_cb filter_swa  = [&](int32_t il) { return  model.hparams.is_swa(il); };
 
     const uint32_t size_base = kv_size;
 
@@ -2975,3 +2975,227 @@ bool llama_kv_cache_recurrent::state_read_data(llama_io_read_i & io, uint32_t ce
 
     return true;
 }
+
+//
+// llama_kv_cache_hybrid_recurrent
+//
+
+class llama_kv_cache_hybrid_recurrent_decode_state_t : public llama_memory_decode_state_i {
+public:
+    llama_kv_cache_hybrid_recurrent_decode_state_t(llama_memory_status status) : status(status) {}
+
+    llama_kv_cache_hybrid_recurrent_decode_state_t(
+        llama_kv_cache_hybrid_recurrent * kv,
+                           llama_sbatch   sbatch,
+                  std::vector<uint32_t>   heads_attn,
+              std::vector<llama_ubatch>   ubatches)
+            : status(LLAMA_MEMORY_STATUS_SUCCESS),
+              kv(kv),
+              sbatch(std::move(sbatch)),
+              heads_attn(std::move(heads_attn)),
+              ubatches(std::move(ubatches)) {
+    }
+
+    ~llama_kv_cache_hybrid_recurrent_decode_state_t() = default;
+
+    llama_ubatch * next() override {
+        assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+        if (i_next >= ubatches.size()) {
+            return nullptr;
+        }
+
+        kv->get_kv_attn()     ->fill_slot(heads_attn[i_next], ubatches[i_next]);
+        kv->get_kv_recurrent()->find_slot(ubatches[i_next]);
+
+        return &ubatches[i_next++];
+    }
+
+    std::vector<int64_t> & out_ids() override {
+        assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+        return sbatch.out_ids;
+    }
+
+    llama_memory_status get_status() const override {
+        return status;
+    }
+
+private:
+    const llama_memory_status status;
+
+    llama_kv_cache_hybrid_recurrent * kv;
+
+    llama_sbatch sbatch;
+
+    // the index of the next ubatch to process
+    size_t i_next = 0;
+
+    std::vector<uint32_t>     heads_attn;
+    std::vector<llama_ubatch> ubatches;
+};
+
+llama_kv_cache_hybrid_recurrent::llama_kv_cache_hybrid_recurrent(
+    const llama_model & model,
+                        /* attn */
+            ggml_type   attn_type_k,
+            ggml_type   attn_type_v,
+                 bool   attn_v_trans,
+             uint32_t   attn_kv_size,
+             uint32_t   attn_n_pad,
+             uint32_t   attn_n_swa,
+       llama_swa_type   attn_swa_type,
+                        /* recurrent */
+            ggml_type   recurrent_type_k,
+            ggml_type   recurrent_type_v,
+             uint32_t   recurrent_kv_size,
+                        /* common */
+             uint32_t   n_seq_max,
+                 bool   offload) :
+    hparams(model.hparams),
+    kv_attn(new llama_kv_cache_unified(
+        model,
+        [&](int32_t il) { return !model.hparams.recurrent_layer(il); },
+        attn_type_k,
+        attn_type_v,
+        attn_v_trans,
+        offload,
+        attn_kv_size,
+        n_seq_max,
+        attn_n_pad,
+        attn_n_swa,
+        attn_swa_type
+    )),
+    kv_recurrent(new llama_kv_cache_recurrent(
+        model,
+        [&](int32_t il) { return model.hparams.recurrent_layer(il); },
+        recurrent_type_k,
+        recurrent_type_v,
+        offload,
+        recurrent_kv_size,
+        n_seq_max
+    )) {}
+
+void llama_kv_cache_hybrid_recurrent::clear() {
+    kv_attn     ->clear();
+    kv_recurrent->clear();
+}
+
+bool llama_kv_cache_hybrid_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    // Try removing from the recurrent cache first since it may fail. If it does
+    // fail, the cache will not have been mutated.
+    if (!kv_recurrent->seq_rm(seq_id, p0, p1)) {
+        return false;
+    }
+    return kv_attn->seq_rm(seq_id, p0, p1);
+}
+
+void llama_kv_cache_hybrid_recurrent::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+    kv_attn     ->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+    kv_recurrent->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+}
+
+void llama_kv_cache_hybrid_recurrent::seq_keep(llama_seq_id seq_id) {
+    kv_attn     ->seq_keep(seq_id);
+    kv_recurrent->seq_keep(seq_id);
+}
+
+void llama_kv_cache_hybrid_recurrent::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+    kv_attn->seq_add(seq_id, p0, p1, shift);
+    kv_recurrent->seq_add(seq_id, p0, p1, shift);
+}
+
+void llama_kv_cache_hybrid_recurrent::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+    kv_attn     ->seq_div(seq_id, p0, p1, d);
+    kv_recurrent->seq_div(seq_id, p0, p1, d);
+}
+
+llama_pos llama_kv_cache_hybrid_recurrent::seq_pos_min(llama_seq_id seq_id) const {
+    // the min of the total cache is the max of the two caches' min values
+    return std::max(kv_attn->seq_pos_min(seq_id), kv_recurrent->seq_pos_min(seq_id));
+}
+
+llama_pos llama_kv_cache_hybrid_recurrent::seq_pos_max(llama_seq_id seq_id) const {
+    // the max of the total cache is the min of the two caches' max values
+    return std::min(kv_attn->seq_pos_max(seq_id), kv_recurrent->seq_pos_max(seq_id));
+}
+
+llama_memory_decode_state_ptr llama_kv_cache_hybrid_recurrent::init(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled, bool logits_all) {
+
+    // since this includes a recurrent cache, we cannot use split_simple
+    auto sbatch = llama_sbatch(batch, hparams.n_embd, true, logits_all);
+
+    // follow the recurrent pattern for creating the ubatch splits
+    std::vector<llama_ubatch> ubatches;
+    while (sbatch.n_tokens > 0) {
+        llama_ubatch ubatch;
+
+        if (embd_pooled) {
+            // Pooled embeddings cannot be split across ubatches (yet)
+            ubatch = sbatch.split_seq(n_ubatch);
+        } else {
+            ubatch = sbatch.split_equal(n_ubatch);
+        }
+
+        ubatches.push_back(ubatch);
+    }
+
+    // prepare the recurrent batches first
+    if (!kv_recurrent->prepare(ubatches)) {
+        // TODO: will the recurrent cache be in an undefined state at this point?
+        LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__);
+        return std::make_unique<llama_kv_cache_hybrid_recurrent_decode_state_t>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+    }
+
+    // prepare the attention cache
+    auto heads_attn = kv_attn->prepare(ubatches);
+    if (heads_attn.empty()) {
+        LLAMA_LOG_ERROR("%s: failed to prepare attention ubatches\n", __func__);
+        return std::make_unique<llama_kv_cache_hybrid_recurrent_decode_state_t>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+    }
+
+    return std::make_unique<llama_kv_cache_hybrid_recurrent_decode_state_t>(
+        this, std::move(sbatch), std::move(heads_attn), std::move(ubatches));
+}
+
+bool llama_kv_cache_hybrid_recurrent::update(llama_context & lctx) {
+    bool res = false;
+
+    res = res | kv_attn     ->update(lctx);
+    res = res | kv_recurrent->update(lctx);
+
+    return res;
+}
+
+void llama_kv_cache_hybrid_recurrent::defrag_sched(float thold) {
+    kv_attn     ->defrag_sched(thold);
+    kv_recurrent->defrag_sched(thold);
+}
+
+void llama_kv_cache_hybrid_recurrent::set_full() {
+    kv_attn     ->set_full();
+    kv_recurrent->set_full();
+}
+
+bool llama_kv_cache_hybrid_recurrent::get_can_shift() const {
+    // TODO: Should this return true if the attention cache can shift?
+    return false;
+}
+
+void llama_kv_cache_hybrid_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
+    kv_attn     ->state_write(io, seq_id);
+    kv_recurrent->state_write(io, seq_id);
+}
+
+void llama_kv_cache_hybrid_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
+    kv_attn     ->state_read(io, seq_id);
+    kv_recurrent->state_read(io, seq_id);
+}
+
+llama_kv_cache_unified * llama_kv_cache_hybrid_recurrent::get_kv_attn() const {
+    return kv_attn.get();
+}
+
+llama_kv_cache_recurrent * llama_kv_cache_hybrid_recurrent::get_kv_recurrent() const {
+    return kv_recurrent.get();
+}

src/llama-kv-cache.h

@@ -428,3 +428,84 @@ class llama_kv_cache_recurrent : public llama_kv_cache {
     bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
     bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
 };
+
+//
+// llama_kv_cache_hybrid_recurrent
+//
+
+// utilizes instances of llama_kv_cache_recurrent and llama_kv_cache_unified to
+//   support models where each layer may be either attention-based or recurrent
+
+class llama_kv_cache_hybrid_recurrent : public llama_kv_cache {
+public:
+    llama_kv_cache_hybrid_recurrent(
+            const llama_model & model,
+                                /* attn */
+                    ggml_type   attn_type_k,
+                    ggml_type   attn_type_v,
+                         bool   attn_v_trans,
+                     uint32_t   attn_kv_size,
+                     uint32_t   attn_n_pad,
+                     uint32_t   attn_n_swa,
+               llama_swa_type   attn_swa_type,
+                                /* recurrent */
+                    ggml_type   recurrent_type_k,
+                    ggml_type   recurrent_type_v,
+                     uint32_t   recurrent_kv_size,
+                                /* common */
+                     uint32_t   n_seq_max,
+                         bool   offload);
+
+    ~llama_kv_cache_hybrid_recurrent() = default;
+
+    //
+    // llama_memory_i
+    //
+
+    void clear() override;
+
+    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
+    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
+    void seq_keep(llama_seq_id seq_id)                                                          override;
+    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) override;
+    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;
+
+    llama_pos seq_pos_min(llama_seq_id seq_id) const override;
+    llama_pos seq_pos_max(llama_seq_id seq_id) const override;
+
+    //
+    // llama_kv_cache
+    //
+
+    llama_memory_decode_state_ptr init(
+            const llama_batch & batch,
+            uint32_t n_ubatch,
+            bool embd_pooled,
+            bool logits_all) override;
+
+    bool update(llama_context & lctx) override;
+
+    void defrag_sched(float thold) override;
+
+    void set_full() override;
+
+    bool get_can_shift() const override;
+
+    // state write/load
+
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1)       override;
+
+    //
+    // llama_kv_cache_hybrid_recurrent specific API
+    //
+
+    llama_kv_cache_unified   * get_kv_attn     () const;
+    llama_kv_cache_recurrent * get_kv_recurrent() const;
+
+private:
+    const llama_hparams & hparams;
+
+    const std::unique_ptr<llama_kv_cache_unified>   kv_attn;
+    const std::unique_ptr<llama_kv_cache_recurrent> kv_recurrent;
+};