llama : reuse compute graphs (#14482)

* llama : reuse compute graphs ggml-ci * llama-bench : add graph reuse parameter ggml-ci * cont : remove the parameter and the sched resets ggml-ci * graph : rename update() to can_reuse() ggml-ci * params : remove is_same() ggml-ci * graph : set res->params in llm_graph_context constructor ggml-ci * graph : avoid set_max_nodes in llm_graph_result ggml-ci * kv-cache : reuse llama_context's graph result instance ggml-ci * context : reset the previous graph result upon memory updates ggml-ci * batch : llama_ubatch now carries its data instead of pointing to balloc ggml-ci * merge : fix build ggml-ci * graph : fix can_reuse() checks when flash-attention is disabled * graph : move llm_graph_result impl in source file + debug env ggml-ci
2025-07-29 05:33:37 -04:00 · 2025-07-17 19:08:33 +03:00
parent 086cf81e88
commit 01612b7409
12 changed files with 548 additions and 289 deletions
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -28,6 +28,15 @@ void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
    }
 }

+bool llm_graph_input_embd::can_reuse(const llm_graph_params & params) {
+    bool res = true;
+
+    res &= (!tokens && !params.ubatch.token) || (tokens && tokens->ne[0] == params.ubatch.n_tokens);
+    res &= (!embd   && !params.ubatch.embd)  || (embd   &&   embd->ne[0] == params.ubatch.n_tokens);
+
+    return res;
+}
+
 void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) {
    if (ubatch->pos && pos) {
        const int64_t n_tokens = ubatch->n_tokens;
@@ -50,6 +59,14 @@ void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) {
    }
 }

+bool llm_graph_input_pos::can_reuse(const llm_graph_params & params) {
+    bool res = true;
+
+    res &= pos->ne[0] == params.ubatch.n_tokens;
+
+    return res;
+}
+
 void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
    if (ubatch->pos && attn_scale) {
        const int64_t n_tokens = ubatch->n_tokens;
@@ -71,7 +88,7 @@ void llm_graph_input_pos_bucket::set_input(const llama_ubatch * ubatch) {
        const int64_t n_tokens = ubatch->n_tokens;

        GGML_ASSERT(ggml_backend_buffer_is_host(pos_bucket->buffer));
-        GGML_ASSERT(!ubatch->equal_seqs); // TODO: use ubatch->n_seqs instead of failing
+        GGML_ASSERT(!ubatch->equal_seqs()); // TODO: use ubatch->n_seqs instead of failing

        int32_t * data = (int32_t *) pos_bucket->data;

@@ -118,6 +135,14 @@ void llm_graph_input_out_ids::set_input(const llama_ubatch * ubatch) {
    }
 }

+bool llm_graph_input_out_ids::can_reuse(const llm_graph_params & params) {
+    bool res = true;
+
+    res &= n_outputs == params.n_outputs;
+
+    return res;
+}
+
 void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) {
    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
        const int64_t n_tokens     = ubatch->n_tokens;
@@ -287,6 +312,24 @@ void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
    mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
 }

+bool llm_graph_input_attn_kv_unified::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_kv_cache_unified_context *>(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
+  //res &= self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
+
+    res &= self_kq_mask->ne[0] == mctx->get_n_kv();
+    res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
+
+    res &= mctx->get_supports_set_rows(); // TODO: tmp
+
+    return res;
+}
+
 void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
    mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
    mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
@@ -299,6 +342,30 @@ void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch
    mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
 }

+bool llm_graph_input_attn_kv_unified_iswa::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_kv_cache_unified_iswa_context *>(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
+  //res &= self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
+
+    res &= self_k_idxs_swa->ne[0] == params.ubatch.n_tokens;
+  //res &= self_v_idxs_swa->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
+
+    res &= self_kq_mask->ne[0] == mctx->get_base()->get_n_kv();
+    res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
+
+    res &= self_kq_mask_swa->ne[0] == mctx->get_swa()->get_n_kv();
+    res &= self_kq_mask_swa->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
+
+    res &= mctx->get_base()->get_supports_set_rows(); // TODO: tmp
+
+    return res;
+}
+
 void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
    GGML_ASSERT(cross_kq_mask);

@@ -306,7 +373,7 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
    const int64_t n_tokens = ubatch->n_tokens;

    GGML_ASSERT(ggml_backend_buffer_is_host(cross_kq_mask->buffer));
-    GGML_ASSERT(!ubatch->equal_seqs); // TODO: use ubatch->n_seqs instead of failing
+    GGML_ASSERT(!ubatch->equal_seqs()); // TODO: use ubatch->n_seqs instead of failing

    float * data = (float *) cross_kq_mask->data;

@@ -340,6 +407,83 @@ void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
    inp_rs->set_input(ubatch);
 }

+//
+// llm_graph_result
+//
+
+llm_graph_result::llm_graph_result(int64_t max_nodes) : max_nodes(max_nodes) {
+    reset();
+
+    const char * LLAMA_GRAPH_RESULT_DEBUG = getenv("LLAMA_GRAPH_RESULT_DEBUG");
+    debug = LLAMA_GRAPH_RESULT_DEBUG ? atoi(LLAMA_GRAPH_RESULT_DEBUG) : 0;
+}
+
+int64_t llm_graph_result::get_max_nodes() const {
+    return max_nodes;
+}
+
+void llm_graph_result::reset() {
+    t_tokens      = nullptr;
+    t_logits      = nullptr;
+    t_embd        = nullptr;
+    t_embd_pooled = nullptr;
+
+    inputs.clear();
+
+    buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));
+
+    ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute_meta.size(),
+        /*.mem_buffer =*/ buf_compute_meta.data(),
+        /*.no_alloc   =*/ true,
+    };
+
+    ctx_compute.reset(ggml_init(params));
+
+    gf = ggml_new_graph_custom(ctx_compute.get(), max_nodes, false);
+}
+
+void llm_graph_result::set_inputs(const llama_ubatch * ubatch) {
+    for (auto & input : inputs) {
+        input->set_input(ubatch);
+    }
+}
+
+bool llm_graph_result::can_reuse(const llm_graph_params & params) {
+    if (!this->params.allow_reuse(params)) {
+        if (debug > 1) {
+            LLAMA_LOG_DEBUG("%s: cannot reuse graph due to incompatible graph parameters\n", __func__);
+        }
+
+        return false;
+    }
+
+    if (debug > 1) {
+        LLAMA_LOG_DEBUG("%s: checking compatibility of %d inputs:\n", __func__, (int) inputs.size());
+    }
+
+    bool res = true;
+
+    for (auto & input : inputs) {
+        const bool cur = input->can_reuse(params);
+
+        LLAMA_LOG_DEBUG("  %s: can_reuse = %d\n", "placeholder", cur);
+
+        res = res && cur;
+    }
+
+    if (debug > 0) {
+        LLAMA_LOG_DEBUG("%s: can reuse graph = %d\n", __func__, res);
+    }
+
+    return res;
+}
+
+llm_graph_input_i * llm_graph_result::add_input(llm_graph_input_ptr input) {
+    inputs.emplace_back(std::move(input));
+    return inputs.back().get();
+}
+
 //
 // llm_graph_context
 //
@@ -374,7 +518,6 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
    n_ctx_orig       (cparams.n_ctx_orig_yarn),
    pooling_type     (cparams.pooling_type),
    rope_type        (hparams.rope_type),
-    ctx0             (params.ctx),
    sched            (params.sched),
    backend_cpu      (params.backend_cpu),
    cvec             (params.cvec),
@@ -382,7 +525,9 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
    mctx             (params.mctx),
    cross            (params.cross),
    cb_func          (params.cb),
-    res              (std::make_unique<llm_graph_result>()) {
+    res              (static_cast<llm_graph_result *>(params.res)),
+    ctx0             (res->get_ctx()) {
+        res->params = params;
    }

 void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const {
@@ -1127,8 +1272,8 @@ ggml_tensor * llm_graph_context::build_attn(
    const auto & kq_mask = inp->get_kq_mask();

    // [TAG_NO_CACHE_PAD]
-    // TODO: if ubatch.equal_seqs == true, we can split the three tensors below into ubatch.n_seqs_unq streams
-    assert(ubatch.equal_seqs == false);
+    // TODO: if ubatch.equal_seqs() == true, we can split the three tensors below into ubatch.n_seqs_unq streams
+    assert(!ubatch.equal_seqs());

    ggml_tensor * q = q_cur;
    ggml_tensor * k = k_cur;