sampling : delegate input allocation to the scheduler (#19266)

* sampling : delegate input allocation to the scheduler

* graph : compute backend samplers only if needed

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 203852d0f1267b04e0ec08b043e19aec7c25b848..95b207e9e11497a0d4b67feca6b93b22a7f4098c 100644 (file)
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1027,11 +1027,7 @@ bool llama_context::set_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
         llama_sampler_chain_n(sampler) > 0;
 
     if (sampler && can_offload) {
-        ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(model.dev_output());
-        auto * host_buft = ggml_backend_dev_host_buffer_type(model.dev_output());
-        if (host_buft) {
-            buft = host_buft;
-        }
+        auto * buft = ggml_backend_dev_buffer_type(model.dev_output());
 
         sampler->iface->backend_init(sampler, buft);
 

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 16d42c4ae3de244befd15f72fa798dea6983f0a8..54f4ed248124a9b211dd8b360e2d621baa9c2dba 100644 (file)
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -2419,6 +2419,9 @@ void llm_graph_context::build_sampling() const {
         return;
     }
 
+    std::array<ggml_tensor *, 2> outs;
+    outs[0] = res->t_logits;
+
     auto inp_sampling = std::make_unique<llm_graph_input_sampling>(samplers);
     res->add_input(std::move(inp_sampling));
 
@@ -2439,14 +2442,14 @@ void llm_graph_context::build_sampling() const {
     // add a dummy row of logits
     // this trick makes the graph static, regardless of which samplers are activated
     // this is important in order to minimize graph reallocations
-    // TODO: use `ggml_build_forward_select()` when available (https://github.com/ggml-org/llama.cpp/pull/18550)
     ggml_tensor * logits_t = ggml_pad(ctx0, res->t_logits, 0, 1, 0, 0);
 
     for (const auto & [seq_id, sampler] : samplers) {
         const auto it = seq_to_logit_row.find(seq_id);
 
         // inactive samplers always work on the first row
-        const auto row_idx = seq_to_logit_row.find(seq_id) != seq_to_logit_row.end() ? it->second : 0;
+        const auto row_idx = it != seq_to_logit_row.end() ? it->second : 0;
+        const int i_out    = it != seq_to_logit_row.end() ? 1          : 0;
 
         ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, logits_t->ne[0], row_idx * logits_t->nb[1]);
         ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
@@ -2463,22 +2466,26 @@ void llm_graph_context::build_sampling() const {
 
         if (data.sampled != nullptr) {
             res->t_sampled[seq_id] = data.sampled;
-            ggml_build_forward_expand(gf, data.sampled);
+            outs[1] = data.sampled;
+            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
         }
 
         if (data.probs != nullptr) {
             res->t_sampled_probs[seq_id] = data.probs;
-            ggml_build_forward_expand(gf, data.probs);
+            outs[1] = data.probs;
+            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
         }
 
         if (data.logits != nullptr) {
             res->t_sampled_logits[seq_id] = data.logits;
-            ggml_build_forward_expand(gf, data.logits);
+            outs[1] = data.logits;
+            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
         }
 
         if (data.candidates != nullptr) {
             res->t_candidates[seq_id] = data.candidates;
-            ggml_build_forward_expand(gf, data.candidates);
+            outs[1] = data.candidates;
+            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
         }
     }
 

diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 5dde513065b5dd6ba630d1fc5f44c6367098d9e0..515d6c163b111c55f4cb67f3a4b79bc4e920e5d9 100644 (file)
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1025,11 +1025,7 @@ struct llama_sampler_dist : public llama_sampler_backend {
 
     std::mt19937 rng;
 
-    // backend input
-    struct ggml_tensor * inp_uniform;
-
-    ggml_context_ptr        inp_ctx;
-    ggml_backend_buffer_ptr inp_buf;
+    ggml_tensor * inp_uniform;
 };
 
 static const char * llama_sampler_dist_name(const struct llama_sampler * smpl) {
@@ -1138,37 +1134,10 @@ static bool llama_sampler_dist_backend_init(
         ggml_backend_buffer_type_t   buft) {
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
-    // allocate inputs
-    {
-        ggml_init_params params = {
-            /*.mem_size   =*/ ggml_tensor_overhead(),
-            /*.mem_buffer =*/ nullptr,
-            /*.no_alloc   =*/ true,
-        };
-
-        sctx->inp_ctx.reset(ggml_init(params));
-
-        // Create the uniform random scalar input tensor. This will be set by
-        // llama_sampler_dist_backend_set_input after this graph is built.
-        sctx->inp_uniform = ggml_new_tensor_1d(sctx->inp_ctx.get(), GGML_TYPE_F32, 1);
-        ggml_set_name (sctx->inp_uniform, "uniform");
-        ggml_set_input(sctx->inp_uniform);
-
-        // Allocate all tensors from our context to the backend
-        sctx->inp_buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(sctx->inp_ctx.get(), buft));
-
-        ggml_backend_buffer_clear(sctx->inp_buf.get(), 0);
-    }
-
     const bool res = llama_sampler_backend_support(smpl, buft);
 
     sctx->init(res);
 
-    if (!res) {
-        sctx->inp_ctx.reset(nullptr);
-        sctx->inp_buf.reset(nullptr);
-    }
-
     return res;
 }
 
@@ -1178,8 +1147,13 @@ static void llama_sampler_dist_backend_apply(
         struct ggml_cgraph        * gf,
         struct llama_sampler_data * data) {
     GGML_UNUSED(gf);
+
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
+    sctx->inp_uniform = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+    ggml_set_name (sctx->inp_uniform, "uniform");
+    ggml_set_input(sctx->inp_uniform);
+
     struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits);
     ggml_set_name(probs, "dist_probs");
 
@@ -1226,6 +1200,7 @@ static void llama_sampler_dist_backend_apply(
 
 static void llama_sampler_dist_backend_set_input(struct llama_sampler * smpl) {
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
+
     GGML_ASSERT(sctx->inp_uniform != nullptr);
 
     // We sample in double precision and cast to float to match rnd numbers of
@@ -1262,8 +1237,6 @@ struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
             /* .seed_cur    = */ seed_cur,
             /* .rng         = */ std::mt19937(seed_cur),
             /* .inp_uniform = */ nullptr,
-            /* .inp_ctx     = */ nullptr,
-            /* .inp_buf     = */ nullptr,
         }
     );
 }
@@ -3461,9 +3434,6 @@ struct llama_sampler_logit_bias : public llama_sampler_backend {
 
     struct ggml_tensor * inp_logit_bias;
     struct ggml_tensor * inp_logit_idxs;
-
-    ggml_context_ptr        inp_ctx;
-    ggml_backend_buffer_ptr inp_buf;
 };
 
 static const char * llama_sampler_logit_bias_name(const struct llama_sampler * smpl) {
@@ -3526,6 +3496,16 @@ static void llama_sampler_logit_bias_backend_apply(
         return;
     }
 
+    const size_t n = sctx->logit_bias.size();
+
+    sctx->inp_logit_bias = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n);
+    ggml_set_name(sctx->inp_logit_bias, "logit_bias");
+    ggml_set_input(sctx->inp_logit_bias);
+
+    sctx->inp_logit_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n);
+    ggml_set_name(sctx->inp_logit_idxs, "logit_idxs");
+    ggml_set_input(sctx->inp_logit_idxs);
+
     ggml_tensor * cur = ggml_fill(ctx, data->logits, 0.0f);
 
     cur = ggml_reshape_2d(ctx, cur, 1, ggml_nelements(cur));
@@ -3562,6 +3542,8 @@ static void llama_sampler_logit_bias_backend_set_input(struct llama_sampler * sm
 static bool llama_sampler_logit_bias_backend_init(
         struct llama_sampler       * smpl,
         ggml_backend_buffer_type_t   buft) {
+    GGML_UNUSED(buft);
+
     auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
 
     sctx->init(true);
@@ -3570,29 +3552,6 @@ static bool llama_sampler_logit_bias_backend_init(
         return true;
     }
 
-    ggml_init_params params = {
-        /*.mem_size   =*/ 2*ggml_tensor_overhead(),
-        /*.mem_buffer =*/ nullptr,
-        /*.no_alloc   =*/ true,
-    };
-
-    sctx->inp_ctx.reset(ggml_init(params));
-
-    const size_t n = sctx->logit_bias.size();
-
-    sctx->inp_logit_bias = ggml_new_tensor_2d(sctx->inp_ctx.get(), GGML_TYPE_F32, 1, n);
-    ggml_set_name(sctx->inp_logit_bias, "logit_bias");
-    ggml_set_input(sctx->inp_logit_bias);
-
-    sctx->inp_logit_idxs = ggml_new_tensor_1d(sctx->inp_ctx.get(), GGML_TYPE_I32, n);
-    ggml_set_name(sctx->inp_logit_idxs, "logit_idxs");
-    ggml_set_input(sctx->inp_logit_idxs);
-
-    // Allocate all tensors from our context to the backend
-    sctx->inp_buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(sctx->inp_ctx.get(), buft));
-
-    ggml_backend_buffer_clear(sctx->inp_buf.get(), 0);
-
     return true;
 }
 
@@ -3628,8 +3587,6 @@ struct llama_sampler * llama_sampler_init_logit_bias(
             /* .to_search      = */ {},
             /* .inp_logit_bias = */ nullptr,
             /* .inp_logit_idxs = */ nullptr,
-            /* .inp_ctx        = */ nullptr,
-            /* .inp_buf        = */ nullptr,
         }
     );
 }