From 7884b0e0ac658c3772363286cb585384a86d3894 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 17 Nov 2025 15:19:34 +0100
Subject: [PATCH 01/59] sampling : add support for backend sampling

This commit adds support for performing sampling operations on the
backend (e.g. GPU) as part of the model computation graph.

The motivation for this feature is to enable sampling to be performed
directly on the backend as part of the computation graph being executed,
allowing for some or all of the sampling to be done on the backend.

For example, the backend sampler chain might select/sample a token
directly in which case only the sampled token needs to be transferred
from device memory to host memory.

It is also possible for the backend samplers to perform filtering of
the logits, or compute and filter the probability distribution, in
which case only the filtered logits or probabilites need to be
transferred back to system memory for further processing by CPU
samplers.

Currently the backend sampling works in a similar manner to how
pooling works, it is a function that is called by build_graph and the
sampler operations become part of the models computation graph.
---
 common/arg.cpp                 |  15 +
 common/common.cpp              |   3 +
 common/common.h                |   7 +
 common/llguidance.cpp          |  16 +-
 common/sampling.cpp            |  91 +++-
 common/sampling.h              |   7 +
 include/llama.h                |  90 ++++
 src/CMakeLists.txt             |   1 +
 src/llama-backend-sampler.cpp  | 489 +++++++++++++++++++++
 src/llama-context.cpp          | 383 ++++++++++++++---
 src/llama-context.h            |  22 +
 src/llama-graph.cpp            | 125 ++++++
 src/llama-graph.h              |  60 ++-
 src/llama-model.cpp            |   3 +
 src/llama-sampling.cpp         | 429 ++++++++++++++-----
 src/llama-sampling.h           |   3 +
 tests/CMakeLists.txt           |  12 +
 tests/test-backend-sampler.cpp | 760 +++++++++++++++++++++++++++++++++
 18 files changed, 2328 insertions(+), 188 deletions(-)
 create mode 100644 src/llama-backend-sampler.cpp
 create mode 100644 tests/test-backend-sampler.cpp

diff --git a/common/arg.cpp b/common/arg.cpp
index 430ab45dfe2..ab3386b1dfa 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1501,6 +1501,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.grammar = json_schema_to_grammar(json::parse(schema));
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"--backend-sampling"},
+        "enable backend sampling (default: disabled)",
+        [](common_params & params) {
+            params.sampling.backend_sampling = true;
+        }
+    ).set_sparam());
+    add_opt(common_arg(
+        {"--backend-dist"},
+        "perform final (distribution) sampling on backend (default: disabled)",
+        [](common_params & params) {
+            params.sampling.backend_dist = true;
+            params.sampling.backend_sampling = true;
+        }
+    ).set_sparam());
     add_opt(common_arg(
         {"--pooling"}, "{none,mean,cls,last,rank}",
         "pooling type for embeddings, use model default if unspecified",
diff --git a/common/common.cpp b/common/common.cpp
index 4dc95dcba22..c31619ac365 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -8,6 +8,7 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "sampling.h"
 
 #include <algorithm>
 #include <cinttypes>
@@ -956,6 +957,8 @@ struct common_init_result common_init_from_params(common_params & params) {
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     auto cparams = common_context_params_to_llama(params);
+    cparams.samplers = params.backend_samplers;
+    cparams.n_samplers = params.n_backend_samplers;
 
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
diff --git a/common/common.h b/common/common.h
index f42c083faa2..b320d891f5a 100644
--- a/common/common.h
+++ b/common/common.h
@@ -188,6 +188,10 @@ struct common_params_sampling {
     std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
     std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
 
+    // Backend sampling flags
+    bool    backend_sampling        = false; // enable backend sampling
+    bool    backend_dist            = false; // backend performs final sampling (dist)
+
     // print the parameters into a string
     std::string print() const;
 };
@@ -512,6 +516,9 @@ struct common_params {
     bool has_speculative() const {
         return !speculative.model.path.empty() || !speculative.model.hf_repo.empty();
     }
+
+    struct llama_sampler_seq_config * backend_samplers = NULL;
+    size_t                            n_backend_samplers = 0;
 };
 
 // call once at the start of a program if it uses libcommon
diff --git a/common/llguidance.cpp b/common/llguidance.cpp
index adce620e4d6..27d15516e94 100644
--- a/common/llguidance.cpp
+++ b/common/llguidance.cpp
@@ -106,12 +106,16 @@ static void llama_sampler_llg_free(llama_sampler * smpl) {
 }
 
 static llama_sampler_i llama_sampler_llg_i = {
-    /* .name   = */ llama_sampler_llg_name,
-    /* .accept = */ llama_sampler_llg_accept_impl,
-    /* .apply  = */ llama_sampler_llg_apply,
-    /* .reset  = */ llama_sampler_llg_reset,
-    /* .clone  = */ llama_sampler_llg_clone,
-    /* .free   = */ llama_sampler_llg_free,
+    /* .name                = */ llama_sampler_llg_name,
+    /* .accept              = */ llama_sampler_llg_accept_impl,
+    /* .apply               = */ llama_sampler_llg_apply,
+    /* .reset               = */ llama_sampler_llg_reset,
+    /* .clone               = */ llama_sampler_llg_clone,
+    /* .free                = */ llama_sampler_llg_free,
+    /* .apply_ggml          = */ NULL,
+    /* .accept_ggml         = */ NULL,
+    /* .set_input_ggml      = */ NULL,
+    /* .set_backend_context = */ NULL,
 };
 
 static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,
diff --git a/common/sampling.cpp b/common/sampling.cpp
index c69d525b5b3..1fc5c7ce0aa 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -113,23 +113,61 @@ struct common_sampler {
     llama_token_data_array cur_p;
 
     void set_logits(struct llama_context * ctx, int idx) {
-        const auto * logits = llama_get_logits_ith(ctx, idx);
+        const float *       sampled_probs        = llama_get_backend_sampled_probs_ith(ctx, idx);
+        const float *       sampled_logits       = llama_get_backend_sampled_logits_ith(ctx, idx);
+        const llama_token * sampled_ids          = llama_get_backend_sampled_token_ids_ith(ctx, idx);
 
         const llama_model * model = llama_get_model(ctx);
         const llama_vocab * vocab = llama_model_get_vocab(model);
 
         const int n_vocab = llama_vocab_n_tokens(vocab);
 
-        cur.resize(n_vocab);
+        // Use the member variable instead of allocating locally
+        cur.clear();
 
-        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-            cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
+        if (sampled_probs) {
+            const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
+            cur.reserve(sampled_probs_count);
+            // The backend sampler has filtered the probabilities so we need to use the sampled ids.
+            if (sampled_ids != nullptr) {
+                for (uint32_t i = 0; i < sampled_probs_count; ++i) {
+                    cur.emplace_back(llama_token_data{sampled_ids[i], 0.0f, sampled_probs[i]});
+                }
+            } else {
+                for (llama_token token_id = 0; token_id < (int) sampled_probs_count; token_id++) {
+                    cur.emplace_back(llama_token_data{token_id, 0.0f, sampled_probs[token_id]});
+                }
+            }
+        } else if (sampled_logits) {
+            const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
+            cur.reserve(sampled_logits_count);
+            // The backend sampler has filtered the logits so we need to use the sampled ids.
+            if (sampled_ids != nullptr) {
+                for (llama_token i = 0; i < (int)sampled_logits_count; i++) {
+                    cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
+                }
+            } else {
+                for (llama_token token_id = 0; token_id < (int)sampled_logits_count; token_id++) {
+                    cur.emplace_back(llama_token_data{token_id, sampled_logits[token_id], 0.0f});
+                }
+            }
+        } else {
+            const auto * logits = llama_get_logits_ith(ctx, idx);
+            GGML_ASSERT(logits != nullptr);
+            cur.reserve(n_vocab);
+            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+                cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+            }
         }
 
         cur_p = { cur.data(), cur.size(), -1, false };
     }
 };
 
+static bool sampler_enabled(const struct common_params_sampling & params, enum common_sampler_type type) {
+    return std::find(params.samplers.begin(), params.samplers.end(), type) != params.samplers.end();
+}
+
 std::string common_params_sampling::print() const {
     char result[1024];
 
@@ -287,6 +325,43 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
     return result;
 }
 
+struct llama_sampler * common_sampler_backend_init(const struct llama_model * model, const struct common_params_sampling & params) {
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+    chain_params.no_perf = params.no_perf;
+
+    struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+    if (!params.backend_sampling) {
+        return chain; // return empty chain
+    }
+
+    const bool enable_temp  = params.temp  > 0.0f && sampler_enabled(params, COMMON_SAMPLER_TYPE_TEMPERATURE);
+    const bool enable_top_k = params.top_k > 0    && sampler_enabled(params, COMMON_SAMPLER_TYPE_TOP_K);
+    const bool enable_dist  = params.backend_dist;
+
+    if (!params.logit_bias.empty()) {
+        llama_sampler_chain_add(chain, llama_sampler_backend_init_logit_bias(
+                    llama_vocab_n_tokens(vocab),
+                    params.logit_bias.size(),
+                    params.logit_bias.data()));
+    }
+
+    if (enable_temp) {
+        llama_sampler_chain_add(chain, llama_sampler_backend_init_temp(params.temp));
+    }
+
+    if (enable_top_k) {
+        llama_sampler_chain_add(chain, llama_sampler_backend_init_top_k(params.top_k));
+    }
+
+    if (enable_dist) {
+        llama_sampler_chain_add(chain, llama_sampler_backend_init_dist(params.seed));
+    }
+
+    return chain;
+}
+
 void common_sampler_free(struct common_sampler * gsmpl) {
     if (gsmpl) {
         llama_sampler_free(gsmpl->grmr);
@@ -337,6 +412,14 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
 }
 
 llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
+    // Check if a backend sampler has already sampled a token in which case we
+    // return that token id directly.
+    const llama_token backend_sampled_token = llama_get_backend_sampled_token_ith(ctx, idx);
+    if (backend_sampled_token != LLAMA_TOKEN_NULL) {
+        LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, backend_sampled_token);
+        return backend_sampled_token;
+    }
+
     gsmpl->set_logits(ctx, idx);
 
     auto & grmr  = gsmpl->grmr;
diff --git a/common/sampling.h b/common/sampling.h
index e198eecda38..0ec164de053 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -38,6 +38,13 @@ struct common_sampler;
 
 struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
 
+// Create a backend sampler chain from common sampling parameters
+// Returns a llama_sampler chain configured with backend samplers based on the parameters
+// This chain can be used per-sequence for backend-based sampling
+// Note: Only samplers that have backend equivalents will be added to the chain
+// The returned sampler should be freed with llama_sampler_free()
+struct llama_sampler * common_sampler_backend_init(const struct llama_model * model, const struct common_params_sampling & params);
+
 void common_sampler_free(struct common_sampler * gsmpl);
 
 // if accept_grammar is true, the token is accepted both by the sampling chain and the grammar
diff --git a/include/llama.h b/include/llama.h
index 8547226ff21..cbf23c7bcf1 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -210,6 +210,13 @@ extern "C" {
         bool sorted;      // note: do not assume the data is sorted - always check this flag
     } llama_token_data_array;
 
+    struct llama_sampler_ggml_data {
+        struct ggml_tensor * logits;
+        struct ggml_tensor * probs;
+        struct ggml_tensor * sampled_token;
+        struct ggml_tensor * filtered_ids;
+    };
+
     typedef bool (*llama_progress_callback)(float progress, void * user_data);
 
     // Input data for llama_encode/llama_decode
@@ -300,6 +307,11 @@ extern "C" {
         bool no_host;         // bypass host buffer allowing extra buffers to be used
     };
 
+    struct llama_sampler_seq_config {
+        llama_seq_id           seq_id;
+        struct llama_sampler * sampler;
+    };
+
     // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
     //       https://github.com/ggml-org/llama.cpp/pull/7544
     struct llama_context_params {
@@ -348,6 +360,10 @@ extern "C" {
         bool kv_unified;  // use a unified buffer across the input sequences when computing the attention
                           // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix
                           // ref: https://github.com/ggml-org/llama.cpp/pull/14363
+
+        // backend sampler chain configuration
+        struct llama_sampler_seq_config * samplers;
+        size_t                            n_samplers;
     };
 
     // model quantization parameters
@@ -950,6 +966,29 @@ extern "C" {
     // otherwise: float[n_embd] (1-dimensional)
     LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
 
+    // Get the backend sampled token for the ith token.
+    // Returns LLAMA_TOKEN_NULL if no token was sampled.
+    LLAMA_API llama_token llama_get_backend_sampled_token_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the backend sampled probabilites for the ith token
+    // The index matches llama_get_backend_sampled_token_ith().
+    // Returns NULL if no probabilites were generated.
+    LLAMA_API float * llama_get_backend_sampled_probs_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the backend sampled logits for the ith token
+    // Returns NULL if no logits were sampled.
+    LLAMA_API float * llama_get_backend_sampled_logits_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the backend sampled token ids associated with the sampled logits for the ith token
+    // Returns NULL if no logits were sampled.
+    LLAMA_API llama_token * llama_get_backend_sampled_token_ids_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the number of backend sampled logits for the ith token.
+    LLAMA_API uint32_t llama_get_backend_sampled_logits_count_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the number of backend sampled probabilites for the ith token.
+    LLAMA_API uint32_t llama_get_backend_sampled_probs_count_ith(struct llama_context * ctx, int32_t i);
+
     //
     // Vocab
     //
@@ -1135,6 +1174,22 @@ extern "C" {
         struct llama_sampler * (*clone) (const struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
         void                   (*free)  (      struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
 
+        void                   (*apply_ggml)(  struct llama_sampler           * smpl,
+                                               struct ggml_context            * ctx,
+                                               struct  ggml_cgraph            * gf,
+                                               struct llama_sampler_ggml_data * ggml_data);
+
+        void                   (*accept_ggml)( struct llama_sampler * smpl,
+                                               struct ggml_context  * ctx,
+                                               struct ggml_cgraph   * gf,
+                                               struct ggml_tensor   * selected_token);
+
+        void                   (*set_input_ggml)(struct llama_sampler * smpl);
+
+        void                   (*init_ggml)(struct llama_sampler      * smpl,
+                                            ggml_backend_buffer_type_t  buft);
+
+
         // TODO: API for internal libllama usage for appending the sampling to an existing ggml_cgraph
         //void (*apply_ggml) (struct llama_sampler * smpl, ...);
     };
@@ -1144,6 +1199,8 @@ extern "C" {
         llama_sampler_context_t        ctx;
     };
 
+    LLAMA_API void llama_set_backend_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
+
     // mirror of llama_sampler_i:
     LLAMA_API struct llama_sampler * llama_sampler_init  (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
     LLAMA_API const char *           llama_sampler_name  (const struct llama_sampler * smpl);
@@ -1153,6 +1210,18 @@ extern "C" {
     LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl);
     // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add)
     LLAMA_API void                   llama_sampler_free  (      struct llama_sampler * smpl);
+    LLAMA_API void                   llama_sampler_init_ggml(struct llama_sampler      * smpl,
+                                                             ggml_backend_buffer_type_t  buft);
+    LLAMA_API void                   llama_sampler_set_input_ggml(struct llama_sampler * smpl);
+    LLAMA_API void                   llama_sampler_apply_ggml(  struct llama_sampler           * smpl,
+                                                                struct ggml_context            * ctx,
+                                                                struct ggml_cgraph             * gf,
+                                                                struct llama_sampler_ggml_data * ggml_data);
+
+    LLAMA_API void                   llama_sampler_accept_ggml( struct llama_sampler * smpl,
+                                                                struct ggml_context  * ctx,
+                                                                struct  ggml_cgraph  * gf,
+                                                                struct ggml_tensor   * selected_token);
 
     // llama_sampler_chain
     // a type of llama_sampler that can chain multiple samplers one after another
@@ -1166,6 +1235,7 @@ extern "C" {
 
     // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed
     LLAMA_API struct llama_sampler * llama_sampler_chain_remove(   struct llama_sampler * chain, int32_t i);
+    LLAMA_API uint64_t               llama_sampler_chain_get_version(const struct llama_sampler * chain);
 
     // available samplers:
 
@@ -1299,9 +1369,29 @@ extern "C" {
     //
     LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab);
 
+    //
+    // Backend samplers
+    //
+
+    /// @details Greedy sampling on backend - always selects the token with the highest probability
+    LLAMA_API struct llama_sampler * llama_sampler_backend_init_greedy(void);
+
+    /// @details Temperature scaling on backend - scales logits by 1/temperature
+    LLAMA_API struct llama_sampler * llama_sampler_backend_init_temp(float temp);
+
+    /// @details Top-K filtering on backend - keeps only the k tokens with highest probabilities
+    LLAMA_API struct llama_sampler * llama_sampler_backend_init_top_k(int32_t k);
+
+    /// @details Distribution sampling on backend - final sampling step that selects a token
+    LLAMA_API struct llama_sampler * llama_sampler_backend_init_dist(uint32_t seed);
+
     // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
     LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
 
+    LLAMA_API struct llama_sampler * llama_sampler_backend_init_logit_bias(int32_t   n_vocab,
+                                                                 int32_t   n_logit_bias,
+                                                  const llama_logit_bias * logit_bias);
+
     /// @details Sample and accept a token from the idx-th output of the last evaluation
     //
     // Shorthand for:
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 8ec95ee1762..c17b8900894 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -31,6 +31,7 @@ add_library(llama
             llama-model.cpp
             llama-quant.cpp
             llama-sampling.cpp
+            llama-backend-sampler.cpp
             llama-vocab.cpp
             unicode-data.cpp
             unicode.cpp
diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
new file mode 100644
index 00000000000..42c8d85aeba
--- /dev/null
+++ b/src/llama-backend-sampler.cpp
@@ -0,0 +1,489 @@
+#include "llama.h"
+#include "ggml.h"
+#include <cstdio>
+#include <chrono>
+#include <random>
+#include <unordered_map>
+#include <vector>
+
+static void llama_sampler_backend_greedy_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    GGML_UNUSED(gf);
+    GGML_UNUSED(smpl);
+    struct ggml_tensor * argmax_result = ggml_argmax(ctx, ggml_data->logits);
+    ggml_set_name(argmax_result, "argmax_result");
+    ggml_data->sampled_token = argmax_result;
+}
+
+static const char * llama_sampler_backend_greedy_sampler_name(const struct llama_sampler *) {
+    return "test-ggml";
+}
+
+static struct llama_sampler * llama_sampler_backend_greedy_clone(const struct llama_sampler * smpl) {
+    (void) smpl;
+    return llama_sampler_backend_init_greedy();
+}
+
+struct llama_sampler * llama_sampler_backend_init_greedy() {
+    static const llama_sampler_i iface = {
+        /*.name                =*/ llama_sampler_backend_greedy_sampler_name,
+        /*.accept              =*/ nullptr,
+        /*.apply               =*/ nullptr,
+        /*.reset               =*/ nullptr,
+        /*.clone               =*/ llama_sampler_backend_greedy_clone,
+        /*.free                =*/ nullptr,
+        /*.apply_ggml          =*/ llama_sampler_backend_greedy_apply_ggml,
+        /*.accept_ggml         =*/ nullptr,
+        /*.set_input_ggml      =*/ nullptr,
+        /*.init_ggml           =*/ nullptr,
+    };
+
+    auto * sampler = new llama_sampler {
+        /*.iface =*/ &iface,
+        /*.ctx   =*/ nullptr,
+    };
+
+    return sampler;
+}
+
+struct llama_sampler_backend_temp_ctx {
+    float temp;
+};
+
+
+static void llama_sampler_backend_temp_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+
+    auto * ctx_data = (llama_sampler_backend_temp_ctx *) smpl->ctx;
+
+    if (ctx_data->temp <= 0.0f) {
+        return;
+    }
+
+    struct ggml_tensor * scaled = ggml_scale(ctx, ggml_data->logits, 1.0f / ctx_data->temp);
+    ggml_set_name(scaled, "temp_scaled");
+
+    // Make sure the scaled tensor is contiguous for subsequent operations
+    ggml_data->logits = ggml_cont(ctx, scaled);
+    ggml_set_name(ggml_data->logits, "temp_scaled_logits");
+
+    ggml_build_forward_expand(gf, ggml_data->logits);
+}
+
+static const char * llama_sampler_backend_temp_name(const struct llama_sampler *) {
+    return "backend-temp";
+}
+
+static void llama_sampler_backend_temp_free(struct llama_sampler * smpl) {
+    auto * ctx_data = (llama_sampler_backend_temp_ctx *) smpl->ctx;
+    delete ctx_data;
+}
+
+static struct llama_sampler * llama_sampler_backend_temp_clone(const struct llama_sampler * smpl) {
+    auto * ctx = (llama_sampler_backend_temp_ctx *) smpl->ctx;
+    return llama_sampler_backend_init_temp(ctx->temp);
+}
+
+struct llama_sampler * llama_sampler_backend_init_temp(float temp) {
+    static const llama_sampler_i iface = {
+        /*.name                =*/ llama_sampler_backend_temp_name,
+        /*.accept              =*/ nullptr,
+        /*.apply               =*/ nullptr,
+        /*.reset               =*/ nullptr,
+        /*.clone               =*/ llama_sampler_backend_temp_clone,
+        /*.free                =*/ llama_sampler_backend_temp_free,
+        /*.apply_ggml          =*/ llama_sampler_backend_temp_apply_ggml,
+        /*.accept_ggml         =*/ nullptr,
+        /*.set_input_ggml      =*/ nullptr,
+        /*.set_backend_context =*/ nullptr,
+    };
+
+    auto * ctx_data = new llama_sampler_backend_temp_ctx {
+        /*.temp    =*/ temp,
+    };
+
+    auto * sampler = new llama_sampler {
+        /*.iface =*/ &iface,
+        /*.ctx   =*/ ctx_data,
+    };
+
+    return sampler;
+}
+
+
+struct llama_sampler_backend_top_k_ctx {
+    int32_t k;
+
+    // Only required for checking operation support and can be removed later.
+    ggml_backend_dev_t device;
+};
+
+static void llama_sampler_backend_top_k_init_ggml(
+        struct llama_sampler           * smpl,
+        ggml_backend_buffer_type_t       buft) {
+    auto * ctx_data = (llama_sampler_backend_top_k_ctx *) smpl->ctx;
+    ctx_data->device = ggml_backend_buft_get_device(buft);
+}
+
+static void llama_sampler_backend_top_k_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+
+    auto * ctx_data = (llama_sampler_backend_top_k_ctx *) smpl->ctx;
+
+    struct ggml_tensor * top_k = ggml_top_k(ctx, ggml_data->logits, ctx_data->k);
+    ggml_set_name(top_k, "top_k");
+
+    // top_k is a view of argsort - check if backend supports the underlying argsort operation
+    // by checking the source tensor (which is the argsort result)
+    if (ctx_data->device && top_k->src[0] && !ggml_backend_dev_supports_op(ctx_data->device, top_k->src[0])) {
+        fprintf(stderr, "Warning: backend does not support argsort operation required for top-k sampling\n");
+        fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
+    }
+
+    ggml_data->filtered_ids = top_k;
+
+    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
+    struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);
+    ggml_set_name(top_k_rows, "top_k_rows");
+
+    ggml_data->logits = ggml_reshape_1d(ctx, top_k_rows, ctx_data->k);
+    ggml_build_forward_expand(gf, ggml_data->logits);
+}
+
+static const char * llama_sampler_backend_top_k_name(const struct llama_sampler *) {
+    return "backend-top-k";
+}
+
+static void llama_sampler_backend_top_k_free(struct llama_sampler * smpl) {
+    auto * ctx_data = (llama_sampler_backend_top_k_ctx *) smpl->ctx;
+    delete ctx_data;
+}
+
+static struct llama_sampler * llama_sampler_backend_top_k_clone(const struct llama_sampler * smpl) {
+    auto * ctx = (llama_sampler_backend_top_k_ctx *) smpl->ctx;
+    return llama_sampler_backend_init_top_k(ctx->k);
+}
+
+struct llama_sampler * llama_sampler_backend_init_top_k(int32_t k) {
+    static const llama_sampler_i iface = {
+        /*.name                =*/ llama_sampler_backend_top_k_name,
+        /*.accept              =*/ nullptr,
+        /*.apply               =*/ nullptr,
+        /*.reset               =*/ nullptr,
+        /*.clone               =*/ llama_sampler_backend_top_k_clone,
+        /*.free                =*/ llama_sampler_backend_top_k_free,
+        /*.apply_ggml          =*/ llama_sampler_backend_top_k_apply_ggml,
+        /*.accept_ggml         =*/ nullptr,
+        /*.set_input_ggml      =*/ nullptr,
+        /*.init_ggml           =*/ llama_sampler_backend_top_k_init_ggml,
+    };
+
+    auto * ctx_data = new llama_sampler_backend_top_k_ctx {
+        /*.k       =*/ k,
+        /*.device  =*/ nullptr,
+    };
+
+    auto * sampler = new llama_sampler {
+        /*.iface =*/ &iface,
+        /*.ctx   =*/ ctx_data,
+    };
+
+    return sampler;
+}
+
+
+static uint32_t get_rng_seed(uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
+        // use system clock if std::random_device is not a true RNG
+        static bool is_rd_prng = std::random_device().entropy() == 0;
+        if (is_rd_prng) {
+            return (uint32_t) std::chrono::system_clock::now().time_since_epoch().count();
+        }
+        std::random_device rd;
+        return rd();
+    }
+    return seed;
+}
+
+struct llama_sampler_backend_dist_ctx {
+    const uint32_t seed;
+          uint32_t seed_cur;
+    std::mt19937   rng;
+
+    struct ggml_tensor   * uniform;
+    struct ggml_context  * ctx;
+    ggml_backend_buffer_t  buffer;
+
+    // Only required for checking operation support and can be removed later.
+    ggml_backend_dev_t device;
+};
+
+static void llama_sampler_backend_dist_init_ggml(
+        struct llama_sampler      * smpl,
+        ggml_backend_buffer_type_t  buft) {
+
+    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
+    sctx->device = ggml_backend_buft_get_device(buft);
+    ggml_init_params params = {
+        /*.mem_size   =*/ ggml_tensor_overhead(),
+        /*.mem_buffer =*/ nullptr,
+        /*.no_alloc   =*/ true,
+    };
+    sctx->ctx = ggml_init(params);
+
+    // Create the uniform random scalar input tensor. This will be set by
+    // llama_sampler_backend_dist_set_input_ggml after this graph is built.
+    sctx->uniform = ggml_new_tensor_1d(sctx->ctx, GGML_TYPE_F32, 1);
+    ggml_set_name(sctx->uniform, "uniform");
+    ggml_set_input(sctx->uniform);
+    ggml_set_output(sctx->uniform);
+
+    // Allocate all tensors from our context to the backend
+    sctx->buffer = ggml_backend_alloc_ctx_tensors_from_buft(sctx->ctx, buft);
+}
+
+static void llama_sampler_backend_dist_set_input_ggml(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
+    GGML_ASSERT(sctx->uniform != nullptr);
+
+    std::uniform_real_distribution<float> dist(0.0f, 1.0f);
+    const float rnd = dist(sctx->rng);
+    ggml_backend_tensor_set(sctx->uniform, &rnd, 0, sizeof(float));
+}
+
+static void llama_sampler_backend_dist_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    GGML_UNUSED(gf);
+    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
+
+    struct ggml_tensor * probs = ggml_soft_max(ctx, ggml_data->logits);
+    ggml_set_name(probs, "dist_probs");
+
+    struct ggml_tensor * cumsum = ggml_cumsum(ctx, probs);
+    if (sctx->device && !ggml_backend_dev_supports_op(sctx->device, cumsum)) {
+        fprintf(stderr, "Warning: backend does not support cumsum operation required for dist sampling\n");
+        fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
+    }
+    ggml_set_name(cumsum, "cumsum");
+
+    // The uniform tensor has a random value and we subtract this tensor with
+    // the cumsum tensor (the uniform tensor will be broadcasted by ggml_sub).
+    // Recall that each entry in cumsum is the cumulative probability up to that
+    // index so values stay negative while the cumulative total is below the
+    // random value, and become zero/positive once the threshold is crossed.
+    struct ggml_tensor * diff = ggml_sub(ctx, cumsum, sctx->uniform);
+    ggml_set_name(diff, "dist_cumsum");
+
+    // The ggml_step function produces a tensor where entries are 1 if the
+    // corresponding entry in diff is > 0, and 0 otherwise. So all values up to
+    // the index where the cumulative probability exceeds the random value are 0,
+    // and all entries after that are 1.
+    struct ggml_tensor * mask = ggml_step(ctx, diff);
+    ggml_set_name(mask, "dist_mask");
+
+    // Taking the sum of the mask gives us the sum of elements after the threshold
+    // we are interested in.
+    struct ggml_tensor * idxf = ggml_sum(ctx, mask);
+    ggml_set_name(idxf, "dist_index_f32");
+
+    // Use ggml_scale_bias to scale the index value by -1 and then add the size
+    // of the mask to that value so we get the correct index ((-1 * idxf) + n).
+    struct ggml_tensor * idx = ggml_cast(ctx, ggml_scale_bias(ctx, idxf, -1.0f, mask->ne[0]), GGML_TYPE_I32);
+    ggml_set_name(idx, "dist_index_i32");
+
+    // Map back to original vocab ids if a filtered id tensor is available.
+    struct ggml_tensor * sampled_token = idx;
+    if (ggml_data->filtered_ids != nullptr) {
+        struct ggml_tensor * filtered_ids = ggml_data->filtered_ids;
+        struct ggml_tensor * filtered_ids_reshaped = ggml_view_2d(ctx, filtered_ids, 1, ggml_nelements(filtered_ids),
+                ggml_type_size(filtered_ids->type), 0);
+
+        sampled_token = ggml_get_rows(ctx, filtered_ids_reshaped, idx);
+        ggml_set_name(sampled_token, "dist_sampled_token");
+    }
+
+    ggml_set_output(sampled_token);
+    ggml_data->sampled_token = sampled_token;
+}
+
+static const char * llama_sampler_backend_dist_name(const struct llama_sampler *) {
+    return "backend-dist";
+}
+
+static void llama_sampler_backend_dist_free(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
+    ggml_backend_buffer_free(sctx->buffer);
+    ggml_free(sctx->ctx);
+    delete sctx;
+}
+
+static struct llama_sampler * llama_sampler_backend_dist_clone(const struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
+    return llama_sampler_backend_init_dist(sctx->seed);
+}
+
+
+struct llama_sampler * llama_sampler_backend_init_dist(uint32_t seed) {
+    static const llama_sampler_i iface = {
+        /*.name           =*/ llama_sampler_backend_dist_name,
+        /*.accept         =*/ nullptr,
+        /*.apply          =*/ nullptr,
+        /*.reset          =*/ nullptr,
+        /*.clone          =*/ llama_sampler_backend_dist_clone,
+        /*.free           =*/ llama_sampler_backend_dist_free,
+        /*.apply_ggml     =*/ llama_sampler_backend_dist_apply_ggml,
+        /*.accept_ggml    =*/ nullptr,
+        /*.set_input_ggml =*/ llama_sampler_backend_dist_set_input_ggml,
+        /*.init_ggml      =*/ llama_sampler_backend_dist_init_ggml,
+    };
+
+    auto seed_cur = get_rng_seed(seed);
+    auto * ctx_data = new llama_sampler_backend_dist_ctx {
+        /*.seed     =*/ seed,
+        /*.seed_cur =*/ seed_cur,
+        /*.rng      =*/ std::mt19937(seed_cur),
+        /*.uniform  =*/ nullptr,
+        /*.ctx      =*/ nullptr,
+        /*.buffer   =*/ nullptr,
+        /*.device   =*/ nullptr,
+    };
+
+    auto * sampler = new llama_sampler {
+        /*.iface =*/ &iface,
+        /*.ctx   =*/ ctx_data,
+    };
+
+    return sampler;
+}
+
+struct llama_sampler_backend_logit_bias_ctx {
+    const int32_t n_vocab;
+
+    const std::vector<llama_logit_bias> logit_bias;
+
+    struct ggml_tensor * logit_bias_t;
+    struct ggml_context * ctx;
+    ggml_backend_buffer_t buffer;
+};
+
+static void llama_sampler_backend_logit_bias_init_ggml(
+        struct llama_sampler      * smpl,
+        ggml_backend_buffer_type_t  buft) {
+    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
+    if (sctx->logit_bias.empty()) {
+        return;
+    }
+    ggml_init_params params = {
+        /*.mem_size   =*/ ggml_tensor_overhead() * sctx->n_vocab * sizeof(float),
+        /*.mem_buffer =*/ nullptr,
+        /*.no_alloc   =*/ true,
+    };
+    sctx->ctx = ggml_init(params);
+
+    struct ggml_tensor * logit_bias = ggml_new_tensor_1d(sctx->ctx, GGML_TYPE_F32, sctx->n_vocab);
+    sctx->logit_bias_t = logit_bias;
+    ggml_set_name(sctx->logit_bias_t, "logit_bias");
+    ggml_set_input(sctx->logit_bias_t);
+    ggml_set_output(sctx->logit_bias_t);
+
+    // Allocate all tensors from our context to the backend
+    sctx->buffer = ggml_backend_alloc_ctx_tensors_from_buft(sctx->ctx, buft);
+}
+
+static void llama_sampler_backend_logit_bias_set_input_ggml(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
+    if (sctx->logit_bias.empty()) {
+        return;
+    }
+    GGML_ASSERT(sctx->logit_bias_t != nullptr);
+
+    // Create a sparse logit_bias vector from the logit_bias entries.
+    std::vector<float> logit_bias_sparse(sctx->n_vocab, 0.0f);
+    for (const auto & lb : sctx->logit_bias) {
+        GGML_ASSERT(lb.token >= 0 && lb.token < (int32_t) sctx->n_vocab);
+        logit_bias_sparse[lb.token] = lb.bias;
+    }
+
+    ggml_backend_tensor_set(sctx->logit_bias_t, logit_bias_sparse.data(), 0, ggml_nbytes(sctx->logit_bias_t));
+}
+
+static void llama_sampler_backend_logit_bias_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    GGML_UNUSED(gf);
+    GGML_UNUSED(ctx);
+
+    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
+    if (sctx->logit_bias_t == nullptr) {
+        return;
+    }
+
+    // Add the sparse logit logit_bias to the logits
+    struct ggml_tensor * logit_biased = ggml_add_inplace(sctx->ctx, ggml_data->logits, sctx->logit_bias_t);
+    ggml_build_forward_expand(gf, logit_biased);
+}
+
+static const char * llama_sampler_backend_logit_bias_name(const struct llama_sampler *) {
+    return "backend-logit_bias";
+}
+
+static void llama_sampler_backend_logit_bias_free(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
+    ggml_backend_buffer_free(sctx->buffer);
+    ggml_free(sctx->ctx);
+    delete sctx;
+}
+
+static struct llama_sampler * llama_sampler_backend_logit_bias_clone(const struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
+    return llama_sampler_backend_init_logit_bias(sctx->n_vocab,
+                                      sctx->logit_bias.size(),
+                                      sctx->logit_bias.data());
+}
+
+
+struct llama_sampler * llama_sampler_backend_init_logit_bias(int32_t   n_vocab,
+                                                   int32_t   n_logit_bias,
+                                    const llama_logit_bias * logit_bias) {
+    static const llama_sampler_i iface = {
+        /*.name           =*/ llama_sampler_backend_logit_bias_name,
+        /*.accept         =*/ nullptr,
+        /*.apply          =*/ nullptr,
+        /*.reset          =*/ nullptr,
+        /*.clone          =*/ llama_sampler_backend_logit_bias_clone,
+        /*.free           =*/ llama_sampler_backend_logit_bias_free,
+        /*.apply_ggml     =*/ llama_sampler_backend_logit_bias_apply_ggml,
+        /*.accept_ggml    =*/ nullptr,
+        /*.set_input_ggml =*/ llama_sampler_backend_logit_bias_set_input_ggml,
+        /*.init_ggml      =*/ llama_sampler_backend_logit_bias_init_ggml,
+    };
+
+    auto * ctx_data = new llama_sampler_backend_logit_bias_ctx {
+        /*.n_vocab      =*/ n_vocab,
+        /*.logit_bias   =*/ std::vector<llama_logit_bias>(logit_bias, logit_bias + n_logit_bias),
+        /*.logit_bias_t =*/ nullptr,
+        /*.ctx          =*/ nullptr,
+        /*.buffer       =*/ nullptr,
+    };
+
+    auto * sampler = new llama_sampler {
+        /*.iface =*/ &iface,
+        /*.ctx   =*/ ctx_data,
+    };
+
+    return sampler;
+}
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 70a3ec62dfc..877116cbfe2 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -58,6 +58,16 @@ llama_context::llama_context(
     cparams.cb_eval           = params.cb_eval;
     cparams.cb_eval_user_data = params.cb_eval_user_data;
 
+    // backend samplers
+    if (params.samplers != nullptr && params.n_samplers > 0) {
+        samplers.reserve(params.n_samplers);
+
+        for (size_t i = 0; i < params.n_samplers; ++i) {
+            const auto & config = params.samplers[i];
+            samplers[config.seq_id] = config.sampler;
+        }
+    }
+
     auto rope_scaling_type = params.rope_scaling_type;
     if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
         rope_scaling_type = hparams.rope_scaling_type_train;
@@ -424,6 +434,10 @@ llama_context::llama_context(
 
 llama_context::~llama_context() {
     ggml_opt_free(opt_ctx);
+    // TODO: perhaps use a smart pointer for samplers
+    for (auto const& [seq_id, sampler] : samplers) {
+        llama_sampler_free(sampler);
+    }
 }
 
 void llama_context::synchronize() {
@@ -610,6 +624,10 @@ float * llama_context::get_embeddings() {
     return embd;
 }
 
+llama_token * llama_context::get_backend_sampled_tokens() {
+    return sampled_tokens;
+}
+
 float * llama_context::get_embeddings_ith(int32_t i) {
     int64_t j = -1;
 
@@ -659,6 +677,98 @@ float * llama_context::get_embeddings_seq(llama_seq_id seq_id) {
     return it->second.data();
 }
 
+llama_token llama_context::get_backend_sampled_token_ith(int32_t idx) {
+    // Handle special case where idx == -1 (single sequence exists) which is
+    // a valid index when using common_sampler_sample.
+    if (idx == -1) {
+        if (sampled_tokens_map.size() == 1) {
+            auto it = sampled_tokens_map.begin();
+            return it->second;
+        }
+        return LLAMA_TOKEN_NULL;
+    }
+
+    auto it = sampled_tokens_map.find(idx);
+    if (it == sampled_tokens_map.end()) {
+        return LLAMA_TOKEN_NULL;
+    }
+
+    return it->second;
+}
+
+float * llama_context::get_backend_sampled_probs_ith(int32_t idx) {
+    if (idx == -1) {
+        if (sampled_probs_map.size() == 1) {
+            return sampled_probs_map.begin()->second.data();
+        }
+    }
+
+    auto it = sampled_probs_map.find(idx);
+    if (it == sampled_probs_map.end()) {
+        return nullptr;
+    }
+
+    return it->second.data();
+}
+
+float * llama_context::get_backend_sampled_logits_ith(int32_t idx) {
+    if (idx == -1) {
+        if (sampled_logits_map.size() == 1) {
+            return sampled_logits_map.begin()->second.data();
+        }
+    }
+    auto it = sampled_logits_map.find(idx);
+    if (it == sampled_logits_map.end()) {
+        return nullptr;
+    }
+
+    return it->second.data();
+}
+
+const llama_token * llama_context::get_backend_sampled_token_ids_ith(int32_t idx) {
+    if (idx == -1) {
+        if (sampled_token_ids_map.size() == 1) {
+            return sampled_token_ids_map.begin()->second.data();
+        }
+    }
+    auto it = sampled_token_ids_map.find(idx);
+    if (it == sampled_token_ids_map.end() || it->second.empty()) {
+        return nullptr;
+    }
+
+    return it->second.data();
+}
+
+size_t llama_context::get_backend_sampled_logits_count(int32_t idx) const {
+    if (idx == -1) {
+        if (sampled_logits_map.size() == 1) {
+            return sampled_logits_map.begin()->second.size();
+        }
+    }
+    auto it = sampled_logits_map.find(idx);
+    if (it == sampled_logits_map.end()) {
+        return 0;
+    }
+
+    return it->second.size();
+}
+
+size_t llama_context::get_backend_sampled_probs_count(int32_t idx) const {
+    if (idx == -1) {
+        if (sampled_probs_map.size() == 1) {
+            return sampled_probs_map.begin()->second.size();
+        }
+        return 0;
+    }
+
+    auto it = sampled_probs_map.find(idx);
+    if (it == sampled_probs_map.end()) {
+        return 0;
+    }
+
+    return it->second.size();
+}
+
 void llama_context::attach_threadpool(
            ggml_threadpool_t threadpool,
            ggml_threadpool_t threadpool_batch) {
@@ -715,6 +825,37 @@ void llama_context::set_warmup(bool value) {
     cparams.warmup = value;
 }
 
+void llama_context::set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
+    LLAMA_LOG_DEBUG("%s: seq_id = %d, sampler = %p\n", __func__, (int) seq_id, (void *) sampler);
+
+    auto it = samplers.find(seq_id);
+    if (it != samplers.end()) {
+        // If the sampler to be set is the same that is already set, do nothing.
+        if (it->second == sampler) {
+            return;
+        }
+
+        llama_sampler_free(it->second);
+
+        // If sampler is nullptr, we remove the samppler chain for this seq_id.
+        // chain for this seq_id.
+        if (sampler == nullptr) {
+            samplers.erase(it);
+            return;
+        }
+
+        // Otherwise, we replace the existing sampler with the new one.
+        it->second = sampler;
+        return;
+    }
+
+    // If there is no sampler for this seq_id and the caller provides a non-null
+    // sampler, we set it.
+    if (sampler != nullptr) {
+        samplers[seq_id] = sampler;
+    }
+}
+
 void llama_context::set_adapter_lora(
             llama_adapter_lora * adapter,
             float scale) {
@@ -1029,6 +1170,10 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
     // TODO: this clear of the buffer can easily be forgotten - need something better
     embd_seq.clear();
+    sampled_probs_map.clear();
+    sampled_logits_map.clear();
+    sampled_tokens_map.clear();
+    sampled_token_ids_map.clear();
     output_swaps.clear();
 
     bool did_optimize = false;
@@ -1088,6 +1233,10 @@ int llama_context::decode(const llama_batch & batch_inp) {
     };
 
     int64_t n_outputs_prev = 0;
+    // This flag indicates whether a backend sampler has actually sampled a specific
+    // token, or if it has produced probabilites. If true, we true we can skip
+    // the normal copying of logits and embeddings.
+    bool backend_has_sampled = false;
 
     do {
         const auto & ubatch = mctx->get_ubatch();
@@ -1147,80 +1296,131 @@ int llama_context::decode(const llama_batch & batch_inp) {
         //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
         //}
 
-        auto * t_logits = res->get_logits();
-        auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
-
-        if (t_embd && res->get_embd_pooled()) {
-            t_embd = res->get_embd_pooled();
+        std::unordered_map<llama_seq_id, int32_t> seq_to_idx;
+        for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
+            if (ubatch.output[i]) {
+                llama_seq_id seq_id = ubatch.seq_id[i][0];
+                seq_to_idx[seq_id] = i;
+            }
         }
 
-        // extract logits
-        if (t_logits && n_outputs > 0) {
-            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
-            GGML_ASSERT(backend_res != nullptr);
-            GGML_ASSERT(logits != nullptr);
-
-            float * logits_out = logits + n_outputs_prev*n_vocab;
+        // extract sampled tokens
+        for (const auto & [seq_id, t_token] : res->t_sampled_tokens) {
+            auto idx_it = seq_to_idx.find(seq_id);
+            GGML_ASSERT(idx_it != seq_to_idx.end());
+            const int32_t idx = idx_it->second;
+            ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t_token);
+            ggml_backend_tensor_get_async(backend, t_token, &sampled_tokens_map[idx], 0, sizeof(llama_token));
+        }
+
+        for (const auto & [seq_id, t_ids] : res->t_sampled_token_ids) {
+            auto idx_it = seq_to_idx.find(seq_id);
+            GGML_ASSERT(idx_it != seq_to_idx.end());
+            const int32_t idx = idx_it->second;
+            ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t_ids);
+            sampled_token_ids_map[idx].resize(ggml_nelements(t_ids));
+            ggml_backend_tensor_get_async(backend, t_ids, sampled_token_ids_map[idx].data(), 0, ggml_nbytes(t_ids));
+        }
+
+        if (res->t_sampled_tokens.empty()) {
+            for (const auto & [seq_id, t_logits] : res->t_sampled_logits) {
+                auto idx_it = seq_to_idx.find(seq_id);
+                GGML_ASSERT(idx_it != seq_to_idx.end());
+                const int32_t idx = idx_it->second;
+                ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
+                sampled_logits_map[idx].resize(ggml_nelements(t_logits));
+                ggml_backend_tensor_get_async(backend, t_logits, sampled_logits_map[idx].data(), 0, ggml_nbytes(t_logits));
+            }
 
-            if (n_outputs) {
-                GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
-                ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
+            // extract sampled probabilities
+            for (const auto & [seq_id, t_probs] : res->t_sampled_probs) {
+                auto idx_it = seq_to_idx.find(seq_id);
+                GGML_ASSERT(idx_it != seq_to_idx.end());
+                const int32_t idx = idx_it->second;
+                ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t_probs);
+                sampled_probs_map[idx].resize(ggml_nelements(t_probs));
+                ggml_backend_tensor_get_async(backend, t_probs, sampled_probs_map[idx].data(), 0, ggml_nbytes(t_probs));
             }
         }
 
-        // extract embeddings
-        if (t_embd && n_outputs > 0) {
-            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
-            GGML_ASSERT(backend_embd != nullptr);
+        backend_has_sampled = !res->t_sampled_tokens.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
 
-            switch (cparams.pooling_type) {
-                case LLAMA_POOLING_TYPE_NONE:
-                    {
-                        // extract token embeddings
-                        GGML_ASSERT(embd != nullptr);
-                        float * embd_out = embd + n_outputs_prev*n_embd;
+        if (!backend_has_sampled) {
+            auto * t_logits = res->get_logits();
+            auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
 
-                        if (n_outputs) {
-                            GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                            GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd <= (int64_t) embd_size);
-                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd*sizeof(float));
-                        }
-                    } break;
-                case LLAMA_POOLING_TYPE_MEAN:
-                case LLAMA_POOLING_TYPE_CLS:
-                case LLAMA_POOLING_TYPE_LAST:
-                    {
-                        // extract sequence embeddings (cleared before processing each batch)
-                        auto & embd_seq_out = embd_seq;
-
-                        for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
-                            const llama_seq_id seq_id  = ubatch.seq_id_unq[s];
-                            const int32_t      seq_idx = ubatch.seq_idx[seq_id];
-
-                            embd_seq_out[seq_id].resize(n_embd);
-                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_idx)*sizeof(float), n_embd*sizeof(float));
-                        }
-                    } break;
-                case LLAMA_POOLING_TYPE_RANK:
-                    {
-                        // extract the rerank score - n_cls_out floats per sequence
-                        auto & embd_seq_out = embd_seq;
+            if (t_embd && res->get_embd_pooled()) {
+                t_embd = res->get_embd_pooled();
+            }
+
+            // extract logits
+            if (t_logits && n_outputs > 0) {
+                ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
+                GGML_ASSERT(backend_res != nullptr);
+                GGML_ASSERT(logits != nullptr);
 
-                        const uint32_t n_cls_out = hparams.n_cls_out;
+                float * logits_out = logits + n_outputs_prev*n_vocab;
 
-                        for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
-                            const llama_seq_id seq_id  = ubatch.seq_id_unq[s];
-                            const int32_t      seq_idx = ubatch.seq_idx[seq_id];
+                if (n_outputs) {
+                    GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
+                    GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
+                    ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
+                }
+            }
 
-                            embd_seq_out[seq_id].resize(n_cls_out);
-                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_idx)*sizeof(float), n_cls_out*sizeof(float));
+            // extract embeddings
+            if (t_embd && n_outputs > 0) {
+                ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
+                GGML_ASSERT(backend_embd != nullptr);
+
+                switch (cparams.pooling_type) {
+                    case LLAMA_POOLING_TYPE_NONE:
+                        {
+                            // extract token embeddings
+                            GGML_ASSERT(embd != nullptr);
+                            float * embd_out = embd + n_outputs_prev*n_embd;
+
+                            if (n_outputs) {
+                                GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
+                                GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd <= (int64_t) embd_size);
+                                ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd*sizeof(float));
+                            }
+                        } break;
+                    case LLAMA_POOLING_TYPE_MEAN:
+                    case LLAMA_POOLING_TYPE_CLS:
+                    case LLAMA_POOLING_TYPE_LAST:
+                        {
+                            // extract sequence embeddings (cleared before processing each batch)
+                            auto & embd_seq_out = embd_seq;
+
+                            for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+                                const llama_seq_id seq_id  = ubatch.seq_id_unq[s];
+                                const int32_t      seq_idx = ubatch.seq_idx[seq_id];
+
+                                embd_seq_out[seq_id].resize(n_embd);
+                                ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_idx)*sizeof(float), n_embd*sizeof(float));
+                            }
+                        } break;
+                    case LLAMA_POOLING_TYPE_RANK:
+                        {
+                            // extract the rerank score - n_cls_out floats per sequence
+                            auto & embd_seq_out = embd_seq;
+
+                            const uint32_t n_cls_out = hparams.n_cls_out;
+
+                            for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+                                const llama_seq_id seq_id  = ubatch.seq_id_unq[s];
+                                const int32_t      seq_idx = ubatch.seq_idx[seq_id];
+
+                                embd_seq_out[seq_id].resize(n_cls_out);
+                                ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_idx)*sizeof(float), n_cls_out*sizeof(float));
+                            }
+                        } break;
+                    case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                        {
+                            GGML_ABORT("unknown pooling type");
                         }
-                    } break;
-                case LLAMA_POOLING_TYPE_UNSPECIFIED:
-                    {
-                        GGML_ABORT("unknown pooling type");
-                    }
+                }
             }
         }
 
@@ -1231,7 +1431,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
     n_outputs = n_outputs_all;
 
     // set output mappings
-    if (n_outputs > 0) {
+    if (n_outputs > 0 && !backend_has_sampled) {
         bool sorted_output = true;
 
         auto & out_ids = balloc->get_out_ids();
@@ -1345,9 +1545,12 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     }
 
     float * output_base = (float *) ggml_backend_buffer_get_base(buf_output.get());
+    llama_token * s_output_base = (llama_token *) ggml_backend_buffer_get_base(buf_output.get());
 
-    logits = has_logits ? output_base               : nullptr;
-    embd   = has_embd   ? output_base + logits_size : nullptr;
+    logits         = has_logits        ? output_base               : nullptr;
+    embd           = has_embd          ? output_base + logits_size : nullptr;
+    sampled_tokens = !samplers.empty() ? s_output_base             : nullptr;
+    sampled_probs  = !samplers.empty() ? embd                      : nullptr;
 
     // set all ids as invalid (negative)
     std::fill(output_ids.begin(), output_ids.end(), -1);
@@ -1456,6 +1659,7 @@ llm_graph_params llama_context::graph_params(
         /*.loras       =*/ &loras,
         /*.mctx        =*/ mctx,
         /*.cross       =*/ &cross,
+        /*.samplers    =*/ samplers,
         /*.n_outputs   =*/ n_outputs,
         /*.cb          =*/ graph_get_cb(),
         /*.res         =*/ res,
@@ -2319,6 +2523,8 @@ llama_context_params llama_context_default_params() {
         /*.op_offload                  =*/ true,
         /*.swa_full                    =*/ true,
         /*.kv_unified                  =*/ false,
+        /*.sampler                     =*/ nullptr,
+        /*.n_sampler                   =*/ 0,
     };
 
     return result;
@@ -2478,6 +2684,13 @@ float * llama_get_logits(llama_context * ctx) {
 float * llama_get_logits_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
+    if (ctx->get_backend_sampled_token_ith(i) != LLAMA_TOKEN_NULL) {
+        return nullptr;
+    }
+    if (ctx->get_backend_sampled_probs_ith(i) != nullptr) {
+        return nullptr;
+    }
+
     return ctx->get_logits_ith(i);
 }
 
@@ -2499,6 +2712,46 @@ float * llama_get_embeddings_seq(llama_context * ctx, llama_seq_id seq_id) {
     return ctx->get_embeddings_seq(seq_id);
 }
 
+void llama_set_backend_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * sampler) {
+    ctx->set_backend_sampler(seq_id, sampler);
+}
+
+llama_token llama_get_backend_sampled_token_ith(llama_context * ctx, int32_t i) {
+    ctx->synchronize();
+
+    return ctx->get_backend_sampled_token_ith(i);
+}
+
+float * llama_get_backend_sampled_probs_ith(llama_context * ctx, int32_t i) {
+    ctx->synchronize();
+
+    return ctx->get_backend_sampled_probs_ith(i);
+}
+
+float * llama_get_backend_sampled_logits_ith(llama_context * ctx, int32_t i) {
+    ctx->synchronize();
+
+    return ctx->get_backend_sampled_logits_ith(i);
+}
+
+llama_token * llama_get_backend_sampled_token_ids_ith(llama_context * ctx, int32_t i) {
+    ctx->synchronize();
+
+    return const_cast<llama_token *>(ctx->get_backend_sampled_token_ids_ith(i));
+}
+
+uint32_t llama_get_backend_sampled_logits_count_ith(llama_context * ctx, int32_t i) {
+    ctx->synchronize();
+
+    return static_cast<uint32_t>(ctx->get_backend_sampled_logits_count(i));
+}
+
+uint32_t llama_get_backend_sampled_probs_count_ith(llama_context * ctx, int32_t i) {
+    ctx->synchronize();
+
+    return static_cast<uint32_t>(ctx->get_backend_sampled_probs_count(i));
+}
+
 // llama adapter API
 
 int32_t llama_set_adapter_lora(
diff --git a/src/llama-context.h b/src/llama-context.h
index 20cbd789554..b9020beff1c 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -66,6 +66,16 @@ struct llama_context {
     float * get_embeddings_ith(int32_t i);
     float * get_embeddings_seq(llama_seq_id seq_id);
 
+    llama_token       * get_backend_sampled_tokens();
+    llama_token         get_backend_sampled_token_ith(int32_t idx);
+
+    float             * get_backend_sampled_logits_ith(int32_t idx);
+    const llama_token * get_backend_sampled_token_ids_ith(int32_t idx);
+    size_t              get_backend_sampled_logits_count(int32_t idx) const;
+
+    float             * get_backend_sampled_probs_ith(int32_t idx);
+    size_t              get_backend_sampled_probs_count(int32_t idx) const;
+
     void attach_threadpool(
             ggml_threadpool_t threadpool,
             ggml_threadpool_t threadpool_batch);
@@ -208,6 +218,8 @@ struct llama_context {
     // reserve a graph with a dummy ubatch of the specified size
     ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false);
 
+    void set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler);
+
 private:
     llm_graph_params graph_params(
                         llm_graph_result * res,
@@ -242,6 +254,16 @@ struct llama_context {
     size_t  logits_size = 0; // capacity (of floats) for logits
     float * logits      = nullptr;
 
+    std::unordered_map<llama_seq_id, llama_sampler*> samplers;
+    llama_token * sampled_tokens = nullptr;
+    std::unordered_map<int32_t, llama_token> sampled_tokens_map;
+
+    float * sampled_probs = nullptr;
+    std::unordered_map<int32_t, std::vector<float>> sampled_probs_map;
+
+    std::unordered_map<int32_t, std::vector<float>> sampled_logits_map;
+    std::unordered_map<int32_t, std::vector<llama_token>> sampled_token_ids_map;
+
     // embeddings output (2-dimensional array: [n_outputs][n_embd])
     // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
     size_t  embd_size = 0; // capacity (of floats) for embeddings
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 650e40ec6ff..49aab37f33b 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -3,6 +3,7 @@
 #include "llama-impl.h"
 #include "llama-batch.h"
 #include "llama-cparams.h"
+#include "llama-model.h"
 
 #include "llama-kv-cache.h"
 #include "llama-kv-cache-iswa.h"
@@ -462,6 +463,28 @@ void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
     inp_rs->set_input(ubatch);
 }
 
+void llm_graph_input_sampling::set_input(const llama_ubatch * ubatch) {
+    GGML_UNUSED(ubatch);
+    for (const auto & [seq_id, sampler] : samplers) {
+        if (sampler->iface->set_input_ggml) {
+            sampler->iface->set_input_ggml(sampler);
+        }
+    }
+}
+
+bool llm_graph_input_sampling::can_reuse(const llm_graph_params & params) {
+    if (params.samplers.empty()) {
+        return true;
+    }
+
+    for (const auto & [seq_id, sampler] : params.samplers) {
+        if (sampler_versions[seq_id] != llama_sampler_chain_get_version(sampler)) {
+           return false;
+        }
+    }
+    return true;
+}
+
 //
 // llm_graph_result
 //
@@ -482,6 +505,10 @@ void llm_graph_result::reset() {
     t_logits      = nullptr;
     t_embd        = nullptr;
     t_embd_pooled = nullptr;
+    t_sampled_tokens.clear();
+    t_sampled_probs.clear();
+    t_sampled_logits.clear();
+    t_sampled_token_ids.clear();
 
     params = {};
 
@@ -587,6 +614,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     loras            (params.loras),
     mctx             (params.mctx),
     cross            (params.cross),
+    samplers         (params.samplers),
     cb_func          (params.cb),
     res              (params.res),
     ctx0             (res->get_ctx()),
@@ -2021,6 +2049,103 @@ void llm_graph_context::build_pooling(
     ggml_build_forward_expand(gf, cur);
 }
 
+void llm_graph_context::build_sampling(const llama_model & model, const llm_graph_params & params) const {
+    GGML_UNUSED(params);
+    if (samplers.empty()) {
+        return;
+    }
+
+    std::unordered_map<llama_seq_id, int32_t> seq_to_logit_row;
+    int32_t logit_row_idx = 0;
+
+    for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
+        if (ubatch.output[i]) {
+            llama_seq_id seq_id = ubatch.seq_id[i][0];
+            seq_to_logit_row[seq_id] = logit_row_idx;
+            logit_row_idx++;
+        }
+    }
+    if (seq_to_logit_row.empty()) {
+        return;
+    }
+
+    // res->t_logits will contain logits for all tokens that specied that want
+    // logits calculated (logits=1 or output=1)
+    ggml_tensor * logits_t = res->t_logits;
+    GGML_ASSERT(res->t_logits != nullptr && "missing t_logits tensor");
+
+    const int n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(&model));
+    GGML_ASSERT(logits_t->ne[0] == n_vocab);
+
+    ggml_backend_dev_t device = model.dev_output();
+    ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(device);
+
+    std::unordered_map<llama_seq_id, llama_sampler*> active_samplers;
+
+    for (const auto & [seq_id, sampler] : samplers) {
+        // Only process samplers for sequences that are in the current batch
+        auto it = seq_to_logit_row.find(seq_id);
+        if (it == seq_to_logit_row.end()) {
+            continue;
+        }
+        const int32_t row_idx = it->second;
+
+        // Allow GPU sampler to create input tensors by implementing init_ggml.
+        if (sampler->iface->init_ggml != nullptr) {
+            sampler->iface->init_ggml(sampler, buft);
+        }
+
+        active_samplers[seq_id] = sampler;
+
+        ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, n_vocab, row_idx * logits_t->nb[1]);
+        ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
+
+        struct llama_sampler_ggml_data ggml_data = {
+            /*.logits         =*/ logits_seq,
+            /*.probs          =*/ nullptr,
+            /*.sampled_token  =*/ nullptr,
+            /*.filtered_ids   =*/ nullptr,
+        };
+
+        llama_sampler_apply_ggml(sampler, ctx0, gf, &ggml_data);
+
+        if (ggml_data.sampled_token != nullptr) {
+            res->t_sampled_tokens[seq_id] = ggml_data.sampled_token;
+            ggml_build_forward_expand(gf, ggml_data.sampled_token);
+        }
+
+        if  (ggml_data.probs != nullptr) {
+            res->t_sampled_probs[seq_id] = ggml_data.probs;
+            ggml_build_forward_expand(gf, ggml_data.probs);
+        }
+
+        if (ggml_data.logits != logits_seq) {
+            res->t_sampled_logits[seq_id] = ggml_data.logits;
+            ggml_build_forward_expand(gf, res->t_sampled_logits[seq_id]);
+        }
+
+        if (ggml_data.filtered_ids != nullptr) {
+            res->t_sampled_token_ids[seq_id] = ggml_data.filtered_ids;
+            ggml_build_forward_expand(gf, ggml_data.filtered_ids);
+        }
+    }
+
+    // TODO: Call llama_sampler_accept_ggml after all samplers have been applied.
+    /*
+    for (const auto & [seq_id, sampler] : samplers) {
+        if (auto it = res->t_sampled_tokens.find(seq_id); it != res->t_sampled_tokens.end()) {
+            ggml_tensor * selected_token = it->second;
+            if (selected_token != nullptr) {
+                llama_sampler_accept_ggml(sampler, ctx0, gf, selected_token);
+            }
+        }
+    }
+    */
+
+    auto inp_sampling = std::make_unique<llm_graph_input_sampling>(n_vocab, false, active_samplers);
+    res->add_input(std::move(inp_sampling));
+}
+
 int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
     // TODO move to hparams if a T5 variant appears that uses a different value
     const int64_t max_distance = 128;
diff --git a/src/llama-graph.h b/src/llama-graph.h
index d0c3934f679..bd176e5d385 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -383,6 +383,32 @@ class llm_graph_input_mem_hybrid : public llm_graph_input_i {
     const llama_memory_hybrid_context * mctx;
 };
 
+class llm_graph_input_sampling : public llm_graph_input_i {
+public:
+    llm_graph_input_sampling(int32_t n_vocab, bool sorted,
+            std::unordered_map<llama_seq_id, llama_sampler*> samplers) :
+        n_vocab(n_vocab), sorted_value(sorted), samplers(samplers) {
+
+        sampler_versions.reserve(samplers.size());
+        for (const auto & [seq_id, sampler] : samplers) {
+            sampler_versions[seq_id] = llama_sampler_chain_get_version(sampler);
+        }
+    }
+    virtual ~llm_graph_input_sampling() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+    bool can_reuse(const llm_graph_params & params) override;
+
+    int32_t       n_vocab;
+    bool          sorted_value;
+    ggml_tensor * size           = nullptr; // I32 [1]
+    ggml_tensor * sorted         = nullptr; // I32 [1]
+
+    // Track sampler chain version for reuse
+    std::unordered_map<llama_seq_id, uint64_t> sampler_versions;
+    std::unordered_map<llama_seq_id, llama_sampler*> samplers;
+};
+
 //
 // llm_graph_result
 //
@@ -416,6 +442,23 @@ struct llm_graph_params {
     const llama_memory_context_i * mctx;
     const llama_cross            * cross;
 
+    std::unordered_map<llama_seq_id, llama_sampler*> samplers;
+
+    static bool samplers_equal(
+          const std::unordered_map<llama_seq_id, llama_sampler*> & lhs,
+          const std::unordered_map<llama_seq_id, llama_sampler*> & rhs) {
+        if (lhs.size() != rhs.size()) {
+            return false;
+        }
+        for (const auto & [seq_id, sampler] : lhs) {
+            auto it = rhs.find(seq_id);
+            if (it == rhs.end() || it->second != sampler) {
+                return false;
+            }
+        }
+        return true;
+    }
+
     uint32_t n_outputs;
 
     llm_graph_cb cb;
@@ -463,7 +506,9 @@ struct llm_graph_params {
             cvec      == other.cvec  &&
             loras     == other.loras &&
             cross     == other.cross &&
-            n_outputs == other.n_outputs;
+            n_outputs == other.n_outputs &&
+            samplers_equal(samplers, other.samplers);
+
     }
 };
 
@@ -504,6 +549,11 @@ class llm_graph_result {
     ggml_tensor * t_embd        = nullptr;
     ggml_tensor * t_embd_pooled = nullptr;
 
+    std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled_logits;
+    std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled_token_ids;
+    std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled_tokens;
+    std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled_probs;
+
     std::vector<llm_graph_input_ptr> inputs;
 
     ggml_context_ptr ctx_compute;
@@ -579,6 +629,8 @@ struct llm_graph_context {
     const llama_memory_context_i * mctx;
     const llama_cross            * cross;
 
+    std::unordered_map<llama_seq_id, llama_sampler*> samplers;
+
     const llm_graph_cb & cb_func;
 
     llm_graph_result * res;
@@ -819,6 +871,12 @@ struct llm_graph_context {
             ggml_tensor * cls_out,
             ggml_tensor * cls_out_b) const;
 
+    //
+    // sampling (backend sampling)
+    //
+
+    void build_sampling(const llama_model & model, const llm_graph_params & params) const;
+
     //
     // dense (out)
     //
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index e703181a198..ca75ce4c9ee 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -7412,6 +7412,9 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
     // add on pooling layer
     llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
 
+    // add backend sampling layers (if any)
+    llm->build_sampling(*this, params);
+
     // if the gguf model was converted with --sentence-transformers-dense-modules
     // there will be two additional dense projection layers
     // dense linear projections are applied after pooling
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index adb3f8810ed..dc9227c1a58 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -372,6 +372,39 @@ void llama_sampler_apply(struct llama_sampler * smpl, struct llama_token_data_ar
     smpl->iface->apply(smpl, cur_p);
 }
 
+void llama_sampler_apply_ggml(
+          struct llama_sampler           * smpl,
+          struct ggml_context            * ctx,
+          struct ggml_cgraph             * gf,
+          struct llama_sampler_ggml_data * ggml_data) {
+    GGML_ASSERT(smpl->iface->apply_ggml);
+    smpl->iface->apply_ggml(smpl, ctx, gf, ggml_data);
+}
+
+void llama_sampler_accept_ggml(
+        struct llama_sampler * smpl,
+        ggml_context * ctx,
+        ggml_cgraph * gf,
+        struct ggml_tensor * selected_token) {
+    if (smpl->iface->accept_ggml) {
+        smpl->iface->accept_ggml(smpl, ctx, gf, selected_token);
+    }
+}
+
+void llama_sampler_set_input_ggml(struct llama_sampler * smpl) {
+    if (smpl->iface->set_input_ggml) {
+        smpl->iface->set_input_ggml(smpl);
+    }
+}
+
+void llama_sampler_init_ggml(
+        struct llama_sampler      * smpl,
+        ggml_backend_buffer_type_t  buft) {
+    if (smpl->iface->init_ggml) {
+        smpl->iface->init_ggml(smpl, buft);
+    }
+}
+
 void llama_sampler_reset(struct llama_sampler * smpl) {
     if (smpl->iface->reset) {
         smpl->iface->reset(smpl);
@@ -406,7 +439,15 @@ void llama_sampler_free(struct llama_sampler * smpl) {
 }
 
 llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx) {
-    const auto * logits = llama_get_logits_ith(ctx, idx);
+    const llama_token   sampled_token        = llama_get_backend_sampled_token_ith(ctx, idx);
+    const float *       sampled_probs        = llama_get_backend_sampled_probs_ith(ctx, idx);
+    const float *       sampled_logits       = llama_get_backend_sampled_logits_ith(ctx, idx);
+    const llama_token * sampled_ids          = llama_get_backend_sampled_token_ids_ith(ctx, idx);
+
+    // If a backend sampler has already sampled a token, return it.
+    if (sampled_token != LLAMA_TOKEN_NULL) {
+        return sampled_token;
+    }
 
     const llama_model * model = llama_get_model(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
@@ -415,9 +456,40 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
 
     // TODO: do not allocate each time
     std::vector<llama_token_data> cur;
-    cur.reserve(n_vocab);
-    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-        cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+
+    if (sampled_probs) {
+        const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
+        cur.reserve(sampled_probs_count);
+        // The backend sampler has filtered the probabilities so we need to use the sampled ids.
+        if (sampled_ids != nullptr) {
+            for (uint32_t i = 0; i < sampled_probs_count; ++i) {
+                cur.emplace_back(llama_token_data{sampled_ids[i], 0.0f, sampled_probs[i]});
+            }
+        } else {
+            for (llama_token token_id = 0; token_id < (int) sampled_probs_count; token_id++) {
+                cur.emplace_back(llama_token_data{token_id, 0.0f, sampled_probs[token_id]});
+            }
+        }
+    } else if (sampled_logits) {
+        const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
+        cur.reserve(sampled_logits_count);
+        // The backend sampler has filtered the logits so we need to use the sampled ids.
+        if (sampled_ids != nullptr) {
+            for (llama_token i = 0; i < (int)sampled_logits_count; i++) {
+                cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
+            }
+        } else {
+            for (llama_token token_id = 0; token_id < (int)sampled_logits_count; token_id++) {
+                cur.emplace_back(llama_token_data{token_id, sampled_logits[token_id], 0.0f});
+            }
+        }
+    } else {
+        const auto * logits = llama_get_logits_ith(ctx, idx);
+        GGML_ASSERT(logits != nullptr);
+        cur.reserve(n_vocab);
+        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+            cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+        }
     }
 
     llama_token_data_array cur_p = {
@@ -462,6 +534,10 @@ static void llama_sampler_chain_apply(struct llama_sampler * smpl, llama_token_d
     time_meas tm(chain->t_sample_us, chain->params.no_perf);
 
     for (auto * smpl : chain->samplers) {
+        // Skip GPU samplers - they have apply_ggml but no apply
+        if (smpl->iface->apply == nullptr) {
+            continue;
+        }
         llama_sampler_apply(smpl, cur_p);
     }
 }
@@ -499,13 +575,67 @@ static void llama_sampler_chain_free(struct llama_sampler * smpl) {
     delete chain;
 }
 
+static void llama_sampler_chain_apply_ggml(
+          struct llama_sampler * smpl,
+          struct ggml_context * ctx,
+          struct ggml_cgraph * gf,
+          struct llama_sampler_ggml_data * ggml_data) {
+    auto * chain = (llama_sampler_chain *) smpl->ctx;
+
+    for (auto * smpl : chain->samplers) {
+        if (smpl->iface->apply_ggml) {
+            smpl->iface->apply_ggml(smpl, ctx, gf, ggml_data);
+        }
+    }
+}
+
+static void llama_sampler_chain_accept_ggml(
+        struct llama_sampler * smpl,
+        ggml_context * ctx,
+        ggml_cgraph * gf,
+        struct ggml_tensor * selected_token) {
+    auto * chain = (llama_sampler_chain *) smpl->ctx;
+
+    for (auto * smpl : chain->samplers) {
+        if (smpl->iface->accept_ggml) {
+            smpl->iface->accept_ggml(smpl, ctx, gf, selected_token);
+        }
+    }
+}
+
+static void llama_sampler_chain_set_input_ggml(struct llama_sampler * smpl) {
+    auto * chain = (llama_sampler_chain *) smpl->ctx;
+
+    for (auto * smpl : chain->samplers) {
+        if (smpl->iface->set_input_ggml) {
+            smpl->iface->set_input_ggml(smpl);
+        }
+    }
+}
+
+static void llama_sampler_chain_set_backend_context(
+        struct llama_sampler      * smpl,
+        ggml_backend_buffer_type_t  buft) {
+    auto * chain = (llama_sampler_chain *) smpl->ctx;
+
+    for (auto * smpl : chain->samplers) {
+        if (smpl->iface->init_ggml) {
+            smpl->iface->init_ggml(smpl,buft);
+        }
+    }
+}
+
 static struct llama_sampler_i llama_sampler_chain_i = {
-    /* .name   = */ llama_sampler_chain_name,
-    /* .accept = */ llama_sampler_chain_accept,
-    /* .apply  = */ llama_sampler_chain_apply,
-    /* .reset  = */ llama_sampler_chain_reset,
-    /* .clone  = */ llama_sampler_chain_clone,
-    /* .free   = */ llama_sampler_chain_free,
+    /* .name                = */ llama_sampler_chain_name,
+    /* .accept              = */ llama_sampler_chain_accept,
+    /* .apply               = */ llama_sampler_chain_apply,
+    /* .reset               = */ llama_sampler_chain_reset,
+    /* .clone               = */ llama_sampler_chain_clone,
+    /* .free                = */ llama_sampler_chain_free,
+    /* .apply_ggml          = */ llama_sampler_chain_apply_ggml,
+    /* .accept_ggml         = */ llama_sampler_chain_accept_ggml,
+    /* .set_input_ggml      = */ llama_sampler_chain_set_input_ggml,
+    /* .set_backend_context = */ llama_sampler_chain_set_backend_context,
 };
 
 struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params) {
@@ -523,6 +653,7 @@ struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_param
 void llama_sampler_chain_add(struct llama_sampler * chain, struct llama_sampler * smpl) {
     auto * p = (llama_sampler_chain *) chain->ctx;
     p->samplers.push_back(smpl);
+    p->version++;
 }
 
 struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i) {
@@ -544,6 +675,7 @@ struct llama_sampler * llama_sampler_chain_remove(struct llama_sampler * chain,
 
     auto * result = p->samplers[i];
     p->samplers.erase(p->samplers.begin() + i);
+    p->version++;
 
     return result;
 }
@@ -554,6 +686,11 @@ int llama_sampler_chain_n(const struct llama_sampler * chain) {
     return p->samplers.size();
 }
 
+uint64_t llama_sampler_chain_get_version(const struct llama_sampler * chain) {
+    const auto * p = (const llama_sampler_chain *) chain->ctx;
+    return p->version;
+}
+
 //
 // samplers
 //
@@ -574,12 +711,16 @@ static void llama_sampler_greedy_apply(struct llama_sampler * /*smpl*/, llama_to
 }
 
 static struct llama_sampler_i llama_sampler_greedy_i = {
-    /* .name   = */ llama_sampler_greedy_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_greedy_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ nullptr,
-    /* .free   = */ nullptr,
+    /* .name                = */ llama_sampler_greedy_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_greedy_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ nullptr,
+    /* .free                = */ nullptr,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_greedy() {
@@ -699,12 +840,16 @@ static void llama_sampler_dist_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_dist_i = {
-    /* .name   = */ llama_sampler_dist_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_dist_apply,
-    /* .reset  = */ llama_sampler_dist_reset,
-    /* .clone  = */ llama_sampler_dist_clone,
-    /* .free   = */ llama_sampler_dist_free,
+    /* .name                = */ llama_sampler_dist_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_dist_apply,
+    /* .reset               = */ llama_sampler_dist_reset,
+    /* .clone               = */ llama_sampler_dist_clone,
+    /* .free                = */ llama_sampler_dist_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
@@ -744,12 +889,16 @@ static void llama_sampler_top_k_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_top_k_i = {
-    /* .name   = */ llama_sampler_top_k_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_top_k_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_top_k_clone,
-    /* .free   = */ llama_sampler_top_k_free,
+    /* .name                = */ llama_sampler_top_k_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_top_k_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_top_k_clone,
+    /* .free                = */ llama_sampler_top_k_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_top_k(int32_t k) {
@@ -839,12 +988,16 @@ static void llama_sampler_top_p_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_top_p_i = {
-    /* .name   = */ llama_sampler_top_p_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_top_p_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_top_p_clone,
-    /* .free   = */ llama_sampler_top_p_free,
+    /* .name                = */ llama_sampler_top_p_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_top_p_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_top_p_clone,
+    /* .free                = */ llama_sampler_top_p_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
@@ -933,12 +1086,16 @@ static void llama_sampler_min_p_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_min_p_i = {
-    /* .name   = */ llama_sampler_min_p_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_min_p_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_min_p_clone,
-    /* .free   = */ llama_sampler_min_p_free,
+    /* .name                = */ llama_sampler_min_p_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_min_p_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_min_p_clone,
+    /* .free                = */ llama_sampler_min_p_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) {
@@ -1032,12 +1189,16 @@ static void llama_sampler_typical_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_typical_i = {
-    /* .name   = */ llama_sampler_typical_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_typical_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_typical_clone,
-    /* .free   = */ llama_sampler_typical_free,
+    /* .name                = */ llama_sampler_typical_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_typical_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_typical_clone,
+    /* .free                = */ llama_sampler_typical_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_typical(float p, size_t min_keep) {
@@ -1076,12 +1237,16 @@ static void llama_sampler_temp_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_temp_i = {
-    /* .name   = */ llama_sampler_temp_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_temp_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_temp_clone,
-    /* .free   = */ llama_sampler_temp_free,
+    /* .name                = */ llama_sampler_temp_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_temp_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_temp_clone,
+    /* .free                = */ llama_sampler_temp_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_temp(float temp) {
@@ -1186,12 +1351,16 @@ static void llama_sampler_temp_ext_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_temp_ext_i = {
-    /* .name   = */ llama_sampler_temp_ext_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_temp_ext_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_temp_ext_clone,
-    /* .free   = */ llama_sampler_temp_ext_free,
+    /* .name                = */ llama_sampler_temp_ext_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_temp_ext_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_temp_ext_clone,
+    /* .free                = */ llama_sampler_temp_ext_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, float exponent) {
@@ -1280,12 +1449,16 @@ static void llama_sampler_xtc_reset(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_xtc_i = {
-    /* .name   = */ llama_sampler_xtc_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sample_xtc_apply,
-    /* .reset  = */ llama_sampler_xtc_reset,
-    /* .clone  = */ llama_sampler_xtc_clone,
-    /* .free   = */ llama_sampler_xtc_free,
+    /* .name                = */ llama_sampler_xtc_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sample_xtc_apply,
+    /* .reset               = */ llama_sampler_xtc_reset,
+    /* .clone               = */ llama_sampler_xtc_clone,
+    /* .free                = */ llama_sampler_xtc_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
@@ -1388,12 +1561,16 @@ static void llama_sampler_mirostat_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_mirostat_i = {
-    /* .name   = */ llama_sampler_mirostat_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_mirostat_apply,
-    /* .reset  = */ llama_sampler_mirostat_reset,
-    /* .clone  = */ llama_sampler_mirostat_clone,
-    /* .free   = */ llama_sampler_mirostat_free,
+    /* .name                = */ llama_sampler_mirostat_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_mirostat_apply,
+    /* .reset               = */ llama_sampler_mirostat_reset,
+    /* .clone               = */ llama_sampler_mirostat_clone,
+    /* .free                = */ llama_sampler_mirostat_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t seed, float tau, float eta, int32_t m) {
@@ -1487,12 +1664,16 @@ static void llama_sampler_mirostat_v2_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_mirostat_v2_i = {
-    /* .name   = */ llama_sampler_mirostat_v2_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_mirostat_v2_apply,
-    /* .reset  = */ llama_sampler_mirostat_v2_reset,
-    /* .clone  = */ llama_sampler_mirostat_v2_clone,
-    /* .free   = */ llama_sampler_mirostat_v2_free,
+    /* .name                = */ llama_sampler_mirostat_v2_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_mirostat_v2_apply,
+    /* .reset               = */ llama_sampler_mirostat_v2_reset,
+    /* .clone               = */ llama_sampler_mirostat_v2_clone,
+    /* .free                = */ llama_sampler_mirostat_v2_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau, float eta) {
@@ -1604,12 +1785,16 @@ static void llama_sampler_grammar_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_grammar_i = {
-    /* .name   = */ llama_sampler_grammar_name,
-    /* .accept = */ llama_sampler_grammar_accept_impl,
-    /* .apply  = */ llama_sampler_grammar_apply,
-    /* .reset  = */ llama_sampler_grammar_reset,
-    /* .clone  = */ llama_sampler_grammar_clone,
-    /* .free   = */ llama_sampler_grammar_free,
+    /* .name                = */ llama_sampler_grammar_name,
+    /* .accept              = */ llama_sampler_grammar_accept_impl,
+    /* .apply               = */ llama_sampler_grammar_apply,
+    /* .reset               = */ llama_sampler_grammar_reset,
+    /* .clone               = */ llama_sampler_grammar_clone,
+    /* .free                = */ llama_sampler_grammar_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 static struct llama_sampler * llama_sampler_init_grammar_impl(
@@ -1811,12 +1996,16 @@ static void llama_sampler_penalties_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_penalties_i = {
-    /* .name   = */ llama_sampler_penalties_name,
-    /* .accept = */ llama_sampler_penalties_accept,
-    /* .apply  = */ llama_sampler_penalties_apply,
-    /* .reset  = */ llama_sampler_penalties_reset,
-    /* .clone  = */ llama_sampler_penalties_clone,
-    /* .free   = */ llama_sampler_penalties_free,
+    /* .name                = */ llama_sampler_penalties_name,
+    /* .accept              = */ llama_sampler_penalties_accept,
+    /* .apply               = */ llama_sampler_penalties_apply,
+    /* .reset               = */ llama_sampler_penalties_reset,
+    /* .clone               = */ llama_sampler_penalties_clone,
+    /* .free                = */ llama_sampler_penalties_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_penalties(
@@ -1902,12 +2091,16 @@ static void llama_sampler_top_n_sigma_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_top_n_sigma_i = {
-    /* .name   = */ llama_sampler_top_n_sigma_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_top_n_sigma_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_top_n_sigma_clone,
-    /* .free   = */ llama_sampler_top_n_sigma_free,
+    /* .name                = */ llama_sampler_top_n_sigma_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_top_n_sigma_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_top_n_sigma_clone,
+    /* .free                = */ llama_sampler_top_n_sigma_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_top_n_sigma(float n) {
@@ -2232,12 +2425,16 @@ static void llama_sampler_dry_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_dry_i = {
-    /* .name   = */ llama_sampler_dry_name,
-    /* .accept = */ llama_sampler_dry_accept,
-    /* .apply  = */ llama_sampler_dry_apply,
-    /* .reset  = */ llama_sampler_dry_reset,
-    /* .clone  = */ llama_sampler_dry_clone,
-    /* .free   = */ llama_sampler_dry_free,
+    /* .name                = */ llama_sampler_dry_name,
+    /* .accept              = */ llama_sampler_dry_accept,
+    /* .apply               = */ llama_sampler_dry_apply,
+    /* .reset               = */ llama_sampler_dry_reset,
+    /* .clone               = */ llama_sampler_dry_clone,
+    /* .free                = */ llama_sampler_dry_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, int32_t n_ctx_train, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
@@ -2373,12 +2570,16 @@ static void llama_sampler_logit_bias_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_logit_bias_i = {
-    /* .name   = */ llama_sampler_logit_bias_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_logit_bias_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_logit_bias_clone,
-    /* .free   = */ llama_sampler_logit_bias_free,
+    /* .name                = */ llama_sampler_logit_bias_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_logit_bias_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_logit_bias_clone,
+    /* .free                = */ llama_sampler_logit_bias_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_logit_bias(
@@ -2603,12 +2804,16 @@ static void llama_sampler_infill_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_infill_i = {
-    /* .name   = */ llama_sampler_infill_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_infill_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ llama_sampler_infill_clone,
-    /* .free   = */ llama_sampler_infill_free,
+    /* .name                = */ llama_sampler_infill_name,
+    /* .accept              = */ nullptr,
+    /* .apply               = */ llama_sampler_infill_apply,
+    /* .reset               = */ nullptr,
+    /* .clone               = */ llama_sampler_infill_clone,
+    /* .free                = */ llama_sampler_infill_free,
+    /* .apply_ggml          = */ nullptr,
+    /* .accept_ggml         = */ nullptr,
+    /* .set_input_ggml      = */ nullptr,
+    /* .set_backend_context = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab) {
diff --git a/src/llama-sampling.h b/src/llama-sampling.h
index 759dd7dcb70..d92311f58a8 100644
--- a/src/llama-sampling.h
+++ b/src/llama-sampling.h
@@ -21,6 +21,9 @@ struct llama_sampler_chain {
     mutable int64_t t_sample_us;
 
     mutable int32_t n_sample;
+
+    // simple version tracking for GPU sampling graph can_reuse
+    uint64_t version = 0;
 };
 
 struct llama_sampler * llama_sampler_init_dry_testing(
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index d9cc5e933f4..0db8b4bd888 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -206,6 +206,18 @@ llama_build_and_test(test-backend-ops.cpp)
 llama_build_and_test(test-model-load-cancel.cpp  LABEL "model")
 llama_build_and_test(test-autorelease.cpp        LABEL "model")
 
+llama_build_and_test(test-backend-sampler.cpp       LABEL "model")
+target_include_directories(test-backend-sampler PRIVATE ${PROJECT_SOURCE_DIR}/src)
+llama_test(test-backend-sampler NAME test-backend-sampler-greedy       ARGS --test greedy)
+llama_test(test-backend-sampler NAME test-backend-sampler-temp         ARGS --test temp)
+llama_test(test-backend-sampler NAME test-backend-sampler-top_k        ARGS --test top_k)
+llama_test(test-backend-sampler NAME test-backend-sampler-dist         ARGS --test dist)
+llama_test(test-backend-sampler NAME test-backend-sampler-dist-and-cpu ARGS --test dist_and_cpu)
+llama_test(test-backend-sampler NAME test-backend-sampler-logit-bias   ARGS --test logit_bias)
+llama_test(test-backend-sampler NAME test-backend-sampler-mul_seq      ARGS --test multi_sequence)
+llama_test(test-backend-sampler NAME test-backend-sampler-set-sampler  ARGS --test set_sampler)
+
+
 if (NOT GGML_BACKEND_DL)
     # these tests use the backends directly and cannot be built with dynamic loading
     llama_build_and_test(test-barrier.cpp)
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
new file mode 100644
index 00000000000..191eebba3e0
--- /dev/null
+++ b/tests/test-backend-sampler.cpp
@@ -0,0 +1,760 @@
+#include "ggml.h"
+#include "llama.h"
+#include "get-model.h"
+#include "common.h"
+
+#ifdef NDEBUG
+#undef NDEBUG
+#endif
+
+#include <cstdlib>
+#include <cstring>
+#include <array>
+#include <map>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+struct test_model_context {
+    llama_model * model = nullptr;
+    llama_context * ctx = nullptr;
+    const llama_vocab * vocab = nullptr;
+    int n_vocab = 0;
+    std::unordered_map<llama_seq_id, int32_t> seq_positions;
+    std::unordered_map<llama_seq_id, int32_t> last_batch_info;
+
+    bool setup_model(const char * model_path) {
+        if (model != nullptr) {
+            return true;
+        }
+
+        llama_backend_init();
+
+        llama_model_params mparams = llama_model_default_params();
+        model = llama_model_load_from_file(model_path, mparams);
+        if (model == nullptr) {
+            fprintf(stderr, "Warning: failed to load model '%s', skipping test\n", model_path);
+            cleanup();
+            return false;
+        }
+        vocab = llama_model_get_vocab(model);
+
+        return true;
+    }
+
+    bool setup(const char * model_path, std::vector<llama_sampler_seq_config> & configs) {
+        if (model == nullptr) {
+            setup_model(model_path);
+        }
+
+        if (model != nullptr && ctx != nullptr) {
+            return true;
+        }
+
+        llama_context_params cparams = llama_context_default_params();
+        cparams.n_ctx = 512;
+        cparams.n_batch = 512;
+        cparams.samplers = configs.data();
+        cparams.n_samplers = configs.size();
+
+        int32_t max_seq_id = 0;
+        for (const auto & config : configs) {
+            if (config.seq_id > max_seq_id) {
+                max_seq_id = config.seq_id;
+            }
+        }
+        cparams.n_seq_max = max_seq_id + 1;
+
+        ctx = llama_init_from_model(model, cparams);
+        if (ctx == nullptr) {
+            fprintf(stderr, "Warning: failed to create context, skipping test\n");
+            cleanup();
+            return false;
+        }
+        llama_set_warmup(ctx, false);
+
+        vocab = llama_model_get_vocab(model);
+        n_vocab = llama_vocab_n_tokens(vocab);
+        fprintf(stderr, "Vocabulary size: %d\n", n_vocab);
+
+        return true;
+    }
+
+    bool decode(const std::map<llama_seq_id, std::string> & prompts) {
+        if (ctx == nullptr || vocab == nullptr) {
+            fprintf(stderr, "Error: context not initialized, call setup() first\n");
+            return false;
+        }
+
+        last_batch_info.clear();
+        llama_batch batch = llama_batch_init(512, 0, prompts.size());
+
+        int n_tokens_per_prompt = 0;
+
+        for (const auto & [seq_id, prompt] : prompts) {
+            std::vector<llama_token> tokens;
+            tokens.push_back(llama_vocab_bos(vocab));
+
+            std::vector<llama_token> prompt_tokens(32);
+            int n_tokens = llama_tokenize(vocab, prompt.c_str(), prompt.length(),
+                                           prompt_tokens.data(), prompt_tokens.size(),
+                                           false, false);
+            //TODO: refactor this function to just handle a single prompt at a time
+            //      to avoid this check and complexity.
+            if (n_tokens_per_prompt == 0) {
+                n_tokens_per_prompt = n_tokens;
+            } else {
+                if (n_tokens != n_tokens_per_prompt) {
+                    fprintf(stderr, "Error: prompts must have the same number of tokens\n");
+                    llama_batch_free(batch);
+                    return false;
+                }
+                n_tokens_per_prompt = n_tokens;
+            }
+            if (n_tokens < 0) {
+                fprintf(stderr, "Warning: tokenization failed for seq_id %d\n", seq_id);
+                llama_batch_free(batch);
+                return false;
+            }
+
+            for (int i = 0; i < n_tokens; i++) {
+                tokens.push_back(prompt_tokens[i]);
+            }
+
+            for (size_t i = 0; i < tokens.size(); i++) {
+                common_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+            }
+
+            seq_positions[seq_id] = tokens.size();
+        }
+
+
+        printf("Batch contents:\n");
+        printf("  n_tokens: %d\n", batch.n_tokens);
+        for (int i = 0; i < batch.n_tokens; i++) {
+            printf("  token[%d]: tok=%-5d, pos=%d, n_seq_id=%d, seq_ids=[", i, batch.token[i], batch.pos[i], batch.n_seq_id[i]);
+
+        for (int j = 0; j < batch.n_seq_id[i]; j++) {
+            printf("%d%s", batch.seq_id[i][j], j < batch.n_seq_id[i]-1 ? ", " : "");
+        }
+        printf("], logits=%d\n", batch.logits[i]);
+}
+
+        if (llama_decode(ctx, batch) != 0) {
+            fprintf(stderr, "Warning: llama_decode failed\n");
+            llama_batch_free(batch);
+            return false;
+        }
+
+        // Build mapping from seq id to batch token idx
+        for (int i = 0; i < batch.n_tokens; i++) {
+            if (batch.logits[i]) {
+                llama_seq_id seq_id = batch.seq_id[i][0];
+                last_batch_info[seq_id] = i;
+                printf("seq %d : batch idx %d\n", seq_id, i);
+            }
+        }
+
+        llama_batch_free(batch);
+        return true;
+    }
+
+    int32_t idx_for_seq(llama_seq_id seq_id) {
+        auto it = last_batch_info.find(seq_id);
+        if (it == last_batch_info.end()) {
+            fprintf(stderr, "Error: no batch index found for seq_id %d\n", seq_id);
+            return -1;
+        }
+        return it->second;
+    }
+
+    bool decode_token(llama_token token, llama_seq_id seq_id = 0) {
+        if (ctx == nullptr) {
+            fprintf(stderr, "Error: context not initialized, call setup() first\n");
+            return false;
+        }
+
+        llama_batch batch = llama_batch_init(1, 0, 1);
+        int32_t pos = seq_positions[seq_id];
+        common_batch_add(batch, token, pos, { seq_id }, true);
+
+        if (llama_decode(ctx, batch) != 0) {
+            fprintf(stderr, "Warning: llama_decode failed for token %d in seq %d\n", token, seq_id);
+            llama_batch_free(batch);
+            return false;
+        }
+
+        last_batch_info.clear();
+        for (int i = 0; i < batch.n_tokens; i++) {
+            if (batch.logits[i]) {
+                llama_seq_id cur_seq = batch.seq_id[i][0];
+                last_batch_info[cur_seq] = i;
+            }
+        }
+
+        seq_positions[seq_id]++;
+        llama_batch_free(batch);
+        return true;
+    }
+
+    bool decode_tokens(const std::map<llama_seq_id, llama_token> & seq_tokens) {
+        if (ctx == nullptr) {
+            fprintf(stderr, "Error: context not initialized, call setup() first\n");
+            return false;
+        }
+
+        llama_batch batch = llama_batch_init(seq_tokens.size(), 0, seq_tokens.size());
+
+        for (const auto & [seq_id, token] : seq_tokens) {
+            int32_t pos = seq_positions[seq_id];
+            common_batch_add(batch, token, pos, { seq_id }, true);
+        }
+
+        if (llama_decode(ctx, batch) != 0) {
+            fprintf(stderr, "Warning: llama_decode failed for batch tokens\n");
+            llama_batch_free(batch);
+            return false;
+        }
+
+        for (const auto & [seq_id, _] : seq_tokens) {
+            seq_positions[seq_id]++;
+        }
+
+        last_batch_info.clear();
+        for (int i = 0; i < batch.n_tokens; i++) {
+            if (batch.logits[i]) {
+                llama_seq_id cur_seq = batch.seq_id[i][0];
+                last_batch_info[cur_seq] = i;
+            }
+        }
+
+        llama_batch_free(batch);
+        return true;
+    }
+
+    std::string token_to_piece(llama_token token, bool special) {
+        std::string piece;
+        piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
+        const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
+        if (n_chars < 0) {
+            piece.resize(-n_chars);
+            int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
+            GGML_ASSERT(check == -n_chars);
+        }
+        else {
+            piece.resize(n_chars);
+        }
+
+        return piece;
+    }
+
+    void cleanup() {
+        if (ctx) llama_free(ctx);
+        if (model) llama_model_free(model);
+        llama_backend_free();
+        ctx = nullptr;
+        model = nullptr;
+        vocab = nullptr;
+    }
+
+    ~test_model_context() {
+        cleanup();
+    }
+};
+
+static void test_backend_greedy_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    const int seq_id = 0;
+
+    struct llama_sampler_chain_params backend_sampler_params = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_sampler_params);
+
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_greedy());
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{seq_id, "Some"}})) {
+        return;
+    }
+
+    int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
+
+    llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+    printf("greedy sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
+    GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+
+    token = llama_get_backend_sampled_token_ith(test_ctx.ctx, -1);
+    printf("greedy sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
+    GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+
+    for (int i = 0; i < 10; i++) {
+        int32_t loop_idx = test_ctx.idx_for_seq(seq_id);
+        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, loop_idx);
+        printf("Generation step %d: token id:%d, string: %s\n", i, token, test_ctx.token_to_piece(token, false).c_str());
+        test_ctx.decode_token(token, 0);
+    }
+}
+
+static void test_backend_top_k_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    const int seq_id = 0;
+    const int32_t k = 8;
+    struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_top_k(k));
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{seq_id, "Hello"}})) {
+        return;
+    }
+
+    int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
+
+    float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);
+    uint32_t n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+    for (size_t i = 0; i < n_logits; ++i) {
+        printf("top_k logit[%zu] = %.6f\n", i, logits[i]);
+    }
+
+    // Sample using CPU sampler for verification that it is possible to do hybrid
+    // sampling, first top_k on the backend and then dist on the CPU.
+    struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+    GGML_ASSERT(chain->iface->apply_ggml != nullptr);
+
+    llama_sampler_chain_add(chain, llama_sampler_init_dist(18));
+    llama_token token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+    const std::string token_str = test_ctx.token_to_piece(token, false);
+    GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+
+    printf("backend top-k hybrid sampling test PASSED\n");
+
+    llama_sampler_free(chain);
+}
+
+static void test_backend_temp_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    const float temp_0 = 0.8f;
+    struct llama_sampler_chain_params backend_chain_params_0 = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain_0 = llama_sampler_chain_init(backend_chain_params_0);
+    llama_sampler_chain_add(backend_sampler_chain_0, llama_sampler_backend_init_temp(temp_0));
+
+    const float temp_1 = 0.1f;
+    struct llama_sampler_chain_params backend_chain_params_1 = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain_1 = llama_sampler_chain_init(backend_chain_params_1);
+    llama_sampler_chain_add(backend_sampler_chain_1, llama_sampler_backend_init_temp(temp_1));
+
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {
+        { 0, backend_sampler_chain_0 },
+        { 1, backend_sampler_chain_1 }
+    };
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{0, "Some where over"}, {1, "Once upon a"}})) {
+        return;
+    }
+
+    int32_t batch_idx_0 = test_ctx.idx_for_seq(0);
+    int32_t batch_idx_1 = test_ctx.idx_for_seq(1);
+
+    // Sample from sequence 0 using CPU sampler
+    struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
+    struct llama_sampler * chain_0 = llama_sampler_chain_init(chain_params_0);
+    llama_sampler_chain_add(chain_0, llama_sampler_init_dist(18));
+
+    llama_token token_0 = llama_sampler_sample(chain_0, test_ctx.ctx, batch_idx_0);
+    const std::string token_0_str = test_ctx.token_to_piece(token_0, false);
+    printf("Sequence 0 sampled token id:%d, string: '%s'\n", token_0, token_0_str.c_str());
+    GGML_ASSERT(token_0 >= 0 && token_0 < test_ctx.n_vocab);
+
+    // Sample from sequence 1 using CPU sampler
+    struct llama_sampler_chain_params chain_params_1 = llama_sampler_chain_default_params();
+    struct llama_sampler * chain_1 = llama_sampler_chain_init(chain_params_1);
+    llama_sampler_chain_add(chain_1, llama_sampler_init_dist(18));
+
+    llama_token token_1 = llama_sampler_sample(chain_1, test_ctx.ctx, batch_idx_1);
+    const std::string token_1_str = test_ctx.token_to_piece(token_1, false);
+    printf("Sequence 1 sampled token id:%d, string: '%s'\n", token_1, token_1_str.c_str());
+    GGML_ASSERT(token_1 >= 0 && token_1 < test_ctx.n_vocab);
+
+    printf("backend temp sampling test PASSED\n");
+
+    llama_sampler_free(chain_0);
+    llama_sampler_free(chain_1);
+}
+
+static void test_backend_multi_sequence_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
+    struct llama_sampler * sampler_chain_0 = llama_sampler_chain_init(chain_params_0);
+    llama_sampler_chain_add(sampler_chain_0, llama_sampler_backend_init_greedy());
+
+    struct llama_sampler_chain_params chain_params_1 = llama_sampler_chain_default_params();
+    struct llama_sampler * sampler_chain_1 = llama_sampler_chain_init(chain_params_1);
+    llama_sampler_chain_add(sampler_chain_1, llama_sampler_backend_init_temp(0.8f));
+    llama_sampler_chain_add(sampler_chain_1, llama_sampler_backend_init_greedy());
+
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {
+        { 0, sampler_chain_0 },
+        { 1, sampler_chain_1 }
+    };
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    std::map<llama_seq_id, std::string> prompts = {
+        {0, "Hello"},
+        {1, "Some"}
+    };
+
+    if (!test_ctx.decode(prompts)) {
+        return;
+    }
+
+    int32_t batch_idx_0 = test_ctx.idx_for_seq(0);
+    llama_token seq0_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx_0);
+    const std::string seq0_token_str = test_ctx.token_to_piece(seq0_token, false);
+    printf("Seq 0 sampled token id=%d, string='%s'\n", seq0_token, seq0_token_str.c_str());
+    GGML_ASSERT(seq0_token >= 0 && seq0_token < test_ctx.n_vocab);
+
+    int32_t batch_idx_1 = test_ctx.idx_for_seq(1);
+    llama_token seq1_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx_1);
+    const std::string seq1_token_str = test_ctx.token_to_piece(seq1_token, false);
+    printf("Seq 1 sampled token id=%d, string='%s'\n", seq1_token, seq1_token_str.c_str());
+    GGML_ASSERT(seq1_token >= 0 && seq1_token < test_ctx.n_vocab);
+
+    // Generate tokens for each sequence
+    printf("\nMulti-sequence generation:\n");
+    for (int step = 0; step < 4; step++) {
+        std::map<llama_seq_id, llama_token> tokens;
+
+        for (llama_seq_id seq_id : {0, 1}) {
+            int32_t idx = test_ctx.idx_for_seq(seq_id);
+            llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, idx);
+            const std::string token_str = test_ctx.token_to_piece(token, false);
+            printf("  Seq %d, step %d: token id=%d, string='%s'\n", seq_id, step, token, token_str.c_str());
+            tokens[seq_id] = token;
+        }
+
+        // Decode all tokens in a single batch
+        if (!test_ctx.decode_tokens(tokens)) {
+            break;
+        }
+    }
+
+    printf("backend multi-sequence sampling test PASSED\n");
+}
+
+static void test_backend_dist_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    const int32_t seed = 88;
+    struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ 0, backend_sampler_chain }};
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{0, "Hello"}})) {
+        return;
+    }
+
+    llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(0));
+    printf("greedy sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
+    GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+
+    token = llama_get_backend_sampled_token_ith(test_ctx.ctx, -1);
+    printf("greedy sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
+    GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+}
+
+static void test_backend_dist_sampling_and_cpu(const char * model_path) {
+    test_model_context test_ctx;
+
+    const int seq_id = 0;
+    const int32_t seed = 88;
+    struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{seq_id, "Hello"}})) {
+        return;
+    }
+
+    int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
+
+    // Sample using CPU sampler
+    struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+    llama_sampler_chain_add(chain, llama_sampler_init_dist(18));
+
+    llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+    llama_token cpu_token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+    GGML_ASSERT(backend_token == cpu_token);
+}
+
+static void test_backend_logit_bias_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    // Calling setup_model to ensure vocab is loaded and can be accessed
+    if (!test_ctx.setup_model(model_path)) {
+        return;
+    }
+
+    const int seq_id = 0;
+
+    // Create the logit biases vector.
+    std::vector<llama_logit_bias> logit_bias;
+
+    // Get the token for the piece "World".
+    const std::string piece = "World";
+    std::vector<llama_token> tokens(16);
+    llama_tokenize(test_ctx.vocab, piece.c_str(), piece.size(), tokens.data(), tokens.size(), false, false);
+    llama_token bias_token = tokens[0];
+    logit_bias.push_back({ bias_token, +100.0f });
+    printf("biasing token piece '%s' -> token id %d\n", piece.c_str(), bias_token);
+
+    struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_logit_bias(
+                llama_vocab_n_tokens(test_ctx.vocab),
+                logit_bias.size(),
+                logit_bias.data()));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(88));
+
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {
+        { seq_id, backend_sampler_chain },
+    };
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{seq_id, "Hello"}})) {
+        return;
+    }
+
+    llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(seq_id));
+    const std::string backend_token_str = test_ctx.token_to_piece(backend_token, false);
+    printf("logit bias sampled token = %d, string='%s'\n", backend_token, backend_token_str.c_str());
+    GGML_ASSERT(backend_token == bias_token);
+}
+
+static void test_backend_set_sampler(const char * model_path) {
+    test_model_context test_ctx;
+
+    const int32_t seed = 88;
+    const int seq_id = 0;
+    struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{seq_id, "Hello"}})) {
+        return;
+    }
+
+    int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
+
+    // Sample using backend sampler configured above
+    llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+    const std::string backend_token_str = test_ctx.token_to_piece(backend_token, false);
+    printf("dist sampled token = %d, string='%s'\n", backend_token, backend_token_str.c_str());
+
+    // Now clear the backend sampler for this sequence.
+    llama_set_backend_sampler(test_ctx.ctx, seq_id, nullptr);
+    printf("Cleared backend sampler for seq_id %d\n", seq_id);
+
+    // Sample using CPU sampler
+    struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+    llama_sampler_chain_add(chain, llama_sampler_init_dist(18));
+
+    std::map<llama_seq_id, llama_token> tokens = { { seq_id, backend_token}, };
+    if (!test_ctx.decode_tokens(tokens)) {
+        return;
+    }
+
+    // Should not have any sampled token or probs after clearing the backend sampler.
+    const int32_t idx = test_ctx.idx_for_seq(seq_id);
+    GGML_ASSERT(llama_get_backend_sampled_token_ith(test_ctx.ctx, idx) == LLAMA_TOKEN_NULL);
+    GGML_ASSERT(llama_get_backend_sampled_probs_ith(test_ctx.ctx, idx) == nullptr);
+
+    // Sample the token using the CPU sampler chain.
+    llama_token token2 = llama_sampler_sample(chain, test_ctx.ctx, seq_id);
+    const std::string token2_str = test_ctx.token_to_piece(token2, false);
+    printf("CPU sampled token after clearing backend sampler: id=%d, string='%s'\n", token2, token2_str.c_str());
+    std::map<llama_seq_id, llama_token> tokens2 = { { seq_id, token2}, };
+
+    // Set a new backend sampler for the sequence.
+    struct llama_sampler_chain_params new_backend_chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * new_backend_sampler_chain = llama_sampler_chain_init(new_backend_chain_params);
+    llama_sampler_chain_add(new_backend_sampler_chain, llama_sampler_backend_init_top_k(20));
+    llama_sampler_chain_add(new_backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    llama_set_backend_sampler(test_ctx.ctx, seq_id, new_backend_sampler_chain);
+
+    if (!test_ctx.decode_tokens(tokens2)) {
+        return;
+    }
+
+    llama_token new_backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(seq_id));
+    const std::string new_backend_token_str = test_ctx.token_to_piece(new_backend_token, false);
+    printf("dist sampled token = %d, string='%s'\n", new_backend_token, new_backend_token_str.c_str());
+}
+
+struct backend_test_case {
+    const char * name;
+    void (*fn)(const char *);
+    bool enabled_by_default;
+};
+
+static const backend_test_case BACKEND_TESTS[] = {
+    { "greedy",          test_backend_greedy_sampling,         true  },
+    { "logit_bias",      test_backend_logit_bias_sampling,     true  },
+    { "temp",            test_backend_temp_sampling,           true  },
+    { "top_k",           test_backend_top_k_sampling,          false },
+    { "multi_sequence",  test_backend_multi_sequence_sampling, false },
+    { "dist",            test_backend_dist_sampling,           false },
+    { "dist_and_cpu",    test_backend_dist_sampling_and_cpu,   false },
+    { "set_sampler",     test_backend_set_sampler,             true  },
+};
+
+struct backend_cli_args {
+    const char * model = nullptr;
+    const char * test = nullptr;
+};
+
+static backend_cli_args parse_backend_cli(int argc, char ** argv) {
+    backend_cli_args out;
+
+    for (int i = 1; i < argc; ++i) {
+        const char * arg = argv[i];
+
+        if (std::strcmp(arg, "--test") == 0) {
+            if (i + 1 >= argc) {
+                fprintf(stderr, "--test expects a value\n");
+                exit(EXIT_FAILURE);
+            }
+            out.test = argv[++i];
+            continue;
+        }
+        if (std::strncmp(arg, "--test=", 7) == 0) {
+            out.test = arg + 7;
+            continue;
+        }
+        if (std::strcmp(arg, "--model") == 0) {
+            if (i + 1 >= argc) {
+                fprintf(stderr, "--model expects a value\n");
+                exit(EXIT_FAILURE);
+            }
+            out.model = argv[++i];
+            continue;
+        }
+        if (std::strncmp(arg, "--model=", 8) == 0) {
+            out.model = arg + 8;
+            continue;
+        }
+        if (!out.model) {
+            out.model = arg;
+            continue;
+        }
+
+        fprintf(stderr, "Unexpected argument: %s\n", arg);
+        exit(EXIT_FAILURE);
+    }
+
+    return out;
+}
+
+static std::vector<const backend_test_case *> collect_tests_to_run(const char * requested) {
+    std::vector<const backend_test_case *> selected;
+
+    if (requested != nullptr) {
+        for (const auto & test : BACKEND_TESTS) {
+            if (std::strcmp(test.name, requested) == 0) {
+                selected.push_back(&test);
+                break;
+            }
+        }
+        if (selected.empty()) {
+            fprintf(stderr, "Unknown test '%s'. Available tests:\n", requested);
+            for (const auto & test : BACKEND_TESTS) {
+                fprintf(stderr, "  %s\n", test.name);
+            }
+            exit(EXIT_FAILURE);
+        }
+    } else {
+        for (const auto & test : BACKEND_TESTS) {
+            if (test.enabled_by_default) {
+                selected.push_back(&test);
+            }
+        }
+    }
+
+    if (selected.empty()) {
+        fprintf(stderr, "No backend sampling tests selected. Use --test=<name> to pick one.\n");
+    }
+
+    return selected;
+}
+
+static void run_tests(const std::vector<const backend_test_case *> & tests, const char * model_path) {
+    for (const auto * test : tests) {
+        fprintf(stderr, "\n=== %s ===\n", test->name);
+        test->fn(model_path);
+    }
+}
+
+
+int main(int argc, char *argv[] ) {
+    const backend_cli_args args = parse_backend_cli(argc, argv);
+
+    std::array<char *, 2> model_argv { argv[0], const_cast<char *>(args.model) };
+    const int model_argc = args.model ? 2 : 1;
+    char * model_path = get_model_or_exit(model_argc, model_argv.data());
+
+    auto * file = fopen(model_path, "r");
+    if (file == nullptr) {
+        fprintf(stderr, "no model at '%s' found\n", model_path);
+        return EXIT_FAILURE;
+    }
+
+    fprintf(stderr, "using '%s'\n", model_path);
+    fclose(file);
+
+    ggml_time_init();
+
+    const std::vector<const backend_test_case *> tests = collect_tests_to_run(args.test);
+    if (!tests.empty()) {
+        run_tests(tests, model_path);
+    }
+
+    return 0;
+}

From 9fe9a00a8a778f60df2d4ea6e435863b24694695 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 17 Nov 2025 15:30:16 +0100
Subject: [PATCH 02/59] llama-cli : add backend sampler configuration

---
 tools/main/main.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tools/main/main.cpp b/tools/main/main.cpp
index 33e88623357..f1d0fd4b60e 100644
--- a/tools/main/main.cpp
+++ b/tools/main/main.cpp
@@ -147,6 +147,9 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    // Configure backend sampler chain
+    llama_set_backend_sampler(ctx, 0, common_sampler_backend_init(model, sparams));
+
     auto * mem = llama_get_memory(ctx);
 
     const llama_vocab * vocab = llama_model_get_vocab(model);

From f1f3e685118724573453f902dd9e045bed6b5cdd Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 17 Nov 2025 15:31:30 +0100
Subject: [PATCH 03/59] server : add backend sampling options/configuration

---
 tools/server/server.cpp | 54 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 0fc3cf9195c..21d2f0f417c 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -197,6 +197,8 @@ struct slot_params {
                 {"speculative.p_min",         speculative.p_min},
                 {"timings_per_token",         timings_per_token},
                 {"post_sampling_probs",       post_sampling_probs},
+                {"backend_sampling",          sampling.backend_sampling},
+                {"backend_dist",              sampling.backend_dist},
                 {"lora",                      lora},
             };
         }
@@ -255,6 +257,8 @@ struct slot_params {
             {"speculative.p_min",         speculative.p_min},
             {"timings_per_token",         timings_per_token},
             {"post_sampling_probs",       post_sampling_probs},
+            {"backend_sampling",          sampling.backend_sampling},
+            {"backend_dist",              sampling.backend_dist},
             {"lora",                      lora},
         };
     }
@@ -357,6 +361,11 @@ struct server_task {
         params.sampling.min_keep           = json_value(data, "min_keep",            defaults.sampling.min_keep);
         params.post_sampling_probs         = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);
 
+        const bool request_backend_sampling    = json_value(data, "backend_sampling",        defaults.sampling.backend_sampling);
+        const bool request_backend_dist        = json_value(data, "backend_dist",            defaults.sampling.backend_dist);
+        params.sampling.backend_sampling       = defaults.sampling.backend_sampling && request_backend_sampling;
+        params.sampling.backend_dist           = params.sampling.backend_sampling && request_backend_dist;
+
         params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
         params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
         params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min);
@@ -1702,6 +1711,7 @@ struct server_slot {
     json json_schema;
 
     struct common_sampler * smpl = nullptr;
+    llama_sampler * backend_sampler = nullptr;
 
     llama_token sampled;
 
@@ -1747,6 +1757,13 @@ struct server_slot {
         n_draft_total = 0;
         n_draft_accepted = 0;
 
+        if (backend_sampler != nullptr) {
+            if (ctx != nullptr) {
+                llama_set_backend_sampler(ctx, id, nullptr);
+            }
+            backend_sampler = nullptr;
+        }
+
         task.reset();
         task_prev.reset();
 
@@ -2368,6 +2385,13 @@ struct server_context {
             common_sampler_free(slot.smpl);
             slot.smpl = nullptr;
 
+            if (slot.backend_sampler != nullptr) {
+                if (ctx != nullptr) {
+                    llama_set_backend_sampler(ctx, slot.id, nullptr);
+                }
+                slot.backend_sampler = nullptr;
+            }
+
             llama_free(slot.ctx_dft);
             slot.ctx_dft = nullptr;
 
@@ -2840,6 +2864,11 @@ struct server_context {
             SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl).c_str());
         }
 
+        if (!configure_slot_backend_sampler(slot, task.params.sampling)) {
+            send_error(task, "Failed to configure backend samplers", ERROR_TYPE_SERVER);
+            return false;
+        }
+
         // initialize draft batch
         // TODO: rework speculative decoding [TAG_SERVER_SPEC_REWORK]
         if (slot.ctx_dft) {
@@ -2857,6 +2886,31 @@ struct server_context {
         return true;
     }
 
+    bool configure_slot_backend_sampler(server_slot & slot, const common_params_sampling & sampling) {
+        if (!sampling.backend_sampling) {
+            if (slot.backend_sampler != nullptr) {
+                llama_set_backend_sampler(ctx, slot.id, nullptr);
+                slot.backend_sampler = nullptr;
+            }
+            return true;
+        }
+
+        llama_sampler * backend_chain = common_sampler_backend_init(model, sampling);
+        if (backend_chain == nullptr) {
+            SLT_ERR(slot, "%s", "failed to initialize backend sampler\n");
+            return false;
+        }
+
+        if (slot.backend_sampler != nullptr) {
+            llama_set_backend_sampler(ctx, slot.id, nullptr);
+            slot.backend_sampler = nullptr;
+        }
+
+        slot.backend_sampler = backend_chain;
+        llama_set_backend_sampler(ctx, slot.id, backend_chain);
+        return true;
+    }
+
     bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
         const std::string token_str = result.text_to_send;

From a3eb847d240c0ed3037f6c388e0e4bf9834e9345 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 17 Nov 2025 15:32:33 +0100
Subject: [PATCH 04/59] webui : add backend sampling options

---
 tools/server/public/index.html.gz             | Bin 1122488 -> 1126283 bytes
 .../ChatSettings/ChatSettingsDialog.svelte    |  14 ++++++++++++++
 .../ChatSettings/ChatSettingsFields.svelte    |  10 ++++++++--
 .../src/lib/constants/settings-config.ts      |   6 ++++++
 tools/server/webui/src/lib/services/chat.ts   |   5 +++++
 .../webui/src/lib/stores/chat.svelte.ts       |   6 ++++++
 tools/server/webui/src/lib/types/api.d.ts     |   2 ++
 .../server/webui/src/lib/types/settings.d.ts  |   2 ++
 8 files changed, 43 insertions(+), 2 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 48e341dbd12b3bae525fe9d55f9fc5c5280dd088..f3f73e9069026f3097bace38ecb61d4e5bb3f624 100644
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zNG`VLqUK+}@c5czO6uxAjoGx}R;iUR<}jo`pG$H;c+J8Fat;^2fwq*Zvr{%J`ChGA
zBAQFfhG0$a*1aOn+3Id0T>oUd34IT-kLvx2;E)5YJh{DN%CIn%Mq&jvJL4l$0@m}L
z`JnLUq?Q#|YyjSQ>_AE=7V}p`*OAE4xIfnJOvY0TWj!&pMH@rQ9wLaIK(&%Cz|fei
z1sGE6)X0MZ&Q~k;;7ureMBQWc(^e$msAE0r#cf;J_VrW0<pTgdb*;O*I4|Yev_3e$
zefuUuJ2&;`^+vO*XTfLm+$1Gms8YH^G{=p{V)8s{YXW7_<l*Vo1W{Y5k?w=9@-V8&
zWc6`ZQ-{z46R+Hq8gvNnTQ~|J1Nc=RRL7wTxewd#;i@T~{vRCW_DBT<a{=7H!+k>-
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za7bb=v-{GrnEY+2)3|-n=0th91-~?mrQ_xAFZ(afkN4(B$?WlRc+fX1Xb;-&Glac|
zuYC;vtCN%MzrI6SHx0_%KKr_FlAwBFVgbbG4H-PI)izo}Tq{h4pM3Jkzwm!Q|Gb1Z
z;OXS;^z`Sy?d=Z_$Lv#ky8~Y?N;xRaqL=*d!_RO-o}QdO!jFfaji>MM$HUL|=Hv56
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte
index d2a0a739c54..fa22a46ba4f 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte
@@ -152,6 +152,16 @@
 					key: 'samplers',
 					label: 'Samplers',
 					type: 'input'
+				},
+				{
+					key: 'backend_sampling',
+					label: 'Backend sampling',
+					type: 'checkbox'
+				},
+				{
+					key: 'backend_dist',
+					label: 'Backend dist sampling',
+					type: 'checkbox'
 				}
 			]
 		},
@@ -277,6 +287,10 @@
 
 	function handleConfigChange(key: string, value: string | boolean) {
 		localConfig[key] = value;
+
+		if (key === 'backend_sampling' && value === false) {
+			localConfig.backend_dist = false;
+		}
 	}
 
 	function handleClose() {
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
index d17f7e4229a..64b815c92ab 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
@@ -198,7 +198,9 @@
 				</p>
 			{/if}
 		{:else if field.type === 'checkbox'}
-			{@const isDisabled = field.key === 'pdfAsImage' && !supportsVision()}
+			{@const pdfDisabled = field.key === 'pdfAsImage' && !supportsVision()}
+			{@const backendDistDisabled = field.key === 'backend_dist' && !localConfig.backend_sampling}
+			{@const isDisabled = pdfDisabled || backendDistDisabled}
 
 			<div class="flex items-start space-x-3">
 				<Checkbox
@@ -223,11 +225,15 @@
 						<p class="text-xs text-muted-foreground">
 							{field.help || SETTING_CONFIG_INFO[field.key]}
 						</p>
-					{:else if field.key === 'pdfAsImage' && !supportsVision()}
+					{:else if pdfDisabled}
 						<p class="text-xs text-muted-foreground">
 							PDF-to-image processing requires a vision-capable model. PDFs will be processed as
 							text.
 						</p>
+					{:else if backendDistDisabled}
+						<p class="text-xs text-muted-foreground">
+							Enable GPU sampling to allow GPU dist sampling.
+						</p>
 					{/if}
 				</div>
 			</div>
diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts
index 7547832d95a..9dbf70cc695 100644
--- a/tools/server/webui/src/lib/constants/settings-config.ts
+++ b/tools/server/webui/src/lib/constants/settings-config.ts
@@ -18,6 +18,8 @@ export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean> =
 	modelSelectorEnabled: false,
 	// make sure these default values are in sync with `common.h`
 	samplers: 'top_k;typ_p;top_p;min_p;temperature',
+	backend_sampling: false,
+	backend_dist: false,
 	temperature: 0.8,
 	dynatemp_range: 0.0,
 	dynatemp_exponent: 1.0,
@@ -50,6 +52,10 @@ export const SETTING_CONFIG_INFO: Record<string, string> = {
 		'On pasting long text, it will be converted to a file. You can control the file length by setting the value of this parameter. Value 0 means disable.',
 	samplers:
 		'The order at which samplers are applied, in simplified way. Default is "top_k;typ_p;top_p;min_p;temperature": top_k->typ_p->top_p->min_p->temperature',
+	backend_sampling:
+		'Enable backend-based samplers. When enabled, supported samplers run on the accelerator backend for faster sampling.',
+	backend_dist:
+		'Perform the final distribution sampling step on the backend. Requires backend sampling to be enabled.',
 	temperature:
 		'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
 	dynatemp_range:
diff --git a/tools/server/webui/src/lib/services/chat.ts b/tools/server/webui/src/lib/services/chat.ts
index 1908d83909e..980d1137e91 100644
--- a/tools/server/webui/src/lib/services/chat.ts
+++ b/tools/server/webui/src/lib/services/chat.ts
@@ -98,6 +98,8 @@ export class ChatService {
 			dry_penalty_last_n,
 			// Other parameters
 			samplers,
+			backend_sampling,
+			backend_dist,
 			custom,
 			timings_per_token
 		} = options;
@@ -182,6 +184,9 @@ export class ChatService {
 					: samplers;
 		}
 
+		if (backend_sampling !== undefined) requestBody.backend_sampling = backend_sampling;
+		if (backend_dist !== undefined) requestBody.backend_dist = backend_dist;
+
 		if (timings_per_token !== undefined) requestBody.timings_per_token = timings_per_token;
 
 		if (custom) {
diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts
index 5b5a9d74a5b..12d0f7d36b8 100644
--- a/tools/server/webui/src/lib/stores/chat.svelte.ts
+++ b/tools/server/webui/src/lib/stores/chat.svelte.ts
@@ -298,6 +298,12 @@ class ChatStore {
 		if (currentConfig.samplers) {
 			apiOptions.samplers = currentConfig.samplers;
 		}
+		if (currentConfig.backend_sampling !== undefined) {
+			apiOptions.backend_sampling = Boolean(currentConfig.backend_sampling);
+		}
+		if (currentConfig.backend_dist !== undefined) {
+			apiOptions.backend_dist = Boolean(currentConfig.backend_dist);
+		}
 		if (currentConfig.custom) {
 			apiOptions.custom = currentConfig.custom;
 		}
diff --git a/tools/server/webui/src/lib/types/api.d.ts b/tools/server/webui/src/lib/types/api.d.ts
index 1a8bc649899..149d4fb118f 100644
--- a/tools/server/webui/src/lib/types/api.d.ts
+++ b/tools/server/webui/src/lib/types/api.d.ts
@@ -181,6 +181,8 @@ export interface ApiChatCompletionRequest {
 	dry_penalty_last_n?: number;
 	// Sampler configuration
 	samplers?: string[];
+	backend_sampling?: boolean;
+	backend_dist?: boolean;
 	// Custom parameters (JSON string)
 	custom?: Record<string, unknown>;
 	timings_per_token?: boolean;
diff --git a/tools/server/webui/src/lib/types/settings.d.ts b/tools/server/webui/src/lib/types/settings.d.ts
index b85b0597d00..9bba5cfbc27 100644
--- a/tools/server/webui/src/lib/types/settings.d.ts
+++ b/tools/server/webui/src/lib/types/settings.d.ts
@@ -36,6 +36,8 @@ export interface SettingsChatServiceOptions {
 	dry_penalty_last_n?: number;
 	// Sampler configuration
 	samplers?: string | string[];
+	backend_sampling?: boolean;
+	backend_dist?: boolean;
 	// Custom parameters
 	custom?: string;
 	timings_per_token?: boolean;

From 67d3b8e84d3915f01cc7fa50a28cfbe3a4d9c974 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 17 Nov 2025 16:03:04 +0100
Subject: [PATCH 05/59] ggml : add initial cumsum implementation for CUDA

---
 ggml/src/ggml-cuda/cumsum.cu    | 69 +++++++++++++++++++++++++++++++++
 ggml/src/ggml-cuda/cumsum.cuh   |  5 +++
 ggml/src/ggml-cuda/ggml-cuda.cu |  5 +++
 3 files changed, 79 insertions(+)
 create mode 100644 ggml/src/ggml-cuda/cumsum.cu
 create mode 100644 ggml/src/ggml-cuda/cumsum.cuh

diff --git a/ggml/src/ggml-cuda/cumsum.cu b/ggml/src/ggml-cuda/cumsum.cu
new file mode 100644
index 00000000000..041dc7cdb51
--- /dev/null
+++ b/ggml/src/ggml-cuda/cumsum.cu
@@ -0,0 +1,69 @@
+#include "cumsum.cuh"
+
+#ifdef GGML_CUDA_USE_CUB
+#include <cub/cub.cuh>
+using namespace cub;
+#endif  // GGML_CUDA_USE_CUB
+
+#include <cstdint>
+
+__global__ void cumsum_f32_kernel(const float * x, float * dst, int64_t n) {
+    // TODO: this is a naive implementation just for getting something working.
+    if (threadIdx.x == 0 && blockIdx.x == 0) {
+        dst[0] = x[0];
+        for (int64_t i = 1; i < n; i++) {
+            dst[i] = dst[i-1] + x[i];
+        }
+    }
+}
+
+void cumsum_f32_cuda(ggml_cuda_pool & pool, const float * x, float * dst, const int64_t ne, cudaStream_t stream) {
+#ifdef GGML_CUDA_USE_CUB
+    size_t tmp_size = 0;
+
+    // Query how much temp storage CUDA UnBound (CUB) needs
+    cub::DeviceScan::InclusiveSum(
+        nullptr,       // d_temp_storage (null = just query size)
+        tmp_size,      // reference to size (will be set by CUB)
+        x,             // input pointer
+        dst,           // output pointer
+        ne,            // number of elements
+        stream         // CUDA stream to use
+    );
+
+    ggml_cuda_pool_alloc<uint8_t> tmp_alloc(pool, tmp_size);
+
+    // Perform the inclusive scan
+    cub::DeviceScan::InclusiveSum(tmp_alloc.ptr, tmp_size, x, dst, ne, stream);
+
+#else
+    GGML_UNUSED(pool);
+    cumsum_f32_kernel<<<1, 1, 0, stream>>>(x, dst, ne);
+#endif // GGML_CUDA_USE_CUB
+}
+
+void ggml_cuda_op_cumsum(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguously_allocated(src0));
+
+    const float * src0_d = (const float *) src0->data;
+    float * dst_d = (float *) dst->data;
+
+    const int64_t ne0 = src0->ne[0];  // row length (cumsum computed along this dimension)
+    const int64_t ne1 = src0->ne[1];
+    const int64_t ne2 = src0->ne[2];
+    const int64_t ne3 = src0->ne[3];
+    const int64_t nrows = ne1 * ne2 * ne3;  // total number of rows
+
+    ggml_cuda_pool & pool = ctx.pool();
+    cudaStream_t stream = ctx.stream();
+
+    for (int64_t i = 0; i < nrows; i++) {
+        const float * src_row = src0_d + i * ne0;
+        float * dst_row = dst_d + i * ne0;
+        cumsum_f32_cuda(pool, src_row, dst_row, ne0, stream);
+    }
+}
diff --git a/ggml/src/ggml-cuda/cumsum.cuh b/ggml/src/ggml-cuda/cumsum.cuh
new file mode 100644
index 00000000000..7fca7e14564
--- /dev/null
+++ b/ggml/src/ggml-cuda/cumsum.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+void cumsum_f32_cuda(ggml_cuda_pool & pool, const float * x, float * dst, const int64_t ne, cudaStream_t stream);
+
+void ggml_cuda_op_cumsum(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 7d792e60cf9..8648e31413c 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -19,6 +19,7 @@
 #include "ggml-cuda/count-equal.cuh"
 #include "ggml-cuda/cpy.cuh"
 #include "ggml-cuda/cross-entropy-loss.cuh"
+#include "ggml-cuda/cumsum.cuh"
 #include "ggml-cuda/diagmask.cuh"
 #include "ggml-cuda/fattn.cuh"
 #include "ggml-cuda/getrows.cuh"
@@ -2678,6 +2679,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_SUM:
             ggml_cuda_op_sum(ctx, dst);
             break;
+        case GGML_OP_CUMSUM:
+            ggml_cuda_op_cumsum(ctx, dst);
+            break;
         case GGML_OP_SUM_ROWS:
             ggml_cuda_op_sum_rows(ctx, dst);
             break;
@@ -4119,6 +4123,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_POOL_2D:
         case GGML_OP_ACC:
             return true;
+        case GGML_OP_CUMSUM:
         case GGML_OP_SUM:
             return ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_ARGSORT:

From 71574f92739e7b0adf3a126969ab7832162f2e7b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 18 Nov 2025 07:31:54 +0100
Subject: [PATCH 06/59] sampling : enable all backend sampler tests

This commit enables all exisiting backend sampler tests in the
test-backend-sampler. Previously, some tests were disabled because
there were missing ggml operation implementations.
---
 tests/test-backend-sampler.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index 191eebba3e0..aa018e645af 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -639,10 +639,10 @@ static const backend_test_case BACKEND_TESTS[] = {
     { "greedy",          test_backend_greedy_sampling,         true  },
     { "logit_bias",      test_backend_logit_bias_sampling,     true  },
     { "temp",            test_backend_temp_sampling,           true  },
-    { "top_k",           test_backend_top_k_sampling,          false },
-    { "multi_sequence",  test_backend_multi_sequence_sampling, false },
-    { "dist",            test_backend_dist_sampling,           false },
-    { "dist_and_cpu",    test_backend_dist_sampling_and_cpu,   false },
+    { "top_k",           test_backend_top_k_sampling,          true  },
+    { "multi_sequence",  test_backend_multi_sequence_sampling, true  },
+    { "dist",            test_backend_dist_sampling,           true  },
+    { "dist_and_cpu",    test_backend_dist_sampling_and_cpu,   true  },
     { "set_sampler",     test_backend_set_sampler,             true  },
 };
 

From 4b52e5990396753503247ffc1053ba444cbddbc0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 18 Nov 2025 13:53:25 +0200
Subject: [PATCH 07/59] graph : do not include llama-model.h

---
 common/common.h       |  8 ++++----
 common/sampling.cpp   | 20 +++++++++++---------
 src/llama-context.cpp |  9 +++++----
 src/llama-graph.cpp   | 11 ++++-------
 src/llama-graph.h     |  9 ++++++---
 src/llama-model.cpp   |  2 +-
 6 files changed, 31 insertions(+), 28 deletions(-)

diff --git a/common/common.h b/common/common.h
index b320d891f5a..be34bcb78c6 100644
--- a/common/common.h
+++ b/common/common.h
@@ -189,8 +189,8 @@ struct common_params_sampling {
     std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
 
     // Backend sampling flags
-    bool    backend_sampling        = false; // enable backend sampling
-    bool    backend_dist            = false; // backend performs final sampling (dist)
+    bool backend_sampling = false; // enable backend sampling
+    bool backend_dist     = false; // backend performs final sampling (dist)
 
     // print the parameters into a string
     std::string print() const;
@@ -517,8 +517,8 @@ struct common_params {
         return !speculative.model.path.empty() || !speculative.model.hf_repo.empty();
     }
 
-    struct llama_sampler_seq_config * backend_samplers = NULL;
-    size_t                            n_backend_samplers = 0;
+    llama_sampler_seq_config * backend_samplers   = NULL;
+    size_t                     n_backend_samplers = 0;
 };
 
 // call once at the start of a program if it uses libcommon
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 1fc5c7ce0aa..ec61c18832c 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -113,9 +113,9 @@ struct common_sampler {
     llama_token_data_array cur_p;
 
     void set_logits(struct llama_context * ctx, int idx) {
-        const float *       sampled_probs        = llama_get_backend_sampled_probs_ith(ctx, idx);
-        const float *       sampled_logits       = llama_get_backend_sampled_logits_ith(ctx, idx);
-        const llama_token * sampled_ids          = llama_get_backend_sampled_token_ids_ith(ctx, idx);
+        const float *       sampled_probs  = llama_get_backend_sampled_probs_ith    (ctx, idx);
+        const float *       sampled_logits = llama_get_backend_sampled_logits_ith   (ctx, idx);
+        const llama_token * sampled_ids    = llama_get_backend_sampled_token_ids_ith(ctx, idx);
 
         const llama_model * model = llama_get_model(ctx);
         const llama_vocab * vocab = llama_model_get_vocab(model);
@@ -143,11 +143,11 @@ struct common_sampler {
             cur.reserve(sampled_logits_count);
             // The backend sampler has filtered the logits so we need to use the sampled ids.
             if (sampled_ids != nullptr) {
-                for (llama_token i = 0; i < (int)sampled_logits_count; i++) {
+                for (uint32_t i = 0; i < sampled_logits_count; i++) {
                     cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
                 }
             } else {
-                for (llama_token token_id = 0; token_id < (int)sampled_logits_count; token_id++) {
+                for (llama_token token_id = 0; token_id < (int) sampled_logits_count; token_id++) {
                     cur.emplace_back(llama_token_data{token_id, sampled_logits[token_id], 0.0f});
                 }
             }
@@ -414,10 +414,12 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
 llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
     // Check if a backend sampler has already sampled a token in which case we
     // return that token id directly.
-    const llama_token backend_sampled_token = llama_get_backend_sampled_token_ith(ctx, idx);
-    if (backend_sampled_token != LLAMA_TOKEN_NULL) {
-        LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, backend_sampled_token);
-        return backend_sampled_token;
+    {
+        const llama_token id = llama_get_backend_sampled_token_ith(ctx, idx);
+        if (id != LLAMA_TOKEN_NULL) {
+            LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, id);
+            return id;
+        }
     }
 
     gsmpl->set_logits(ctx, idx);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 877116cbfe2..f931881c9cd 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1233,9 +1233,9 @@ int llama_context::decode(const llama_batch & batch_inp) {
     };
 
     int64_t n_outputs_prev = 0;
+
     // This flag indicates whether a backend sampler has actually sampled a specific
-    // token, or if it has produced probabilites. If true, we true we can skip
-    // the normal copying of logits and embeddings.
+    // token, or if it has produced probabilites. If true, we can skip the normal copying of logits and embeddings.
     bool backend_has_sampled = false;
 
     do {
@@ -1655,6 +1655,7 @@ llm_graph_params llama_context::graph_params(
         /*.gtype       =*/ gtype,
         /*.sched       =*/ sched.get(),
         /*.backend_cpu =*/ backend_cpu,
+        /*.dev_out     =*/ model.dev_output(),
         /*.cvec        =*/ &cvec,
         /*.loras       =*/ &loras,
         /*.mctx        =*/ mctx,
@@ -2712,8 +2713,8 @@ float * llama_get_embeddings_seq(llama_context * ctx, llama_seq_id seq_id) {
     return ctx->get_embeddings_seq(seq_id);
 }
 
-void llama_set_backend_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * sampler) {
-    ctx->set_backend_sampler(seq_id, sampler);
+void llama_set_backend_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
+    ctx->set_backend_sampler(seq_id, smpl);
 }
 
 llama_token llama_get_backend_sampled_token_ith(llama_context * ctx, int32_t i) {
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 49aab37f33b..561e629869b 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -3,7 +3,6 @@
 #include "llama-impl.h"
 #include "llama-batch.h"
 #include "llama-cparams.h"
-#include "llama-model.h"
 
 #include "llama-kv-cache.h"
 #include "llama-kv-cache-iswa.h"
@@ -610,6 +609,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     rope_type        (hparams.rope_type),
     sched            (params.sched),
     backend_cpu      (params.backend_cpu),
+    dev_out          (params.dev_out),
     cvec             (params.cvec),
     loras            (params.loras),
     mctx             (params.mctx),
@@ -2049,8 +2049,7 @@ void llm_graph_context::build_pooling(
     ggml_build_forward_expand(gf, cur);
 }
 
-void llm_graph_context::build_sampling(const llama_model & model, const llm_graph_params & params) const {
-    GGML_UNUSED(params);
+void llm_graph_context::build_sampling() const {
     if (samplers.empty()) {
         return;
     }
@@ -2074,11 +2073,9 @@ void llm_graph_context::build_sampling(const llama_model & model, const llm_grap
     ggml_tensor * logits_t = res->t_logits;
     GGML_ASSERT(res->t_logits != nullptr && "missing t_logits tensor");
 
-    const int n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(&model));
-    GGML_ASSERT(logits_t->ne[0] == n_vocab);
+    const int64_t n_vocab = logits_t->ne[0];
 
-    ggml_backend_dev_t device = model.dev_output();
-    ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(device);
+    ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(dev_out);
 
     std::unordered_map<llama_seq_id, llama_sampler*> active_samplers;
 
diff --git a/src/llama-graph.h b/src/llama-graph.h
index bd176e5d385..552c3e724f0 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -436,6 +436,7 @@ struct llm_graph_params {
 
     ggml_backend_sched_t sched;
     ggml_backend_t backend_cpu;
+    ggml_backend_dev_t dev_out;
 
     const llama_adapter_cvec     * cvec;
     const llama_adapter_loras    * loras;
@@ -445,8 +446,8 @@ struct llm_graph_params {
     std::unordered_map<llama_seq_id, llama_sampler*> samplers;
 
     static bool samplers_equal(
-          const std::unordered_map<llama_seq_id, llama_sampler*> & lhs,
-          const std::unordered_map<llama_seq_id, llama_sampler*> & rhs) {
+          const std::unordered_map<llama_seq_id, llama_sampler *> & lhs,
+          const std::unordered_map<llama_seq_id, llama_sampler *> & rhs) {
         if (lhs.size() != rhs.size()) {
             return false;
         }
@@ -624,6 +625,8 @@ struct llm_graph_context {
 
     ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
 
+    ggml_backend_dev_t dev_out;
+
     const llama_adapter_cvec     * cvec;
     const llama_adapter_loras    * loras;
     const llama_memory_context_i * mctx;
@@ -875,7 +878,7 @@ struct llm_graph_context {
     // sampling (backend sampling)
     //
 
-    void build_sampling(const llama_model & model, const llm_graph_params & params) const;
+    void build_sampling() const;
 
     //
     // dense (out)
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index ca75ce4c9ee..1647b85453f 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -7413,7 +7413,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
     llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
 
     // add backend sampling layers (if any)
-    llm->build_sampling(*this, params);
+    llm->build_sampling();
 
     // if the gguf model was converted with --sentence-transformers-dense-modules
     // there will be two additional dense projection layers

From 82957a90f21f37b6862a3eb900b54b0de6687e23 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 18 Nov 2025 14:54:49 +0100
Subject: [PATCH 08/59] sampling : always expose sampled_ids

This commit precomputes and caches the full-vocab token id list in
llama_context's constructor, so llama_get_backend_sampled_token_ids_ith
always returns a valid pointer.

The motivation for this is that this enables both common/sampling.cpp
and src/llama-sampling.cpp can simplify their logic.

Not all backends samplers that process logits need to set the
sampled_tokens_id as they may not change the order of the logits, for
example the temperature sampler only scales the logits but does not
change their order. Simliar the logit bias sampler only adds bias to
specific token ids but does not change the order of the logits. In
these cases there will not be a device to host copy of the sampled
token ids, and this is the use case where having this precomputed
list is useful.
---
 common/sampling.cpp    | 22 ++++------------------
 src/llama-context.cpp  | 21 +++++++++++++++++----
 src/llama-context.h    |  1 +
 src/llama-sampling.cpp | 22 ++++------------------
 4 files changed, 26 insertions(+), 40 deletions(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index ec61c18832c..9c707a5bb9f 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -128,28 +128,14 @@ struct common_sampler {
         if (sampled_probs) {
             const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
             cur.reserve(sampled_probs_count);
-            // The backend sampler has filtered the probabilities so we need to use the sampled ids.
-            if (sampled_ids != nullptr) {
-                for (uint32_t i = 0; i < sampled_probs_count; ++i) {
-                    cur.emplace_back(llama_token_data{sampled_ids[i], 0.0f, sampled_probs[i]});
-                }
-            } else {
-                for (llama_token token_id = 0; token_id < (int) sampled_probs_count; token_id++) {
-                    cur.emplace_back(llama_token_data{token_id, 0.0f, sampled_probs[token_id]});
-                }
+            for (uint32_t i = 0; i < sampled_probs_count; ++i) {
+                cur.emplace_back(llama_token_data{sampled_ids[i], 0.0f, sampled_probs[i]});
             }
         } else if (sampled_logits) {
             const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
             cur.reserve(sampled_logits_count);
-            // The backend sampler has filtered the logits so we need to use the sampled ids.
-            if (sampled_ids != nullptr) {
-                for (uint32_t i = 0; i < sampled_logits_count; i++) {
-                    cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
-                }
-            } else {
-                for (llama_token token_id = 0; token_id < (int) sampled_logits_count; token_id++) {
-                    cur.emplace_back(llama_token_data{token_id, sampled_logits[token_id], 0.0f});
-                }
+            for (uint32_t i = 0; i < sampled_logits_count; i++) {
+                cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
             }
         } else {
             const auto * logits = llama_get_logits_ith(ctx, idx);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index f931881c9cd..25d35284340 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -430,6 +430,16 @@ llama_context::llama_context(
             LLAMA_LOG_INFO("%s: graph splits = %d (with bs=%d), %d (with bs=1)\n", __func__, n_splits_pp, n_tokens, n_splits_tg);
         }
     }
+
+    // Initialize the full vocabulary token ids for backend samplers.
+    {
+        const llama_vocab * vocab = llama_model_get_vocab(&model);
+        const int n_vocab = llama_vocab_n_tokens(vocab);
+        sampled_token_ids_full_vocab.resize(n_vocab);
+        for (int i = 0; i < n_vocab; ++i) {
+            sampled_token_ids_full_vocab[i] = i;
+        }
+    }
 }
 
 llama_context::~llama_context() {
@@ -728,15 +738,18 @@ float * llama_context::get_backend_sampled_logits_ith(int32_t idx) {
 const llama_token * llama_context::get_backend_sampled_token_ids_ith(int32_t idx) {
     if (idx == -1) {
         if (sampled_token_ids_map.size() == 1) {
-            return sampled_token_ids_map.begin()->second.data();
+            const auto & vec = sampled_token_ids_map.begin()->second;
+            if (!vec.empty()) {
+                return vec.data();
+            }
         }
     }
     auto it = sampled_token_ids_map.find(idx);
-    if (it == sampled_token_ids_map.end() || it->second.empty()) {
-        return nullptr;
+    if (it != sampled_token_ids_map.end() && !it->second.empty()) {
+        return it->second.data();
     }
 
-    return it->second.data();
+    return sampled_token_ids_full_vocab.data();
 }
 
 size_t llama_context::get_backend_sampled_logits_count(int32_t idx) const {
diff --git a/src/llama-context.h b/src/llama-context.h
index b9020beff1c..aba62e6e38e 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -263,6 +263,7 @@ struct llama_context {
 
     std::unordered_map<int32_t, std::vector<float>> sampled_logits_map;
     std::unordered_map<int32_t, std::vector<llama_token>> sampled_token_ids_map;
+    std::vector<llama_token> sampled_token_ids_full_vocab;
 
     // embeddings output (2-dimensional array: [n_outputs][n_embd])
     // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index dc9227c1a58..d210b826c70 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -460,28 +460,14 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
     if (sampled_probs) {
         const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
         cur.reserve(sampled_probs_count);
-        // The backend sampler has filtered the probabilities so we need to use the sampled ids.
-        if (sampled_ids != nullptr) {
-            for (uint32_t i = 0; i < sampled_probs_count; ++i) {
-                cur.emplace_back(llama_token_data{sampled_ids[i], 0.0f, sampled_probs[i]});
-            }
-        } else {
-            for (llama_token token_id = 0; token_id < (int) sampled_probs_count; token_id++) {
-                cur.emplace_back(llama_token_data{token_id, 0.0f, sampled_probs[token_id]});
-            }
+        for (uint32_t i = 0; i < sampled_probs_count; ++i) {
+            cur.emplace_back(llama_token_data{sampled_ids[i], 0.0f, sampled_probs[i]});
         }
     } else if (sampled_logits) {
         const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
         cur.reserve(sampled_logits_count);
-        // The backend sampler has filtered the logits so we need to use the sampled ids.
-        if (sampled_ids != nullptr) {
-            for (llama_token i = 0; i < (int)sampled_logits_count; i++) {
-                cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
-            }
-        } else {
-            for (llama_token token_id = 0; token_id < (int)sampled_logits_count; token_id++) {
-                cur.emplace_back(llama_token_data{token_id, sampled_logits[token_id], 0.0f});
-            }
+        for (llama_token i = 0; i < (int)sampled_logits_count; i++) {
+            cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
         }
     } else {
         const auto * logits = llama_get_logits_ith(ctx, idx);

From 311c1a347f5ac73382b4f2301e5361b74c707953 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 18 Nov 2025 16:01:54 +0100
Subject: [PATCH 09/59] sampling : ensure at most one output token per seq

This commit adds a check in the batch allocator to ensure that when
backend sampling is enabled, at most one output token is specified per
sequence.
---
 src/llama-batch.cpp            | 21 ++++++++++++++++-
 src/llama-batch.h              |  3 ++-
 src/llama-context.cpp          |  5 ++++-
 tests/test-backend-sampler.cpp | 41 ++++++++++++++++++++++++++++++++++
 4 files changed, 67 insertions(+), 3 deletions(-)

diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp
index 86a1a4ba187..f0866a9ca19 100644
--- a/src/llama-batch.cpp
+++ b/src/llama-batch.cpp
@@ -28,7 +28,8 @@ bool llama_batch_allocr::init(
         const llama_memory_i * memory,
         uint32_t n_embd,
         uint32_t n_seq_max,
-        bool output_all) {
+        bool output_all,
+        bool backend_sampling) {
     clear();
 
     batch = batch_inp;
@@ -145,6 +146,24 @@ bool llama_batch_allocr::init(
         }
     }
 
+    if (backend_sampling) {
+        std::vector<int32_t> seq_output_count(n_seq_max, 0);
+
+        for (int32_t i = 0; i < batch.n_tokens; ++i) {
+            if (batch.logits[i] == 0) {
+                continue;
+            }
+            for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
+                const llama_seq_id seq_id = batch.seq_id[i][s];
+                seq_output_count[seq_id]++;
+                if (seq_output_count[seq_id] > 1) {
+                    LLAMA_LOG_ERROR("%s: backend sampling allows at most one output token per sequence (%d)\n", __func__, seq_id);
+                    return false;
+                }
+            }
+        }
+    }
+
     //
     // compute stats
     //
diff --git a/src/llama-batch.h b/src/llama-batch.h
index 209cf3699de..d8751274f37 100644
--- a/src/llama-batch.h
+++ b/src/llama-batch.h
@@ -79,7 +79,8 @@ class llama_batch_allocr {
             const llama_memory_i * memory,
             uint32_t n_embd,
             uint32_t n_seq_max,
-            bool output_all);
+            bool output_all,
+            bool backend_sampling = false);
 
     const llama_batch & get_batch() const;
 
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 25d35284340..0b7f3adf9b5 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1155,7 +1155,10 @@ int llama_context::decode(const llama_batch & batch_inp) {
     // when computing embeddings, all tokens are output
     const bool output_all = cparams.embeddings;
 
-    if (!balloc->init(batch_inp, vocab, memory.get(), n_embd, cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max, output_all)) {
+    if (!balloc->init(batch_inp, vocab, memory.get(), n_embd,
+                      cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max,
+                      output_all,
+                      !samplers.empty())) {
         LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
         return -1;
     }
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index aa018e645af..c6d0d1a38d7 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -629,6 +629,46 @@ static void test_backend_set_sampler(const char * model_path) {
     printf("dist sampled token = %d, string='%s'\n", new_backend_token, new_backend_token_str.c_str());
 }
 
+static void test_backend_max_outputs(const char * model_path) {
+    test_model_context test_ctx;
+
+    const int seq_id = 0;
+    const int32_t seed = 88;
+    llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
+    llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    llama_batch batch = llama_batch_init(512, 0, 1);
+    std::string prompt = "Hello";
+
+    std::vector<llama_token> tokens;
+    tokens.push_back(llama_vocab_bos(test_ctx.vocab));
+
+    std::vector<llama_token> prompt_tokens(32);
+    int n_tokens = llama_tokenize(test_ctx.vocab, prompt.c_str(), prompt.length(),
+                                   prompt_tokens.data(), prompt_tokens.size(),
+                                   false, false);
+    for (int i = 0; i < n_tokens; i++) {
+        tokens.push_back(prompt_tokens[i]);
+    }
+
+    for (size_t i = 0; i < tokens.size(); i++) {
+        // set all tokens as output to trigger error
+        common_batch_add(batch, tokens[i], i, { seq_id }, true);
+    }
+
+    printf(">>> test_max_outputs expected error start:\n");
+    const int ret = llama_decode(test_ctx.ctx, batch);
+    GGML_ASSERT(ret != 0 && "llama_decode should not succeed multiple outputs per sequence");
+    printf("<<< test_max_outputs expected error end.\n");
+    llama_batch_free(batch);
+}
+
 struct backend_test_case {
     const char * name;
     void (*fn)(const char *);
@@ -644,6 +684,7 @@ static const backend_test_case BACKEND_TESTS[] = {
     { "dist",            test_backend_dist_sampling,           true  },
     { "dist_and_cpu",    test_backend_dist_sampling_and_cpu,   true  },
     { "set_sampler",     test_backend_set_sampler,             true  },
+    { "max_outputs",     test_backend_max_outputs,             true  },
 };
 
 struct backend_cli_args {

From 26be108be8e34fe48c36cb6224df0f5e5a09c8e9 Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Tue, 18 Nov 2025 18:17:44 +0100
Subject: [PATCH 10/59] CUDA: Optimize argsort for gpu-based token sampling

Argsort is used for top-k currently. WE optimize argsort by 2 things:

1. Use `DeviceRadixSort` for single-row/sequence to parallelize it
   across our SMs
2. Use `DeviceSegmentedSort` for multi-row/sequence as this is the
   correct entrypoint (the function chooses different execution paths,
   it contains `DeviceSegmentedRadixSort` as one of the paths and will
   choose the best one according to heuristics.
   https://nvidia.github.io/cccl/cub/api/structcub_1_1DeviceSegmentedSort.html#overview

Some perf numbers for a RTX PRO 6000:

On the kernel level, tested with
`GGML_CUDA_DISABLE_GRAPHS=1 ./test-backend-ops -o ARGSORT perf`
Before:
```
  ARGSORT(type=f32,ne=[65000,16,1,1],order=0):                  4130 runs -   359.24 us/run
  ARGSORT(type=f32,ne=[200000,1,1,1],order=0):                  8192 runs -   861.34 us/run
  ARGSORT(type=f32,ne=[200000,16,1,1],order=0):                 1343 runs -  1020.01 us/run
```

After:
```
  ARGSORT(type=f32,ne=[65000,16,1,1],order=0):                  4130 runs -   312.41 us/run
  ARGSORT(type=f32,ne=[200000,1,1,1],order=0):                 16384 runs -    63.48 us/run
  ARGSORT(type=f32,ne=[200000,16,1,1],order=0):                 1343 runs -   874.36 us/run
```

---
On the model level, tested with
`llama-cli -m gpt-oss-20b-mxfp4.gguf -n 200 -p "What is
the Capital of Sweden?" -no-cnv -fa 1 --backend-sampling`

Before:
```
llama_perf_sampler_print:    sampling time =       0.25 ms /   207 runs   (    0.00 ms per token, 824701.20 tokens per second)
llama_perf_context_print:        load time =   18215.58 ms
llama_perf_context_print: prompt eval time =      28.20 ms /     7 tokens (    4.03 ms per token,   248.19 tokens per second)
llama_perf_context_print:        eval time =     714.79 ms /   199 runs   (    3.59 ms per token,   278.40 tokens per second)
llama_perf_context_print:       total time =     857.62 ms /   206 tokens
```

After
```
llama_perf_sampler_print:    sampling time =       0.25 ms /   207 runs   (    0.00 ms per token, 828000.00 tokens per second)
llama_perf_context_print:        load time =   18366.92 ms
llama_perf_context_print: prompt eval time =      35.92 ms /     7 tokens (    5.13 ms per token,   194.87 tokens per second)
llama_perf_context_print:        eval time =     532.79 ms /   199 runs   (    2.68 ms per token,   373.50 tokens per second)
llama_perf_context_print:       total time =     683.65 ms /   206 tokens
```
---
 ggml/src/ggml-cuda/argsort.cu | 49 +++++++++++++++++++++++++----------
 tests/test-backend-ops.cpp    |  2 ++
 2 files changed, 37 insertions(+), 14 deletions(-)

diff --git a/ggml/src/ggml-cuda/argsort.cu b/ggml/src/ggml-cuda/argsort.cu
index 3722cf3ab26..b8003c48c51 100644
--- a/ggml/src/ggml-cuda/argsort.cu
+++ b/ggml/src/ggml-cuda/argsort.cu
@@ -49,28 +49,49 @@ static void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
     size_t temp_storage_bytes = 0;
 
     if (order == GGML_SORT_ORDER_ASC) {
-        DeviceSegmentedRadixSort::SortPairs(nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
-                                            temp_indices, dst,                                  // values (indices)
-                                            ncols * nrows, nrows,                            // num items, num segments
-                                            d_offsets, d_offsets + 1, 0, sizeof(float) * 8,  // all bits
-                                            stream);
+        if (nrows == 1) {
+            DeviceRadixSort::SortPairs(nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
+                                       temp_indices, dst,                                  // values (indices)
+                                       ncols, 0, sizeof(float) * 8, stream);
+        } else {
+            DeviceSegmentedSort::SortPairs(nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
+                                           temp_indices, dst,                                  // values (indices)
+                                           ncols * nrows, nrows,  // num items, num segments
+                                           d_offsets, d_offsets + 1, stream);
+        }
     } else {
-        DeviceSegmentedRadixSort::SortPairsDescending(nullptr, temp_storage_bytes, temp_keys, temp_keys, temp_indices,
-                                                      dst, ncols * nrows, nrows, d_offsets, d_offsets + 1, 0,
-                                                      sizeof(float) * 8, stream);
+        if (nrows == 1) {
+            DeviceRadixSort::SortPairsDescending(nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
+                                                 temp_indices, dst,                                  // values (indices)
+                                                 ncols, 0, sizeof(float) * 8, stream);
+        } else {
+            DeviceSegmentedSort::SortPairsDescending(nullptr, temp_storage_bytes, temp_keys, temp_keys, temp_indices,
+                                                     dst, ncols * nrows, nrows, d_offsets, d_offsets + 1, stream);
+        }
     }
 
     ggml_cuda_pool_alloc<uint8_t> temp_storage_alloc(pool, temp_storage_bytes);
     void *                        d_temp_storage = temp_storage_alloc.get();
 
     if (order == GGML_SORT_ORDER_ASC) {
-        DeviceSegmentedRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys, temp_indices, dst,
-                                            ncols * nrows, nrows, d_offsets, d_offsets + 1, 0, sizeof(float) * 8,
-                                            stream);
+        if (nrows == 1) {
+            DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
+                                       temp_indices, dst,  // values (indices)
+                                       ncols, 0, sizeof(float) * 8, stream);
+        } else {
+            DeviceSegmentedSort::SortPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys, temp_indices, dst,
+                                           ncols * nrows, nrows, d_offsets, d_offsets + 1, stream);
+        }
     } else {
-        DeviceSegmentedRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,
-                                                      temp_indices, dst, ncols * nrows, nrows, d_offsets, d_offsets + 1,
-                                                      0, sizeof(float) * 8, stream);
+        if (nrows == 1) {
+            DeviceRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
+                                                 temp_indices, dst,                                  // values (indices)
+                                                 ncols, 0, sizeof(float) * 8, stream);
+        } else {
+            DeviceSegmentedSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,
+                                                     temp_indices, dst, ncols * nrows, nrows, d_offsets, d_offsets + 1,
+                                                     stream);
+        }
     }
 }
 #endif  // GGML_CUDA_USE_CUB
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 267bead8c4a..fab8465934e 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -7886,6 +7886,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     }
 
     test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {65000, 16, 1, 1}));
+    test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {200000, 1, 1, 1}));
+    test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {200000, 16, 1, 1}));
 
     return test_cases;
 }

From 0da7e7dcccfac8a75bf3f65ac54cb4ea6b200c56 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 19 Nov 2025 06:59:03 +0100
Subject: [PATCH 11/59] sampling : remove version from sampler chain

This commit removes the version field from the sampler chain and instead
used the sampler pointer itself for change detection.
---
 include/llama.h        |  1 -
 src/llama-graph.cpp    |  2 +-
 src/llama-graph.h      | 10 +---------
 src/llama-sampling.cpp |  7 -------
 src/llama-sampling.h   |  3 ---
 5 files changed, 2 insertions(+), 21 deletions(-)

diff --git a/include/llama.h b/include/llama.h
index cbf23c7bcf1..2bc41f36d8d 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1235,7 +1235,6 @@ extern "C" {
 
     // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed
     LLAMA_API struct llama_sampler * llama_sampler_chain_remove(   struct llama_sampler * chain, int32_t i);
-    LLAMA_API uint64_t               llama_sampler_chain_get_version(const struct llama_sampler * chain);
 
     // available samplers:
 
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 561e629869b..fbf4475aa4e 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -477,7 +477,7 @@ bool llm_graph_input_sampling::can_reuse(const llm_graph_params & params) {
     }
 
     for (const auto & [seq_id, sampler] : params.samplers) {
-        if (sampler_versions[seq_id] != llama_sampler_chain_get_version(sampler)) {
+        if (samplers[seq_id] != sampler) {
            return false;
         }
     }
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 552c3e724f0..f7508046e40 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -387,13 +387,7 @@ class llm_graph_input_sampling : public llm_graph_input_i {
 public:
     llm_graph_input_sampling(int32_t n_vocab, bool sorted,
             std::unordered_map<llama_seq_id, llama_sampler*> samplers) :
-        n_vocab(n_vocab), sorted_value(sorted), samplers(samplers) {
-
-        sampler_versions.reserve(samplers.size());
-        for (const auto & [seq_id, sampler] : samplers) {
-            sampler_versions[seq_id] = llama_sampler_chain_get_version(sampler);
-        }
-    }
+        n_vocab(n_vocab), sorted_value(sorted), samplers(samplers) { }
     virtual ~llm_graph_input_sampling() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
@@ -404,8 +398,6 @@ class llm_graph_input_sampling : public llm_graph_input_i {
     ggml_tensor * size           = nullptr; // I32 [1]
     ggml_tensor * sorted         = nullptr; // I32 [1]
 
-    // Track sampler chain version for reuse
-    std::unordered_map<llama_seq_id, uint64_t> sampler_versions;
     std::unordered_map<llama_seq_id, llama_sampler*> samplers;
 };
 
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index d210b826c70..456e0502012 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -639,7 +639,6 @@ struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_param
 void llama_sampler_chain_add(struct llama_sampler * chain, struct llama_sampler * smpl) {
     auto * p = (llama_sampler_chain *) chain->ctx;
     p->samplers.push_back(smpl);
-    p->version++;
 }
 
 struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i) {
@@ -661,7 +660,6 @@ struct llama_sampler * llama_sampler_chain_remove(struct llama_sampler * chain,
 
     auto * result = p->samplers[i];
     p->samplers.erase(p->samplers.begin() + i);
-    p->version++;
 
     return result;
 }
@@ -672,11 +670,6 @@ int llama_sampler_chain_n(const struct llama_sampler * chain) {
     return p->samplers.size();
 }
 
-uint64_t llama_sampler_chain_get_version(const struct llama_sampler * chain) {
-    const auto * p = (const llama_sampler_chain *) chain->ctx;
-    return p->version;
-}
-
 //
 // samplers
 //
diff --git a/src/llama-sampling.h b/src/llama-sampling.h
index d92311f58a8..759dd7dcb70 100644
--- a/src/llama-sampling.h
+++ b/src/llama-sampling.h
@@ -21,9 +21,6 @@ struct llama_sampler_chain {
     mutable int64_t t_sample_us;
 
     mutable int32_t n_sample;
-
-    // simple version tracking for GPU sampling graph can_reuse
-    uint64_t version = 0;
 };
 
 struct llama_sampler * llama_sampler_init_dry_testing(

From 51fee298222be59730499e0c5dbad2922b60b8fb Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 19 Nov 2025 07:14:11 +0100
Subject: [PATCH 12/59] sampling : always populate logits for sampled probs

This commit updates common/sampler.cpp set_logits and
src/llama-sampling.cpp llama_sampler_sample to always populate the
logits field when backend sampled probabilities are available.

The motivation for this is that this ensure that CPU sampler always have
access to the logits values even when probabilites have been produced by
backend samplers.
---
 common/sampling.cpp    | 2 +-
 src/llama-sampling.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index 9c707a5bb9f..9813762eca9 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -129,7 +129,7 @@ struct common_sampler {
             const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
             cur.reserve(sampled_probs_count);
             for (uint32_t i = 0; i < sampled_probs_count; ++i) {
-                cur.emplace_back(llama_token_data{sampled_ids[i], 0.0f, sampled_probs[i]});
+                cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]});
             }
         } else if (sampled_logits) {
             const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 456e0502012..2cffa524cd3 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -461,7 +461,7 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
         const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
         cur.reserve(sampled_probs_count);
         for (uint32_t i = 0; i < sampled_probs_count; ++i) {
-            cur.emplace_back(llama_token_data{sampled_ids[i], 0.0f, sampled_probs[i]});
+            cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]});
         }
     } else if (sampled_logits) {
         const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);

From 7e98ebcc6b2e0ceaa62350cf92c1d353ea61eff5 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 19 Nov 2025 09:31:33 +0100
Subject: [PATCH 13/59] sampling : simplify backend sampling logic decode

This commit tries to simplify the backend sampling logic in
llama_context::decode.
---
 src/llama-context.cpp | 114 +++++++++++++++++++++++++-----------------
 1 file changed, 67 insertions(+), 47 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 0b7f3adf9b5..8485416c3e2 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1133,6 +1133,54 @@ int llama_context::encode(const llama_batch & batch_inp) {
     return 0;
 }
 
+static std::unordered_map<llama_seq_id, int32_t> build_seq_to_batch_idx(const llama_ubatch & ubatch) {
+    std::unordered_map<llama_seq_id, int32_t> seq_to_batch_idx;
+    for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
+        if (ubatch.output[i]) {
+            seq_to_batch_idx[ubatch.seq_id[i][0]] = i;
+        }
+    }
+    return seq_to_batch_idx;
+}
+
+static void copy_tensor_async_int(
+    const std::unordered_map<llama_seq_id, ggml_tensor*> & tensor_map,
+    std::unordered_map<int32_t, llama_token> & output_map,
+    const std::unordered_map<llama_seq_id, int32_t> & seq_to_batch_idx,
+    ggml_backend_sched_t sched) {
+    for (const auto & [seq_id, tensor] : tensor_map) {
+        const int32_t idx = seq_to_batch_idx.at(seq_id);
+        ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
+        ggml_backend_tensor_get_async(backend, tensor, &output_map[idx], 0, sizeof(output_map[idx]));
+    }
+}
+
+static void copy_tensor_async_floats(
+    const std::unordered_map<llama_seq_id, ggml_tensor*> & tensor_map,
+    std::unordered_map<int32_t, std::vector<float>> & output_map,
+    const std::unordered_map<llama_seq_id, int32_t> & seq_to_batch_idx,
+    ggml_backend_sched_t sched) {
+    for (const auto & [seq_id, tensor] : tensor_map) {
+        const int32_t idx = seq_to_batch_idx.at(seq_id);
+        ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
+        output_map[idx].resize(ggml_nelements(tensor));
+        ggml_backend_tensor_get_async(backend, tensor, output_map[idx].data(), 0, ggml_nbytes(tensor));
+    }
+}
+
+static void copy_tensor_async_token_ids(
+    const std::unordered_map<llama_seq_id, ggml_tensor*> & tensor_map,
+    std::unordered_map<int32_t, std::vector<llama_token>> & output_map,
+    const std::unordered_map<llama_seq_id, int32_t> & seq_to_batch_idx,
+    ggml_backend_sched_t sched) {
+    for (const auto & [seq_id, tensor] : tensor_map) {
+        const int32_t idx = seq_to_batch_idx.at(seq_id);
+        ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
+        output_map[idx].resize(ggml_nelements(tensor));
+        ggml_backend_tensor_get_async(backend, tensor, output_map[idx].data(), 0, ggml_nbytes(tensor));
+    }
+}
+
 int llama_context::decode(const llama_batch & batch_inp) {
     GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT
 
@@ -1154,11 +1202,12 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
     // when computing embeddings, all tokens are output
     const bool output_all = cparams.embeddings;
+    const bool has_backend_samplers = !samplers.empty();
 
     if (!balloc->init(batch_inp, vocab, memory.get(), n_embd,
                       cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max,
                       output_all,
-                      !samplers.empty())) {
+                      has_backend_samplers)) {
         LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
         return -1;
     }
@@ -1312,56 +1361,27 @@ int llama_context::decode(const llama_batch & batch_inp) {
         //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
         //}
 
-        std::unordered_map<llama_seq_id, int32_t> seq_to_idx;
-        for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
-            if (ubatch.output[i]) {
-                llama_seq_id seq_id = ubatch.seq_id[i][0];
-                seq_to_idx[seq_id] = i;
-            }
-        }
-
-        // extract sampled tokens
-        for (const auto & [seq_id, t_token] : res->t_sampled_tokens) {
-            auto idx_it = seq_to_idx.find(seq_id);
-            GGML_ASSERT(idx_it != seq_to_idx.end());
-            const int32_t idx = idx_it->second;
-            ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t_token);
-            ggml_backend_tensor_get_async(backend, t_token, &sampled_tokens_map[idx], 0, sizeof(llama_token));
-        }
-
-        for (const auto & [seq_id, t_ids] : res->t_sampled_token_ids) {
-            auto idx_it = seq_to_idx.find(seq_id);
-            GGML_ASSERT(idx_it != seq_to_idx.end());
-            const int32_t idx = idx_it->second;
-            ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t_ids);
-            sampled_token_ids_map[idx].resize(ggml_nelements(t_ids));
-            ggml_backend_tensor_get_async(backend, t_ids, sampled_token_ids_map[idx].data(), 0, ggml_nbytes(t_ids));
-        }
-
-        if (res->t_sampled_tokens.empty()) {
-            for (const auto & [seq_id, t_logits] : res->t_sampled_logits) {
-                auto idx_it = seq_to_idx.find(seq_id);
-                GGML_ASSERT(idx_it != seq_to_idx.end());
-                const int32_t idx = idx_it->second;
-                ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
-                sampled_logits_map[idx].resize(ggml_nelements(t_logits));
-                ggml_backend_tensor_get_async(backend, t_logits, sampled_logits_map[idx].data(), 0, ggml_nbytes(t_logits));
-            }
+        if (has_backend_samplers) {
+            const auto seq_to_batch_idx = build_seq_to_batch_idx(ubatch);
 
-            // extract sampled probabilities
-            for (const auto & [seq_id, t_probs] : res->t_sampled_probs) {
-                auto idx_it = seq_to_idx.find(seq_id);
-                GGML_ASSERT(idx_it != seq_to_idx.end());
-                const int32_t idx = idx_it->second;
-                ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t_probs);
-                sampled_probs_map[idx].resize(ggml_nelements(t_probs));
-                ggml_backend_tensor_get_async(backend, t_probs, sampled_probs_map[idx].data(), 0, ggml_nbytes(t_probs));
+            // If a backend sampler has sampled a token we only want to copy the
+            // sampled tokens and avoid copying logits and probabilites.
+            if (!res->t_sampled_tokens.empty()) {
+                // async copy the sampled tokens from the backend to the host.
+                copy_tensor_async_int(res->t_sampled_tokens, sampled_tokens_map, seq_to_batch_idx, sched.get());
+            } else {
+                // async copy the sampled logits/probs from the backend to the host.
+                copy_tensor_async_floats(res->t_sampled_logits, sampled_logits_map, seq_to_batch_idx, sched.get());
+                copy_tensor_async_floats(res->t_sampled_probs,  sampled_probs_map,  seq_to_batch_idx, sched.get());
             }
-        }
 
-        backend_has_sampled = !res->t_sampled_tokens.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
+            // async copy the filtered token ids from the backend to the host.
+            // These are needed for:
+            // 1) Backend dist sampler to map indices to vocab token ids.
+            // 2) CPU samplers to associate filtered logits with their token ids.
+            copy_tensor_async_token_ids(res->t_sampled_token_ids, sampled_token_ids_map, seq_to_batch_idx, sched.get());
 
-        if (!backend_has_sampled) {
+        } else {
             auto * t_logits = res->get_logits();
             auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
 

From d74eb61aa7a45ee63a6ac63a281d3f6e6985c6db Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 19 Nov 2025 11:29:26 +0100
Subject: [PATCH 14/59] squash! sampling : simplify backend sampling logic
 decode

Fix condition to check if backend actually sampled tokens, not just that
backend samplers are available.
---
 src/llama-context.cpp | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 8485416c3e2..15ca80a735a 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1361,7 +1361,9 @@ int llama_context::decode(const llama_batch & batch_inp) {
         //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
         //}
 
-        if (has_backend_samplers) {
+        const bool backend_has_sampled = !res->t_sampled_tokens.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
+
+        if (has_backend_samplers && backend_has_sampled) {
             const auto seq_to_batch_idx = build_seq_to_batch_idx(ubatch);
 
             // If a backend sampler has sampled a token we only want to copy the
@@ -1381,7 +1383,9 @@ int llama_context::decode(const llama_batch & batch_inp) {
             // 2) CPU samplers to associate filtered logits with their token ids.
             copy_tensor_async_token_ids(res->t_sampled_token_ids, sampled_token_ids_map, seq_to_batch_idx, sched.get());
 
-        } else {
+        }
+
+        if (!backend_has_sampled) {
             auto * t_logits = res->get_logits();
             auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
 

From 38f408c2530871a24a96b318b13f89836e3b10a3 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 19 Nov 2025 13:43:29 +0200
Subject: [PATCH 15/59] common : fix regression caused by extra memory
 allocations during sampling

---
 common/sampling.cpp | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index 9813762eca9..bccec35dacf 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -122,27 +122,24 @@ struct common_sampler {
 
         const int n_vocab = llama_vocab_n_tokens(vocab);
 
-        // Use the member variable instead of allocating locally
-        cur.clear();
-
         if (sampled_probs) {
             const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
-            cur.reserve(sampled_probs_count);
+            cur.resize(sampled_probs_count);
             for (uint32_t i = 0; i < sampled_probs_count; ++i) {
-                cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]});
+                cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]};
             }
         } else if (sampled_logits) {
             const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
-            cur.reserve(sampled_logits_count);
+            cur.resize(sampled_logits_count);
             for (uint32_t i = 0; i < sampled_logits_count; i++) {
-                cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
+                cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f};
             }
         } else {
             const auto * logits = llama_get_logits_ith(ctx, idx);
             GGML_ASSERT(logits != nullptr);
-            cur.reserve(n_vocab);
+            cur.resize(n_vocab);
             for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-                cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+                cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
             }
         }
 

From 18ed4d8f962c46b819f517c3eef2e90678c3939f Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 19 Nov 2025 15:10:15 +0100
Subject: [PATCH 16/59] squash! sampling : simplify backend sampling logic
 decode

The commit fixes a variable shadowing issue in the
`llama_context::decode` function which was introduced in a previous
refactoring.
---
 src/llama-context.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 15ca80a735a..5868f6246f9 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1361,7 +1361,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
         //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
         //}
 
-        const bool backend_has_sampled = !res->t_sampled_tokens.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
+        backend_has_sampled = !res->t_sampled_tokens.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
 
         if (has_backend_samplers && backend_has_sampled) {
             const auto seq_to_batch_idx = build_seq_to_batch_idx(ubatch);

From ed4345bdd9d55f3a21ac95f562bb2eb2212c9a8b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 20 Nov 2025 07:56:33 +0100
Subject: [PATCH 17/59] squash! common : fix regression caused by extra memory
 allocations during sampling

Apply the same changes to llama-sampling.cpp, llama_sampler_sample as
were applied in commit 38f408c25.
---
 src/llama-sampling.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 2cffa524cd3..11679c6c9eb 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -459,22 +459,22 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
 
     if (sampled_probs) {
         const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
-        cur.reserve(sampled_probs_count);
+        cur.resize(sampled_probs_count);
         for (uint32_t i = 0; i < sampled_probs_count; ++i) {
-            cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]});
+            cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]};
         }
     } else if (sampled_logits) {
         const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
-        cur.reserve(sampled_logits_count);
+        cur.resize(sampled_logits_count);
         for (llama_token i = 0; i < (int)sampled_logits_count; i++) {
-            cur.emplace_back(llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f});
+            cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f};
         }
     } else {
         const auto * logits = llama_get_logits_ith(ctx, idx);
         GGML_ASSERT(logits != nullptr);
-        cur.reserve(n_vocab);
+        cur.resize(n_vocab);
         for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-            cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+            cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
         }
     }
 

From 0d28b16bdcda2e0973202989e6385864f4e85b5a Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 20 Nov 2025 14:31:37 +0100
Subject: [PATCH 18/59] sampling : introduce sampling_info struct

This commit introduces a sampling_info struct to encapsulate all
backend sampling related data within the llama_context class.

It also updates to use more descriptive names for sampled tokens and
candidates in the backend sampler ggml data structure.
---
 include/llama.h               |   4 +-
 src/llama-backend-sampler.cpp |  18 +++---
 src/llama-context.cpp         | 103 +++++++++++++++++-----------------
 src/llama-context.h           |  21 ++++---
 src/llama-graph.cpp           |  26 ++++-----
 src/llama-graph.h             |   4 +-
 6 files changed, 91 insertions(+), 85 deletions(-)

diff --git a/include/llama.h b/include/llama.h
index 2bc41f36d8d..9c4862ad893 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -213,8 +213,8 @@ extern "C" {
     struct llama_sampler_ggml_data {
         struct ggml_tensor * logits;
         struct ggml_tensor * probs;
-        struct ggml_tensor * sampled_token;
-        struct ggml_tensor * filtered_ids;
+        struct ggml_tensor * sampled;
+        struct ggml_tensor * candidates;
     };
 
     typedef bool (*llama_progress_callback)(float progress, void * user_data);
diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
index 42c8d85aeba..cd6b8bb7526 100644
--- a/src/llama-backend-sampler.cpp
+++ b/src/llama-backend-sampler.cpp
@@ -15,7 +15,7 @@ static void llama_sampler_backend_greedy_apply_ggml(
     GGML_UNUSED(smpl);
     struct ggml_tensor * argmax_result = ggml_argmax(ctx, ggml_data->logits);
     ggml_set_name(argmax_result, "argmax_result");
-    ggml_data->sampled_token = argmax_result;
+    ggml_data->sampled = argmax_result;
 }
 
 static const char * llama_sampler_backend_greedy_sampler_name(const struct llama_sampler *) {
@@ -149,7 +149,7 @@ static void llama_sampler_backend_top_k_apply_ggml(
         fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
     }
 
-    ggml_data->filtered_ids = top_k;
+    ggml_data->candidates = top_k;
 
     struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
     struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);
@@ -303,19 +303,19 @@ static void llama_sampler_backend_dist_apply_ggml(
     struct ggml_tensor * idx = ggml_cast(ctx, ggml_scale_bias(ctx, idxf, -1.0f, mask->ne[0]), GGML_TYPE_I32);
     ggml_set_name(idx, "dist_index_i32");
 
-    // Map back to original vocab ids if a filtered id tensor is available.
+    // Map back to original vocab ids if a candidates tensor is available.
     struct ggml_tensor * sampled_token = idx;
-    if (ggml_data->filtered_ids != nullptr) {
-        struct ggml_tensor * filtered_ids = ggml_data->filtered_ids;
-        struct ggml_tensor * filtered_ids_reshaped = ggml_view_2d(ctx, filtered_ids, 1, ggml_nelements(filtered_ids),
-                ggml_type_size(filtered_ids->type), 0);
+    if (ggml_data->candidates != nullptr) {
+        struct ggml_tensor * candidates = ggml_data->candidates;
+        struct ggml_tensor * candidates_reshaped = ggml_view_2d(ctx, candidates, 1, ggml_nelements(candidates),
+                ggml_type_size(candidates->type), 0);
 
-        sampled_token = ggml_get_rows(ctx, filtered_ids_reshaped, idx);
+        sampled_token = ggml_get_rows(ctx, candidates_reshaped, idx);
         ggml_set_name(sampled_token, "dist_sampled_token");
     }
 
     ggml_set_output(sampled_token);
-    ggml_data->sampled_token = sampled_token;
+    ggml_data->sampled = sampled_token;
 }
 
 static const char * llama_sampler_backend_dist_name(const struct llama_sampler *) {
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 5868f6246f9..7bebf58b9e1 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -60,11 +60,11 @@ llama_context::llama_context(
 
     // backend samplers
     if (params.samplers != nullptr && params.n_samplers > 0) {
-        samplers.reserve(params.n_samplers);
+        sampling.samplers.reserve(params.n_samplers);
 
         for (size_t i = 0; i < params.n_samplers; ++i) {
             const auto & config = params.samplers[i];
-            samplers[config.seq_id] = config.sampler;
+            sampling.samplers[config.seq_id] = config.sampler;
         }
     }
 
@@ -435,9 +435,9 @@ llama_context::llama_context(
     {
         const llama_vocab * vocab = llama_model_get_vocab(&model);
         const int n_vocab = llama_vocab_n_tokens(vocab);
-        sampled_token_ids_full_vocab.resize(n_vocab);
+        sampling.token_ids_full_vocab.resize(n_vocab);
         for (int i = 0; i < n_vocab; ++i) {
-            sampled_token_ids_full_vocab[i] = i;
+            sampling.token_ids_full_vocab[i] = i;
         }
     }
 }
@@ -445,7 +445,7 @@ llama_context::llama_context(
 llama_context::~llama_context() {
     ggml_opt_free(opt_ctx);
     // TODO: perhaps use a smart pointer for samplers
-    for (auto const& [seq_id, sampler] : samplers) {
+    for (auto const& [seq_id, sampler] : sampling.samplers) {
         llama_sampler_free(sampler);
     }
 }
@@ -635,7 +635,7 @@ float * llama_context::get_embeddings() {
 }
 
 llama_token * llama_context::get_backend_sampled_tokens() {
-    return sampled_tokens;
+    return sampling.sampled;
 }
 
 float * llama_context::get_embeddings_ith(int32_t i) {
@@ -691,15 +691,15 @@ llama_token llama_context::get_backend_sampled_token_ith(int32_t idx) {
     // Handle special case where idx == -1 (single sequence exists) which is
     // a valid index when using common_sampler_sample.
     if (idx == -1) {
-        if (sampled_tokens_map.size() == 1) {
-            auto it = sampled_tokens_map.begin();
+        if (sampling.map_sampled.size() == 1) {
+            auto it = sampling.map_sampled.begin();
             return it->second;
         }
         return LLAMA_TOKEN_NULL;
     }
 
-    auto it = sampled_tokens_map.find(idx);
-    if (it == sampled_tokens_map.end()) {
+    auto it = sampling.map_sampled.find(idx);
+    if (it == sampling.map_sampled.end()) {
         return LLAMA_TOKEN_NULL;
     }
 
@@ -708,13 +708,13 @@ llama_token llama_context::get_backend_sampled_token_ith(int32_t idx) {
 
 float * llama_context::get_backend_sampled_probs_ith(int32_t idx) {
     if (idx == -1) {
-        if (sampled_probs_map.size() == 1) {
-            return sampled_probs_map.begin()->second.data();
+        if (sampling.map_probs.size() == 1) {
+            return sampling.map_probs.begin()->second.data();
         }
     }
 
-    auto it = sampled_probs_map.find(idx);
-    if (it == sampled_probs_map.end()) {
+    auto it = sampling.map_probs.find(idx);
+    if (it == sampling.map_probs.end()) {
         return nullptr;
     }
 
@@ -723,12 +723,12 @@ float * llama_context::get_backend_sampled_probs_ith(int32_t idx) {
 
 float * llama_context::get_backend_sampled_logits_ith(int32_t idx) {
     if (idx == -1) {
-        if (sampled_logits_map.size() == 1) {
-            return sampled_logits_map.begin()->second.data();
+        if (sampling.map_logits.size() == 1) {
+            return sampling.map_logits.begin()->second.data();
         }
     }
-    auto it = sampled_logits_map.find(idx);
-    if (it == sampled_logits_map.end()) {
+    auto it = sampling.map_logits.find(idx);
+    if (it == sampling.map_logits.end()) {
         return nullptr;
     }
 
@@ -737,29 +737,29 @@ float * llama_context::get_backend_sampled_logits_ith(int32_t idx) {
 
 const llama_token * llama_context::get_backend_sampled_token_ids_ith(int32_t idx) {
     if (idx == -1) {
-        if (sampled_token_ids_map.size() == 1) {
-            const auto & vec = sampled_token_ids_map.begin()->second;
+        if (sampling.map_cadidates.size() == 1) {
+            const auto & vec = sampling.map_cadidates.begin()->second;
             if (!vec.empty()) {
                 return vec.data();
             }
         }
     }
-    auto it = sampled_token_ids_map.find(idx);
-    if (it != sampled_token_ids_map.end() && !it->second.empty()) {
+    auto it = sampling.map_cadidates.find(idx);
+    if (it != sampling.map_cadidates.end() && !it->second.empty()) {
         return it->second.data();
     }
 
-    return sampled_token_ids_full_vocab.data();
+    return sampling.token_ids_full_vocab.data();
 }
 
 size_t llama_context::get_backend_sampled_logits_count(int32_t idx) const {
     if (idx == -1) {
-        if (sampled_logits_map.size() == 1) {
-            return sampled_logits_map.begin()->second.size();
+        if (sampling.map_logits.size() == 1) {
+            return sampling.map_logits.begin()->second.size();
         }
     }
-    auto it = sampled_logits_map.find(idx);
-    if (it == sampled_logits_map.end()) {
+    auto it = sampling.map_logits.find(idx);
+    if (it == sampling.map_logits.end()) {
         return 0;
     }
 
@@ -768,14 +768,14 @@ size_t llama_context::get_backend_sampled_logits_count(int32_t idx) const {
 
 size_t llama_context::get_backend_sampled_probs_count(int32_t idx) const {
     if (idx == -1) {
-        if (sampled_probs_map.size() == 1) {
-            return sampled_probs_map.begin()->second.size();
+        if (sampling.map_probs.size() == 1) {
+            return sampling.map_probs.begin()->second.size();
         }
         return 0;
     }
 
-    auto it = sampled_probs_map.find(idx);
-    if (it == sampled_probs_map.end()) {
+    auto it = sampling.map_probs.find(idx);
+    if (it == sampling.map_probs.end()) {
         return 0;
     }
 
@@ -841,8 +841,8 @@ void llama_context::set_warmup(bool value) {
 void llama_context::set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
     LLAMA_LOG_DEBUG("%s: seq_id = %d, sampler = %p\n", __func__, (int) seq_id, (void *) sampler);
 
-    auto it = samplers.find(seq_id);
-    if (it != samplers.end()) {
+    auto it = sampling.samplers.find(seq_id);
+    if (it != sampling.samplers.end()) {
         // If the sampler to be set is the same that is already set, do nothing.
         if (it->second == sampler) {
             return;
@@ -853,7 +853,7 @@ void llama_context::set_backend_sampler(llama_seq_id seq_id, llama_sampler * sam
         // If sampler is nullptr, we remove the samppler chain for this seq_id.
         // chain for this seq_id.
         if (sampler == nullptr) {
-            samplers.erase(it);
+            sampling.samplers.erase(it);
             return;
         }
 
@@ -865,7 +865,7 @@ void llama_context::set_backend_sampler(llama_seq_id seq_id, llama_sampler * sam
     // If there is no sampler for this seq_id and the caller provides a non-null
     // sampler, we set it.
     if (sampler != nullptr) {
-        samplers[seq_id] = sampler;
+        sampling.samplers[seq_id] = sampler;
     }
 }
 
@@ -1202,7 +1202,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
     // when computing embeddings, all tokens are output
     const bool output_all = cparams.embeddings;
-    const bool has_backend_samplers = !samplers.empty();
+    const bool has_backend_samplers = !sampling.samplers.empty();
 
     if (!balloc->init(batch_inp, vocab, memory.get(), n_embd,
                       cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max,
@@ -1235,10 +1235,10 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
     // TODO: this clear of the buffer can easily be forgotten - need something better
     embd_seq.clear();
-    sampled_probs_map.clear();
-    sampled_logits_map.clear();
-    sampled_tokens_map.clear();
-    sampled_token_ids_map.clear();
+    sampling.map_probs.clear();
+    sampling.map_logits.clear();
+    sampling.map_sampled.clear();
+    sampling.map_cadidates.clear();
     output_swaps.clear();
 
     bool did_optimize = false;
@@ -1361,27 +1361,27 @@ int llama_context::decode(const llama_batch & batch_inp) {
         //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
         //}
 
-        backend_has_sampled = !res->t_sampled_tokens.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
+        backend_has_sampled = !res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
 
         if (has_backend_samplers && backend_has_sampled) {
             const auto seq_to_batch_idx = build_seq_to_batch_idx(ubatch);
 
             // If a backend sampler has sampled a token we only want to copy the
             // sampled tokens and avoid copying logits and probabilites.
-            if (!res->t_sampled_tokens.empty()) {
+            if (!res->t_sampled.empty()) {
                 // async copy the sampled tokens from the backend to the host.
-                copy_tensor_async_int(res->t_sampled_tokens, sampled_tokens_map, seq_to_batch_idx, sched.get());
+                copy_tensor_async_int(res->t_sampled, sampling.map_sampled, seq_to_batch_idx, sched.get());
             } else {
                 // async copy the sampled logits/probs from the backend to the host.
-                copy_tensor_async_floats(res->t_sampled_logits, sampled_logits_map, seq_to_batch_idx, sched.get());
-                copy_tensor_async_floats(res->t_sampled_probs,  sampled_probs_map,  seq_to_batch_idx, sched.get());
+                copy_tensor_async_floats(res->t_sampled_logits, sampling.map_logits, seq_to_batch_idx, sched.get());
+                copy_tensor_async_floats(res->t_sampled_probs,  sampling.map_probs,  seq_to_batch_idx, sched.get());
             }
 
-            // async copy the filtered token ids from the backend to the host.
+            // async copy the candidate token ids from the backend to the host.
             // These are needed for:
             // 1) Backend dist sampler to map indices to vocab token ids.
-            // 2) CPU samplers to associate filtered logits with their token ids.
-            copy_tensor_async_token_ids(res->t_sampled_token_ids, sampled_token_ids_map, seq_to_batch_idx, sched.get());
+            // 2) CPU samplers to associate candidate logits with their token ids.
+            copy_tensor_async_token_ids(res->t_candidates, sampling.map_cadidates, seq_to_batch_idx, sched.get());
 
         }
 
@@ -1589,8 +1589,9 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     logits         = has_logits        ? output_base               : nullptr;
     embd           = has_embd          ? output_base + logits_size : nullptr;
-    sampled_tokens = !samplers.empty() ? s_output_base             : nullptr;
-    sampled_probs  = !samplers.empty() ? embd                      : nullptr;
+
+    sampling.sampled = !sampling.samplers.empty() ? s_output_base    : nullptr;
+    sampling.probs   = !sampling.samplers.empty() ? embd             : nullptr;
 
     // set all ids as invalid (negative)
     std::fill(output_ids.begin(), output_ids.end(), -1);
@@ -1700,7 +1701,7 @@ llm_graph_params llama_context::graph_params(
         /*.loras       =*/ &loras,
         /*.mctx        =*/ mctx,
         /*.cross       =*/ &cross,
-        /*.samplers    =*/ samplers,
+        /*.samplers    =*/ sampling.samplers,
         /*.n_outputs   =*/ n_outputs,
         /*.cb          =*/ graph_get_cb(),
         /*.res         =*/ res,
diff --git a/src/llama-context.h b/src/llama-context.h
index aba62e6e38e..8e6a111e611 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -254,16 +254,21 @@ struct llama_context {
     size_t  logits_size = 0; // capacity (of floats) for logits
     float * logits      = nullptr;
 
-    std::unordered_map<llama_seq_id, llama_sampler*> samplers;
-    llama_token * sampled_tokens = nullptr;
-    std::unordered_map<int32_t, llama_token> sampled_tokens_map;
+    struct sampling_info {
+        std::unordered_map<llama_seq_id, llama_sampler*> samplers;
 
-    float * sampled_probs = nullptr;
-    std::unordered_map<int32_t, std::vector<float>> sampled_probs_map;
+        llama_token * sampled = nullptr;
+        float       * probs = nullptr;
 
-    std::unordered_map<int32_t, std::vector<float>> sampled_logits_map;
-    std::unordered_map<int32_t, std::vector<llama_token>> sampled_token_ids_map;
-    std::vector<llama_token> sampled_token_ids_full_vocab;
+        std::unordered_map<int32_t, llama_token>              map_sampled;
+        std::unordered_map<int32_t, std::vector<float>>       map_probs;
+        std::unordered_map<int32_t, std::vector<float>>       map_logits;
+        std::unordered_map<int32_t, std::vector<llama_token>> map_cadidates;
+
+        std::vector<llama_token> token_ids_full_vocab;
+    };
+
+    sampling_info sampling;
 
     // embeddings output (2-dimensional array: [n_outputs][n_embd])
     // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index fbf4475aa4e..8af9188d05d 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -504,10 +504,10 @@ void llm_graph_result::reset() {
     t_logits      = nullptr;
     t_embd        = nullptr;
     t_embd_pooled = nullptr;
-    t_sampled_tokens.clear();
+    t_sampled.clear();
     t_sampled_probs.clear();
     t_sampled_logits.clear();
-    t_sampled_token_ids.clear();
+    t_candidates.clear();
 
     params = {};
 
@@ -2098,17 +2098,17 @@ void llm_graph_context::build_sampling() const {
         ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
 
         struct llama_sampler_ggml_data ggml_data = {
-            /*.logits         =*/ logits_seq,
-            /*.probs          =*/ nullptr,
-            /*.sampled_token  =*/ nullptr,
-            /*.filtered_ids   =*/ nullptr,
+            /*.logits      =*/ logits_seq,
+            /*.probs       =*/ nullptr,
+            /*.sampled     =*/ nullptr,
+            /*.candidates  =*/ nullptr,
         };
 
         llama_sampler_apply_ggml(sampler, ctx0, gf, &ggml_data);
 
-        if (ggml_data.sampled_token != nullptr) {
-            res->t_sampled_tokens[seq_id] = ggml_data.sampled_token;
-            ggml_build_forward_expand(gf, ggml_data.sampled_token);
+        if (ggml_data.sampled != nullptr) {
+            res->t_sampled[seq_id] = ggml_data.sampled;
+            ggml_build_forward_expand(gf, ggml_data.sampled);
         }
 
         if  (ggml_data.probs != nullptr) {
@@ -2121,16 +2121,16 @@ void llm_graph_context::build_sampling() const {
             ggml_build_forward_expand(gf, res->t_sampled_logits[seq_id]);
         }
 
-        if (ggml_data.filtered_ids != nullptr) {
-            res->t_sampled_token_ids[seq_id] = ggml_data.filtered_ids;
-            ggml_build_forward_expand(gf, ggml_data.filtered_ids);
+        if (ggml_data.candidates != nullptr) {
+            res->t_candidates[seq_id] = ggml_data.candidates;
+            ggml_build_forward_expand(gf, ggml_data.candidates);
         }
     }
 
     // TODO: Call llama_sampler_accept_ggml after all samplers have been applied.
     /*
     for (const auto & [seq_id, sampler] : samplers) {
-        if (auto it = res->t_sampled_tokens.find(seq_id); it != res->t_sampled_tokens.end()) {
+        if (auto it = res->t_sampled.find(seq_id); it != res->t_sampled.end()) {
             ggml_tensor * selected_token = it->second;
             if (selected_token != nullptr) {
                 llama_sampler_accept_ggml(sampler, ctx0, gf, selected_token);
diff --git a/src/llama-graph.h b/src/llama-graph.h
index f7508046e40..6797d78a20e 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -543,8 +543,8 @@ class llm_graph_result {
     ggml_tensor * t_embd_pooled = nullptr;
 
     std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled_logits;
-    std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled_token_ids;
-    std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled_tokens;
+    std::unordered_map<llama_seq_id, ggml_tensor*> t_candidates;
+    std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled;
     std::unordered_map<llama_seq_id, ggml_tensor*> t_sampled_probs;
 
     std::vector<llm_graph_input_ptr> inputs;

From c1625620f6d2af9581b0138ff70103219d8d103b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 21 Nov 2025 08:44:25 +0100
Subject: [PATCH 19/59] sampling : return early if backend sampling is disabled

---
 common/sampling.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index bccec35dacf..ebe61f32cad 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -309,15 +309,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 }
 
 struct llama_sampler * common_sampler_backend_init(const struct llama_model * model, const struct common_params_sampling & params) {
+    if (!params.backend_sampling) {
+        return nullptr;
+    }
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
     chain_params.no_perf = params.no_perf;
 
     struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
-    if (!params.backend_sampling) {
-        return chain; // return empty chain
-    }
 
     const bool enable_temp  = params.temp  > 0.0f && sampler_enabled(params, COMMON_SAMPLER_TYPE_TEMPERATURE);
     const bool enable_top_k = params.top_k > 0    && sampler_enabled(params, COMMON_SAMPLER_TYPE_TOP_K);

From 61ffe41dc1cdafb2c71b650c4265c22ec769f88b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 21 Nov 2025 14:02:16 +0100
Subject: [PATCH 20/59] sampling : use pinned memory for backend sampling
 buffers

---
 common/build-info.cpp          |   4 +
 common/sampling.cpp            |   6 +-
 include/llama.h                |  17 +-
 src/llama-context.cpp          | 401 +++++++++++++++++++++++++--------
 src/llama-context.h            |  30 ++-
 src/llama-sampling.cpp         |   2 +-
 tests/test-backend-sampler.cpp |  10 +
 7 files changed, 354 insertions(+), 116 deletions(-)
 create mode 100644 common/build-info.cpp

diff --git a/common/build-info.cpp b/common/build-info.cpp
new file mode 100644
index 00000000000..6e8240fbb16
--- /dev/null
+++ b/common/build-info.cpp
@@ -0,0 +1,4 @@
+int LLAMA_BUILD_NUMBER = 5590;
+char const *LLAMA_COMMIT = "0d398442";
+char const *LLAMA_COMPILER = "cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0";
+char const *LLAMA_BUILD_TARGET = "x86_64-linux-gnu";
diff --git a/common/sampling.cpp b/common/sampling.cpp
index ebe61f32cad..ae2276c7121 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -113,9 +113,9 @@ struct common_sampler {
     llama_token_data_array cur_p;
 
     void set_logits(struct llama_context * ctx, int idx) {
-        const float *       sampled_probs  = llama_get_backend_sampled_probs_ith    (ctx, idx);
-        const float *       sampled_logits = llama_get_backend_sampled_logits_ith   (ctx, idx);
-        const llama_token * sampled_ids    = llama_get_backend_sampled_token_ids_ith(ctx, idx);
+        const float *       sampled_probs  = llama_get_backend_sampled_probs_ith     (ctx, idx);
+        const float *       sampled_logits = llama_get_backend_sampled_logits_ith    (ctx, idx);
+        const llama_token * sampled_ids    = llama_get_backend_sampled_candidates_ith(ctx, idx);
 
         const llama_model * model = llama_get_model(ctx);
         const llama_vocab * vocab = llama_model_get_vocab(model);
diff --git a/include/llama.h b/include/llama.h
index 9c4862ad893..9fbce771d74 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -974,20 +974,23 @@ extern "C" {
     // The index matches llama_get_backend_sampled_token_ith().
     // Returns NULL if no probabilites were generated.
     LLAMA_API float * llama_get_backend_sampled_probs_ith(struct llama_context * ctx, int32_t i);
+    //
+    // Get the number of backend sampled probabilites for the ith token.
+    LLAMA_API uint32_t llama_get_backend_sampled_probs_count_ith(struct llama_context * ctx, int32_t i);
 
     // Get the backend sampled logits for the ith token
     // Returns NULL if no logits were sampled.
     LLAMA_API float * llama_get_backend_sampled_logits_ith(struct llama_context * ctx, int32_t i);
-
-    // Get the backend sampled token ids associated with the sampled logits for the ith token
-    // Returns NULL if no logits were sampled.
-    LLAMA_API llama_token * llama_get_backend_sampled_token_ids_ith(struct llama_context * ctx, int32_t i);
-
+    //
     // Get the number of backend sampled logits for the ith token.
     LLAMA_API uint32_t llama_get_backend_sampled_logits_count_ith(struct llama_context * ctx, int32_t i);
 
-    // Get the number of backend sampled probabilites for the ith token.
-    LLAMA_API uint32_t llama_get_backend_sampled_probs_count_ith(struct llama_context * ctx, int32_t i);
+    // Get the backend sampled candidates (token ids) for the ith token
+    // Returns NULL if no candidates were sampled.
+    LLAMA_API llama_token * llama_get_backend_sampled_candidates_ith(struct llama_context * ctx, int32_t i);
+    //
+    // Get the number of backend sampled candidates for the ith token.
+    LLAMA_API uint32_t llama_get_backend_sampled_candidates_count_ith(struct llama_context * ctx, int32_t i);
 
     //
     // Vocab
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 7bebf58b9e1..1694e447200 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -588,6 +588,35 @@ float * llama_context::get_logits() {
     return logits;
 }
 
+int64_t llama_context::resolve_output_row(int32_t i) const {
+    int64_t j = -1;
+
+    // support negative indices (last output row)
+    if (i < 0) {
+        j = n_outputs + i;
+        if (j < 0) {
+            throw std::runtime_error(format("negative index out of range [0, %d)", n_outputs));
+        }
+    } else if ((size_t) i >= output_ids.size()) {
+        throw std::runtime_error(format("out of range [0, %zu)", output_ids.size()));
+    } else {
+        // use output_ids to translate the batch token index into a row number
+        // that holds this token's data.
+        j = output_ids[i];
+    }
+
+    if (j < 0) {
+        // the batch token was not configured to output anything
+        throw std::runtime_error(format("batch.logits[%d] != true", i));
+    }
+
+    if (j >= n_outputs) {
+        throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
+    }
+
+    return j;
+}
+
 float * llama_context::get_logits_ith(int32_t i) {
     int64_t j = -1;
 
@@ -688,100 +717,135 @@ float * llama_context::get_embeddings_seq(llama_seq_id seq_id) {
 }
 
 llama_token llama_context::get_backend_sampled_token_ith(int32_t idx) {
-    // Handle special case where idx == -1 (single sequence exists) which is
-    // a valid index when using common_sampler_sample.
-    if (idx == -1) {
-        if (sampling.map_sampled.size() == 1) {
-            auto it = sampling.map_sampled.begin();
-            return it->second;
-        }
+    output_reorder();
+
+    if (sampling.sampled == nullptr) {
         return LLAMA_TOKEN_NULL;
     }
 
-    auto it = sampling.map_sampled.find(idx);
-    if (it == sampling.map_sampled.end()) {
+    try {
+        const int64_t row = resolve_output_row(idx);
+        GGML_ASSERT(row < (int64_t) sampling.sampled_size);
+        return sampling.sampled[row];
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid backend sampled token id %d, reason: %s\n", __func__, idx, err.what());
         return LLAMA_TOKEN_NULL;
     }
-
-    return it->second;
 }
 
 float * llama_context::get_backend_sampled_probs_ith(int32_t idx) {
-    if (idx == -1) {
-        if (sampling.map_probs.size() == 1) {
-            return sampling.map_probs.begin()->second.data();
-        }
-    }
+    output_reorder();
 
-    auto it = sampling.map_probs.find(idx);
-    if (it == sampling.map_probs.end()) {
+    if (sampling.probs == nullptr) {
         return nullptr;
     }
 
-    return it->second.data();
+    try {
+        const int64_t row = resolve_output_row(idx);
+        if ((size_t) row >= sampling.probs_count.size() || sampling.probs_count[row] == 0) {
+            return nullptr;
+        }
+        return sampling.probs + row*model.vocab.n_tokens();
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid backend sampled probs id %d, reason: %s\n", __func__, idx, err.what());
+        return nullptr;
+    }
 }
 
 float * llama_context::get_backend_sampled_logits_ith(int32_t idx) {
-    if (idx == -1) {
-        if (sampling.map_logits.size() == 1) {
-            return sampling.map_logits.begin()->second.data();
-        }
-    }
-    auto it = sampling.map_logits.find(idx);
-    if (it == sampling.map_logits.end()) {
+    output_reorder();
+
+    if (sampling.logits == nullptr) {
         return nullptr;
     }
 
-    return it->second.data();
+    try {
+        const int64_t row = resolve_output_row(idx);
+        if ((size_t) row >= sampling.logits_count.size() || sampling.logits_count[row] == 0) {
+            return nullptr;
+        }
+        return sampling.logits + row*model.vocab.n_tokens();
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid backend sampled logits id %d, reason: %s\n", __func__, idx, err.what());
+        return nullptr;
+    }
 }
 
-const llama_token * llama_context::get_backend_sampled_token_ids_ith(int32_t idx) {
-    if (idx == -1) {
-        if (sampling.map_cadidates.size() == 1) {
-            const auto & vec = sampling.map_cadidates.begin()->second;
-            if (!vec.empty()) {
-                return vec.data();
-            }
+const llama_token * llama_context::get_backend_sampled_candidates_ith(int32_t idx) {
+    output_reorder();
+
+    try {
+        const int64_t row = resolve_output_row(idx);
+        if (sampling.candidates != nullptr &&
+            (size_t) row < sampling.candidates_count.size() &&
+            sampling.candidates_count[row] > 0) {
+            return sampling.candidates + row*model.vocab.n_tokens();
         }
-    }
-    auto it = sampling.map_cadidates.find(idx);
-    if (it != sampling.map_cadidates.end() && !it->second.empty()) {
-        return it->second.data();
+    } catch (const std::exception & err) {
+        // fallback to full vocab list
     }
 
     return sampling.token_ids_full_vocab.data();
 }
 
-size_t llama_context::get_backend_sampled_logits_count(int32_t idx) const {
-    if (idx == -1) {
-        if (sampling.map_logits.size() == 1) {
-            return sampling.map_logits.begin()->second.size();
-        }
-    }
-    auto it = sampling.map_logits.find(idx);
-    if (it == sampling.map_logits.end()) {
+size_t llama_context::get_backend_sampled_candidates_count(int32_t idx) {
+    output_reorder();
+
+    if (sampling.candidates == nullptr) {
         return 0;
     }
 
-    return it->second.size();
+    try {
+        const int64_t row = resolve_output_row(idx);
+        if ((size_t) row >= sampling.candidates_count.size()) {
+            return 0;
+        }
+        return sampling.candidates_count[row];
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid backend sampled candidates count id %d, reason: %s\n", __func__, idx, err.what());
+        return 0;
+    }
 }
 
-size_t llama_context::get_backend_sampled_probs_count(int32_t idx) const {
-    if (idx == -1) {
-        if (sampling.map_probs.size() == 1) {
-            return sampling.map_probs.begin()->second.size();
+size_t llama_context::get_backend_sampled_logits_count(int32_t idx) {
+    output_reorder();
+
+    if (sampling.logits == nullptr) {
+        return 0;
+    }
+
+    try {
+        const int64_t row = resolve_output_row(idx);
+        if ((size_t) row >= sampling.logits_count.size()) {
+            return 0;
         }
+        return sampling.logits_count[row];
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid backend sampled logits count id %d, reason: %s\n", __func__, idx, err.what());
         return 0;
     }
+}
 
-    auto it = sampling.map_probs.find(idx);
-    if (it == sampling.map_probs.end()) {
+size_t llama_context::get_backend_sampled_probs_count(int32_t idx) {
+    output_reorder();
+
+    if (sampling.probs == nullptr) {
         return 0;
     }
 
-    return it->second.size();
+    try {
+        const int64_t row = resolve_output_row(idx);
+        if ((size_t) row >= sampling.probs_count.size()) {
+            return 0;
+        }
+        return sampling.probs_count[row];
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid backend sampled probs count id %d, reason: %s\n", __func__, idx, err.what());
+        return 0;
+    }
 }
 
+
 void llama_context::attach_threadpool(
            ggml_threadpool_t threadpool,
            ggml_threadpool_t threadpool_batch) {
@@ -1133,51 +1197,94 @@ int llama_context::encode(const llama_batch & batch_inp) {
     return 0;
 }
 
-static std::unordered_map<llama_seq_id, int32_t> build_seq_to_batch_idx(const llama_ubatch & ubatch) {
-    std::unordered_map<llama_seq_id, int32_t> seq_to_batch_idx;
-    for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
-        if (ubatch.output[i]) {
-            seq_to_batch_idx[ubatch.seq_id[i][0]] = i;
+static std::unordered_map<llama_seq_id, uint32_t> build_seq_to_output_row(const llama_ubatch & ubatch, uint32_t row_offset) {
+    std::unordered_map<llama_seq_id, uint32_t> seq_to_row;
+    // how many output tokens we have seen so far for this ubatch.
+    uint32_t local = 0;
+    for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+        // skip tokens that are not output.
+        if (!ubatch.output[i]) {
+            continue;
         }
+
+        const llama_seq_id seq_id = ubatch.seq_id[i][0];
+        // row_offset is the number of output tokens before this ubatch.
+        seq_to_row[seq_id] = row_offset + local;
+        ++local;
     }
-    return seq_to_batch_idx;
+    return seq_to_row;
 }
 
-static void copy_tensor_async_int(
+static void copy_tensor_async_ints(
     const std::unordered_map<llama_seq_id, ggml_tensor*> & tensor_map,
-    std::unordered_map<int32_t, llama_token> & output_map,
-    const std::unordered_map<llama_seq_id, int32_t> & seq_to_batch_idx,
+    llama_token * sampled,
+    size_t sampled_size,
+    const std::unordered_map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
+    if (sampled == nullptr || sampled_size == 0) {
+        return;
+    }
+
     for (const auto & [seq_id, tensor] : tensor_map) {
-        const int32_t idx = seq_to_batch_idx.at(seq_id);
+        auto it = seq_to_row.find(seq_id);
+        GGML_ASSERT(it != seq_to_row.end());
+        const uint32_t row = it->second;
+        GGML_ASSERT(row < sampled_size);
+
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        ggml_backend_tensor_get_async(backend, tensor, &output_map[idx], 0, sizeof(output_map[idx]));
+        ggml_backend_tensor_get_async(backend, tensor, sampled + row, 0, sizeof(sampled[row]));
     }
 }
 
 static void copy_tensor_async_floats(
     const std::unordered_map<llama_seq_id, ggml_tensor*> & tensor_map,
-    std::unordered_map<int32_t, std::vector<float>> & output_map,
-    const std::unordered_map<llama_seq_id, int32_t> & seq_to_batch_idx,
+    float * dst,
+    size_t stride,
+    std::vector<uint32_t> & counts,
+    const std::unordered_map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
+    if (dst == nullptr || stride == 0) {
+        return;
+    }
+
     for (const auto & [seq_id, tensor] : tensor_map) {
-        const int32_t idx = seq_to_batch_idx.at(seq_id);
+        auto it = seq_to_row.find(seq_id);
+        GGML_ASSERT(it != seq_to_row.end());
+        const uint32_t row = it->second;
+        GGML_ASSERT(row < counts.size());
+
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        output_map[idx].resize(ggml_nelements(tensor));
-        ggml_backend_tensor_get_async(backend, tensor, output_map[idx].data(), 0, ggml_nbytes(tensor));
+        float * row_ptr = dst + (size_t) row * stride;
+        ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
+
+        // Update the actual number of logits/probabilities that were written for this row.
+        counts[row] = ggml_nelements(tensor);
     }
 }
 
-static void copy_tensor_async_token_ids(
+static void copy_tensor_async_candidates(
     const std::unordered_map<llama_seq_id, ggml_tensor*> & tensor_map,
-    std::unordered_map<int32_t, std::vector<llama_token>> & output_map,
-    const std::unordered_map<llama_seq_id, int32_t> & seq_to_batch_idx,
+    llama_token * dst,
+    size_t stride,
+    std::vector<uint32_t> & counts,
+    const std::unordered_map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
+    if (dst == nullptr || stride == 0) {
+        return;
+    }
+
     for (const auto & [seq_id, tensor] : tensor_map) {
-        const int32_t idx = seq_to_batch_idx.at(seq_id);
+        auto it = seq_to_row.find(seq_id);
+        GGML_ASSERT(it != seq_to_row.end());
+        const uint32_t row = it->second;
+        GGML_ASSERT(row < counts.size());
+
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        output_map[idx].resize(ggml_nelements(tensor));
-        ggml_backend_tensor_get_async(backend, tensor, output_map[idx].data(), 0, ggml_nbytes(tensor));
+        llama_token * row_ptr = dst + (size_t) row * stride;
+        ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
+
+        // Update the actual number of candidates that were written.
+        counts[row] = ggml_nelements(tensor);
     }
 }
 
@@ -1235,10 +1342,6 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
     // TODO: this clear of the buffer can easily be forgotten - need something better
     embd_seq.clear();
-    sampling.map_probs.clear();
-    sampling.map_logits.clear();
-    sampling.map_sampled.clear();
-    sampling.map_cadidates.clear();
     output_swaps.clear();
 
     bool did_optimize = false;
@@ -1364,24 +1467,24 @@ int llama_context::decode(const llama_batch & batch_inp) {
         backend_has_sampled = !res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
 
         if (has_backend_samplers && backend_has_sampled) {
-            const auto seq_to_batch_idx = build_seq_to_batch_idx(ubatch);
+            const auto seq_to_output_row = build_seq_to_output_row(ubatch, n_outputs_prev);
 
             // If a backend sampler has sampled a token we only want to copy the
             // sampled tokens and avoid copying logits and probabilites.
             if (!res->t_sampled.empty()) {
                 // async copy the sampled tokens from the backend to the host.
-                copy_tensor_async_int(res->t_sampled, sampling.map_sampled, seq_to_batch_idx, sched.get());
+                copy_tensor_async_ints(res->t_sampled, sampling.sampled, sampling.sampled_size, seq_to_output_row, sched.get());
             } else {
                 // async copy the sampled logits/probs from the backend to the host.
-                copy_tensor_async_floats(res->t_sampled_logits, sampling.map_logits, seq_to_batch_idx, sched.get());
-                copy_tensor_async_floats(res->t_sampled_probs,  sampling.map_probs,  seq_to_batch_idx, sched.get());
+                copy_tensor_async_floats(res->t_sampled_logits, sampling.logits, n_vocab, sampling.logits_count, seq_to_output_row, sched.get());
+                copy_tensor_async_floats(res->t_sampled_probs,  sampling.probs,  n_vocab, sampling.probs_count,  seq_to_output_row, sched.get());
             }
 
             // async copy the candidate token ids from the backend to the host.
             // These are needed for:
             // 1) Backend dist sampler to map indices to vocab token ids.
             // 2) CPU samplers to associate candidate logits with their token ids.
-            copy_tensor_async_token_ids(res->t_candidates, sampling.map_cadidates, seq_to_batch_idx, sched.get());
+            copy_tensor_async_candidates(res->t_candidates, sampling.candidates, n_vocab, sampling.candidates_count, seq_to_output_row, sched.get());
 
         }
 
@@ -1471,7 +1574,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
     n_outputs = n_outputs_all;
 
     // set output mappings
-    if (n_outputs > 0 && !backend_has_sampled) {
+    if (n_outputs > 0) {
         bool sorted_output = true;
 
         auto & out_ids = balloc->get_out_ids();
@@ -1546,8 +1649,31 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         has_embd   = true;
     }
 
-    logits_size = has_logits ? n_vocab*n_outputs_max : 0;
-    embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
+    const bool backend_sampling = !sampling.samplers.empty();
+    size_t backend_float_count = 0;
+    size_t backend_token_count = 0;
+
+    if (!backend_sampling) {
+        logits_size = has_logits ? n_vocab*n_outputs_max : 0;
+        embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
+
+        // reset backend sampling values.
+        sampling.logits_size       = 0;
+        sampling.probs_size        = 0;
+        sampling.sampled_size      = 0;
+        sampling.candidates_size   = 0;
+    } else {
+        logits_size = 0;
+        embd_size   = 0;
+
+        sampling.logits_size     = n_vocab*n_outputs_max;
+        sampling.probs_size      = n_vocab*n_outputs_max;
+        sampling.sampled_size    = n_outputs_max;
+        sampling.candidates_size = n_vocab*n_outputs_max;
+
+        backend_float_count      = sampling.logits_size + sampling.probs_size;
+        backend_token_count      = sampling.sampled_size + sampling.candidates_size;
+    }
 
     if (output_ids.empty()) {
         // init, never resized afterwards
@@ -1555,7 +1681,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     }
 
     const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
-    const size_t new_size  = (logits_size + embd_size) * sizeof(float);
+    const size_t new_size  = (logits_size + embd_size + backend_float_count) * sizeof(float)
+                         + backend_token_count * sizeof(llama_token);
 
     // alloc only when more than the current capacity is required
     // TODO: also consider shrinking the buffer
@@ -1585,13 +1712,57 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     }
 
     float * output_base = (float *) ggml_backend_buffer_get_base(buf_output.get());
-    llama_token * s_output_base = (llama_token *) ggml_backend_buffer_get_base(buf_output.get());
 
-    logits         = has_logits        ? output_base               : nullptr;
-    embd           = has_embd          ? output_base + logits_size : nullptr;
+    logits = nullptr;
+    embd   = nullptr;
+
+    // reset sampling pointers.
+    sampling.logits     = nullptr;
+    sampling.probs      = nullptr;
+    sampling.sampled    = nullptr;
+    sampling.candidates = nullptr;
 
-    sampling.sampled = !sampling.samplers.empty() ? s_output_base    : nullptr;
-    sampling.probs   = !sampling.samplers.empty() ? embd             : nullptr;
+    if (!backend_sampling) {
+        logits = has_logits ? output_base : nullptr;
+        embd   = has_embd   ? output_base + logits_size : nullptr;
+    } else {
+        size_t offset = 0;
+        uint8_t * base = (uint8_t *) output_base;
+
+        if (sampling.logits_size > 0) {
+            sampling.logits = (float *) (base + offset);
+            offset += sampling.logits_size * sizeof(float);
+        }
+        if (sampling.probs_size > 0) {
+            sampling.probs = (float *) (base + offset);
+            offset += sampling.probs_size * sizeof(float);
+        }
+        if (sampling.sampled_size > 0) {
+            sampling.sampled = (llama_token *) (base + offset);
+            offset += sampling.sampled_size * sizeof(llama_token);
+        }
+        if (sampling.candidates_size > 0) {
+            sampling.candidates = (llama_token *) (base + offset);
+            offset += sampling.candidates_size * sizeof(llama_token);
+        }
+
+        const size_t n_rows = (size_t) n_outputs_max;
+        if (sampling.outputs_capacity < n_rows) {
+            sampling.outputs_capacity = n_rows;
+
+            sampling.logits_count.assign(n_rows, 0);
+            sampling.probs_count.assign(n_rows, 0);
+            sampling.candidates_count.assign(n_rows, 0);
+        } else {
+            std::fill(sampling.logits_count.begin(), sampling.logits_count.end(), 0);
+            std::fill(sampling.probs_count.begin(),  sampling.probs_count.end(),  0);
+            std::fill(sampling.candidates_count.begin(), sampling.candidates_count.end(), 0);
+        }
+
+        if (sampling.sampled && sampling.sampled_size > 0) {
+            std::fill_n(sampling.sampled, sampling.sampled_size, LLAMA_TOKEN_NULL);
+        }
+    }
 
     // set all ids as invalid (negative)
     std::fill(output_ids.begin(), output_ids.end(), -1);
@@ -1620,6 +1791,38 @@ void llama_context::output_reorder() {
                 std::swap(embd[i0*n_embd + k], embd[i1*n_embd + k]);
             }
         }
+
+        if (sampling.logits && sampling.logits_size > 0) {
+            for (uint64_t k = 0; k < n_vocab; ++k) {
+                std::swap(sampling.logits[i0*n_vocab + k], sampling.logits[i1*n_vocab + k]);
+            }
+        }
+
+        if (sampling.probs && sampling.probs_size > 0) {
+            for (uint64_t k = 0; k < n_vocab; ++k) {
+                std::swap(sampling.probs[i0*n_vocab + k], sampling.probs[i1*n_vocab + k]);
+            }
+        }
+
+        if (sampling.candidates && sampling.candidates_size > 0) {
+            for (uint64_t k = 0; k < n_vocab; ++k) {
+                std::swap(sampling.candidates[i0*n_vocab + k], sampling.candidates[i1*n_vocab + k]);
+            }
+        }
+
+        if (sampling.sampled && sampling.sampled_size > 0) {
+            std::swap(sampling.sampled[i0], sampling.sampled[i1]);
+        }
+
+        if (!sampling.logits_count.empty()) {
+            std::swap(sampling.logits_count[i0], sampling.logits_count[i1]);
+        }
+        if (!sampling.probs_count.empty()) {
+            std::swap(sampling.probs_count[i0], sampling.probs_count[i1]);
+        }
+        if (!sampling.candidates_count.empty()) {
+            std::swap(sampling.candidates_count[i0], sampling.candidates_count[i1]);
+        }
     }
 
     output_swaps.clear();
@@ -2776,10 +2979,16 @@ float * llama_get_backend_sampled_logits_ith(llama_context * ctx, int32_t i) {
     return ctx->get_backend_sampled_logits_ith(i);
 }
 
-llama_token * llama_get_backend_sampled_token_ids_ith(llama_context * ctx, int32_t i) {
+llama_token * llama_get_backend_sampled_candidates_ith(llama_context * ctx, int32_t i) {
+    ctx->synchronize();
+
+    return const_cast<llama_token *>(ctx->get_backend_sampled_candidates_ith(i));
+}
+
+uint32_t llama_get_backend_sampled_candidates_count_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    return const_cast<llama_token *>(ctx->get_backend_sampled_token_ids_ith(i));
+    return static_cast<uint32_t>(ctx->get_backend_sampled_candidates_count(i));
 }
 
 uint32_t llama_get_backend_sampled_logits_count_ith(llama_context * ctx, int32_t i) {
diff --git a/src/llama-context.h b/src/llama-context.h
index 8e6a111e611..2bdbf8a5532 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -70,11 +70,13 @@ struct llama_context {
     llama_token         get_backend_sampled_token_ith(int32_t idx);
 
     float             * get_backend_sampled_logits_ith(int32_t idx);
-    const llama_token * get_backend_sampled_token_ids_ith(int32_t idx);
-    size_t              get_backend_sampled_logits_count(int32_t idx) const;
+    size_t              get_backend_sampled_logits_count(int32_t idx);
 
     float             * get_backend_sampled_probs_ith(int32_t idx);
-    size_t              get_backend_sampled_probs_count(int32_t idx) const;
+    size_t              get_backend_sampled_probs_count(int32_t idx);
+
+    const llama_token * get_backend_sampled_candidates_ith(int32_t idx);
+    size_t              get_backend_sampled_candidates_count(int32_t idx);
 
     void attach_threadpool(
             ggml_threadpool_t threadpool,
@@ -201,6 +203,7 @@ struct llama_context {
     uint32_t output_reserve(int32_t n_outputs);
 
     void output_reorder();
+    int64_t resolve_output_row(int32_t i) const;
 
     //
     // graph
@@ -257,13 +260,22 @@ struct llama_context {
     struct sampling_info {
         std::unordered_map<llama_seq_id, llama_sampler*> samplers;
 
-        llama_token * sampled = nullptr;
-        float       * probs = nullptr;
+        float       * logits      = nullptr;
+        size_t        logits_size = 0;
+
+        llama_token * sampled      = nullptr;
+        size_t        sampled_size = 0;
+
+        float       * probs        = nullptr;
+        size_t        probs_size   = 0;
+
+        llama_token * candidates   = nullptr;
+        size_t        candidates_size = 0;
 
-        std::unordered_map<int32_t, llama_token>              map_sampled;
-        std::unordered_map<int32_t, std::vector<float>>       map_probs;
-        std::unordered_map<int32_t, std::vector<float>>       map_logits;
-        std::unordered_map<int32_t, std::vector<llama_token>> map_cadidates;
+        size_t outputs_capacity = 0;
+        std::vector<uint32_t> logits_count;
+        std::vector<uint32_t> probs_count;
+        std::vector<uint32_t> candidates_count;
 
         std::vector<llama_token> token_ids_full_vocab;
     };
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 11679c6c9eb..c126b702264 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -442,7 +442,7 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
     const llama_token   sampled_token        = llama_get_backend_sampled_token_ith(ctx, idx);
     const float *       sampled_probs        = llama_get_backend_sampled_probs_ith(ctx, idx);
     const float *       sampled_logits       = llama_get_backend_sampled_logits_ith(ctx, idx);
-    const llama_token * sampled_ids          = llama_get_backend_sampled_token_ids_ith(ctx, idx);
+    const llama_token * sampled_ids          = llama_get_backend_sampled_candidates_ith(ctx, idx);
 
     // If a backend sampler has already sampled a token, return it.
     if (sampled_token != LLAMA_TOKEN_NULL) {
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index c6d0d1a38d7..2ed13688c98 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -325,6 +325,13 @@ static void test_backend_top_k_sampling(const char * model_path) {
         printf("top_k logit[%zu] = %.6f\n", i, logits[i]);
     }
 
+    llama_token * candidates = llama_get_backend_sampled_candidates_ith(test_ctx.ctx, batch_idx);
+    uint32_t n_candidates = llama_get_backend_sampled_candidates_count_ith(test_ctx.ctx, batch_idx);
+    for (size_t i = 0; i < n_candidates; ++i) {
+        printf("top_k candidate[%zu] = %d : %s\n", i, candidates[i],
+               test_ctx.token_to_piece(candidates[i], false).c_str());
+    }
+
     // Sample using CPU sampler for verification that it is possible to do hybrid
     // sampling, first top_k on the backend and then dist on the CPU.
     struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
@@ -370,6 +377,9 @@ static void test_backend_temp_sampling(const char * model_path) {
     int32_t batch_idx_0 = test_ctx.idx_for_seq(0);
     int32_t batch_idx_1 = test_ctx.idx_for_seq(1);
 
+    int n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx_0);
+    GGML_ASSERT(n_logits == test_ctx.n_vocab);
+
     // Sample from sequence 0 using CPU sampler
     struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
     struct llama_sampler * chain_0 = llama_sampler_chain_init(chain_params_0);

From 9b2439347fa9e4a031ad0618e7728b4f74f882ae Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 21 Nov 2025 14:26:52 +0100
Subject: [PATCH 21/59] common, tools : refactor model loading to support
 backend samplers

This commit refactors the model loading process in common/common.cpp
to enable backend sampler to be configure prior to the llama_context
creation.

The motivation for this change is that just being able to set/reset the
backend samplers after the llama_context has been created will cause a
resize to occur in llama_context::output_reserve which we want to avoid.
---
 common/common.cpp   | 24 ++++++++++++++++++++++--
 common/common.h     |  8 ++++++++
 tools/main/main.cpp | 23 +++++++++++++++++------
 3 files changed, 47 insertions(+), 8 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index c31619ac365..40dea7fd4b4 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -943,14 +943,26 @@ std::vector<common_file_info> fs_list_files(const std::string & path) {
 // Model utils
 //
 
-struct common_init_result common_init_from_params(common_params & params) {
-    common_init_result iparams;
+llama_model * common_load_model_from_params(common_params & params) {
     auto mparams = common_model_params_to_llama(params);
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
     if (model == NULL) {
         LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
             __func__, params.model.path.c_str());
+        return nullptr;
+    }
+
+    return model;
+}
+
+struct common_init_result common_init_context_from_model(
+    llama_model * model,
+    common_params & params) {
+    common_init_result iparams;
+
+    if (model == NULL) {
+        LOG_ERR("%s: model is NULL\n", __func__);
         return iparams;
     }
 
@@ -1125,6 +1137,14 @@ struct common_init_result common_init_from_params(common_params & params) {
     return iparams;
 }
 
+struct common_init_result common_init_from_params(common_params & params) {
+    llama_model * model = common_load_model_from_params(params);
+    if (model == NULL) {
+        return common_init_result();
+    }
+    return common_init_context_from_model(model, params);
+}
+
 std::string get_model_endpoint() {
     const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
     // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
diff --git a/common/common.h b/common/common.h
index be34bcb78c6..5d289a116b5 100644
--- a/common/common.h
+++ b/common/common.h
@@ -640,6 +640,14 @@ struct common_init_result {
 
 struct common_init_result     common_init_from_params(common_params & params);
 
+// Load model only (allows creating backend samplers before context initialization)
+llama_model * common_load_model_from_params(common_params & params);
+
+// Initialize context from an already-loaded model (allows pre-configuring backend samplers)
+struct common_init_result common_init_context_from_model(
+    llama_model * model,
+    common_params & params);
+
 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
 struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
diff --git a/tools/main/main.cpp b/tools/main/main.cpp
index f1d0fd4b60e..06185e47eb8 100644
--- a/tools/main/main.cpp
+++ b/tools/main/main.cpp
@@ -137,18 +137,29 @@ int main(int argc, char ** argv) {
 
     // load the model and apply lora adapter, if any
     LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
-    common_init_result llama_init = common_init_from_params(params);
-
-    model = llama_init.model.get();
-    ctx = llama_init.context.get();
 
+    model = common_load_model_from_params(params);
     if (model == NULL) {
         LOG_ERR("%s: error: unable to load model\n", __func__);
         return 1;
     }
 
-    // Configure backend sampler chain
-    llama_set_backend_sampler(ctx, 0, common_sampler_backend_init(model, sparams));
+    // Configure backend sampler if configured
+    llama_sampler * backend_sampler = common_sampler_backend_init(model, sparams);
+    if (backend_sampler) {
+        llama_sampler_seq_config sampler_config = { 0, backend_sampler };
+        params.backend_samplers = &sampler_config;
+        params.n_backend_samplers = 1;
+    }
+
+    common_init_result llama_init = common_init_context_from_model(model, params);
+    ctx = llama_init.context.get();
+    model = llama_init.model.get(); // Update pointer (now managed by llama_init)
+
+    if (ctx == NULL) {
+        LOG_ERR("%s: error: unable to create context\n", __func__);
+        return 1;
+    }
 
     auto * mem = llama_get_memory(ctx);
 

From 65500d05ab955750774c232bb5d5c6202b228a36 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Sun, 23 Nov 2025 11:27:54 +0100
Subject: [PATCH 22/59] sampling : add stride variable for clarity

---
 src/llama-context.cpp | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 1694e447200..081d2aad7dd 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1468,6 +1468,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
         if (has_backend_samplers && backend_has_sampled) {
             const auto seq_to_output_row = build_seq_to_output_row(ubatch, n_outputs_prev);
+            const auto stride = n_vocab;
 
             // If a backend sampler has sampled a token we only want to copy the
             // sampled tokens and avoid copying logits and probabilites.
@@ -1476,15 +1477,15 @@ int llama_context::decode(const llama_batch & batch_inp) {
                 copy_tensor_async_ints(res->t_sampled, sampling.sampled, sampling.sampled_size, seq_to_output_row, sched.get());
             } else {
                 // async copy the sampled logits/probs from the backend to the host.
-                copy_tensor_async_floats(res->t_sampled_logits, sampling.logits, n_vocab, sampling.logits_count, seq_to_output_row, sched.get());
-                copy_tensor_async_floats(res->t_sampled_probs,  sampling.probs,  n_vocab, sampling.probs_count,  seq_to_output_row, sched.get());
+                copy_tensor_async_floats(res->t_sampled_logits, sampling.logits, stride, sampling.logits_count, seq_to_output_row, sched.get());
+                copy_tensor_async_floats(res->t_sampled_probs,  sampling.probs,  stride, sampling.probs_count,  seq_to_output_row, sched.get());
             }
 
             // async copy the candidate token ids from the backend to the host.
             // These are needed for:
             // 1) Backend dist sampler to map indices to vocab token ids.
             // 2) CPU samplers to associate candidate logits with their token ids.
-            copy_tensor_async_candidates(res->t_candidates, sampling.candidates, n_vocab, sampling.candidates_count, seq_to_output_row, sched.get());
+            copy_tensor_async_candidates(res->t_candidates, sampling.candidates, stride, sampling.candidates_count, seq_to_output_row, sched.get());
 
         }
 

From ae23d2d2c169711b0e9af41361ed0e50ffb626fa Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Sun, 23 Nov 2025 11:28:19 +0100
Subject: [PATCH 23/59] sampling: clarify candidate ids usage in comments

---
 src/llama-context.cpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 081d2aad7dd..c03a23e701f 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1482,9 +1482,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
             }
 
             // async copy the candidate token ids from the backend to the host.
-            // These are needed for:
-            // 1) Backend dist sampler to map indices to vocab token ids.
-            // 2) CPU samplers to associate candidate logits with their token ids.
+            // These are needed by CPU samplers to map probability/logit indices to vocab token ids.
             copy_tensor_async_candidates(res->t_candidates, sampling.candidates, stride, sampling.candidates_count, seq_to_output_row, sched.get());
 
         }

From 9e273f7aa4584b05bc9eef30569957805c495b07 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Sun, 23 Nov 2025 13:08:08 +0100
Subject: [PATCH 24/59] sampling : fix copying both sampled tokens and
 logits/probs from backend

This commit fixes the issue where both sampled tokens and logits/probs
were not being copied correctly from the backend to the host when
multiple backend samplers were used.

A test for this scenario has also been added to ensure that both types
of data are copied correctly when different backend samplers are
employed.
---
 src/llama-context.cpp          | 18 +++++------
 tests/test-backend-sampler.cpp | 57 ++++++++++++++++++++++++++++++++++
 2 files changed, 65 insertions(+), 10 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index c03a23e701f..9ccd8f39983 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1470,16 +1470,14 @@ int llama_context::decode(const llama_batch & batch_inp) {
             const auto seq_to_output_row = build_seq_to_output_row(ubatch, n_outputs_prev);
             const auto stride = n_vocab;
 
-            // If a backend sampler has sampled a token we only want to copy the
-            // sampled tokens and avoid copying logits and probabilites.
-            if (!res->t_sampled.empty()) {
-                // async copy the sampled tokens from the backend to the host.
-                copy_tensor_async_ints(res->t_sampled, sampling.sampled, sampling.sampled_size, seq_to_output_row, sched.get());
-            } else {
-                // async copy the sampled logits/probs from the backend to the host.
-                copy_tensor_async_floats(res->t_sampled_logits, sampling.logits, stride, sampling.logits_count, seq_to_output_row, sched.get());
-                copy_tensor_async_floats(res->t_sampled_probs,  sampling.probs,  stride, sampling.probs_count,  seq_to_output_row, sched.get());
-            }
+            // async copy the sampled tokens from the backend to the host.
+            copy_tensor_async_ints(res->t_sampled, sampling.sampled, sampling.sampled_size, seq_to_output_row, sched.get());
+
+            // async copy the sampled logits from the backend to the host.
+            copy_tensor_async_floats(res->t_sampled_logits, sampling.logits, stride, sampling.logits_count, seq_to_output_row, sched.get());
+
+            // async copy the sampled probablities from the backend to the host.
+            copy_tensor_async_floats(res->t_sampled_probs,  sampling.probs,  stride, sampling.probs_count,  seq_to_output_row, sched.get());
 
             // async copy the candidate token ids from the backend to the host.
             // These are needed by CPU samplers to map probability/logit indices to vocab token ids.
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index 2ed13688c98..ebbb6e039ee 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -573,6 +573,62 @@ static void test_backend_logit_bias_sampling(const char * model_path) {
     GGML_ASSERT(backend_token == bias_token);
 }
 
+// This test verifies that it is possible to have two different backend sampler,
+// one that used the backend dist sampler, and another that uses CPU dist sampler.
+static void test_backend_mixed_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
+    struct llama_sampler * sampler_chain_0 = llama_sampler_chain_init(chain_params_0);
+    llama_sampler_chain_add(sampler_chain_0, llama_sampler_backend_init_dist(88));
+
+    int k = 40;
+    struct llama_sampler_chain_params chain_params_1 = llama_sampler_chain_default_params();
+    struct llama_sampler * sampler_chain_1 = llama_sampler_chain_init(chain_params_1);
+    llama_sampler_chain_add(sampler_chain_1, llama_sampler_backend_init_top_k(k));
+
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {
+        { 0, sampler_chain_0 },
+        { 1, sampler_chain_1 }
+    };
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    std::map<llama_seq_id, std::string> prompts = {
+        {0, "Hello"},
+        {1, "Some"}
+    };
+
+    if (!test_ctx.decode(prompts)) {
+        return;
+    }
+
+    // Verfiy sequence 0 that used the dist backend sampler.
+    {
+        int32_t batch_idx = test_ctx.idx_for_seq(0);
+        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        const std::string token_str = test_ctx.token_to_piece(token, false);
+        printf("sampled token id=%d, string='%s'\n", token, token_str.c_str());
+        GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+        GGML_ASSERT(llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx) == nullptr);
+        GGML_ASSERT(llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx) == 0);
+    }
+
+    // Verfiy sequence 0 that used the top-k backend sampler.
+    {
+        int32_t batch_idx = test_ctx.idx_for_seq(1);
+        float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);
+        GGML_ASSERT(logits != nullptr);
+        size_t n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+        GGML_ASSERT(n_logits == (size_t) k);
+        GGML_ASSERT(llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx) == LLAMA_TOKEN_NULL);
+    }
+
+    printf("backend mixed sampling test PASSED\n");
+}
+
 static void test_backend_set_sampler(const char * model_path) {
     test_model_context test_ctx;
 
@@ -695,6 +751,7 @@ static const backend_test_case BACKEND_TESTS[] = {
     { "dist_and_cpu",    test_backend_dist_sampling_and_cpu,   true  },
     { "set_sampler",     test_backend_set_sampler,             true  },
     { "max_outputs",     test_backend_max_outputs,             true  },
+    { "mixed",           test_backend_mixed_sampling,          true  },
 };
 
 struct backend_cli_args {

From 50d21aa4a4a4b63dc1023d34066d89f1331f6b8f Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 24 Nov 2025 07:18:39 +0100
Subject: [PATCH 25/59] tests : cleanup test-backend-sampler.cpp

---
 tests/test-backend-sampler.cpp | 150 +++++++++++++++++++--------------
 1 file changed, 85 insertions(+), 65 deletions(-)

diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index ebbb6e039ee..19e9855aeda 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -16,38 +16,40 @@
 #include <vector>
 
 struct test_model_context {
-    llama_model * model = nullptr;
-    llama_context * ctx = nullptr;
-    const llama_vocab * vocab = nullptr;
-    int n_vocab = 0;
+    llama_model       * model   = nullptr;
+    llama_context     * ctx     = nullptr;
+    const llama_vocab * vocab   = nullptr;
+    int                 n_vocab = 0;
+
     std::unordered_map<llama_seq_id, int32_t> seq_positions;
     std::unordered_map<llama_seq_id, int32_t> last_batch_info;
 
-    bool setup_model(const char * model_path) {
+    bool load_model(const char * model_path) {
         if (model != nullptr) {
             return true;
         }
 
         llama_backend_init();
 
-        llama_model_params mparams = llama_model_default_params();
-        model = llama_model_load_from_file(model_path, mparams);
+        model = llama_model_load_from_file(model_path, llama_model_default_params());
         if (model == nullptr) {
             fprintf(stderr, "Warning: failed to load model '%s', skipping test\n", model_path);
             cleanup();
             return false;
         }
-        vocab = llama_model_get_vocab(model);
+        vocab   = llama_model_get_vocab(model);
+        n_vocab = llama_vocab_n_tokens(vocab);
+        fprintf(stderr, "Vocabulary size: %d\n", n_vocab);
 
         return true;
     }
 
     bool setup(const char * model_path, std::vector<llama_sampler_seq_config> & configs) {
         if (model == nullptr) {
-            setup_model(model_path);
+            load_model(model_path);
         }
 
-        if (model != nullptr && ctx != nullptr) {
+        if (ctx != nullptr) {
             return true;
         }
 
@@ -73,10 +75,6 @@ struct test_model_context {
         }
         llama_set_warmup(ctx, false);
 
-        vocab = llama_model_get_vocab(model);
-        n_vocab = llama_vocab_n_tokens(vocab);
-        fprintf(stderr, "Vocabulary size: %d\n", n_vocab);
-
         return true;
     }
 
@@ -130,15 +128,15 @@ struct test_model_context {
 
 
         printf("Batch contents:\n");
-        printf("  n_tokens: %d\n", batch.n_tokens);
+        printf("n_tokens: %d\n", batch.n_tokens);
         for (int i = 0; i < batch.n_tokens; i++) {
-            printf("  token[%d]: tok=%-5d, pos=%d, n_seq_id=%d, seq_ids=[", i, batch.token[i], batch.pos[i], batch.n_seq_id[i]);
+            printf("token[%d]: tok=%-5d, pos=%d, n_seq_id=%d, seq_ids=[", i, batch.token[i], batch.pos[i], batch.n_seq_id[i]);
 
-        for (int j = 0; j < batch.n_seq_id[i]; j++) {
-            printf("%d%s", batch.seq_id[i][j], j < batch.n_seq_id[i]-1 ? ", " : "");
+            for (int j = 0; j < batch.n_seq_id[i]; j++) {
+                printf("%d%s", batch.seq_id[i][j], j < batch.n_seq_id[i]-1 ? ", " : "");
+            }
+            printf("], logits=%d\n", batch.logits[i]);
         }
-        printf("], logits=%d\n", batch.logits[i]);
-}
 
         if (llama_decode(ctx, batch) != 0) {
             fprintf(stderr, "Warning: llama_decode failed\n");
@@ -151,7 +149,6 @@ struct test_model_context {
             if (batch.logits[i]) {
                 llama_seq_id seq_id = batch.seq_id[i][0];
                 last_batch_info[seq_id] = i;
-                printf("seq %d : batch idx %d\n", seq_id, i);
             }
         }
 
@@ -249,10 +246,15 @@ struct test_model_context {
     }
 
     void cleanup() {
-        if (ctx) llama_free(ctx);
-        if (model) llama_model_free(model);
+        if (ctx) {
+            llama_free(ctx);
+        }
+        if (model) {
+            llama_model_free(model);
+        }
         llama_backend_free();
-        ctx = nullptr;
+
+        ctx   = nullptr;
         model = nullptr;
         vocab = nullptr;
     }
@@ -374,36 +376,44 @@ static void test_backend_temp_sampling(const char * model_path) {
         return;
     }
 
-    int32_t batch_idx_0 = test_ctx.idx_for_seq(0);
-    int32_t batch_idx_1 = test_ctx.idx_for_seq(1);
+    // Verfify sequence 0
+    {
+        int32_t batch_idx = test_ctx.idx_for_seq(0);
+        int n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+        GGML_ASSERT(n_logits == test_ctx.n_vocab);
 
-    int n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx_0);
-    GGML_ASSERT(n_logits == test_ctx.n_vocab);
+        // Sample from sequence 0 using CPU sampler
+        struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+        struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+        llama_sampler_chain_add(chain, llama_sampler_init_dist(18));
 
-    // Sample from sequence 0 using CPU sampler
-    struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
-    struct llama_sampler * chain_0 = llama_sampler_chain_init(chain_params_0);
-    llama_sampler_chain_add(chain_0, llama_sampler_init_dist(18));
+        llama_token token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+        const std::string token_str = test_ctx.token_to_piece(token, false);
+        printf("Sequence 0 sampled token id:%d, string: '%s'\n", token, token_str.c_str());
+        GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
 
-    llama_token token_0 = llama_sampler_sample(chain_0, test_ctx.ctx, batch_idx_0);
-    const std::string token_0_str = test_ctx.token_to_piece(token_0, false);
-    printf("Sequence 0 sampled token id:%d, string: '%s'\n", token_0, token_0_str.c_str());
-    GGML_ASSERT(token_0 >= 0 && token_0 < test_ctx.n_vocab);
+        llama_sampler_free(chain);
+    }
 
-    // Sample from sequence 1 using CPU sampler
-    struct llama_sampler_chain_params chain_params_1 = llama_sampler_chain_default_params();
-    struct llama_sampler * chain_1 = llama_sampler_chain_init(chain_params_1);
-    llama_sampler_chain_add(chain_1, llama_sampler_init_dist(18));
+    // Verfify sequence 1
+    {
+        int32_t batch_idx = test_ctx.idx_for_seq(1);
+
+        // Sample from sequence 1 using CPU sampler
+        struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+        struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+        llama_sampler_chain_add(chain, llama_sampler_init_dist(18));
+
+        llama_token token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+        const std::string token_str = test_ctx.token_to_piece(token, false);
+        printf("Sequence 1 sampled token id:%d, string: '%s'\n", token, token_str.c_str());
+        GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
 
-    llama_token token_1 = llama_sampler_sample(chain_1, test_ctx.ctx, batch_idx_1);
-    const std::string token_1_str = test_ctx.token_to_piece(token_1, false);
-    printf("Sequence 1 sampled token id:%d, string: '%s'\n", token_1, token_1_str.c_str());
-    GGML_ASSERT(token_1 >= 0 && token_1 < test_ctx.n_vocab);
+        llama_sampler_free(chain);
+    }
 
     printf("backend temp sampling test PASSED\n");
 
-    llama_sampler_free(chain_0);
-    llama_sampler_free(chain_1);
 }
 
 static void test_backend_multi_sequence_sampling(const char * model_path) {
@@ -436,17 +446,23 @@ static void test_backend_multi_sequence_sampling(const char * model_path) {
         return;
     }
 
-    int32_t batch_idx_0 = test_ctx.idx_for_seq(0);
-    llama_token seq0_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx_0);
-    const std::string seq0_token_str = test_ctx.token_to_piece(seq0_token, false);
-    printf("Seq 0 sampled token id=%d, string='%s'\n", seq0_token, seq0_token_str.c_str());
-    GGML_ASSERT(seq0_token >= 0 && seq0_token < test_ctx.n_vocab);
+    // Verfiy sequence 0
+    {
+        int32_t batch_idx = test_ctx.idx_for_seq(0);
+        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        const std::string token_str = test_ctx.token_to_piece(token, false);
+        printf("Seq 0 sampled token id=%d, string='%s'\n", token, token_str.c_str());
+        GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+    }
 
-    int32_t batch_idx_1 = test_ctx.idx_for_seq(1);
-    llama_token seq1_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx_1);
-    const std::string seq1_token_str = test_ctx.token_to_piece(seq1_token, false);
-    printf("Seq 1 sampled token id=%d, string='%s'\n", seq1_token, seq1_token_str.c_str());
-    GGML_ASSERT(seq1_token >= 0 && seq1_token < test_ctx.n_vocab);
+    // Verify sequence 1
+    {
+        int32_t batch_idx= test_ctx.idx_for_seq(1);
+        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        const std::string token_str = test_ctx.token_to_piece(token, false);
+        printf("Seq 1 sampled token id=%d, string='%s'\n", token, token_str.c_str());
+        GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+    }
 
     // Generate tokens for each sequence
     printf("\nMulti-sequence generation:\n");
@@ -473,26 +489,29 @@ static void test_backend_multi_sequence_sampling(const char * model_path) {
 static void test_backend_dist_sampling(const char * model_path) {
     test_model_context test_ctx;
 
+    const int seq_id = 189;
     const int32_t seed = 88;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
     llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
-    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ 0, backend_sampler_chain }};
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {
         return;
     }
 
-    if (!test_ctx.decode({{0, "Hello"}})) {
+    if (!test_ctx.decode({{seq_id, "Some"}})) {
         return;
     }
 
-    llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(0));
-    printf("greedy sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
+    int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
+    llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+    printf("dist sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
     GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+    GGML_ASSERT(llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx) == nullptr);
 
     token = llama_get_backend_sampled_token_ith(test_ctx.ctx, -1);
-    printf("greedy sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
+    printf("dist sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
     GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
 }
 
@@ -510,7 +529,7 @@ static void test_backend_dist_sampling_and_cpu(const char * model_path) {
         return;
     }
 
-    if (!test_ctx.decode({{seq_id, "Hello"}})) {
+    if (!test_ctx.decode({{seq_id, "Some"}})) {
         return;
     }
 
@@ -523,14 +542,15 @@ static void test_backend_dist_sampling_and_cpu(const char * model_path) {
 
     llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
     llama_token cpu_token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+    printf("dist & cpu sampled id:%d, string:'%s'\n", cpu_token, test_ctx.token_to_piece(cpu_token, false).c_str());
     GGML_ASSERT(backend_token == cpu_token);
 }
 
 static void test_backend_logit_bias_sampling(const char * model_path) {
     test_model_context test_ctx;
 
-    // Calling setup_model to ensure vocab is loaded and can be accessed
-    if (!test_ctx.setup_model(model_path)) {
+    // Calling load_model to ensure vocab is loaded and can be accessed
+    if (!test_ctx.load_model(model_path)) {
         return;
     }
 
@@ -616,7 +636,7 @@ static void test_backend_mixed_sampling(const char * model_path) {
         GGML_ASSERT(llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx) == 0);
     }
 
-    // Verfiy sequence 0 that used the top-k backend sampler.
+    // Verfiy sequence 1 that used the top-k backend sampler.
     {
         int32_t batch_idx = test_ctx.idx_for_seq(1);
         float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);

From d88ba1813cdf5e6893c546965c37c4187f944144 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 24 Nov 2025 09:31:14 +0100
Subject: [PATCH 26/59] common : remove build-info.cpp from commit [no ci]

This file was generated during the build process and should not be
included in previous commits.
---
 common/build-info.cpp | 4 ----
 1 file changed, 4 deletions(-)
 delete mode 100644 common/build-info.cpp

diff --git a/common/build-info.cpp b/common/build-info.cpp
deleted file mode 100644
index 6e8240fbb16..00000000000
--- a/common/build-info.cpp
+++ /dev/null
@@ -1,4 +0,0 @@
-int LLAMA_BUILD_NUMBER = 5590;
-char const *LLAMA_COMMIT = "0d398442";
-char const *LLAMA_COMPILER = "cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0";
-char const *LLAMA_BUILD_TARGET = "x86_64-linux-gnu";

From 4a90583d7d6f0786f2b8e501860d248835ad0c73 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 24 Nov 2025 13:26:18 +0100
Subject: [PATCH 27/59] sampling : cleanup and clarify output_reserve

---
 src/llama-context.cpp | 37 ++++++++++++++++++++-----------------
 1 file changed, 20 insertions(+), 17 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 9ccd8f39983..0ed729f498d 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1723,40 +1723,41 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         logits = has_logits ? output_base : nullptr;
         embd   = has_embd   ? output_base + logits_size : nullptr;
     } else {
+        // Allocate worst case (full vocabulary size) for backend sampled
+        // data in the pinned memory buffer.
         size_t offset = 0;
         uint8_t * base = (uint8_t *) output_base;
 
-        if (sampling.logits_size > 0) {
-            sampling.logits = (float *) (base + offset);
-            offset += sampling.logits_size * sizeof(float);
-        }
-        if (sampling.probs_size > 0) {
-            sampling.probs = (float *) (base + offset);
-            offset += sampling.probs_size * sizeof(float);
-        }
-        if (sampling.sampled_size > 0) {
-            sampling.sampled = (llama_token *) (base + offset);
-            offset += sampling.sampled_size * sizeof(llama_token);
-        }
-        if (sampling.candidates_size > 0) {
-            sampling.candidates = (llama_token *) (base + offset);
-            offset += sampling.candidates_size * sizeof(llama_token);
-        }
+        sampling.logits = (float *) (base + offset);
+        offset += sampling.logits_size * sizeof(float);
+
+        sampling.probs = (float *) (base + offset);
+        offset += sampling.probs_size * sizeof(float);
 
+        sampling.sampled = (llama_token *) (base + offset);
+        offset += sampling.sampled_size * sizeof(llama_token);
+
+        sampling.candidates = (llama_token *) (base + offset);
+        offset += sampling.candidates_size * sizeof(llama_token);
+
+        // The count vectors keep track of the actual number of logits/probs/candidates
+        // copied from the backend for each output row.
         const size_t n_rows = (size_t) n_outputs_max;
         if (sampling.outputs_capacity < n_rows) {
+            // The output size has increased, so resize and reset the count vectors.
             sampling.outputs_capacity = n_rows;
 
             sampling.logits_count.assign(n_rows, 0);
             sampling.probs_count.assign(n_rows, 0);
             sampling.candidates_count.assign(n_rows, 0);
         } else {
+            // The output size has not increased so just reset the counts to zero.
             std::fill(sampling.logits_count.begin(), sampling.logits_count.end(), 0);
             std::fill(sampling.probs_count.begin(),  sampling.probs_count.end(),  0);
             std::fill(sampling.candidates_count.begin(), sampling.candidates_count.end(), 0);
         }
 
-        if (sampling.sampled && sampling.sampled_size > 0) {
+        if (sampling.sampled) {
             std::fill_n(sampling.sampled, sampling.sampled_size, LLAMA_TOKEN_NULL);
         }
     }
@@ -1814,9 +1815,11 @@ void llama_context::output_reorder() {
         if (!sampling.logits_count.empty()) {
             std::swap(sampling.logits_count[i0], sampling.logits_count[i1]);
         }
+
         if (!sampling.probs_count.empty()) {
             std::swap(sampling.probs_count[i0], sampling.probs_count[i1]);
         }
+
         if (!sampling.candidates_count.empty()) {
             std::swap(sampling.candidates_count[i0], sampling.candidates_count[i1]);
         }

From 8eb9b4769d948225af04ec628fc3156d409395d5 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 24 Nov 2025 13:53:29 +0100
Subject: [PATCH 28/59] sampling : remove redundant checks for stride and size
 [no ci]

---
 src/llama-context.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 0ed729f498d..dabe64986e5 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1221,7 +1221,7 @@ static void copy_tensor_async_ints(
     size_t sampled_size,
     const std::unordered_map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (sampled == nullptr || sampled_size == 0) {
+    if (sampled == nullptr) {
         return;
     }
 
@@ -1243,7 +1243,7 @@ static void copy_tensor_async_floats(
     std::vector<uint32_t> & counts,
     const std::unordered_map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (dst == nullptr || stride == 0) {
+    if (dst == nullptr) {
         return;
     }
 
@@ -1269,7 +1269,7 @@ static void copy_tensor_async_candidates(
     std::vector<uint32_t> & counts,
     const std::unordered_map<llama_seq_id, uint32_t> & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (dst == nullptr || stride == 0) {
+    if (dst == nullptr) {
         return;
     }
 

From 25f33806d35032a3ebc5eddfc3eeae6538e6c9c3 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 24 Nov 2025 15:03:41 +0100
Subject: [PATCH 29/59] sampling : add debug log when backend sampler selects
 token

This commit adds a debug log statement in the llama_sampler_sample
to indicate when a backend sampler has selected a token for a given
index.

The modification helps in tracing the sampling process and understanding
the flow of control when backend samplers are used.
---
 src/llama-sampling.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 621438a9cf4..5c3214f029f 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -446,6 +446,7 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
 
     // If a backend sampler has already sampled a token, return it.
     if (sampled_token != LLAMA_TOKEN_NULL) {
+        LLAMA_LOG_DEBUG("%s: Backend sampler selected token for idx %d. Skipping CPU samplers\n", __func__, idx);
         return sampled_token;
     }
 

From d0bea21a3cfcf14f4b70a6d194b1f50008aa2f09 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 24 Nov 2025 16:37:22 +0100
Subject: [PATCH 30/59] examples : update batched to use backend sampling

This commit updates the batched example to demonstrate how to use
backend samplers.
---
 examples/batched/README.md   | 12 ++++++++++++
 examples/batched/batched.cpp | 13 +++++++++++++
 2 files changed, 25 insertions(+)

diff --git a/examples/batched/README.md b/examples/batched/README.md
index 6013aab01fd..5f2c59e1d67 100644
--- a/examples/batched/README.md
+++ b/examples/batched/README.md
@@ -42,3 +42,15 @@ llama_print_timings: prompt eval time =  4089.11 ms /   118 tokens (   34.65 ms
 llama_print_timings:        eval time =     0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
 llama_print_timings:       total time =  4156.04 ms
 ```
+
+### Using backend samplers
+It is possible to run this example using backend samplers so that sampling is
+performed on the backend device, like a GPU.
+```bash
+./llama-batched \
+    -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf -p "Hello my name is" \
+    -np 4 -kvu \
+    --backend_sampling --top-k 80 --backend_dist
+```
+The `--verbose` flag can be added to see more detailed output and also show
+that the backend samplers are being used.
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 1a5de5928a5..e9d1fc95c26 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -2,6 +2,7 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "sampling.h"
 
 #include <algorithm>
 #include <cstdio>
@@ -64,6 +65,18 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_predict, n_parallel);
 
+    std::vector<llama_sampler_seq_config> sampler_configs(n_parallel);
+    if (params.sampling.backend_sampling) {
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            llama_sampler * backend_sampler = common_sampler_backend_init(model, params.sampling);
+            if (backend_sampler) {
+                sampler_configs[i] = { i, backend_sampler };
+            }
+        }
+        ctx_params.samplers   = sampler_configs.data();
+        ctx_params.n_samplers = n_parallel;
+    }
+
     llama_context * ctx = llama_init_from_model(model, ctx_params);
 
     auto sparams = llama_sampler_chain_default_params();

From e2d4f0829c0d332997f79925fee7a6398731ce5c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 24 Nov 2025 19:51:32 +0200
Subject: [PATCH 31/59] llama-cli : fix dangling reference to sampler config

---
 common/common.cpp   | 5 +++--
 tools/main/main.cpp | 3 ++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 6a6f5fec3d5..8f2dfd82155 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -976,8 +976,6 @@ struct common_init_result common_init_context_from_model(
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     auto cparams = common_context_params_to_llama(params);
-    cparams.samplers = params.backend_samplers;
-    cparams.n_samplers = params.n_backend_samplers;
 
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
@@ -1247,6 +1245,9 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.type_k = params.cache_type_k;
     cparams.type_v = params.cache_type_v;
 
+    cparams.samplers   = params.backend_samplers;
+    cparams.n_samplers = params.n_backend_samplers;
+
     return cparams;
 }
 
diff --git a/tools/main/main.cpp b/tools/main/main.cpp
index 64855b646fe..cae778e551d 100644
--- a/tools/main/main.cpp
+++ b/tools/main/main.cpp
@@ -146,8 +146,9 @@ int main(int argc, char ** argv) {
 
     // Configure backend sampler if configured
     llama_sampler * backend_sampler = common_sampler_backend_init(model, sparams);
+    llama_sampler_seq_config sampler_config = { 0, backend_sampler };
+
     if (backend_sampler) {
-        llama_sampler_seq_config sampler_config = { 0, backend_sampler };
         params.backend_samplers = &sampler_config;
         params.n_backend_samplers = 1;
     }

From b26c7069fbbbe8709808dd6b04bbc8bc96baecf1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 24 Nov 2025 20:25:44 +0200
Subject: [PATCH 32/59] common : initialize backend samplers

---
 common/common.cpp   | 37 ++++++++++++-------------------------
 common/common.h     | 14 +++-----------
 tools/main/main.cpp | 17 +----------------
 3 files changed, 16 insertions(+), 52 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 8f2dfd82155..8e7cbfe1da7 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -950,26 +950,14 @@ std::vector<common_file_info> fs_list_files(const std::string & path) {
 // Model utils
 //
 
-llama_model * common_load_model_from_params(common_params & params) {
+struct common_init_result common_init_from_params(common_params & params) {
+    common_init_result iparams;
     auto mparams = common_model_params_to_llama(params);
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
     if (model == NULL) {
         LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
             __func__, params.model.path.c_str());
-        return nullptr;
-    }
-
-    return model;
-}
-
-struct common_init_result common_init_context_from_model(
-    llama_model * model,
-    common_params & params) {
-    common_init_result iparams;
-
-    if (model == NULL) {
-        LOG_ERR("%s: model is NULL\n", __func__);
         return iparams;
     }
 
@@ -977,6 +965,16 @@ struct common_init_result common_init_context_from_model(
 
     auto cparams = common_context_params_to_llama(params);
 
+    // backend sampling initialization
+    if (params.sampling.backend_sampling) {
+        iparams.samplers_seq_config.resize(cparams.n_seq_max);
+        for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
+            iparams.samplers_seq_config[i] = { i, common_sampler_backend_init(model, params.sampling) };
+        }
+        cparams.samplers   = iparams.samplers_seq_config.data();
+        cparams.n_samplers = cparams.n_seq_max;
+    }
+
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
@@ -1142,14 +1140,6 @@ struct common_init_result common_init_context_from_model(
     return iparams;
 }
 
-struct common_init_result common_init_from_params(common_params & params) {
-    llama_model * model = common_load_model_from_params(params);
-    if (model == NULL) {
-        return common_init_result();
-    }
-    return common_init_context_from_model(model, params);
-}
-
 std::string get_model_endpoint() {
     const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
     // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
@@ -1245,9 +1235,6 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.type_k = params.cache_type_k;
     cparams.type_v = params.cache_type_v;
 
-    cparams.samplers   = params.backend_samplers;
-    cparams.n_samplers = params.n_backend_samplers;
-
     return cparams;
 }
 
diff --git a/common/common.h b/common/common.h
index 01e6dfe59b4..8c04715709a 100644
--- a/common/common.h
+++ b/common/common.h
@@ -523,9 +523,6 @@ struct common_params {
     bool has_speculative() const {
         return !speculative.model.path.empty() || !speculative.model.hf_repo.empty();
     }
-
-    llama_sampler_seq_config * backend_samplers   = NULL;
-    size_t                     n_backend_samplers = 0;
 };
 
 // call once at the start of a program if it uses libcommon
@@ -643,18 +640,13 @@ struct common_init_result {
     llama_context_ptr context;
 
     std::vector<llama_adapter_lora_ptr> lora;
+
+    std::vector<llama_sampler_ptr> samplers;
+    std::vector<llama_sampler_seq_config> samplers_seq_config;
 };
 
 struct common_init_result     common_init_from_params(common_params & params);
 
-// Load model only (allows creating backend samplers before context initialization)
-llama_model * common_load_model_from_params(common_params & params);
-
-// Initialize context from an already-loaded model (allows pre-configuring backend samplers)
-struct common_init_result common_init_context_from_model(
-    llama_model * model,
-    common_params & params);
-
 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
 struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
diff --git a/tools/main/main.cpp b/tools/main/main.cpp
index cae778e551d..263387f4175 100644
--- a/tools/main/main.cpp
+++ b/tools/main/main.cpp
@@ -138,22 +138,7 @@ int main(int argc, char ** argv) {
     // load the model and apply lora adapter, if any
     LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
 
-    model = common_load_model_from_params(params);
-    if (model == NULL) {
-        LOG_ERR("%s: error: unable to load model\n", __func__);
-        return 1;
-    }
-
-    // Configure backend sampler if configured
-    llama_sampler * backend_sampler = common_sampler_backend_init(model, sparams);
-    llama_sampler_seq_config sampler_config = { 0, backend_sampler };
-
-    if (backend_sampler) {
-        params.backend_samplers = &sampler_config;
-        params.n_backend_samplers = 1;
-    }
-
-    common_init_result llama_init = common_init_context_from_model(model, params);
+    common_init_result llama_init = common_init_from_params(params);
     ctx = llama_init.context.get();
     model = llama_init.model.get(); // Update pointer (now managed by llama_init)
 

From 883a87043a5987ac46a6519d23d754b93b6535af Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 24 Nov 2025 21:46:57 +0200
Subject: [PATCH 33/59] samplers : add missing cont

---
 src/llama-backend-sampler.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
index cd6b8bb7526..361e48ed688 100644
--- a/src/llama-backend-sampler.cpp
+++ b/src/llama-backend-sampler.cpp
@@ -149,7 +149,8 @@ static void llama_sampler_backend_top_k_apply_ggml(
         fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
     }
 
-    ggml_data->candidates = top_k;
+    // TODO: temporary cont until https://github.com/ggml-org/llama.cpp/pull/17365 is merged
+    ggml_data->candidates = ggml_cont(ctx, top_k);
 
     struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
     struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);

From a02adf42116485b399162d0b2f776d621d9ff95e Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 24 Nov 2025 21:00:03 +0100
Subject: [PATCH 34/59] sampling : add assertions for contiguous tensors in
 async copy functions

---
 src/llama-context.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index dabe64986e5..f4ffe620c34 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1231,6 +1231,8 @@ static void copy_tensor_async_ints(
         const uint32_t row = it->second;
         GGML_ASSERT(row < sampled_size);
 
+        GGML_ASSERT(ggml_is_contiguous(tensor) && "sampled tokens tensor must be contiguous for async copy");
+
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
         ggml_backend_tensor_get_async(backend, tensor, sampled + row, 0, sizeof(sampled[row]));
     }
@@ -1253,6 +1255,8 @@ static void copy_tensor_async_floats(
         const uint32_t row = it->second;
         GGML_ASSERT(row < counts.size());
 
+        GGML_ASSERT(ggml_is_contiguous(tensor) && "logits/probs tensor must be contiguous for async copy");
+
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
         float * row_ptr = dst + (size_t) row * stride;
         ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
@@ -1279,6 +1283,8 @@ static void copy_tensor_async_candidates(
         const uint32_t row = it->second;
         GGML_ASSERT(row < counts.size());
 
+        GGML_ASSERT(ggml_is_contiguous(tensor) && "candidates tensor must be contiguous for async copy");
+
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
         llama_token * row_ptr = dst + (size_t) row * stride;
         ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));

From 0f17ccdee7e8cd8d1d452f45d2f9d1cc8448276f Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 25 Nov 2025 08:12:42 +0100
Subject: [PATCH 35/59] examples : add info about hybrid sampling in batched
 [no ci]

---
 examples/batched/README.md | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/examples/batched/README.md b/examples/batched/README.md
index de2aa41fbae..f10639220e2 100644
--- a/examples/batched/README.md
+++ b/examples/batched/README.md
@@ -53,4 +53,17 @@ performed on the backend device, like a GPU.
     --backend_sampling --top-k 80 --backend_dist
 ```
 The `--verbose` flag can be added to see more detailed output and also show
-that the backend samplers are being used.
+that the backend samplers are being used. The above example will perform distribution
+sampling on the backend device and only transfer the sampled token ids back to the host.
+
+It is also possible to perform partial sampling on the backend, and then allow CPU samplers
+to process those results further. This is sometimes referred to as hybrid sampling.
+For an example of this we can remove `--backend_dist` from the above command:
+```bash
+./llama-batched \
+    -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf -p "Hello my name is" \
+    -np 4 -kvu \
+    --backend_sampling --top-k 80 -v
+```
+This will perform the top-k filtering on the backend device, and then transfer the filtered logits
+back to the host for sampling.

From 9e5e09d087db1038e69c4b040d42ec290eaa7675 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 25 Nov 2025 13:45:02 +0100
Subject: [PATCH 36/59] sampling : remove backend-dist option (wip)

This commit removes the `--backend-dist` option and instead uses the
configured --samplers chain to determine which samplers run on the
backend.

Backend sampling is still enabled using With `--backend_sampling`, and
the sampler chain, either explictly specified using `--samplers` or the
default, is automatically analyzed to determine which samplers can run
on the backend. The system finds the longest contiguous chain of
backend supported samplers from the start of the sampler sequence.
For example:

* If the chain is `top-k -> temperature -> top-p`, and both `top-k` and
  `temperature` are backend-supported but `top-p` is not, then `top-k`
  and `temperature` will run on the backend, while `top-p` and
  subsequent samplers run on the CPU.

* If all configured samplers are supported, the final distribution
  sampling will also happen on the backend, transferring only the
  sampled token IDs back to the host.

* If the sampler chain starts with an unsupported sampler (e.g.,
  `penalties`), all sampling runs on the CPU. Note that this is
  currently the case with the default sampler so to use backend sampling
  it is required to specify a sampler chain. See below for an example.

The following shows how llama-cli can be run with backend sampling:
```console
$ llama-cli -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf \
    --prompt 'What is the capital of Sweden?' \
    -n 20 \
    -no-cnv \
    --verbose-prompt \
    -ngl 40 \
    --backend-sampling \
    --samplers 'top_k;temperature'
```
In this case the all sampling will happen on the backend since both
`top_k` and `temperature` are supported backend samplers.

To enable a partial backend sampling (hybrid sampling), for example
running `top_k` and `temperature` on the backend and `typ_p` on the CPU
the following sampler chain could be specified:
```console
$ llama-cli -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf \
    --prompt 'What is the capital of Sweden?' \
    -n 20 \
    -no-cnv \
    --verbose-prompt \
    -ngl 40 \
    --backend-sampling \
    --samplers 'top_k;temperature;top_p'
```

If this looks good then I'll follow up with updates the llama-cli and
llama-server documentation to reflect these changes.
---
 common/arg.cpp                                |   8 -
 common/common.cpp                             |  15 +-
 common/common.h                               |   2 -
 common/sampling.cpp                           | 196 +++++++++++++++---
 examples/batched/README.md                    |  47 +++--
 tools/server/public/index.html.gz             | Bin 1123267 -> 1126465 bytes
 tools/server/server-task.cpp                  |   4 -
 tools/server/server.cpp                       |  12 +-
 .../app/chat/ChatSettings/ChatSettings.svelte |   9 -
 .../ChatSettings/ChatSettingsFields.svelte    |   7 +-
 .../src/lib/constants/settings-config.ts      |   3 -
 tools/server/webui/src/lib/services/chat.ts   |   2 -
 .../webui/src/lib/stores/chat.svelte.ts       |   3 -
 tools/server/webui/src/lib/types/api.d.ts     |   1 -
 .../server/webui/src/lib/types/settings.d.ts  |   1 -
 15 files changed, 214 insertions(+), 96 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index c7d4b22c9bc..e29c3619b65 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1520,14 +1520,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.backend_sampling = true;
         }
     ).set_sparam());
-    add_opt(common_arg(
-        {"--backend-dist"},
-        "perform final (distribution) sampling on backend (default: disabled)",
-        [](common_params & params) {
-            params.sampling.backend_dist = true;
-            params.sampling.backend_sampling = true;
-        }
-    ).set_sparam());
     add_opt(common_arg(
         {"--pooling"}, "{none,mean,cls,last,rank}",
         "pooling type for embeddings, use model default if unspecified",
diff --git a/common/common.cpp b/common/common.cpp
index ff07ba4f23b..bf813707304 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1021,12 +1021,17 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     // backend sampling initialization
     if (params.sampling.backend_sampling) {
-        iparams.samplers_seq_config.resize(cparams.n_seq_max);
-        for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
-            iparams.samplers_seq_config[i] = { i, common_sampler_backend_init(model, params.sampling) };
+        llama_sampler * backend_chain = common_sampler_backend_init(model, params.sampling);
+        if (backend_chain != nullptr) {
+            iparams.samplers_seq_config.resize(cparams.n_seq_max);
+            for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
+                iparams.samplers_seq_config[i] = { i, llama_sampler_clone(backend_chain) };
+            }
+            cparams.samplers   = iparams.samplers_seq_config.data();
+            cparams.n_samplers = cparams.n_seq_max;
+
+            llama_sampler_free(backend_chain);
         }
-        cparams.samplers   = iparams.samplers_seq_config.data();
-        cparams.n_samplers = cparams.n_seq_max;
     }
 
     llama_context * lctx = llama_init_from_model(model, cparams);
diff --git a/common/common.h b/common/common.h
index e48fe336ea1..8e78ab32ead 100644
--- a/common/common.h
+++ b/common/common.h
@@ -213,9 +213,7 @@ struct common_params_sampling {
     std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
     std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
 
-    // Backend sampling flags
     bool backend_sampling = false; // enable backend sampling
-    bool backend_dist     = false; // backend performs final sampling (dist)
 
     // print the parameters into a string
     std::string print() const;
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 0a2be6bf7df..c1167719819 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -105,7 +105,8 @@ struct common_sampler {
     common_params_sampling params;
 
     struct llama_sampler * grmr;
-    struct llama_sampler * chain;
+    struct llama_sampler * chain;          // CPU sampling chain
+    struct llama_sampler * backend_chain;  // Backend sampling chain
 
     ring_buffer<llama_token> prev;
 
@@ -118,6 +119,9 @@ struct common_sampler {
 
         llama_sampler_reset(grmr);
         llama_sampler_reset(chain);
+        if (backend_chain) {
+            llama_sampler_reset(backend_chain);
+        }
     }
 
     void set_logits(struct llama_context * ctx, int idx) {
@@ -165,6 +169,20 @@ static bool sampler_enabled(const struct common_params_sampling & params, enum c
     return std::find(params.samplers.begin(), params.samplers.end(), type) != params.samplers.end();
 }
 
+static bool sampler_backend_supported(enum common_sampler_type type) {
+    switch (type) {
+        case COMMON_SAMPLER_TYPE_TOP_K:
+        case COMMON_SAMPLER_TYPE_TEMPERATURE:
+            return true;
+        default:
+            return false;
+    }
+}
+
+static bool has_logit_bias(const struct common_params_sampling & params) {
+    return !params.logit_bias.empty();
+}
+
 std::string common_params_sampling::print() const {
     char result[1024];
 
@@ -249,22 +267,86 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
     }
 
     auto * result = new common_sampler {
-        /* .params = */ params,
-        /* .grmr   = */ grmr,
-        /* .chain  = */ llama_sampler_chain_init(lparams),
-        /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
-        /* .cur    = */ {},
-        /* .cur_p  = */ {},
+        /* .params        = */ params,
+        /* .grmr          = */ grmr,
+        /* .chain         = */ llama_sampler_chain_init(lparams),
+        /* .backend_chain = */ nullptr,
+        /* .prev          = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
+        /* .cur           = */ {},
+        /* .cur_p         = */ {},
     };
 
-    llama_sampler_chain_add(result->chain,
-            llama_sampler_init_logit_bias(
-                llama_vocab_n_tokens(vocab),
-                params.logit_bias.size(),
-                params.logit_bias.data()));
+    size_t backend_sampler_count = 0;
+    if (params.backend_sampling && params.mirostat == 0) {
+        if (has_logit_bias(params)) {
+            backend_sampler_count++;
+        }
+
+        // Find the longest contiguous chain of backend-supported samplers from the start
+        for (const auto & sampler_type : params.samplers) {
+            if (sampler_backend_supported(sampler_type)) {
+                backend_sampler_count++;
+            } else {
+                break;
+            }
+        }
+    }
+
+    // If the samplers combination is supported then we can build the backend chain.
+    if (backend_sampler_count > 0 || (params.backend_sampling && has_logit_bias(params))) {
+        llama_sampler_chain_params backend_params = llama_sampler_chain_default_params();
+        backend_params.no_perf = params.no_perf;
+        result->backend_chain = llama_sampler_chain_init(backend_params);
+
+        if (has_logit_bias(params)) {
+            llama_sampler_chain_add(result->backend_chain,
+                llama_sampler_backend_init_logit_bias(
+                    llama_vocab_n_tokens(vocab),
+                    params.logit_bias.size(),
+                    params.logit_bias.data()));
+        }
+
+        size_t backend_idx = 0;
+        for (const auto & sampler_type : params.samplers) {
+            if (backend_idx >= backend_sampler_count - has_logit_bias(params)) {
+                break;
+            }
+
+            switch (sampler_type) {
+                case COMMON_SAMPLER_TYPE_TOP_K:
+                    if (params.top_k > 0) {
+                        llama_sampler_chain_add(result->backend_chain, llama_sampler_backend_init_top_k(params.top_k));
+                    }
+                    backend_idx++;
+                    break;
+                case COMMON_SAMPLER_TYPE_TEMPERATURE:
+                    if (params.temp > 0.0f) {
+                        llama_sampler_chain_add(result->backend_chain, llama_sampler_backend_init_temp(params.temp));
+                    }
+                    backend_idx++;
+                    break;
+                default:
+                    GGML_ASSERT(false && "unsupported backend sampler");
+            }
+        }
+    }
+
+    size_t cpu_start_idx = backend_sampler_count - has_logit_bias(params);
+    bool cpu_has_samplers = cpu_start_idx < params.samplers.size();
+
+    // Build CPU chain
+    if (!params.backend_sampling || !has_logit_bias(params)) {
+        llama_sampler_chain_add(result->chain,
+                llama_sampler_init_logit_bias(
+                    llama_vocab_n_tokens(vocab),
+                    params.logit_bias.size(),
+                    params.logit_bias.data()));
+    }
 
     if (params.mirostat == 0) {
-        for (const auto & cnstr : params.samplers) {
+        // Add remaining CPU samplers
+        for (size_t i = cpu_start_idx; i < params.samplers.size(); i++) {
+            const auto & cnstr = params.samplers[i];
             switch (cnstr) {
                 case COMMON_SAMPLER_TYPE_DRY:
                     {
@@ -308,7 +390,13 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                     GGML_ASSERT(false && "unknown sampler type");
             }
         }
-        llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
+
+        // If all samplers are on backend, add dist to backend; otherwise add to CPU
+        if (result->backend_chain && !cpu_has_samplers) {
+            llama_sampler_chain_add(result->backend_chain, llama_sampler_backend_init_dist(params.seed));
+        } else {
+            llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
+        }
     } else if (params.mirostat == 1) {
         llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
         llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
@@ -323,36 +411,74 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 }
 
 struct llama_sampler * common_sampler_backend_init(const struct llama_model * model, const struct common_params_sampling & params) {
-    if (!params.backend_sampling) {
+    if (!params.backend_sampling || params.mirostat != 0) {
         return nullptr;
     }
+
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
+    // Determine the split point for backend sampling using the same logic as common_sampler_init
+    size_t backend_sampler_count = 0;
+    if (has_logit_bias(params)) {
+        backend_sampler_count++;
+    }
+
+    // Find the longest contiguous chain of backend-supported samplers from the start
+    for (const auto & sampler_type : params.samplers) {
+        if (sampler_backend_supported(sampler_type)) {
+            backend_sampler_count++;
+        } else {
+            break;
+        }
+    }
+
+    if (backend_sampler_count == 0 && !has_logit_bias(params)) {
+        return nullptr;
+    }
+
     llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
     chain_params.no_perf = params.no_perf;
 
     struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
 
-    const bool enable_temp  = params.temp  > 0.0f && sampler_enabled(params, COMMON_SAMPLER_TYPE_TEMPERATURE);
-    const bool enable_top_k = params.top_k > 0    && sampler_enabled(params, COMMON_SAMPLER_TYPE_TOP_K);
-    const bool enable_dist  = params.backend_dist;
-
-    if (!params.logit_bias.empty()) {
+    // Add logit_bias to backend chain if present
+    if (has_logit_bias(params)) {
         llama_sampler_chain_add(chain, llama_sampler_backend_init_logit_bias(
                     llama_vocab_n_tokens(vocab),
                     params.logit_bias.size(),
                     params.logit_bias.data()));
     }
 
-    if (enable_temp) {
-        llama_sampler_chain_add(chain, llama_sampler_backend_init_temp(params.temp));
-    }
+    size_t backend_idx = 0;
+    for (const auto & sampler_type : params.samplers) {
+        if (backend_idx >= backend_sampler_count - has_logit_bias(params)) {
+            break;
+        }
 
-    if (enable_top_k) {
-        llama_sampler_chain_add(chain, llama_sampler_backend_init_top_k(params.top_k));
+        switch (sampler_type) {
+            case COMMON_SAMPLER_TYPE_TOP_K:
+                if (params.top_k > 0) {
+                    llama_sampler_chain_add(chain, llama_sampler_backend_init_top_k(params.top_k));
+                }
+                backend_idx++;
+                break;
+            case COMMON_SAMPLER_TYPE_TEMPERATURE:
+                if (params.temp > 0.0f) {
+                    llama_sampler_chain_add(chain, llama_sampler_backend_init_temp(params.temp));
+                }
+                backend_idx++;
+                break;
+            default:
+                GGML_ASSERT(false && "unsupported backend sampler");
+        }
     }
 
-    if (enable_dist) {
+    // Determine if we should add dist sampler to backend chain
+    // Only add it if all samplers from params.samplers are on the backend
+    size_t cpu_start_idx = backend_sampler_count - has_logit_bias(params);
+    bool cpu_has_samplers = cpu_start_idx < params.samplers.size();
+
+    if (!cpu_has_samplers) {
         llama_sampler_chain_add(chain, llama_sampler_backend_init_dist(params.seed));
     }
 
@@ -362,9 +488,12 @@ struct llama_sampler * common_sampler_backend_init(const struct llama_model * mo
 void common_sampler_free(struct common_sampler * gsmpl) {
     if (gsmpl) {
         llama_sampler_free(gsmpl->grmr);
-
         llama_sampler_free(gsmpl->chain);
 
+        if (gsmpl->backend_chain) {
+            llama_sampler_free(gsmpl->backend_chain);
+        }
+
         delete gsmpl;
     }
 }
@@ -387,12 +516,13 @@ void common_sampler_reset(struct common_sampler * gsmpl) {
 
 struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
     return new common_sampler {
-        /* .params = */ gsmpl->params,
-        /* .grmr   = */ llama_sampler_clone(gsmpl->grmr),
-        /* .chain  = */ llama_sampler_clone(gsmpl->chain),
-        /* .prev   = */ gsmpl->prev,
-        /* .cur    = */ gsmpl->cur,
-        /* .cur_p  = */ gsmpl->cur_p,
+        /* .params        = */ gsmpl->params,
+        /* .grmr          = */ llama_sampler_clone(gsmpl->grmr),
+        /* .chain         = */ llama_sampler_clone(gsmpl->chain),
+        /* .backend_chain = */ gsmpl->backend_chain ? llama_sampler_clone(gsmpl->backend_chain) : nullptr,
+        /* .prev          = */ gsmpl->prev,
+        /* .cur           = */ gsmpl->cur,
+        /* .cur_p         = */ gsmpl->cur_p,
     };
 }
 
diff --git a/examples/batched/README.md b/examples/batched/README.md
index f10639220e2..a68b45b2901 100644
--- a/examples/batched/README.md
+++ b/examples/batched/README.md
@@ -45,25 +45,38 @@ llama_print_timings:       total time =  4156.04 ms
 
 ### Using backend samplers
 It is possible to run this example using backend samplers so that sampling is
-performed on the backend device, like a GPU.
+performed on a backend device, like a GPU.
 ```bash
 ./llama-batched \
     -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf -p "Hello my name is" \
-    -np 4 -kvu \
-    --backend_sampling --top-k 80 --backend_dist
+    -np 4 \
+    -kvu \
+    --backend_sampling \
+    --samplers 'top_k;temperature' \
+    --top-k 80
 ```
-The `--verbose` flag can be added to see more detailed output and also show
-that the backend samplers are being used. The above example will perform distribution
-sampling on the backend device and only transfer the sampled token ids back to the host.
+The samplers specified with `--samplers` must be supported by the backend and
+this is why we are explicitly specifying only `top_k` and `temperature` here as
+at the time of writing these are supported.
 
-It is also possible to perform partial sampling on the backend, and then allow CPU samplers
-to process those results further. This is sometimes referred to as hybrid sampling.
-For an example of this we can remove `--backend_dist` from the above command:
-```bash
-./llama-batched \
-    -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf -p "Hello my name is" \
-    -np 4 -kvu \
-    --backend_sampling --top-k 80 -v
-```
-This will perform the top-k filtering on the backend device, and then transfer the filtered logits
-back to the host for sampling.
+The `--verbose` flag can be added to see more detailed output and also show
+that the backend samplers are being used.
+
+With `--backend_sampling` enabled, the sampler chain is automatically analyzed
+to determine which samplers can run on the backend. The system finds the longest
+contiguous chain of backend-supported samplers from the start of the sampler
+sequence. For example:
+* If the chain is `top-k -> temperature -> top-p`, and both `top-k` and
+  `temperature` are backend-supported but `top-p` is not, then `top-k` and
+  `temperature` will run on the backend, while `top-p` and subsequent samplers
+  run on the CPU.
+* If all configured samplers are supported, the final distribution sampling will
+  also happen on the backend, transferring only the sampled token IDs back to the
+  host.
+* If the sampler chain starts with an unsupported sampler (e.g., `penalties`),
+  all sampling runs on the CPU.
+
+**Note:** The default sampler chain includes `penalties` as the first sampler,
+which is not backend-supported yet. To use backend sampling, you must explicitly
+configure a sampler chain that starts with backend-supported samplers using
+`--samplers` like shown above.
diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
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FF#!M3it_*f

diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index 9503262fe4d..d083777fa7e 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -79,7 +79,6 @@ json task_params::to_json(bool only_metrics) const {
             {"timings_per_token",         timings_per_token},
             {"post_sampling_probs",       post_sampling_probs},
             {"backend_sampling",          sampling.backend_sampling},
-            {"backend_dist",              sampling.backend_dist},
             {"lora",                      lora},
         };
     }
@@ -139,7 +138,6 @@ json task_params::to_json(bool only_metrics) const {
         {"timings_per_token",         timings_per_token},
         {"post_sampling_probs",       post_sampling_probs},
         {"backend_sampling",          sampling.backend_sampling},
-        {"backend_dist",              sampling.backend_dist},
         {"lora",                      lora},
     };
 }
@@ -210,9 +208,7 @@ task_params server_task::params_from_json_cmpl(
     params.post_sampling_probs         = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);
 
     const bool request_backend_sampling    = json_value(data, "backend_sampling",        defaults.sampling.backend_sampling);
-    const bool request_backend_dist        = json_value(data, "backend_dist",            defaults.sampling.backend_dist);
     params.sampling.backend_sampling       = defaults.sampling.backend_sampling && request_backend_sampling;
-    params.sampling.backend_dist           = params.sampling.backend_sampling && request_backend_dist;
 
     params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
     params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 37882f5ff11..48a7cf2c826 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -1047,9 +1047,16 @@ struct server_context {
         }
 
         llama_sampler * backend_chain = common_sampler_backend_init(model, sampling);
+        // The sampler types configured with --samplers might not be supported
+        // by backend samplers in which case we disable backend sampling and
+        // fallback to CPU only sampling.
         if (backend_chain == nullptr) {
-            SLT_ERR(slot, "%s", "failed to initialize backend sampler\n");
-            return false;
+            if (slot.backend_sampler != nullptr) {
+                llama_set_backend_sampler(ctx, slot.id, nullptr);
+                slot.backend_sampler = nullptr;
+            }
+            SLT_INF(slot, "%s", "no backend samplers configured (sampler chain doesn't start with backend-supported samplers)\n");
+            return true;
         }
 
         if (slot.backend_sampler != nullptr) {
@@ -1059,6 +1066,7 @@ struct server_context {
 
         slot.backend_sampler = backend_chain;
         llama_set_backend_sampler(ctx, slot.id, backend_chain);
+        SLT_INF(slot, "%s", "configured backend samplers\n");
         return true;
     }
 
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
index 78ad97a41c6..b6f345c241e 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
@@ -179,11 +179,6 @@
 					key: 'backend_sampling',
 					label: 'Backend sampling',
 					type: 'checkbox'
-				},
-				{
-					key: 'backend_dist',
-					label: 'Backend dist sampling',
-					type: 'checkbox'
 				}
 			]
 		},
@@ -297,10 +292,6 @@
 
 	function handleConfigChange(key: string, value: string | boolean) {
 		localConfig[key] = value;
-
-		if (key === 'backend_sampling' && value === false) {
-			localConfig.backend_dist = false;
-		}
 	}
 
 	function handleReset() {
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
index 1bafaf137a4..57862bc05ea 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
@@ -211,8 +211,7 @@
 			{/if}
 		{:else if field.type === 'checkbox'}
 			{@const pdfDisabled = field.key === 'pdfAsImage' && !supportsVision()}
-			{@const backendDistDisabled = field.key === 'backend_dist' && !localConfig.backend_sampling}
-			{@const isDisabled = pdfDisabled || backendDistDisabled}
+			{@const isDisabled = pdfDisabled}
 
 			<div class="flex items-start space-x-3">
 				<Checkbox
@@ -246,10 +245,6 @@
 							PDF-to-image processing requires a vision-capable model. PDFs will be processed as
 							text.
 						</p>
-					{:else if backendDistDisabled}
-						<p class="text-xs text-muted-foreground">
-							Enable GPU sampling to allow GPU dist sampling.
-						</p>
 					{/if}
 				</div>
 			</div>
diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts
index 67323bb720d..f8187bbf49f 100644
--- a/tools/server/webui/src/lib/constants/settings-config.ts
+++ b/tools/server/webui/src/lib/constants/settings-config.ts
@@ -20,7 +20,6 @@ export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean> =
 	// make sure these default values are in sync with `common.h`
 	samplers: 'top_k;typ_p;top_p;min_p;temperature',
 	backend_sampling: false,
-	backend_dist: false,
 	temperature: 0.8,
 	dynatemp_range: 0.0,
 	dynatemp_exponent: 1.0,
@@ -56,8 +55,6 @@ export const SETTING_CONFIG_INFO: Record<string, string> = {
 		'The order at which samplers are applied, in simplified way. Default is "top_k;typ_p;top_p;min_p;temperature": top_k->typ_p->top_p->min_p->temperature',
 	backend_sampling:
 		'Enable backend-based samplers. When enabled, supported samplers run on the accelerator backend for faster sampling.',
-	backend_dist:
-		'Perform the final distribution sampling step on the backend. Requires backend sampling to be enabled.',
 	temperature:
 		'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
 	dynatemp_range:
diff --git a/tools/server/webui/src/lib/services/chat.ts b/tools/server/webui/src/lib/services/chat.ts
index 5cda8c88686..66134a524e4 100644
--- a/tools/server/webui/src/lib/services/chat.ts
+++ b/tools/server/webui/src/lib/services/chat.ts
@@ -99,7 +99,6 @@ export class ChatService {
 			// Other parameters
 			samplers,
 			backend_sampling,
-			backend_dist,
 			custom,
 			timings_per_token
 		} = options;
@@ -185,7 +184,6 @@ export class ChatService {
 		}
 
 		if (backend_sampling !== undefined) requestBody.backend_sampling = backend_sampling;
-		if (backend_dist !== undefined) requestBody.backend_dist = backend_dist;
 
 		if (timings_per_token !== undefined) requestBody.timings_per_token = timings_per_token;
 
diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts
index e00994bc469..d8978558e1b 100644
--- a/tools/server/webui/src/lib/stores/chat.svelte.ts
+++ b/tools/server/webui/src/lib/stores/chat.svelte.ts
@@ -301,9 +301,6 @@ class ChatStore {
 		if (currentConfig.backend_sampling !== undefined) {
 			apiOptions.backend_sampling = Boolean(currentConfig.backend_sampling);
 		}
-		if (currentConfig.backend_dist !== undefined) {
-			apiOptions.backend_dist = Boolean(currentConfig.backend_dist);
-		}
 		if (currentConfig.custom) {
 			apiOptions.custom = currentConfig.custom;
 		}
diff --git a/tools/server/webui/src/lib/types/api.d.ts b/tools/server/webui/src/lib/types/api.d.ts
index 149d4fb118f..592923b6536 100644
--- a/tools/server/webui/src/lib/types/api.d.ts
+++ b/tools/server/webui/src/lib/types/api.d.ts
@@ -182,7 +182,6 @@ export interface ApiChatCompletionRequest {
 	// Sampler configuration
 	samplers?: string[];
 	backend_sampling?: boolean;
-	backend_dist?: boolean;
 	// Custom parameters (JSON string)
 	custom?: Record<string, unknown>;
 	timings_per_token?: boolean;
diff --git a/tools/server/webui/src/lib/types/settings.d.ts b/tools/server/webui/src/lib/types/settings.d.ts
index e68d107faa3..25b232582ba 100644
--- a/tools/server/webui/src/lib/types/settings.d.ts
+++ b/tools/server/webui/src/lib/types/settings.d.ts
@@ -38,7 +38,6 @@ export interface SettingsChatServiceOptions {
 	// Sampler configuration
 	samplers?: string | string[];
 	backend_sampling?: boolean;
-	backend_dist?: boolean;
 	// Custom parameters
 	custom?: string;
 	timings_per_token?: boolean;

From f23b306cc5f76941d07c433f9d3a789b1b8b0988 Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Fri, 21 Nov 2025 12:01:32 +0100
Subject: [PATCH 37/59] CUDA: Add top-k implementation

---
 cmake/CPM.cmake                   |  25 +++++++
 ggml/src/ggml-cuda/CMakeLists.txt |  17 +++++
 ggml/src/ggml-cuda/argsort.cu     |  26 ++++----
 ggml/src/ggml-cuda/argsort.cuh    |  16 +++++
 ggml/src/ggml-cuda/ggml-cuda.cu   |   6 ++
 ggml/src/ggml-cuda/top-k.cu       | 104 ++++++++++++++++++++++++++++++
 ggml/src/ggml-cuda/top-k.cuh      |   3 +
 tests/test-backend-ops.cpp        |   5 ++
 8 files changed, 189 insertions(+), 13 deletions(-)
 create mode 100644 cmake/CPM.cmake
 create mode 100644 ggml/src/ggml-cuda/top-k.cu
 create mode 100644 ggml/src/ggml-cuda/top-k.cuh

diff --git a/cmake/CPM.cmake b/cmake/CPM.cmake
new file mode 100644
index 00000000000..978a1b7e394
--- /dev/null
+++ b/cmake/CPM.cmake
@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: MIT
+#
+# SPDX-FileCopyrightText: Copyright (c) 2019-2023 Lars Melchior and contributors
+
+# TODO: Remove this file once CCCL 3.2 is released & bundled with the CUDA Toolkit
+set(CPM_DOWNLOAD_VERSION 0.42.0)
+set(CPM_HASH_SUM "2020b4fc42dba44817983e06342e682ecfc3d2f484a581f11cc5731fbe4dce8a")
+
+if(CPM_SOURCE_CACHE)
+  set(CPM_DOWNLOAD_LOCATION "${CPM_SOURCE_CACHE}/cpm/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
+elseif(DEFINED ENV{CPM_SOURCE_CACHE})
+  set(CPM_DOWNLOAD_LOCATION "$ENV{CPM_SOURCE_CACHE}/cpm/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
+else()
+  set(CPM_DOWNLOAD_LOCATION "${CMAKE_BINARY_DIR}/cmake/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
+endif()
+
+# Expand relative path. This is important if the provided path contains a tilde (~)
+get_filename_component(CPM_DOWNLOAD_LOCATION ${CPM_DOWNLOAD_LOCATION} ABSOLUTE)
+
+file(DOWNLOAD
+     https://github.com/cpm-cmake/CPM.cmake/releases/download/v${CPM_DOWNLOAD_VERSION}/CPM.cmake
+     ${CPM_DOWNLOAD_LOCATION} EXPECTED_HASH SHA256=${CPM_HASH_SUM}
+)
+
+include(${CPM_DOWNLOAD_LOCATION})
diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt
index 67af1d8ccc1..05a9b49e83a 100644
--- a/ggml/src/ggml-cuda/CMakeLists.txt
+++ b/ggml/src/ggml-cuda/CMakeLists.txt
@@ -2,6 +2,17 @@ cmake_minimum_required(VERSION 3.18)  # for CMAKE_CUDA_ARCHITECTURES
 
 find_package(CUDAToolkit)
 
+# Remove once CCCL 3.2 has been released and bundled with CUDA Toolkit
+if (GGML_CUDA_CUB_3DOT2)
+    include(../../../cmake/CPM.cmake)
+    # This will automatically clone CCCL from GitHub and make the exported cmake targets available
+    CPMAddPackage(
+        NAME CCCL
+        GITHUB_REPOSITORY nvidia/cccl
+        GIT_TAG v3.2.0-rc0 # Fetches the latest commit on the main branch
+    )
+endif()
+
 if (CUDAToolkit_FOUND)
     message(STATUS "CUDA Toolkit found")
 
@@ -102,6 +113,9 @@ if (CUDAToolkit_FOUND)
             # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library
             target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas)
         else ()
+            if (GGML_CUDA_CUB_3DOT2)
+                target_link_libraries(ggml-cuda PRIVATE  CCCL::CCCL)
+            endif()
             if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "10.1")
                 target_link_libraries(ggml-cuda PRIVATE  CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
             else()
@@ -109,6 +123,9 @@ if (CUDAToolkit_FOUND)
             endif()
         endif()
     else()
+        if (GGML_CUDA_CUB_3DOT2)
+            target_link_libraries(ggml-cuda PRIVATE  CCCL::CCCL)
+        endif()
         target_link_libraries(ggml-cuda PRIVATE CUDA::cudart CUDA::cublas)
     endif()
 
diff --git a/ggml/src/ggml-cuda/argsort.cu b/ggml/src/ggml-cuda/argsort.cu
index b8003c48c51..eb83e6547a5 100644
--- a/ggml/src/ggml-cuda/argsort.cu
+++ b/ggml/src/ggml-cuda/argsort.cu
@@ -22,13 +22,13 @@ static __global__ void init_offsets(int * offsets, const int ncols, const int nr
 }
 
 #ifdef GGML_CUDA_USE_CUB
-static void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
-                                     const float *    x,
-                                     int *            dst,
-                                     const int        ncols,
-                                     const int        nrows,
-                                     ggml_sort_order  order,
-                                     cudaStream_t     stream) {
+void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
+                              const float *    x,
+                              int *            dst,
+                              const int        ncols,
+                              const int        nrows,
+                              ggml_sort_order  order,
+                              cudaStream_t     stream) {
     ggml_cuda_pool_alloc<int>   temp_indices_alloc(pool, ncols * nrows);
     ggml_cuda_pool_alloc<float> temp_keys_alloc(pool, ncols * nrows);
     ggml_cuda_pool_alloc<int>   offsets_alloc(pool, nrows + 1);
@@ -162,12 +162,12 @@ static int next_power_of_2(int x) {
     return n;
 }
 
-static void argsort_f32_i32_cuda_bitonic(const float *   x,
-                                         int *           dst,
-                                         const int       ncols,
-                                         const int       nrows,
-                                         ggml_sort_order order,
-                                         cudaStream_t    stream) {
+void argsort_f32_i32_cuda_bitonic(const float *   x,
+                                  int *           dst,
+                                  const int       ncols,
+                                  const int       nrows,
+                                  ggml_sort_order order,
+                                  cudaStream_t    stream) {
     // bitonic sort requires ncols to be power of 2
     const int ncols_pad = next_power_of_2(ncols);
 
diff --git a/ggml/src/ggml-cuda/argsort.cuh b/ggml/src/ggml-cuda/argsort.cuh
index 68a001547ff..22b7306f202 100644
--- a/ggml/src/ggml-cuda/argsort.cuh
+++ b/ggml/src/ggml-cuda/argsort.cuh
@@ -1,3 +1,19 @@
 #include "common.cuh"
 
 void ggml_cuda_op_argsort(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+#ifdef GGML_CUDA_USE_CUB
+void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
+                              const float *    x,
+                              int *            dst,
+                              const int        ncols,
+                              const int        nrows,
+                              ggml_sort_order  order,
+                              cudaStream_t     stream);
+#endif  // GGML_CUDA_USE_CUB
+void argsort_f32_i32_cuda_bitonic(const float *   x,
+                                  int *           dst,
+                                  const int       ncols,
+                                  const int       nrows,
+                                  ggml_sort_order order,
+                                  cudaStream_t    stream);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 98b0cea33a0..ea4763fa6e6 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -44,6 +44,7 @@
 #include "ggml-cuda/ssm-scan.cuh"
 #include "ggml-cuda/sum.cuh"
 #include "ggml-cuda/sumrows.cuh"
+#include "ggml-cuda/top-k.cuh"
 #include "ggml-cuda/mean.cuh"
 #include "ggml-cuda/tsembd.cuh"
 #include "ggml-cuda/topk-moe.cuh"
@@ -2694,6 +2695,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_SSM_SCAN:
             ggml_cuda_op_ssm_scan(ctx, dst);
             break;
+        case GGML_OP_TOP_K:
+            ggml_cuda_op_top_k(ctx, dst);
+            break;
         case GGML_OP_ARGSORT:
             ggml_cuda_op_argsort(ctx, dst);
             break;
@@ -4233,6 +4237,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_CUMSUM:
         case GGML_OP_SUM:
             return ggml_is_contiguous_rows(op->src[0]);
+        case GGML_OP_TOP_K:
+            return true;
         case GGML_OP_ARGSORT:
 #ifndef GGML_CUDA_USE_CUB
             return op->src[0]->ne[0] <= 1024;
diff --git a/ggml/src/ggml-cuda/top-k.cu b/ggml/src/ggml-cuda/top-k.cu
new file mode 100644
index 00000000000..ae5989cacc8
--- /dev/null
+++ b/ggml/src/ggml-cuda/top-k.cu
@@ -0,0 +1,104 @@
+#include "argsort.cuh"
+#include "top-k.cuh"
+
+#ifdef GGML_CUDA_USE_CUB
+#    include <cub/cub.cuh>
+#    if (CCCL_MAJOR_VERSION >= 3 && CCCL_MINOR_VERSION >= 2)
+#        define CUB_TOP_K_AVAILABLE
+using namespace cub;
+#    endif  // CCCL_MAJOR_VERSION >= 3 && CCCL_MINOR_VERSION >= 2
+#endif      // GGML_CUDA_USE_CUB
+
+#ifdef CUB_TOP_K_AVAILABLE
+static __global__ void init_indices(int * indices, const int ncols) {
+    const int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (col < ncols) {
+        indices[col] = col;
+    }
+}
+
+static void top_k_cub(ggml_cuda_pool & pool,
+                      const float *    src,
+                      int *            dst,
+                      const int        ncols,
+                      const int        k,
+                      cudaStream_t     stream) {
+    auto requirements = cuda::execution::require(cuda::execution::determinism::not_guaranteed,
+                                                 cuda::execution::output_ordering::unsorted);
+    auto stream_env   = cuda::stream_ref{ stream };
+    auto env          = cuda::std::execution::env{ stream_env, requirements };
+
+    ggml_cuda_pool_alloc<int>   temp_indices_alloc(pool, ncols);
+    ggml_cuda_pool_alloc<float> temp_keys_alloc(pool, ncols);
+
+    int *   temp_indices = temp_indices_alloc.get();
+    float * temp_keys    = temp_keys_alloc.get();
+
+    static const int block_size = 256;
+    const dim3       grid_size((ncols + block_size - 1) / block_size, 1);
+    init_indices<<<grid_size, block_size, 0, stream>>>(temp_indices, ncols);
+
+    CUDA_CHECK(cudaMemcpyAsync(temp_keys, src, ncols * sizeof(float), cudaMemcpyDeviceToDevice, stream));
+
+    size_t temp_storage_bytes = 0;
+    DeviceTopK::MaxPairs(nullptr, temp_storage_bytes, temp_keys, temp_keys, temp_indices, dst, ncols, k, env);
+
+    ggml_cuda_pool_alloc<uint8_t> temp_storage_alloc(pool, temp_storage_bytes);
+    void *                        d_temp_storage = temp_storage_alloc.get();
+
+    DeviceTopK::MaxPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys, temp_indices, dst, ncols, k, env);
+}
+
+#else
+
+static int next_power_of_2(int x) {
+    int n = 1;
+    while (n < x) {
+        n *= 2;
+    }
+    return n;
+}
+
+#endif  // CUB_TOP_K_AVAILABLE
+
+void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0   = dst->src[0];
+    const float *       src0_d = (const float *) src0->data;
+    int *               dst_d  = (int *) dst->data;
+    cudaStream_t        stream = ctx.stream();
+
+    // are these asserts truly necessary?
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_I32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    const int64_t    ncols = src0->ne[0];
+    const int64_t    nrows = ggml_nrows(src0);
+    const int64_t    k     = dst->ne[0];
+    ggml_cuda_pool & pool  = ctx.pool();
+#ifdef CUB_TOP_K_AVAILABLE
+    // TODO: Switch to `DeviceSegmentedTopK` for multi-row TopK once implemented
+    // https://github.com/NVIDIA/cccl/issues/6391
+    // TODO: investigate if there exists a point where parallelized argsort is faster than sequential top-k
+    for (int i = 0; i < nrows; i++) {
+        top_k_cub(pool, src0_d + i * ncols, dst_d + i * k, ncols, k, stream);
+    }
+#else
+    // Fall back to argsort + copy
+    const int    ncols_pad      = next_power_of_2(ncols);
+    const size_t shared_mem     = ncols_pad * sizeof(int);
+    const size_t max_shared_mem = ggml_cuda_info().devices[ggml_cuda_get_device()].smpb;
+
+    ggml_cuda_pool_alloc<int> temp_dst_alloc(pool, ncols * nrows);
+    int *                     tmp_dst = temp_dst_alloc.get();
+
+    if (shared_mem > max_shared_mem || ncols > 1024) {
+        argsort_f32_i32_cuda_cub(pool, src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
+    } else {
+        argsort_f32_i32_cuda_bitonic(src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
+    }
+    CUDA_CHECK(cudaMemcpy2DAsync(dst_d, k * sizeof(int), tmp_dst, ncols * sizeof(int), k * sizeof(int), nrows,
+                                 cudaMemcpyDeviceToDevice, stream));
+#endif  // CUB_TOP_K_AVAILABLE
+}
diff --git a/ggml/src/ggml-cuda/top-k.cuh b/ggml/src/ggml-cuda/top-k.cuh
new file mode 100644
index 00000000000..85a95bc1bcd
--- /dev/null
+++ b/ggml/src/ggml-cuda/top-k.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
\ No newline at end of file
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index eb49eb41a6d..f4072bf4a61 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8035,6 +8035,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {200000, 1, 1, 1}));
     test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {200000, 16, 1, 1}));
     test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {65000, 16, 1, 1}, 40));
+    test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {65000, 1, 1, 1}, 40));
+    test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {65000, 1, 1, 1}, 1));
+    test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {200000, 1, 1, 1}, 400));
+    test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {200000, 1, 1, 1}, 40));
+    test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {200000, 1, 1, 1}, 1));
 
     return test_cases;
 }

From b45d504e70e53a3a5a2f66d52b0174666ae789f2 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 26 Nov 2025 10:50:58 +0100
Subject: [PATCH 38/59] sampling : add min-p backend sampler

---
 common/sampling.cpp            |  13 ++++
 include/llama.h                |   3 +
 src/llama-backend-sampler.cpp  | 115 +++++++++++++++++++++++++++++++++
 tests/test-backend-sampler.cpp |  57 ++++++++++++++++
 4 files changed, 188 insertions(+)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index c1167719819..9f7795aa416 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -173,6 +173,7 @@ static bool sampler_backend_supported(enum common_sampler_type type) {
     switch (type) {
         case COMMON_SAMPLER_TYPE_TOP_K:
         case COMMON_SAMPLER_TYPE_TEMPERATURE:
+        case COMMON_SAMPLER_TYPE_MIN_P:
             return true;
         default:
             return false;
@@ -325,6 +326,12 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                     }
                     backend_idx++;
                     break;
+                case COMMON_SAMPLER_TYPE_MIN_P:
+                    if (params.min_p > 0.0f) {
+                        llama_sampler_chain_add(result->backend_chain, llama_sampler_backend_init_min_p(params.min_p));
+                    }
+                    backend_idx++;
+                    break;
                 default:
                     GGML_ASSERT(false && "unsupported backend sampler");
             }
@@ -468,6 +475,12 @@ struct llama_sampler * common_sampler_backend_init(const struct llama_model * mo
                 }
                 backend_idx++;
                 break;
+            case COMMON_SAMPLER_TYPE_MIN_P:
+                if (params.min_p > 0.0f) {
+                    llama_sampler_chain_add(chain, llama_sampler_backend_init_min_p(params.min_p));
+                }
+                backend_idx++;
+                break;
             default:
                 GGML_ASSERT(false && "unsupported backend sampler");
         }
diff --git a/include/llama.h b/include/llama.h
index d68504d8733..50a4cc7c139 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1405,6 +1405,9 @@ extern "C" {
     /// @details Distribution sampling on backend - final sampling step that selects a token
     LLAMA_API struct llama_sampler * llama_sampler_backend_init_dist(uint32_t seed);
 
+    /// @details Min-P filtering on backend - filter tokens with a probability less than p times the maximum probability.
+    LLAMA_API struct llama_sampler * llama_sampler_backend_init_min_p(float p);
+
     // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
     LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
 
diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
index 361e48ed688..34361d925a7 100644
--- a/src/llama-backend-sampler.cpp
+++ b/src/llama-backend-sampler.cpp
@@ -488,3 +488,118 @@ struct llama_sampler * llama_sampler_backend_init_logit_bias(int32_t   n_vocab,
 
     return sampler;
 }
+
+struct llama_sampler_backend_min_p_ctx {
+    float p;
+
+    // Only required for checking operation support and can be removed later.
+    ggml_backend_dev_t device;
+};
+
+static void llama_sampler_backend_min_p_init_ggml(
+        struct llama_sampler           * smpl,
+        ggml_backend_buffer_type_t       buft) {
+    auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
+    sctx->device = ggml_backend_buft_get_device(buft);
+}
+
+static void llama_sampler_backend_min_p_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    GGML_UNUSED(gf);
+
+    auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
+
+    struct ggml_tensor * softmax = ggml_soft_max(ctx, ggml_data->logits);
+    ggml_set_name(softmax, "softmax");
+
+    // Get the sorted indices of the softmax probabilities in descending order.
+    struct ggml_tensor * sorted_idx = ggml_argsort(ctx, softmax, GGML_SORT_ORDER_DESC);
+    ggml_set_name(sorted_idx, "sorted_idx");
+
+    // Reshape into a row vector.
+    struct ggml_tensor * softmax_rows = ggml_reshape_2d(ctx, softmax, 1, softmax->ne[0]);
+    ggml_set_name(softmax_rows, "softmax_rows");
+
+    // Get the sorted probabilities using the sorted indices so that we can get
+    // the max probability value, which will be the first entry in sorted_probs.
+    struct ggml_tensor * sorted_probs = ggml_get_rows(ctx, softmax_rows, sorted_idx);
+    ggml_set_name(sorted_probs, "sorted_probs");
+
+    // Get the max probability value from sorted_probs.
+    struct ggml_tensor * p_max = ggml_view_1d(ctx, sorted_probs, 1, 0);
+    ggml_set_name(p_max, "p_max");
+
+    // Calculate the threshold value.
+    struct ggml_tensor * threshold = ggml_scale(ctx, p_max, sctx->p);
+    ggml_set_name(threshold, "min_p_threshold");
+
+    // Broadcast the threshold to match the shape of softmax.
+    struct ggml_tensor * threshold_b = ggml_repeat(ctx, threshold, softmax);
+    ggml_set_name(threshold_b, "min_p_threshold_b");
+
+    // Subtract the threshold from softmax probabilities.
+    struct ggml_tensor * sub = ggml_sub(ctx, softmax, threshold_b);
+
+    // Create a mask where probabilities below the threshold are 0 (discard),
+    // and others are 1 (keep).
+    struct ggml_tensor * mask = ggml_step(ctx, sub);
+    ggml_set_name(mask, "min_p_mask");
+
+    // Use ggml_scale_bias (output = (a * s) + b) which in this case becomes:
+    // min_p_bias = (mask * 1e9f) - 1e9f.
+    // So entries in the mask that we want to discard will become -1e9f, and
+    // others will be 0 (meaning that will not effect the logits).
+    const float large_val = 1e9f;
+    struct ggml_tensor * min_p_bias = ggml_scale_bias(ctx, mask, large_val, -large_val);
+    ggml_set_name(min_p_bias, "min_p_bias");
+
+    // Add the min_p bias to the logits.
+    ggml_data->logits = ggml_add(ctx, ggml_data->logits, min_p_bias);
+    ggml_set_name(ggml_data->logits, "min_p_logits");
+
+    ggml_build_forward_expand(gf, ggml_data->logits);
+}
+
+static const char * llama_sampler_backend_min_p_name(const struct llama_sampler *) {
+    return "backend-min-p";
+}
+
+static void llama_sampler_backend_min_p_free(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
+    delete sctx;
+}
+
+static struct llama_sampler * llama_sampler_backend_min_p_clone(const struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
+    return llama_sampler_backend_init_min_p(sctx->p);
+}
+
+struct llama_sampler * llama_sampler_backend_init_min_p(float p) {
+    static const llama_sampler_i iface = {
+        /*.name                =*/ llama_sampler_backend_min_p_name,
+        /*.accept              =*/ nullptr,
+        /*.apply               =*/ nullptr,
+        /*.reset               =*/ nullptr,
+        /*.clone               =*/ llama_sampler_backend_min_p_clone,
+        /*.free                =*/ llama_sampler_backend_min_p_free,
+        /*.apply_ggml          =*/ llama_sampler_backend_min_p_apply_ggml,
+        /*.accept_ggml         =*/ nullptr,
+        /*.set_input_ggml      =*/ nullptr,
+        /*.init_ggml           =*/ llama_sampler_backend_min_p_init_ggml,
+    };
+
+    auto * sctx = new llama_sampler_backend_min_p_ctx {
+        /*.p       =*/ p,
+        /*.device  =*/ nullptr,
+    };
+
+    auto * sampler = new llama_sampler {
+        /*.iface =*/ &iface,
+        /*.ctx   =*/ sctx,
+    };
+
+    return sampler;
+}
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index 19e9855aeda..b668b884857 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -416,6 +416,62 @@ static void test_backend_temp_sampling(const char * model_path) {
 
 }
 
+static void test_backend_min_p_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    const int seq_id = 0;
+    const float p = 0.1;
+    struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_min_p(p));
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{seq_id, "Hello"}})) {
+        return;
+    }
+
+    int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
+
+    float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);
+    uint32_t n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+
+    // Print the logits that are above the min-p threshold
+    std::vector<float> filtered_logits;
+    for (size_t i = 0; i < n_logits; ++i) {
+        if (logits[i] > -1e9f) {
+            filtered_logits.push_back(logits[i]);
+            //printf("min_p logit[%zu] = %.6f\n", i, logits[i]);
+        }
+    }
+    GGML_ASSERT(filtered_logits.size() < (size_t) test_ctx.n_vocab);
+
+    // Sample using CPU sampler for verification to inspect they are reasonable
+    struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+    llama_sampler_chain_add(chain, llama_sampler_init_dist(88));
+
+    llama_token token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+    const std::string token_str = test_ctx.token_to_piece(token, false);
+    printf("min-p cpu sampled token id:%d, string: '%s'\n", token, token_str.c_str());
+    GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+
+    // Decode and sampler 10 more tokens
+    for (int i = 0; i < 10; i++) {
+        int32_t loop_idx = test_ctx.idx_for_seq(seq_id);
+        llama_token token = llama_sampler_sample(chain, test_ctx.ctx, loop_idx);
+        printf("min-p gen step %d: token id :%5.d, string: %s\n", i, token, test_ctx.token_to_piece(token, false).c_str());
+        test_ctx.decode_token(token, 0);
+    }
+
+    printf("min-p sampling test PASSED\n");
+
+    llama_sampler_free(chain);
+}
+
 static void test_backend_multi_sequence_sampling(const char * model_path) {
     test_model_context test_ctx;
 
@@ -772,6 +828,7 @@ static const backend_test_case BACKEND_TESTS[] = {
     { "set_sampler",     test_backend_set_sampler,             true  },
     { "max_outputs",     test_backend_max_outputs,             true  },
     { "mixed",           test_backend_mixed_sampling,          true  },
+    { "min_p",           test_backend_min_p_sampling,          true  },
 };
 
 struct backend_cli_args {

From 4fea191c66a972824becafc4a9736b2d4fa07596 Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Wed, 26 Nov 2025 15:00:24 +0100
Subject: [PATCH 39/59] Use `FetchContent` over CPM as it's bundled with CMake

Thanks @ggerganov for the suggestion
---
 cmake/CPM.cmake                   | 25 -------------------------
 ggml/src/ggml-cuda/CMakeLists.txt | 15 +++++++++------
 2 files changed, 9 insertions(+), 31 deletions(-)
 delete mode 100644 cmake/CPM.cmake

diff --git a/cmake/CPM.cmake b/cmake/CPM.cmake
deleted file mode 100644
index 978a1b7e394..00000000000
--- a/cmake/CPM.cmake
+++ /dev/null
@@ -1,25 +0,0 @@
-# SPDX-License-Identifier: MIT
-#
-# SPDX-FileCopyrightText: Copyright (c) 2019-2023 Lars Melchior and contributors
-
-# TODO: Remove this file once CCCL 3.2 is released & bundled with the CUDA Toolkit
-set(CPM_DOWNLOAD_VERSION 0.42.0)
-set(CPM_HASH_SUM "2020b4fc42dba44817983e06342e682ecfc3d2f484a581f11cc5731fbe4dce8a")
-
-if(CPM_SOURCE_CACHE)
-  set(CPM_DOWNLOAD_LOCATION "${CPM_SOURCE_CACHE}/cpm/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
-elseif(DEFINED ENV{CPM_SOURCE_CACHE})
-  set(CPM_DOWNLOAD_LOCATION "$ENV{CPM_SOURCE_CACHE}/cpm/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
-else()
-  set(CPM_DOWNLOAD_LOCATION "${CMAKE_BINARY_DIR}/cmake/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
-endif()
-
-# Expand relative path. This is important if the provided path contains a tilde (~)
-get_filename_component(CPM_DOWNLOAD_LOCATION ${CPM_DOWNLOAD_LOCATION} ABSOLUTE)
-
-file(DOWNLOAD
-     https://github.com/cpm-cmake/CPM.cmake/releases/download/v${CPM_DOWNLOAD_VERSION}/CPM.cmake
-     ${CPM_DOWNLOAD_LOCATION} EXPECTED_HASH SHA256=${CPM_HASH_SUM}
-)
-
-include(${CPM_DOWNLOAD_LOCATION})
diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt
index 05a9b49e83a..fa771cdd11f 100644
--- a/ggml/src/ggml-cuda/CMakeLists.txt
+++ b/ggml/src/ggml-cuda/CMakeLists.txt
@@ -4,13 +4,16 @@ find_package(CUDAToolkit)
 
 # Remove once CCCL 3.2 has been released and bundled with CUDA Toolkit
 if (GGML_CUDA_CUB_3DOT2)
-    include(../../../cmake/CPM.cmake)
-    # This will automatically clone CCCL from GitHub and make the exported cmake targets available
-    CPMAddPackage(
-        NAME CCCL
-        GITHUB_REPOSITORY nvidia/cccl
-        GIT_TAG v3.2.0-rc0 # Fetches the latest commit on the main branch
+    include(FetchContent)
+
+    FetchContent_Declare(
+        CCCL
+        GIT_REPOSITORY https://github.com/nvidia/cccl.git
+        GIT_TAG        v3.2.0-rc0
+        GIT_SHALLOW    TRUE
     )
+
+    FetchContent_MakeAvailable(CCCL)
 endif()
 
 if (CUDAToolkit_FOUND)

From 0f7805f32a95fff2aa35f7ef34c3d6cb8f5d456c Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 26 Nov 2025 13:12:36 +0100
Subject: [PATCH 40/59] common : add get_active_samplers function to check
 enabled samplers

This commit adds a function to check if a sampler is actually enabled,
meaning that it does not have values that disables its effect. This is
then used by the backend samplers initialization to avoid considering
samplers that are not enabled when determining the split point between
them.

The motivation for this is that this allows the default sampler chain
for `--samplers` to be used and any sampler that is not enabled will not
cause the backend samplers to be skipped.
For example, before this change if the penalties sampler was included in
the samplers list but had default values that disable it, it would cause
the backend samplers to be skipped entirely.

This commit also contains some refactoring to remove some code
duplication.
---
 common/common.cpp   |   1 -
 common/sampling.cpp | 240 ++++++++++++++++++++++----------------------
 2 files changed, 119 insertions(+), 122 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index bf813707304..6606ab5c599 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1019,7 +1019,6 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     auto cparams = common_context_params_to_llama(params);
 
-    // backend sampling initialization
     if (params.sampling.backend_sampling) {
         llama_sampler * backend_chain = common_sampler_backend_init(model, params.sampling);
         if (backend_chain != nullptr) {
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 9f7795aa416..9f1ce466807 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -165,10 +165,6 @@ struct common_sampler {
     mutable int64_t t_total_us = 0;
 };
 
-static bool sampler_enabled(const struct common_params_sampling & params, enum common_sampler_type type) {
-    return std::find(params.samplers.begin(), params.samplers.end(), type) != params.samplers.end();
-}
-
 static bool sampler_backend_supported(enum common_sampler_type type) {
     switch (type) {
         case COMMON_SAMPLER_TYPE_TOP_K:
@@ -180,10 +176,100 @@ static bool sampler_backend_supported(enum common_sampler_type type) {
     }
 }
 
+static bool is_sampler_enabled(enum common_sampler_type type, const struct common_params_sampling & params) {
+    switch (type) {
+        case COMMON_SAMPLER_TYPE_PENALTIES:
+            if (params.penalty_last_n == 64 &&
+                fabs(params.penalty_repeat) <= 1.0f &&
+                fabs(params.penalty_freq)   <= 0.0f &&
+                fabs(params.penalty_present) <= 0.0f) {
+                return false;
+            }
+            break;
+        case COMMON_SAMPLER_TYPE_DRY:
+            if (params.dry_multiplier == 0.0f && params.dry_base == 1.75f) {
+                return false;
+            }
+            break;
+        case COMMON_SAMPLER_TYPE_TYPICAL_P:
+            if (params.typ_p == 1.0) {
+                return false;
+            }
+            break;
+        case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
+            if (params.top_n_sigma == -1.0) {
+                return false;
+            }
+            break;
+        case COMMON_SAMPLER_TYPE_TOP_K:
+            if (params.top_k <= 0) {
+                return false;
+            }
+            break;
+        case COMMON_SAMPLER_TYPE_TEMPERATURE:
+            if (params.temp <= 0.0f) {
+                return false;
+            }
+            break;
+        case COMMON_SAMPLER_TYPE_MIN_P:
+            if (params.min_p <= 0.0f) {
+                return false;
+            }
+            break;
+        case COMMON_SAMPLER_TYPE_TOP_P:
+            if (params.top_p >= 1.0f) {
+                return false;
+            }
+            break;
+        case COMMON_SAMPLER_TYPE_XTC:
+            if (params.xtc_probability == 0.0f && params.xtc_threshold == 0.10f) {
+                return false;
+            }
+            break;
+        default:
+            break;
+    }
+    return true;
+}
+
 static bool has_logit_bias(const struct common_params_sampling & params) {
     return !params.logit_bias.empty();
 }
 
+struct active_samplers {
+    std::vector<common_sampler_type> backend_samplers;
+    std::vector<common_sampler_type> cpu_samplers;
+};
+
+static struct active_samplers get_active_samplers(const struct common_params_sampling & params) {
+    struct active_samplers result;
+
+    if (params.mirostat != 0) {
+        // Mirostat is CPU-only and overrides other samplers
+        for (const auto & sampler_type : params.samplers) {
+            if (is_sampler_enabled(sampler_type, params)) {
+                result.cpu_samplers.push_back(sampler_type);
+            }
+        }
+        return result;
+    }
+
+    bool backend_supported = params.backend_sampling;
+
+    for (const auto & sampler_type : params.samplers) {
+        if (!is_sampler_enabled(sampler_type, params)) {
+            continue;
+        }
+
+        if (backend_supported && sampler_backend_supported(sampler_type)) {
+            result.backend_samplers.push_back(sampler_type);
+        } else {
+            result.cpu_samplers.push_back(sampler_type);
+        }
+    }
+    return result;
+}
+
 std::string common_params_sampling::print() const {
     char result[1024];
 
@@ -200,6 +286,14 @@ std::string common_params_sampling::print() const {
     return std::string(result);
 }
 
+struct backend_chain_data {
+    struct llama_sampler * chain;
+    size_t count;
+};
+
+static struct backend_chain_data backend_samplers_init(const struct llama_model * model, const struct common_params_sampling & params,
+        struct active_samplers get_active_samplers);
+
 struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
@@ -277,69 +371,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
         /* .cur_p         = */ {},
     };
 
-    size_t backend_sampler_count = 0;
-    if (params.backend_sampling && params.mirostat == 0) {
-        if (has_logit_bias(params)) {
-            backend_sampler_count++;
-        }
-
-        // Find the longest contiguous chain of backend-supported samplers from the start
-        for (const auto & sampler_type : params.samplers) {
-            if (sampler_backend_supported(sampler_type)) {
-                backend_sampler_count++;
-            } else {
-                break;
-            }
-        }
-    }
-
-    // If the samplers combination is supported then we can build the backend chain.
-    if (backend_sampler_count > 0 || (params.backend_sampling && has_logit_bias(params))) {
-        llama_sampler_chain_params backend_params = llama_sampler_chain_default_params();
-        backend_params.no_perf = params.no_perf;
-        result->backend_chain = llama_sampler_chain_init(backend_params);
-
-        if (has_logit_bias(params)) {
-            llama_sampler_chain_add(result->backend_chain,
-                llama_sampler_backend_init_logit_bias(
-                    llama_vocab_n_tokens(vocab),
-                    params.logit_bias.size(),
-                    params.logit_bias.data()));
-        }
-
-        size_t backend_idx = 0;
-        for (const auto & sampler_type : params.samplers) {
-            if (backend_idx >= backend_sampler_count - has_logit_bias(params)) {
-                break;
-            }
-
-            switch (sampler_type) {
-                case COMMON_SAMPLER_TYPE_TOP_K:
-                    if (params.top_k > 0) {
-                        llama_sampler_chain_add(result->backend_chain, llama_sampler_backend_init_top_k(params.top_k));
-                    }
-                    backend_idx++;
-                    break;
-                case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    if (params.temp > 0.0f) {
-                        llama_sampler_chain_add(result->backend_chain, llama_sampler_backend_init_temp(params.temp));
-                    }
-                    backend_idx++;
-                    break;
-                case COMMON_SAMPLER_TYPE_MIN_P:
-                    if (params.min_p > 0.0f) {
-                        llama_sampler_chain_add(result->backend_chain, llama_sampler_backend_init_min_p(params.min_p));
-                    }
-                    backend_idx++;
-                    break;
-                default:
-                    GGML_ASSERT(false && "unsupported backend sampler");
-            }
-        }
-    }
-
-    size_t cpu_start_idx = backend_sampler_count - has_logit_bias(params);
-    bool cpu_has_samplers = cpu_start_idx < params.samplers.size();
+    struct active_samplers active_samplers = get_active_samplers(params);
+    backend_chain_data backend_data = backend_samplers_init(model, params, active_samplers);
+    result->backend_chain = backend_data.chain;
 
     // Build CPU chain
     if (!params.backend_sampling || !has_logit_bias(params)) {
@@ -352,8 +386,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 
     if (params.mirostat == 0) {
         // Add remaining CPU samplers
-        for (size_t i = cpu_start_idx; i < params.samplers.size(); i++) {
-            const auto & cnstr = params.samplers[i];
+        for (const auto & cnstr : active_samplers.cpu_samplers) {
             switch (cnstr) {
                 case COMMON_SAMPLER_TYPE_DRY:
                     {
@@ -398,10 +431,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
             }
         }
 
-        // If all samplers are on backend, add dist to backend; otherwise add to CPU
-        if (result->backend_chain && !cpu_has_samplers) {
-            llama_sampler_chain_add(result->backend_chain, llama_sampler_backend_init_dist(params.seed));
-        } else {
+        if (!active_samplers.cpu_samplers.empty()) {
             llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
         }
     } else if (params.mirostat == 1) {
@@ -417,35 +447,17 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
     return result;
 }
 
-struct llama_sampler * common_sampler_backend_init(const struct llama_model * model, const struct common_params_sampling & params) {
-    if (!params.backend_sampling || params.mirostat != 0) {
-        return nullptr;
-    }
-
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    // Determine the split point for backend sampling using the same logic as common_sampler_init
-    size_t backend_sampler_count = 0;
-    if (has_logit_bias(params)) {
-        backend_sampler_count++;
-    }
 
-    // Find the longest contiguous chain of backend-supported samplers from the start
-    for (const auto & sampler_type : params.samplers) {
-        if (sampler_backend_supported(sampler_type)) {
-            backend_sampler_count++;
-        } else {
-            break;
-        }
+static struct backend_chain_data backend_samplers_init(const struct llama_model * model, const struct common_params_sampling & params,
+        struct active_samplers active_samplers) {
+    if (active_samplers.backend_samplers.empty()) {
+        return { nullptr, 0 };
     }
 
-    if (backend_sampler_count == 0 && !has_logit_bias(params)) {
-        return nullptr;
-    }
+    const llama_vocab * vocab = llama_model_get_vocab(model);
 
     llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
     chain_params.no_perf = params.no_perf;
-
     struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
 
     // Add logit_bias to backend chain if present
@@ -456,46 +468,32 @@ struct llama_sampler * common_sampler_backend_init(const struct llama_model * mo
                     params.logit_bias.data()));
     }
 
-    size_t backend_idx = 0;
-    for (const auto & sampler_type : params.samplers) {
-        if (backend_idx >= backend_sampler_count - has_logit_bias(params)) {
-            break;
-        }
-
+    for (const auto & sampler_type : active_samplers.backend_samplers) {
         switch (sampler_type) {
             case COMMON_SAMPLER_TYPE_TOP_K:
-                if (params.top_k > 0) {
-                    llama_sampler_chain_add(chain, llama_sampler_backend_init_top_k(params.top_k));
-                }
-                backend_idx++;
+                llama_sampler_chain_add(chain, llama_sampler_backend_init_top_k(params.top_k));
                 break;
             case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                if (params.temp > 0.0f) {
-                    llama_sampler_chain_add(chain, llama_sampler_backend_init_temp(params.temp));
-                }
-                backend_idx++;
+                llama_sampler_chain_add(chain, llama_sampler_backend_init_temp(params.temp));
                 break;
             case COMMON_SAMPLER_TYPE_MIN_P:
-                if (params.min_p > 0.0f) {
-                    llama_sampler_chain_add(chain, llama_sampler_backend_init_min_p(params.min_p));
-                }
-                backend_idx++;
+                llama_sampler_chain_add(chain, llama_sampler_backend_init_min_p(params.min_p));
                 break;
             default:
                 GGML_ASSERT(false && "unsupported backend sampler");
         }
     }
 
-    // Determine if we should add dist sampler to backend chain
-    // Only add it if all samplers from params.samplers are on the backend
-    size_t cpu_start_idx = backend_sampler_count - has_logit_bias(params);
-    bool cpu_has_samplers = cpu_start_idx < params.samplers.size();
-
-    if (!cpu_has_samplers) {
+    if (active_samplers.cpu_samplers.empty()) {
         llama_sampler_chain_add(chain, llama_sampler_backend_init_dist(params.seed));
     }
 
-    return chain;
+    return { chain, active_samplers.backend_samplers.size() + has_logit_bias(params) };
+}
+
+struct llama_sampler * common_sampler_backend_init(const struct llama_model * model, const struct common_params_sampling & params) {
+    struct active_samplers active_samplers = get_active_samplers(params);
+    return backend_samplers_init(model, params, active_samplers).chain;
 }
 
 void common_sampler_free(struct common_sampler * gsmpl) {

From 90a3aff2c212e72e0e884a15023c51084fd18453 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 26 Nov 2025 17:44:04 +0100
Subject: [PATCH 41/59] cuda : fix editorconfig-checker warning

---
 ggml/src/ggml-cuda/top-k.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/top-k.cuh b/ggml/src/ggml-cuda/top-k.cuh
index 85a95bc1bcd..f4d8f61e5b3 100644
--- a/ggml/src/ggml-cuda/top-k.cuh
+++ b/ggml/src/ggml-cuda/top-k.cuh
@@ -1,3 +1,3 @@
 #include "common.cuh"
 
-void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
\ No newline at end of file
+void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

From d9d736102be9c0a45a68b0df63b1f28355f519fb Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 27 Nov 2025 07:38:44 +0100
Subject: [PATCH 42/59] sampling : use argmax for min-p sampling

---
 src/llama-backend-sampler.cpp | 17 +++++------------
 1 file changed, 5 insertions(+), 12 deletions(-)

diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
index 34361d925a7..6a3893b1294 100644
--- a/src/llama-backend-sampler.cpp
+++ b/src/llama-backend-sampler.cpp
@@ -516,24 +516,17 @@ static void llama_sampler_backend_min_p_apply_ggml(
     ggml_set_name(softmax, "softmax");
 
     // Get the sorted indices of the softmax probabilities in descending order.
-    struct ggml_tensor * sorted_idx = ggml_argsort(ctx, softmax, GGML_SORT_ORDER_DESC);
-    ggml_set_name(sorted_idx, "sorted_idx");
+    struct ggml_tensor * max_idx = ggml_argmax(ctx, softmax);
+    ggml_set_name(max_idx, "max_idx");
 
-    // Reshape into a row vector.
     struct ggml_tensor * softmax_rows = ggml_reshape_2d(ctx, softmax, 1, softmax->ne[0]);
     ggml_set_name(softmax_rows, "softmax_rows");
 
-    // Get the sorted probabilities using the sorted indices so that we can get
-    // the max probability value, which will be the first entry in sorted_probs.
-    struct ggml_tensor * sorted_probs = ggml_get_rows(ctx, softmax_rows, sorted_idx);
-    ggml_set_name(sorted_probs, "sorted_probs");
-
-    // Get the max probability value from sorted_probs.
-    struct ggml_tensor * p_max = ggml_view_1d(ctx, sorted_probs, 1, 0);
-    ggml_set_name(p_max, "p_max");
+    struct ggml_tensor * max_prob = ggml_get_rows(ctx, softmax_rows, max_idx);
+    ggml_set_name(max_prob, "max_prob");
 
     // Calculate the threshold value.
-    struct ggml_tensor * threshold = ggml_scale(ctx, p_max, sctx->p);
+    struct ggml_tensor * threshold = ggml_scale(ctx, max_prob, sctx->p);
     ggml_set_name(threshold, "min_p_threshold");
 
     // Broadcast the threshold to match the shape of softmax.

From 51107a0b6329994c720aadf1200b59f1e3e04d72 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 27 Nov 2025 09:18:43 +0100
Subject: [PATCH 43/59] sampling : fix temperature check to allow zero
 temperature

This commit modifies the temperature sampling check to allow a
temperature value of zero. Previously, the check only allowed
positive temperature values, which excluded the valid case of
zero temperature.

The motivation for this is to enable a zero temperature setting which is
also currently causing the following test to fail:
```console
(venv) $ cd tools/server/tests
(venv) $ ./tests.sh unit/test_basic.py::test_load_split_model
```
---
 common/sampling.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index 9f1ce466807..2dea7aec17e 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -207,7 +207,7 @@ static bool is_sampler_enabled(enum common_sampler_type type, const struct commo
             }
             break;
         case COMMON_SAMPLER_TYPE_TEMPERATURE:
-            if (params.temp <= 0.0f) {
+            if (params.temp < 0.0f) {
                 return false;
             }
             break;

From 5ea3be265ba6f8916daf52e19e3fb8efe9a03637 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 27 Nov 2025 09:40:13 +0100
Subject: [PATCH 44/59] cuda : fix top-k compilation when CUB is unavailable

This commit adds a macro guard around argsort_f32_i32_cuda_cub usage
in the top-k fallback path, falling back to bitonic sort when
GGML_CUDA_USE_CUB is not defined.

The motivation for this is that some environments like AMD HIP
do not have CUB available, causing compilation failure.

Refs: https://github.com/ggml-org/llama.cpp/actions/runs/19728226426/job/56523606840#step:6:208
---
 ggml/src/ggml-cuda/top-k.cu | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml/src/ggml-cuda/top-k.cu b/ggml/src/ggml-cuda/top-k.cu
index ae5989cacc8..912c41626b1 100644
--- a/ggml/src/ggml-cuda/top-k.cu
+++ b/ggml/src/ggml-cuda/top-k.cu
@@ -94,7 +94,11 @@ void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     int *                     tmp_dst = temp_dst_alloc.get();
 
     if (shared_mem > max_shared_mem || ncols > 1024) {
+#ifdef GGML_CUDA_USE_CUB
         argsort_f32_i32_cuda_cub(pool, src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
+#else
+        argsort_f32_i32_cuda_bitonic(src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
+#endif
     } else {
         argsort_f32_i32_cuda_bitonic(src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
     }

From 172208afbfefc254f8c95960759799fb445cd5cb Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 27 Nov 2025 14:59:52 +0100
Subject: [PATCH 45/59] sampling : add comments about backend sampler [no ci]

This commit adds a comment to llama_context's constructor explaining why
backend samplers are initialized early in the process.
---
 src/llama-context.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index b402f1045fa..094ef0481bc 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -58,7 +58,9 @@ llama_context::llama_context(
     cparams.cb_eval           = params.cb_eval;
     cparams.cb_eval_user_data = params.cb_eval_user_data;
 
-    // backend samplers
+    // Initialize backend samplers here so they are part of the sampling graph
+    // before the reserve passes run later in this function. This avoids a later
+    // re-reserve when graph nodes change.
     if (params.samplers != nullptr && params.n_samplers > 0) {
         sampling.samplers.reserve(params.n_samplers);
 

From e9d070980bf843a0d3d9052a4c7135060635dc09 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 27 Nov 2025 15:28:37 +0100
Subject: [PATCH 46/59] sampling : remove backend sampling chain from
 common_sampler

This commit removes the backend sampling chain from the common_sampler
structure and related functions.

The motivation for this change is that the backend samplers are not
currently set on the context, and if they are they would cause the
a graph reallocation to occur. Instead, the intialization is handled
like it currently is by llama_context's constructor.
---
 common/sampling.cpp | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index 2dea7aec17e..9954e2519d6 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -106,7 +106,6 @@ struct common_sampler {
 
     struct llama_sampler * grmr;
     struct llama_sampler * chain;          // CPU sampling chain
-    struct llama_sampler * backend_chain;  // Backend sampling chain
 
     ring_buffer<llama_token> prev;
 
@@ -119,9 +118,6 @@ struct common_sampler {
 
         llama_sampler_reset(grmr);
         llama_sampler_reset(chain);
-        if (backend_chain) {
-            llama_sampler_reset(backend_chain);
-        }
     }
 
     void set_logits(struct llama_context * ctx, int idx) {
@@ -365,15 +361,12 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
         /* .params        = */ params,
         /* .grmr          = */ grmr,
         /* .chain         = */ llama_sampler_chain_init(lparams),
-        /* .backend_chain = */ nullptr,
         /* .prev          = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
         /* .cur           = */ {},
         /* .cur_p         = */ {},
     };
 
     struct active_samplers active_samplers = get_active_samplers(params);
-    backend_chain_data backend_data = backend_samplers_init(model, params, active_samplers);
-    result->backend_chain = backend_data.chain;
 
     // Build CPU chain
     if (!params.backend_sampling || !has_logit_bias(params)) {
@@ -501,10 +494,6 @@ void common_sampler_free(struct common_sampler * gsmpl) {
         llama_sampler_free(gsmpl->grmr);
         llama_sampler_free(gsmpl->chain);
 
-        if (gsmpl->backend_chain) {
-            llama_sampler_free(gsmpl->backend_chain);
-        }
-
         delete gsmpl;
     }
 }
@@ -530,7 +519,6 @@ struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
         /* .params        = */ gsmpl->params,
         /* .grmr          = */ llama_sampler_clone(gsmpl->grmr),
         /* .chain         = */ llama_sampler_clone(gsmpl->chain),
-        /* .backend_chain = */ gsmpl->backend_chain ? llama_sampler_clone(gsmpl->backend_chain) : nullptr,
         /* .prev          = */ gsmpl->prev,
         /* .cur           = */ gsmpl->cur,
         /* .cur_p         = */ gsmpl->cur_p,

From f9889cf1c7f3115f31e0c5f3e60305a24052f8f9 Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Thu, 27 Nov 2025 16:40:41 +0100
Subject: [PATCH 47/59] Fix top-k comp & behavior for non-CUB path

Some changes were made in 5ea3be265ba6f8916daf52e19e3fb8efe9a03637
which were incomplete. In the case of non-CUB, bitonic sort and its
limitations of ncols < 1024 have to apply, similar to argsort.cu
---
 ggml/src/ggml-cuda/ggml-cuda.cu |  1 -
 ggml/src/ggml-cuda/top-k.cu     | 16 +++++++++-------
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index ea4763fa6e6..b6ac36a23cc 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -4238,7 +4238,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_SUM:
             return ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_TOP_K:
-            return true;
         case GGML_OP_ARGSORT:
 #ifndef GGML_CUDA_USE_CUB
             return op->src[0]->ne[0] <= 1024;
diff --git a/ggml/src/ggml-cuda/top-k.cu b/ggml/src/ggml-cuda/top-k.cu
index 912c41626b1..7d66fec4952 100644
--- a/ggml/src/ggml-cuda/top-k.cu
+++ b/ggml/src/ggml-cuda/top-k.cu
@@ -50,7 +50,7 @@ static void top_k_cub(ggml_cuda_pool & pool,
     DeviceTopK::MaxPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys, temp_indices, dst, ncols, k, env);
 }
 
-#else
+#elif defined(GGML_CUDA_USE_CUB) // CUB_TOP_K_AVAILABLE
 
 static int next_power_of_2(int x) {
     int n = 1;
@@ -84,7 +84,7 @@ void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     for (int i = 0; i < nrows; i++) {
         top_k_cub(pool, src0_d + i * ncols, dst_d + i * k, ncols, k, stream);
     }
-#else
+#elif defined(GGML_CUDA_USE_CUB) // CUB_TOP_K_AVAILABLE
     // Fall back to argsort + copy
     const int    ncols_pad      = next_power_of_2(ncols);
     const size_t shared_mem     = ncols_pad * sizeof(int);
@@ -94,15 +94,17 @@ void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     int *                     tmp_dst = temp_dst_alloc.get();
 
     if (shared_mem > max_shared_mem || ncols > 1024) {
-#ifdef GGML_CUDA_USE_CUB
         argsort_f32_i32_cuda_cub(pool, src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
-#else
-        argsort_f32_i32_cuda_bitonic(src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
-#endif
     } else {
         argsort_f32_i32_cuda_bitonic(src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
     }
     CUDA_CHECK(cudaMemcpy2DAsync(dst_d, k * sizeof(int), tmp_dst, ncols * sizeof(int), k * sizeof(int), nrows,
                                  cudaMemcpyDeviceToDevice, stream));
-#endif  // CUB_TOP_K_AVAILABLE
+#else  // GGML_CUDA_USE_CUB
+    ggml_cuda_pool_alloc<int> temp_dst_alloc(pool, ncols * nrows);
+    int *                     tmp_dst = temp_dst_alloc.get();
+    argsort_f32_i32_cuda_bitonic(src0_d, tmp_dst, ncols, nrows, GGML_SORT_ORDER_DESC, stream);
+    CUDA_CHECK(cudaMemcpy2DAsync(dst_d, k * sizeof(int), tmp_dst, ncols * sizeof(int), k * sizeof(int), nrows,
+                                 cudaMemcpyDeviceToDevice, stream));
+#endif
 }

From 74be332e24e66c939411b5163adabc830ebd66b3 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 27 Nov 2025 19:39:41 +0100
Subject: [PATCH 48/59] sampling : support intermixed backend/cpu samplers

This commit updates the backend sampling implementation to support
intermixed usage of backend and CPU samplers within the same batch.

The initial implementation was developed as an all-or-nothing solution:
either perform backend sampling for the entire batch, or perform CPU
sampling for the entire batch.

The motivation for this change is to support batches with mixed
sequences. For example, we may have a backend sampler configured for
sequence 0, while sequence 1 in the same batch uses CPU sampling. This
was not supported in the initial implementation.

This issue manifested in llama-server with the webui: decoding with
backend samplers would work initially, but after changing to CPU
sampling, a slot (sequence) could still be using a backend sampler.
This meant that logits in output_reserve would not be allocated,
resulting in an error.

The solution in this commit inspects the batch to determine which
sampling modes are needed and allocates buffers accordingly. However,
there is a known inefficiency: when we have intermixed backend/CPU
samplers in the same batch, we currently copy all logits to the host,
even for sequences using backend samplers.

Added test_backend_cpu_mixed_batch to verify correct behavior with
mixed backend/CPU samplers in a single batch, including dynamic
sampler switching between decode calls.
---
 src/llama-context.cpp          | 204 ++++++++++++++++++---------------
 src/llama-context.h            |   2 +-
 tests/test-backend-sampler.cpp | 149 ++++++++++++++++++++----
 3 files changed, 241 insertions(+), 114 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 094ef0481bc..d8fb5d782b9 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -212,7 +212,10 @@ llama_context::llama_context(
         // graph outputs buffer
         {
             // resized during inference when a batch uses more outputs
-            if (output_reserve(params.n_seq_max) < params.n_seq_max) {
+            // Create a dummy batch for initialization.
+            llama_batch dummy_batch = {};
+            dummy_batch.n_tokens = 0;
+            if (output_reserve(params.n_seq_max, dummy_batch) < params.n_seq_max) {
                 throw std::runtime_error("failed to reserve initial output buffer");
             }
 
@@ -1075,7 +1078,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
     n_queued_tokens += n_tokens;
 
     // reserve output buffer
-    if (output_reserve(n_tokens) < n_tokens) {
+    if (output_reserve(n_tokens, batch_inp) < n_tokens) {
         LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
         return -2;
     };
@@ -1403,7 +1406,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
     }
 
     // reserve output buffer
-    if (output_reserve(n_outputs_all) < n_outputs_all) {
+    if (output_reserve(n_outputs_all, balloc->get_batch()) < n_outputs_all) {
         LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all);
         return -2;
     };
@@ -1493,82 +1496,83 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
         }
 
-        if (!backend_has_sampled) {
-            auto * t_logits = res->get_logits();
-            auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
+        auto * t_logits = res->get_logits();
+        auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
 
-            if (t_embd && res->get_embd_pooled()) {
-                t_embd = res->get_embd_pooled();
-            }
+        if (t_embd && res->get_embd_pooled()) {
+            t_embd = res->get_embd_pooled();
+        }
 
-            // extract logits
-            if (t_logits && n_outputs > 0) {
-                ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
-                GGML_ASSERT(backend_res != nullptr);
-                GGML_ASSERT(logits != nullptr);
+        // extract logits
+        // For multipsequence batches that mix backend samplers and CPU sampler
+        // this is currently inefficient as we copy all logits even for the
+        // backend sampled tokens.
+        if (logits && t_logits && n_outputs > 0) {
+            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
+            GGML_ASSERT(backend_res != nullptr);
+            GGML_ASSERT(logits != nullptr);
 
-                float * logits_out = logits + n_outputs_prev*n_vocab;
+            float * logits_out = logits + n_outputs_prev*n_vocab;
 
-                if (n_outputs) {
-                    GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                    GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
-                    ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
-                }
+            if (n_outputs) {
+                GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
+                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
+                ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
             }
+        }
 
-            // extract embeddings
-            if (t_embd && n_outputs > 0) {
-                ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
-                GGML_ASSERT(backend_embd != nullptr);
-
-                switch (cparams.pooling_type) {
-                    case LLAMA_POOLING_TYPE_NONE:
-                        {
-                            // extract token embeddings
-                            GGML_ASSERT(embd != nullptr);
-                            float * embd_out = embd + n_outputs_prev*n_embd;
-
-                            if (n_outputs) {
-                                GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                                GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd <= (int64_t) embd_size);
-                                ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd*sizeof(float));
-                            }
-                        } break;
-                    case LLAMA_POOLING_TYPE_MEAN:
-                    case LLAMA_POOLING_TYPE_CLS:
-                    case LLAMA_POOLING_TYPE_LAST:
-                        {
-                            // extract sequence embeddings (cleared before processing each batch)
-                            auto & embd_seq_out = embd_seq;
-
-                            for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
-                                const llama_seq_id seq_id  = ubatch.seq_id_unq[s];
-                                const int32_t      seq_idx = ubatch.seq_idx[seq_id];
-
-                                embd_seq_out[seq_id].resize(n_embd);
-                                ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_idx)*sizeof(float), n_embd*sizeof(float));
-                            }
-                        } break;
-                    case LLAMA_POOLING_TYPE_RANK:
-                        {
-                            // extract the rerank score - n_cls_out floats per sequence
-                            auto & embd_seq_out = embd_seq;
-
-                            const uint32_t n_cls_out = hparams.n_cls_out;
-
-                            for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
-                                const llama_seq_id seq_id  = ubatch.seq_id_unq[s];
-                                const int32_t      seq_idx = ubatch.seq_idx[seq_id];
-
-                                embd_seq_out[seq_id].resize(n_cls_out);
-                                ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_idx)*sizeof(float), n_cls_out*sizeof(float));
-                            }
-                        } break;
-                    case LLAMA_POOLING_TYPE_UNSPECIFIED:
-                        {
-                            GGML_ABORT("unknown pooling type");
+        // extract embeddings
+        if (embd && t_embd && n_outputs > 0) {
+            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
+            GGML_ASSERT(backend_embd != nullptr);
+
+            switch (cparams.pooling_type) {
+                case LLAMA_POOLING_TYPE_NONE:
+                    {
+                        // extract token embeddings
+                        GGML_ASSERT(embd != nullptr);
+                        float * embd_out = embd + n_outputs_prev*n_embd;
+
+                        if (n_outputs) {
+                            GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
+                            GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd <= (int64_t) embd_size);
+                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd*sizeof(float));
                         }
-                }
+                    } break;
+                case LLAMA_POOLING_TYPE_MEAN:
+                case LLAMA_POOLING_TYPE_CLS:
+                case LLAMA_POOLING_TYPE_LAST:
+                    {
+                        // extract sequence embeddings (cleared before processing each batch)
+                        auto & embd_seq_out = embd_seq;
+
+                        for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+                            const llama_seq_id seq_id  = ubatch.seq_id_unq[s];
+                            const int32_t      seq_idx = ubatch.seq_idx[seq_id];
+
+                            embd_seq_out[seq_id].resize(n_embd);
+                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_idx)*sizeof(float), n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_RANK:
+                    {
+                        // extract the rerank score - n_cls_out floats per sequence
+                        auto & embd_seq_out = embd_seq;
+
+                        const uint32_t n_cls_out = hparams.n_cls_out;
+
+                        for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+                            const llama_seq_id seq_id  = ubatch.seq_id_unq[s];
+                            const int32_t      seq_idx = ubatch.seq_idx[seq_id];
+
+                            embd_seq_out[seq_id].resize(n_cls_out);
+                            ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_idx)*sizeof(float), n_cls_out*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                    {
+                        GGML_ABORT("unknown pooling type");
+                    }
             }
         }
 
@@ -1635,7 +1639,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
 // output
 //
 
-uint32_t llama_context::output_reserve(int32_t n_outputs) {
+uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & batch) {
     const auto & hparams = model.hparams;
     const auto & vocab   = model.vocab;
 
@@ -1654,23 +1658,37 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         has_embd   = true;
     }
 
-    const bool backend_sampling = !sampling.samplers.empty();
+    // Check which sampling modes are needed by sequences in the current batch.
+    bool batch_has_backend_sampling = false;
+    bool batch_needs_cpu_logits     = false;
+
+    for (int32_t i = 0; i < batch.n_tokens; i++) {
+        if (!batch.logits[i]) {
+            continue;
+        }
+        for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
+            llama_seq_id seq_id = batch.seq_id[i][j];
+            if (sampling.samplers.find(seq_id) != sampling.samplers.end()) {
+                batch_has_backend_sampling = true;
+            } else {
+                batch_needs_cpu_logits = true;
+            }
+        }
+    }
+
     size_t backend_float_count = 0;
     size_t backend_token_count = 0;
 
-    if (!backend_sampling) {
-        logits_size = has_logits ? n_vocab*n_outputs_max : 0;
-        embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
+    // Allocate CPU logits buffer only if needed by sequences in this batch
+    logits_size = (has_logits && batch_needs_cpu_logits) ? n_vocab*n_outputs_max : 0;
+    embd_size   = has_embd ? n_embd*n_outputs_max : 0;
 
-        // reset backend sampling values.
+    if (!batch_has_backend_sampling) {
         sampling.logits_size       = 0;
         sampling.probs_size        = 0;
         sampling.sampled_size      = 0;
         sampling.candidates_size   = 0;
     } else {
-        logits_size = 0;
-        embd_size   = 0;
-
         sampling.logits_size     = n_vocab*n_outputs_max;
         sampling.probs_size      = n_vocab*n_outputs_max;
         sampling.sampled_size    = n_outputs_max;
@@ -1727,15 +1745,16 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     sampling.sampled    = nullptr;
     sampling.candidates = nullptr;
 
-    if (!backend_sampling) {
-        logits = has_logits ? output_base : nullptr;
-        embd   = has_embd   ? output_base + logits_size : nullptr;
-    } else {
-        // Allocate worst case (full vocabulary size) for backend sampled
-        // data in the pinned memory buffer.
-        size_t offset = 0;
-        uint8_t * base = (uint8_t *) output_base;
+    size_t offset = 0;
+    uint8_t * base = (uint8_t *) output_base;
+
+    logits = (has_logits && batch_needs_cpu_logits) ? output_base : nullptr;
+    offset += logits_size * sizeof(float);
+
+    embd = has_embd ? (float *) (base + offset) : nullptr;
+    offset += embd_size * sizeof(float);
 
+    if (batch_has_backend_sampling) {
         sampling.logits = (float *) (base + offset);
         offset += sampling.logits_size * sizeof(float);
 
@@ -2400,7 +2419,10 @@ size_t llama_context::state_read_data(llama_io_read_i & io) {
         auto n_outputs = this->n_outputs;
         io.read_to(&n_outputs, sizeof(n_outputs));
 
-        if (n_outputs > output_reserve(n_outputs)) {
+        // Create a dummy batch for state loading.
+        llama_batch dummy_batch = {};
+        dummy_batch.n_tokens = 0;
+        if (n_outputs > output_reserve(n_outputs, dummy_batch)) {
             throw std::runtime_error("could not reserve outputs");
         }
 
@@ -2631,7 +2653,7 @@ void llama_context::opt_epoch_iter(
         }
 
         // reserve output buffer
-        if (output_reserve(n_outputs_all) < n_outputs_all) {
+        if (output_reserve(n_outputs_all, balloc->get_batch()) < n_outputs_all) {
             LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all);
             GGML_ABORT("TODO: handle this error");
         };
diff --git a/src/llama-context.h b/src/llama-context.h
index 2bdbf8a5532..1dcd3bf419b 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -200,7 +200,7 @@ struct llama_context {
 
     // Make sure enough space is available for outputs.
     // Returns max number of outputs for which space was reserved.
-    uint32_t output_reserve(int32_t n_outputs);
+    uint32_t output_reserve(int32_t n_outputs, const llama_batch & batch);
 
     void output_reorder();
     int64_t resolve_output_row(int32_t i) const;
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index b668b884857..cd9aa003b56 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -44,7 +44,7 @@ struct test_model_context {
         return true;
     }
 
-    bool setup(const char * model_path, std::vector<llama_sampler_seq_config> & configs) {
+    bool setup(const char * model_path, std::vector<llama_sampler_seq_config> & configs, int32_t n_seq_max = -1) {
         if (model == nullptr) {
             load_model(model_path);
         }
@@ -59,13 +59,18 @@ struct test_model_context {
         cparams.samplers = configs.data();
         cparams.n_samplers = configs.size();
 
-        int32_t max_seq_id = 0;
-        for (const auto & config : configs) {
-            if (config.seq_id > max_seq_id) {
-                max_seq_id = config.seq_id;
+        // If n_seq_max is not specified, calculate it from configs
+        if (n_seq_max < 0) {
+            int32_t max_seq_id = 0;
+            for (const auto & config : configs) {
+                if (config.seq_id > max_seq_id) {
+                    max_seq_id = config.seq_id;
+                }
             }
+            cparams.n_seq_max = max_seq_id + 1;
+        } else {
+            cparams.n_seq_max = n_seq_max;
         }
-        cparams.n_seq_max = max_seq_id + 1;
 
         ctx = llama_init_from_model(model, cparams);
         if (ctx == nullptr) {
@@ -280,7 +285,7 @@ static void test_backend_greedy_sampling(const char * model_path) {
     }
 
     if (!test_ctx.decode({{seq_id, "Some"}})) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
@@ -297,7 +302,9 @@ static void test_backend_greedy_sampling(const char * model_path) {
         int32_t loop_idx = test_ctx.idx_for_seq(seq_id);
         llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, loop_idx);
         printf("Generation step %d: token id:%d, string: %s\n", i, token, test_ctx.token_to_piece(token, false).c_str());
-        test_ctx.decode_token(token, 0);
+        if (!test_ctx.decode_token(token, 0)) {
+            GGML_ASSERT(false && "Failed to decode token");
+        }
     }
 }
 
@@ -316,7 +323,7 @@ static void test_backend_top_k_sampling(const char * model_path) {
     }
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
@@ -373,7 +380,7 @@ static void test_backend_temp_sampling(const char * model_path) {
     }
 
     if (!test_ctx.decode({{0, "Some where over"}, {1, "Once upon a"}})) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     // Verfify sequence 0
@@ -431,7 +438,7 @@ static void test_backend_min_p_sampling(const char * model_path) {
     }
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
@@ -464,7 +471,9 @@ static void test_backend_min_p_sampling(const char * model_path) {
         int32_t loop_idx = test_ctx.idx_for_seq(seq_id);
         llama_token token = llama_sampler_sample(chain, test_ctx.ctx, loop_idx);
         printf("min-p gen step %d: token id :%5.d, string: %s\n", i, token, test_ctx.token_to_piece(token, false).c_str());
-        test_ctx.decode_token(token, 0);
+        if (!test_ctx.decode_token(token, 0)) {
+            GGML_ASSERT(false && "Failed to decode token");
+        }
     }
 
     printf("min-p sampling test PASSED\n");
@@ -499,7 +508,7 @@ static void test_backend_multi_sequence_sampling(const char * model_path) {
     };
 
     if (!test_ctx.decode(prompts)) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     // Verfiy sequence 0
@@ -535,7 +544,7 @@ static void test_backend_multi_sequence_sampling(const char * model_path) {
 
         // Decode all tokens in a single batch
         if (!test_ctx.decode_tokens(tokens)) {
-            break;
+            GGML_ASSERT(false && "Failed to decode token");
         }
     }
 
@@ -557,7 +566,7 @@ static void test_backend_dist_sampling(const char * model_path) {
     }
 
     if (!test_ctx.decode({{seq_id, "Some"}})) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
@@ -586,7 +595,7 @@ static void test_backend_dist_sampling_and_cpu(const char * model_path) {
     }
 
     if (!test_ctx.decode({{seq_id, "Some"}})) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
@@ -640,7 +649,7 @@ static void test_backend_logit_bias_sampling(const char * model_path) {
     }
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(seq_id));
@@ -650,7 +659,7 @@ static void test_backend_logit_bias_sampling(const char * model_path) {
 }
 
 // This test verifies that it is possible to have two different backend sampler,
-// one that used the backend dist sampler, and another that uses CPU dist sampler.
+// one that uses the backend dist sampler, and another that uses CPU dist sampler.
 static void test_backend_mixed_sampling(const char * model_path) {
     test_model_context test_ctx;
 
@@ -678,7 +687,7 @@ static void test_backend_mixed_sampling(const char * model_path) {
     };
 
     if (!test_ctx.decode(prompts)) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     // Verfiy sequence 0 that used the dist backend sampler.
@@ -720,7 +729,7 @@ static void test_backend_set_sampler(const char * model_path) {
     }
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
@@ -741,7 +750,7 @@ static void test_backend_set_sampler(const char * model_path) {
 
     std::map<llama_seq_id, llama_token> tokens = { { seq_id, backend_token}, };
     if (!test_ctx.decode_tokens(tokens)) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     // Should not have any sampled token or probs after clearing the backend sampler.
@@ -763,7 +772,7 @@ static void test_backend_set_sampler(const char * model_path) {
     llama_set_backend_sampler(test_ctx.ctx, seq_id, new_backend_sampler_chain);
 
     if (!test_ctx.decode_tokens(tokens2)) {
-        return;
+        GGML_ASSERT(false && "Failed to decode token");
     }
 
     llama_token new_backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(seq_id));
@@ -771,6 +780,101 @@ static void test_backend_set_sampler(const char * model_path) {
     printf("dist sampled token = %d, string='%s'\n", new_backend_token, new_backend_token_str.c_str());
 }
 
+static void test_backend_cpu_mixed_batch(const char * model_path) {
+    test_model_context test_ctx;
+
+    // Sequence 0 uses backend sampling
+    struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
+    struct llama_sampler * sampler_chain_0 = llama_sampler_chain_init(chain_params_0);
+    llama_sampler_chain_add(sampler_chain_0, llama_sampler_backend_init_dist(88));
+
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {
+        { 0, sampler_chain_0 },
+    };
+
+    // We need 2 sequences: seq 0 with backend sampling, seq 1 with CPU sampling
+    if (!test_ctx.setup(model_path, backend_sampler_configs, 2)) {
+        return;
+    }
+
+    std::map<llama_seq_id, std::string> prompts = {
+        {0, "Hello"},  // Will use backend sampling
+        {1, "Some"}   // Will use CPU sampling
+    };
+
+    if (!test_ctx.decode(prompts)) {
+        GGML_ASSERT(false && "Failed to decode token");
+    }
+
+    // Verify sequence 0 (backend sampled)
+    {
+        int32_t batch_idx = test_ctx.idx_for_seq(0);
+        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        const std::string token_str = test_ctx.token_to_piece(token, false);
+        printf("Seq 0 (backend) sampled token id=%d, string='%s'\n", token, token_str.c_str());
+        GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+    }
+
+    // Verify sequence 1 (CPU sampled)
+    {
+        int32_t batch_idx = test_ctx.idx_for_seq(1);
+
+        llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        GGML_ASSERT(backend_token == LLAMA_TOKEN_NULL);
+
+        struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+        struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+        llama_sampler_chain_add(chain, llama_sampler_init_greedy());
+
+        llama_token token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+        const std::string token_str = test_ctx.token_to_piece(token, false);
+        printf("Seq 1 (CPU) sampled token id=%d, string='%s'\n", token, token_str.c_str());
+        GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+        llama_sampler_free(chain);
+    }
+
+    // Clear/remove the backend sampler, and sample again
+    {
+        // clear the backend sampler for seq 0 so that there are no backend
+        // samplers.
+        llama_set_backend_sampler(test_ctx.ctx, 0, nullptr);
+
+        // Create a CPU sampler and verify we can sampler from it.
+        struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+        struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+        llama_sampler_chain_add(chain, llama_sampler_init_greedy());
+
+        int32_t batch_idx = test_ctx.idx_for_seq(1);
+        llama_token token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+        if (!test_ctx.decode_token(token, 1)) {
+            GGML_ASSERT(false && "Failed to decode token");
+        }
+
+        llama_sampler_free(chain);
+    }
+
+    // Set a backend sampler so that we can verify that it can be reset
+    {
+        struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+        struct llama_sampler * sampler_chain= llama_sampler_chain_init(chain_params);
+        llama_sampler_chain_add(sampler_chain, llama_sampler_backend_init_dist(88));
+
+        llama_set_backend_sampler(test_ctx.ctx, 0, sampler_chain);
+
+        if (!test_ctx.decode_token(3834, 0)) {
+            GGML_ASSERT(false && "Failed to decode token");
+        }
+
+        int32_t batch_idx = test_ctx.idx_for_seq(0);
+        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        const std::string token_str = test_ctx.token_to_piece(token, false);
+        printf("re-added backend sampled token id=%d, string='%s'\n", token, token_str.c_str());
+        GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+    }
+
+    printf("backend-cpu mixed batch test PASSED\n");
+}
+
 static void test_backend_max_outputs(const char * model_path) {
     test_model_context test_ctx;
 
@@ -829,6 +933,7 @@ static const backend_test_case BACKEND_TESTS[] = {
     { "max_outputs",     test_backend_max_outputs,             true  },
     { "mixed",           test_backend_mixed_sampling,          true  },
     { "min_p",           test_backend_min_p_sampling,          true  },
+    { "cpu_mixed",       test_backend_cpu_mixed_batch,         true  },
 };
 
 struct backend_cli_args {

From 9ad6522be6406a6057bf9796ffac619ab8a54827 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 28 Nov 2025 08:57:48 +0100
Subject: [PATCH 49/59] squash! sampling : support intermixed backend/cpu
 samplers

Add check that logits is not null which is can happen for embeddings.
---
 src/llama-context.cpp | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index d8fb5d782b9..f74248127cb 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1662,16 +1662,18 @@ uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & ba
     bool batch_has_backend_sampling = false;
     bool batch_needs_cpu_logits     = false;
 
-    for (int32_t i = 0; i < batch.n_tokens; i++) {
-        if (!batch.logits[i]) {
-            continue;
-        }
-        for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
-            llama_seq_id seq_id = batch.seq_id[i][j];
-            if (sampling.samplers.find(seq_id) != sampling.samplers.end()) {
-                batch_has_backend_sampling = true;
-            } else {
-                batch_needs_cpu_logits = true;
+    if (batch.logits) {
+        for (int32_t i = 0; i < batch.n_tokens; i++) {
+            if (!batch.logits[i]) {
+                continue;
+            }
+            for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
+                llama_seq_id seq_id = batch.seq_id[i][j];
+                if (sampling.samplers.find(seq_id) != sampling.samplers.end()) {
+                    batch_has_backend_sampling = true;
+                } else {
+                    batch_needs_cpu_logits = true;
+                }
             }
         }
     }

From 459b7ae7b9a6885672576db141caf1c460901481 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 28 Nov 2025 13:46:51 +0100
Subject: [PATCH 50/59] squash! sampling : support intermixed backend/cpu
 samplers

Fix llama-save-load-state which currently fails by handling the case
when batch.logits is nullptr (like when loading state) by allocating
space for all outputs as CPU logits.
---
 src/llama-context.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index f74248127cb..b8c5accff88 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1676,6 +1676,10 @@ uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & ba
                 }
             }
         }
+    } else {
+        // When batch.logits is nullptr (when loading state with a dummy batch),
+        // allocate CPU logits.
+        batch_needs_cpu_logits = true;
     }
 
     size_t backend_float_count = 0;

From 117e2079a912a03cfc16fdb380fbc19942997025 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 28 Nov 2025 11:47:59 +0200
Subject: [PATCH 51/59] refactor : simplify and improve memory management

---
 common/common.cpp                             | 204 ++++++++++------
 common/common.h                               |  34 ++-
 common/sampling.cpp                           | 227 ++++++++----------
 common/sampling.h                             |  18 +-
 examples/batched/batched.cpp                  |  50 ++--
 examples/embedding/embedding.cpp              |   6 +-
 examples/eval-callback/eval-callback.cpp      |   6 +-
 examples/lookahead/lookahead.cpp              |   6 +-
 examples/lookup/lookup-create.cpp             |   8 +-
 examples/lookup/lookup-stats.cpp              |   8 +-
 examples/lookup/lookup.cpp                    |   6 +-
 examples/parallel/parallel.cpp                |   6 +-
 examples/retrieval/retrieval.cpp              |   6 +-
 examples/save-load-state/save-load-state.cpp  |   6 +-
 .../speculative-simple/speculative-simple.cpp |  12 +-
 examples/speculative/speculative.cpp          |  12 +-
 examples/training/finetune.cpp                |  17 +-
 include/llama.h                               |  29 +--
 src/llama-context.cpp                         |  47 ++--
 src/llama-context.h                           |   2 +-
 src/llama-sampling.cpp                        |   8 +-
 tools/cvector-generator/cvector-generator.cpp |   6 +-
 tools/imatrix/imatrix.cpp                     |   6 +-
 tools/main/main.cpp                           |  16 +-
 tools/mtmd/mtmd-cli.cpp                       |   6 +-
 tools/perplexity/perplexity.cpp               |   6 +-
 tools/server/server-task.cpp                  |   4 +-
 tools/server/server.cpp                       |  95 ++------
 tools/tts/tts.cpp                             |  12 +-
 29 files changed, 422 insertions(+), 447 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 6606ab5c599..321ea90e7ef 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -950,31 +950,40 @@ std::vector<common_file_info> fs_list_files(const std::string & path) {
 // Model utils
 //
 
-static inline void common_init_sampler_from_model(
+// TODO: move to common/sampling
+static void common_init_sampler_from_model(
     const llama_model * model,
     common_params_sampling & sparams) {
 
     const uint64_t config = sparams.user_sampling_config;
 
     auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) {
-        if (config & user_config) return;
+        if (config & user_config) {
+            return;
+        }
 
         char buf[64] = {0};
         if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
             char * end = nullptr;
             int32_t v = strtol(buf, &end, 10);
-            if (end && end != buf) dst = v;
+            if (end && end != buf) {
+                dst = v;
+            }
         }
     };
 
     auto get_float = [&](const char * key, float & dst, uint64_t user_config) {
-        if (config & user_config) return;
+        if (config & user_config) {
+            return;
+        }
 
         char buf[128] = {0};
         if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
             char * end = nullptr;
             float v = strtof(buf, &end);
-            if (end && end != buf) dst = v;
+            if (end && end != buf) {
+                dst = v;
+            }
         }
     };
 
@@ -1002,45 +1011,130 @@ static inline void common_init_sampler_from_model(
     get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA),    sparams.mirostat_eta,    common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA);
 }
 
-struct common_init_result common_init_from_params(common_params & params) {
-    common_init_result iparams;
-    auto mparams = common_model_params_to_llama(params);
+struct common_init_result::impl {
+    impl() = default;
+    ~impl() = default;
+
+    llama_model_ptr   model;
+    llama_context_ptr context;
+
+    std::vector<llama_adapter_lora_ptr> lora;
+
+    std::vector<common_sampler_ptr> samplers;
+    std::vector<llama_sampler_seq_config> samplers_seq_config;
+};
+
+common_init_result::common_init_result(common_params & params) :
+    pimpl(new impl{}) {
+    const auto mparams = common_model_params_to_llama(params);
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
     if (model == NULL) {
-        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
-        return iparams;
+        return;
     }
 
-    common_init_sampler_from_model(model, params.sampling);
+    pimpl->model.reset(model);
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
+    // updates params.sampling
+    // TODO: fix naming
+    common_init_sampler_from_model(model, params.sampling);
+
     auto cparams = common_context_params_to_llama(params);
 
-    if (params.sampling.backend_sampling) {
-        llama_sampler * backend_chain = common_sampler_backend_init(model, params.sampling);
-        if (backend_chain != nullptr) {
-            iparams.samplers_seq_config.resize(cparams.n_seq_max);
-            for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
-                iparams.samplers_seq_config[i] = { i, llama_sampler_clone(backend_chain) };
-            }
-            cparams.samplers   = iparams.samplers_seq_config.data();
-            cparams.n_samplers = cparams.n_seq_max;
+    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
+        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
+        params.sampling.ignore_eos = false;
+    }
 
-            llama_sampler_free(backend_chain);
+    // initialize once
+    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
+        if (llama_vocab_is_eog(vocab, i)) {
+            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
+            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
         }
     }
 
+    if (params.sampling.ignore_eos) {
+        // add EOG biases to the active set of logit biases
+        params.sampling.logit_bias.insert(
+                params.sampling.logit_bias.end(),
+                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
+    }
+
+    //if (params.sampling.penalty_last_n == -1) {
+    //    LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    params.sampling.penalty_last_n = llama_n_ctx(lctx);
+    //}
+
+    //if (params.sampling.dry_penalty_last_n == -1) {
+    //    LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
+    //}
+
+    // init the backend samplers as part of the context creation
+    pimpl->samplers.resize(cparams.n_seq_max);
+    pimpl->samplers_seq_config.resize(cparams.n_seq_max);
+
+    for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
+        pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
+        llama_sampler * backend_chain = common_sampler_chain_backend(pimpl->samplers[i].get());
+        pimpl->samplers_seq_config[i] = { i, backend_chain };
+    }
+
+    cparams.samplers   = pimpl->samplers_seq_config.data();
+    cparams.n_samplers = pimpl->samplers_seq_config.size();
+
     llama_context * lctx = llama_init_from_model(model, cparams);
+    if (lctx == NULL) {
+        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
+                __func__, params.model.path.c_str());
+        return;
+    }
+
+    pimpl->context.reset(lctx);
+}
+
+llama_model * common_init_result::model() {
+    return pimpl->model.get();
+}
+
+llama_context * common_init_result::context() {
+    return pimpl->context.get();
+}
+
+common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
+    return pimpl->samplers[seq_id].get();
+}
+
+std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
+    return pimpl->lora;
+}
+
+void common_init_result::free_context() {
+    pimpl->context.reset();
+}
+
+common_init_result_ptr common_init_from_params(common_params & params) {
+    common_init_result_ptr res(new common_init_result(params));
+
+    llama_model * model = res->model();
+    if (model == NULL) {
+        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
+            __func__, params.model.path.c_str());
+        return res;
+    }
+
+    llama_context * lctx = res->context();
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
             __func__, params.model.path.c_str());
-        llama_model_free(model);
-        return iparams;
+        return res;
     }
 
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
         LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
         params.ctx_shift = false;
@@ -1052,10 +1146,7 @@ struct common_init_result common_init_from_params(common_params & params) {
 
         const auto cvec = common_control_vector_load(params.control_vectors);
         if (cvec.n_embd == -1) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
 
         int err = llama_apply_adapter_cvec(
@@ -1066,10 +1157,7 @@ struct common_init_result common_init_from_params(common_params & params) {
                 params.control_vector_layer_start,
                 params.control_vector_layer_end);
         if (err) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
     }
 
@@ -1093,10 +1181,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         }
 
         if (!ok) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
     }
 
@@ -1106,9 +1191,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
         if (lora == nullptr) {
             LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
-            llama_free(lctx);
-            llama_model_free(model);
-            return iparams;
+            return res;
         }
 
         char buf[1024];
@@ -1117,43 +1200,13 @@ struct common_init_result common_init_from_params(common_params & params) {
         la.task_name = buf;
         llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
         la.prompt_prefix = buf;
-        iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
+        res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters
     }
 
     if (!params.lora_init_without_apply) {
         common_set_adapter_lora(lctx, params.lora_adapters);
     }
 
-    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
-        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
-        params.sampling.ignore_eos = false;
-    }
-
-    // initialize once
-    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
-        if (llama_vocab_is_eog(vocab, i)) {
-            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
-            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
-        }
-    }
-
-    if (params.sampling.ignore_eos) {
-        // add EOG biases to the active set of logit biases
-        params.sampling.logit_bias.insert(
-                params.sampling.logit_bias.end(),
-                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
-    }
-
-    if (params.sampling.penalty_last_n == -1) {
-        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.penalty_last_n = llama_n_ctx(lctx);
-    }
-
-    if (params.sampling.dry_penalty_last_n == -1) {
-        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
-    }
-
     if (params.warmup) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
@@ -1192,12 +1245,11 @@ struct common_init_result common_init_from_params(common_params & params) {
         llama_set_warmup(lctx, false);
     }
 
-    iparams.model.reset(model);
-    iparams.context.reset(lctx);
-
-    return iparams;
+    return res;
 }
 
+common_init_result::~common_init_result() = default;
+
 std::string get_model_endpoint() {
     const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
     // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
@@ -1206,7 +1258,9 @@ std::string get_model_endpoint() {
     std::string model_endpoint = "https://huggingface.co/";
     if (endpoint_env) {
         model_endpoint = endpoint_env;
-        if (model_endpoint.back() != '/') model_endpoint += '/';
+        if (model_endpoint.back() != '/') {
+            model_endpoint += '/';
+        }
     }
     return model_endpoint;
 }
diff --git a/common/common.h b/common/common.h
index 8e78ab32ead..9b53d2b56f6 100644
--- a/common/common.h
+++ b/common/common.h
@@ -192,7 +192,6 @@ struct common_params_sampling {
 
     std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"};     // default sequence breakers for DRY
 
-
     std::vector<enum common_sampler_type> samplers = {
         COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
@@ -215,6 +214,16 @@ struct common_params_sampling {
 
     bool backend_sampling = false; // enable backend sampling
 
+    bool has_logit_bias() const {
+        return !logit_bias.empty();
+    }
+
+    bool is_disabled(enum common_sampler_type type) const;
+
+    // remove disabled samplers
+    // TODO: temporary until all samplers have llama_sampler_backend_ API [LLAMA_SAMPLER_BACKEND]
+    void filter_disabled();
+
     // print the parameters into a string
     std::string print() const;
 };
@@ -650,18 +659,29 @@ std::vector<common_file_info> fs_list_files(const std::string & path);
 // Model utils
 //
 
+struct common_sampler;
+
 // note: defines object's lifetime
 struct common_init_result {
-    llama_model_ptr   model;
-    llama_context_ptr context;
+    common_init_result(common_params & params);
+    ~common_init_result();
+
+    llama_model * model();
+    llama_context * context();
+    common_sampler * sampler(llama_seq_id seq_id);
 
-    std::vector<llama_adapter_lora_ptr> lora;
+    std::vector<llama_adapter_lora_ptr> & lora();
 
-    std::vector<llama_sampler_ptr> samplers;
-    std::vector<llama_sampler_seq_config> samplers_seq_config;
+    void free_context();
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
 };
 
-struct common_init_result     common_init_from_params(common_params & params);
+using common_init_result_ptr = std::unique_ptr<common_init_result>;
+
+common_init_result_ptr common_init_from_params(common_params & params);
 
 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 9954e2519d6..a831eac18b6 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -105,7 +105,8 @@ struct common_sampler {
     common_params_sampling params;
 
     struct llama_sampler * grmr;
-    struct llama_sampler * chain;          // CPU sampling chain
+    struct llama_sampler * chain;
+    struct llama_sampler * chain_backend;
 
     ring_buffer<llama_token> prev;
 
@@ -118,6 +119,7 @@ struct common_sampler {
 
         llama_sampler_reset(grmr);
         llama_sampler_reset(chain);
+        llama_sampler_reset(chain_backend);
     }
 
     void set_logits(struct llama_context * ctx, int idx) {
@@ -161,7 +163,8 @@ struct common_sampler {
     mutable int64_t t_total_us = 0;
 };
 
-static bool sampler_backend_supported(enum common_sampler_type type) {
+// TODO: temporary until all samplers have llama_sampler_backend_ API [LLAMA_SAMPLER_BACKEND]
+static bool common_sampler_type_has_backend_support(enum common_sampler_type type) {
     switch (type) {
         case COMMON_SAMPLER_TYPE_TOP_K:
         case COMMON_SAMPLER_TYPE_TEMPERATURE:
@@ -172,98 +175,69 @@ static bool sampler_backend_supported(enum common_sampler_type type) {
     }
 }
 
-static bool is_sampler_enabled(enum common_sampler_type type, const struct common_params_sampling & params) {
+bool common_params_sampling::is_disabled(enum common_sampler_type type) const {
     switch (type) {
         case COMMON_SAMPLER_TYPE_PENALTIES:
-            if (params.penalty_last_n == 64 &&
-                fabs(params.penalty_repeat) <= 1.0f &&
-                fabs(params.penalty_freq)   <= 0.0f &&
-                fabs(params.penalty_present) <= 0.0f) {
-                return false;
+            if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
+                return true;
             }
             break;
         case COMMON_SAMPLER_TYPE_DRY:
-            if (params.dry_multiplier == 0.0f && params.dry_base == 1.75f) {
-                return false;
+            if (dry_multiplier == 0.0f || dry_base < 1.0f || dry_penalty_last_n == 0) {
+                return true;
             }
             break;
         case COMMON_SAMPLER_TYPE_TYPICAL_P:
-            if (params.typ_p == 1.0) {
-                return false;
+            if (typ_p >= 1.0) {
+                return true;
             }
             break;
         case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
-            if (params.top_n_sigma == -1.0) {
-                return false;
+            if (top_n_sigma <= 0.0) {
+                return true;
             }
             break;
         case COMMON_SAMPLER_TYPE_TOP_K:
-            if (params.top_k <= 0) {
-                return false;
+            if (top_k <= 0) {
+                return true;
             }
             break;
         case COMMON_SAMPLER_TYPE_TEMPERATURE:
-            if (params.temp < 0.0f) {
-                return false;
+            if (dynatemp_range <= 0.0f) {
+                return true;
             }
             break;
         case COMMON_SAMPLER_TYPE_MIN_P:
-            if (params.min_p <= 0.0f) {
-                return false;
+            if (min_p <= 0.0f) {
+                return true;
             }
             break;
         case COMMON_SAMPLER_TYPE_TOP_P:
-            if (params.top_p >= 1.0f) {
-                return false;
+            if (top_p >= 1.0f) {
+                return true;
             }
             break;
         case COMMON_SAMPLER_TYPE_XTC:
-            if (params.xtc_probability == 0.0f && params.xtc_threshold == 0.10f) {
-                return false;
+            if (xtc_probability <= 0.0f || xtc_threshold == 0.50f) {
+                return true;
             }
             break;
         default:
             break;
     }
-    return true;
-}
 
-static bool has_logit_bias(const struct common_params_sampling & params) {
-    return !params.logit_bias.empty();
+    return false;
 }
 
-struct active_samplers {
-    std::vector<common_sampler_type> backend_samplers;
-    std::vector<common_sampler_type> cpu_samplers;
-};
-
-static struct active_samplers get_active_samplers(const struct common_params_sampling & params) {
-    struct active_samplers result;
-
-    if (params.mirostat != 0) {
-        // Mirostat is CPU-only and overrides other samplers
-        for (const auto & sampler_type : params.samplers) {
-            if (is_sampler_enabled(sampler_type, params)) {
-                result.cpu_samplers.push_back(sampler_type);
-            }
-        }
-        return result;
-    }
-
-    bool backend_supported = params.backend_sampling;
-
-    for (const auto & sampler_type : params.samplers) {
-        if (!is_sampler_enabled(sampler_type, params)) {
-            continue;
-        }
-
-        if (backend_supported && sampler_backend_supported(sampler_type)) {
-            result.backend_samplers.push_back(sampler_type);
+void common_params_sampling::filter_disabled() {
+    for (auto it = samplers.begin(); it != samplers.end();) {
+        if (is_disabled(*it)) {
+            LOG_WRN("%s: removing disabled sampler %s\n", __func__, common_sampler_type_to_str(*it).c_str());
+            it = samplers.erase(it);
         } else {
-            result.cpu_samplers.push_back(sampler_type);
+            ++it;
         }
     }
-    return result;
 }
 
 std::string common_params_sampling::print() const {
@@ -282,15 +256,7 @@ std::string common_params_sampling::print() const {
     return std::string(result);
 }
 
-struct backend_chain_data {
-    struct llama_sampler * chain;
-    size_t count;
-};
-
-static struct backend_chain_data backend_samplers_init(const struct llama_model * model, const struct common_params_sampling & params,
-        struct active_samplers get_active_samplers);
-
-struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
+struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params) {
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
@@ -357,29 +323,74 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
         }
     }
 
+    // TODO: temporary until all samplers have llama_sampler_backend_ API [LLAMA_SAMPLER_BACKEND]
+    if (params.backend_sampling) {
+        params.filter_disabled();
+    }
+
     auto * result = new common_sampler {
         /* .params        = */ params,
         /* .grmr          = */ grmr,
         /* .chain         = */ llama_sampler_chain_init(lparams),
+        /* .chain_backend = */ llama_sampler_chain_init(lparams),
         /* .prev          = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
         /* .cur           = */ {},
         /* .cur_p         = */ {},
     };
 
-    struct active_samplers active_samplers = get_active_samplers(params);
+    size_t idx_smpl = 0;
 
-    // Build CPU chain
-    if (!params.backend_sampling || !has_logit_bias(params)) {
-        llama_sampler_chain_add(result->chain,
-                llama_sampler_init_logit_bias(
-                    llama_vocab_n_tokens(vocab),
-                    params.logit_bias.size(),
-                    params.logit_bias.data()));
+    bool is_backend = true;
+
+    is_backend = is_backend && params.backend_sampling;
+    is_backend = is_backend && (params.samplers.size() == 0 || common_sampler_type_has_backend_support(params.samplers[idx_smpl]));
+
+    if (params.has_logit_bias()) {
+        if (is_backend) {
+            llama_sampler_chain_add(result->chain_backend,
+                    llama_sampler_backend_init_logit_bias(
+                        llama_vocab_n_tokens(vocab),
+                        params.logit_bias.size(),
+                        params.logit_bias.data()));
+        } else {
+            llama_sampler_chain_add(result->chain,
+                    llama_sampler_init_logit_bias(
+                        llama_vocab_n_tokens(vocab),
+                        params.logit_bias.size(),
+                        params.logit_bias.data()));
+        }
     }
 
     if (params.mirostat == 0) {
+        // backend samplers are added first
+        while (is_backend && idx_smpl < params.samplers.size()) {
+            const auto & cnstr = params.samplers[idx_smpl++];
+
+            if (!common_sampler_type_has_backend_support(cnstr)) {
+                is_backend = false;
+                --idx_smpl;
+                break;
+            }
+
+            switch (cnstr) {
+                case COMMON_SAMPLER_TYPE_TOP_K:
+                    llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_top_k(params.top_k));
+                    break;
+                case COMMON_SAMPLER_TYPE_TEMPERATURE:
+                    llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_temp(params.temp));
+                    break;
+                case COMMON_SAMPLER_TYPE_MIN_P:
+                    llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_min_p(params.min_p));
+                    break;
+                default:
+                    GGML_ASSERT(false && "unsupported backend sampler");
+            }
+        }
+
         // Add remaining CPU samplers
-        for (const auto & cnstr : active_samplers.cpu_samplers) {
+        while (idx_smpl < params.samplers.size()) {
+            const auto & cnstr = params.samplers[idx_smpl++];
+
             switch (cnstr) {
                 case COMMON_SAMPLER_TYPE_DRY:
                     {
@@ -424,7 +435,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
             }
         }
 
-        if (!active_samplers.cpu_samplers.empty()) {
+        if (is_backend) {
+            llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_dist(params.seed));
+        } else {
             llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
         }
     } else if (params.mirostat == 1) {
@@ -440,59 +453,11 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
     return result;
 }
 
-
-static struct backend_chain_data backend_samplers_init(const struct llama_model * model, const struct common_params_sampling & params,
-        struct active_samplers active_samplers) {
-    if (active_samplers.backend_samplers.empty()) {
-        return { nullptr, 0 };
-    }
-
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
-    chain_params.no_perf = params.no_perf;
-    struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
-
-    // Add logit_bias to backend chain if present
-    if (has_logit_bias(params)) {
-        llama_sampler_chain_add(chain, llama_sampler_backend_init_logit_bias(
-                    llama_vocab_n_tokens(vocab),
-                    params.logit_bias.size(),
-                    params.logit_bias.data()));
-    }
-
-    for (const auto & sampler_type : active_samplers.backend_samplers) {
-        switch (sampler_type) {
-            case COMMON_SAMPLER_TYPE_TOP_K:
-                llama_sampler_chain_add(chain, llama_sampler_backend_init_top_k(params.top_k));
-                break;
-            case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                llama_sampler_chain_add(chain, llama_sampler_backend_init_temp(params.temp));
-                break;
-            case COMMON_SAMPLER_TYPE_MIN_P:
-                llama_sampler_chain_add(chain, llama_sampler_backend_init_min_p(params.min_p));
-                break;
-            default:
-                GGML_ASSERT(false && "unsupported backend sampler");
-        }
-    }
-
-    if (active_samplers.cpu_samplers.empty()) {
-        llama_sampler_chain_add(chain, llama_sampler_backend_init_dist(params.seed));
-    }
-
-    return { chain, active_samplers.backend_samplers.size() + has_logit_bias(params) };
-}
-
-struct llama_sampler * common_sampler_backend_init(const struct llama_model * model, const struct common_params_sampling & params) {
-    struct active_samplers active_samplers = get_active_samplers(params);
-    return backend_samplers_init(model, params, active_samplers).chain;
-}
-
 void common_sampler_free(struct common_sampler * gsmpl) {
     if (gsmpl) {
         llama_sampler_free(gsmpl->grmr);
         llama_sampler_free(gsmpl->chain);
+        llama_sampler_free(gsmpl->chain_backend);
 
         delete gsmpl;
     }
@@ -519,6 +484,7 @@ struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
         /* .params        = */ gsmpl->params,
         /* .grmr          = */ llama_sampler_clone(gsmpl->grmr),
         /* .chain         = */ llama_sampler_clone(gsmpl->chain),
+        /* .chain_backend = */ llama_sampler_clone(gsmpl->chain_backend),
         /* .prev          = */ gsmpl->prev,
         /* .cur           = */ gsmpl->cur,
         /* .cur_p         = */ gsmpl->cur_p,
@@ -570,6 +536,10 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
     }
 }
 
+struct llama_sampler * common_sampler_chain_backend(const struct common_sampler * gsmpl) {
+    return gsmpl->chain_backend;
+}
+
 llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
     // Check if a backend sampler has already sampled a token in which case we
     // return that token id directly.
@@ -707,7 +677,12 @@ llama_token common_sampler_last(const struct common_sampler * gsmpl) {
 }
 
 std::string common_sampler_print(const struct common_sampler * gsmpl) {
-    std::string result = "logits ";
+    std::string result = llama_sampler_chain_n(gsmpl->chain_backend) > 0 ? "*logits " : "logits ";
+
+    for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain_backend); i++) {
+        const auto * smpl = llama_sampler_chain_get(gsmpl->chain_backend, i);
+        result += std::string("-> *") + llama_sampler_name(smpl) + " ";
+    }
 
     for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
         const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
diff --git a/common/sampling.h b/common/sampling.h
index 0ec164de053..06f27923a0d 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -36,14 +36,8 @@ struct common_sampler;
 
 // llama_sampler API overloads
 
-struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
-
-// Create a backend sampler chain from common sampling parameters
-// Returns a llama_sampler chain configured with backend samplers based on the parameters
-// This chain can be used per-sequence for backend-based sampling
-// Note: Only samplers that have backend equivalents will be added to the chain
-// The returned sampler should be freed with llama_sampler_free()
-struct llama_sampler * common_sampler_backend_init(const struct llama_model * model, const struct common_params_sampling & params);
+// TODO: params should become const again [LLAMA_SAMPLER_BACKEND]
+struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params);
 
 void common_sampler_free(struct common_sampler * gsmpl);
 
@@ -55,6 +49,8 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
 // arguments can be nullptr to skip printing
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
 
+struct llama_sampler * common_sampler_chain_backend(const struct common_sampler * gsmpl);
+
 // extended sampling implementation:
 //
 // - set logits
@@ -114,3 +110,9 @@ std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std:
 
 llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
                 const char * grammar_kind, const char * grammar_data);
+
+struct common_sampler_deleter {
+    void operator()(common_sampler * s) { common_sampler_free(s); }
+};
+
+typedef std::unique_ptr<common_sampler, common_sampler_deleter> common_sampler_ptr;
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index e9d1fc95c26..e23a3bab215 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -65,30 +65,35 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_predict, n_parallel);
 
-    std::vector<llama_sampler_seq_config> sampler_configs(n_parallel);
-    if (params.sampling.backend_sampling) {
-        for (int32_t i = 0; i < n_parallel; ++i) {
-            llama_sampler * backend_sampler = common_sampler_backend_init(model, params.sampling);
-            if (backend_sampler) {
-                sampler_configs[i] = { i, backend_sampler };
-            }
+    auto sparams = llama_sampler_chain_default_params();
+    sparams.no_perf = false;
+
+    std::vector<llama_sampler_seq_config> sampler_configs;
+
+    for (int32_t i = 0; i < n_parallel; ++i) {
+        llama_sampler * smpl = llama_sampler_chain_init(sparams);
+
+        if (params.sampling.backend_sampling) {
+            llama_sampler_chain_add(smpl, llama_sampler_backend_init_top_k(params.sampling.top_k));
+            llama_sampler_chain_add(smpl, llama_sampler_backend_init_temp (params.sampling.temp));
+            llama_sampler_chain_add(smpl, llama_sampler_backend_init_dist (params.sampling.seed));
+        } else {
+            llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
+            llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
+            llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
+            llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
         }
+
+        sampler_configs.push_back({ i, smpl });
+    }
+
+    if (params.sampling.backend_sampling) {
         ctx_params.samplers   = sampler_configs.data();
-        ctx_params.n_samplers = n_parallel;
+        ctx_params.n_samplers = sampler_configs.size();
     }
 
     llama_context * ctx = llama_init_from_model(model, ctx_params);
 
-    auto sparams = llama_sampler_chain_default_params();
-    sparams.no_perf = false;
-
-    llama_sampler * smpl = llama_sampler_chain_init(sparams);
-
-    llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
-    llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
-    llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
-    llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
-
     if (ctx == NULL) {
         LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);
         return 1;
@@ -186,7 +191,7 @@ int main(int argc, char ** argv) {
                 continue;
             }
 
-            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
+            const llama_token new_token_id = llama_sampler_sample(sampler_configs[i].sampler, ctx, i_batch[i]);
 
             // is it an end of generation? -> mark the stream as finished
             if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
@@ -242,14 +247,17 @@ int main(int argc, char ** argv) {
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
     LOG("\n");
-    llama_perf_sampler_print(smpl);
+    llama_perf_sampler_print(sampler_configs[0].sampler);
     llama_perf_context_print(ctx);
 
     fprintf(stderr, "\n");
 
     llama_batch_free(batch);
 
-    llama_sampler_free(smpl);
+    for (auto & sampler_config : sampler_configs) {
+        llama_sampler_free(sampler_config.sampler);
+    }
+
     llama_free(ctx);
     llama_model_free(model);
 
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 9e3ab5905bb..ccf5bb6f55c 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -130,10 +130,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index 80c693ce61c..408338f1afc 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -202,10 +202,10 @@ int main(int argc, char ** argv) {
     params.warmup = false;
 
     // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == nullptr || ctx == nullptr) {
         LOG_ERR("%s : failed to init\n", __func__);
diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp
index 1e26d8221b8..f54cfdd77f2 100644
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -55,10 +55,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the target model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     auto * mem = llama_get_memory(ctx);
 
diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp
index 3da45ed9e03..bb94a8fe06d 100644
--- a/examples/lookup/lookup-create.cpp
+++ b/examples/lookup/lookup-create.cpp
@@ -18,16 +18,16 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model_ptr & model = llama_init.model;
-    llama_context_ptr & ctx = llama_init.context;
+    auto * model = llama_init->model();
+    auto * ctx = llama_init->context();
 
     GGML_ASSERT(model != nullptr);
 
     // tokenize the prompt
     std::vector<llama_token> inp;
-    inp = common_tokenize(ctx.get(), params.prompt, true, true);
+    inp = common_tokenize(ctx, params.prompt, true, true);
     fprintf(stderr, "%s: tokenization done\n", __func__);
 
     common_ngram_cache ngram_cache;
diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp
index fcb289abe0e..135f6fcab95 100644
--- a/examples/lookup/lookup-stats.cpp
+++ b/examples/lookup/lookup-stats.cpp
@@ -28,13 +28,13 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_context_ptr & ctx = llama_init.context;
+    llama_context * ctx = llama_init->context();
 
     // tokenize the prompt
     std::vector<llama_token> inp;
-    inp = common_tokenize(ctx.get(), params.prompt, true, true);
+    inp = common_tokenize(ctx, params.prompt, true, true);
 
     common_ngram_cache ngram_cache_context;
     common_ngram_cache ngram_cache_dynamic;
@@ -65,7 +65,7 @@ int main(int argc, char ** argv){
     }
 
     const int n_input = inp.size();
-    const int n_ctx = llama_n_ctx(ctx.get());
+    const int n_ctx = llama_n_ctx(ctx);
 
     int n_drafted = 0;
     int n_accept  = 0;
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index 2bfa26b55f0..27f159940a4 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -29,10 +29,10 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index e48f48fc322..c92173ae291 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -192,10 +192,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the target model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     auto * mem = llama_get_memory(ctx);
 
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
index 042e12c2bf8..2c2143ad108 100644
--- a/examples/retrieval/retrieval.cpp
+++ b/examples/retrieval/retrieval.cpp
@@ -149,10 +149,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index 1065ec6bb00..615929c1e0c 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -34,10 +34,10 @@ int main(int argc, char ** argv) {
     std::string result2;
 
     // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == nullptr || ctx == nullptr) {
         fprintf(stderr, "%s : failed to init\n", __func__);
diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp
index a8e53f28eb5..a5bd5c9125e 100644
--- a/examples/speculative-simple/speculative-simple.cpp
+++ b/examples/speculative-simple/speculative-simple.cpp
@@ -40,10 +40,10 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    common_init_result llama_init_tgt = common_init_from_params(params);
+    auto llama_init_tgt = common_init_from_params(params);
 
-    model_tgt = llama_init_tgt.model.get();
-    ctx_tgt   = llama_init_tgt.context.get();
+    model_tgt = llama_init_tgt->model();
+    ctx_tgt   = llama_init_tgt->context();
 
     const llama_vocab * vocab = llama_model_get_vocab(model_tgt);
 
@@ -61,10 +61,10 @@ int main(int argc, char ** argv) {
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
     params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
 
-    common_init_result llama_init_dft = common_init_from_params(params);
+    auto llama_init_dft = common_init_from_params(params);
 
-    //model_dft = llama_init_dft.model.get();
-    ctx_dft   = llama_init_dft.context.get();
+    //model_dft = llama_init_dft->model();
+    ctx_dft   = llama_init_dft->context();
 
     if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
         LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str());
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 5f5ac5eb64d..89d3249431e 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -71,10 +71,10 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    common_init_result llama_init_tgt = common_init_from_params(params);
+    auto llama_init_tgt = common_init_from_params(params);
 
-    model_tgt = llama_init_tgt.model.get();
-    ctx_tgt   = llama_init_tgt.context.get();
+    model_tgt = llama_init_tgt->model();
+    ctx_tgt   = llama_init_tgt->context();
 
     // load the draft model
     params.devices = params.speculative.devices;
@@ -87,10 +87,10 @@ int main(int argc, char ** argv) {
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
     params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
 
-    common_init_result llama_init_dft = common_init_from_params(params);
+    auto llama_init_dft = common_init_from_params(params);
 
-    model_dft = llama_init_dft.model.get();
-    ctx_dft   = llama_init_dft.context.get();
+    model_dft = llama_init_dft->model();
+    ctx_dft   = llama_init_dft->context();
 
     const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
     const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp
index 416d8d8f6c8..c82de8d35d0 100644
--- a/examples/training/finetune.cpp
+++ b/examples/training/finetune.cpp
@@ -39,9 +39,10 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
     // load the model and apply lora adapter, if any
-    common_init_result   llama_init = common_init_from_params(params);
-    llama_model_ptr    & model      = llama_init.model;
-    llama_context_ptr  & ctx        = llama_init.context;
+    auto llama_init = common_init_from_params(params);
+
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
@@ -54,8 +55,8 @@ int main(int argc, char ** argv) {
         LOG_INF("%s\n", common_params_get_system_info(params).c_str());
     }
 
-    std::vector<llama_token> tokens  = common_tokenize(ctx.get(), params.prompt, true);
-    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get()) / 2);
+    std::vector<llama_token> tokens  = common_tokenize(ctx, params.prompt, true);
+    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx, tokens, llama_n_ctx(ctx) / 2);
 
     struct lr_opt & lr = params.lr;
     LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n",
@@ -70,7 +71,7 @@ int main(int argc, char ** argv) {
         /*get_opt_pars_ud =*/&params.lr,
         /*optimizer_type  =*/params.optimizer,
     };
-    llama_opt_init(ctx.get(), model.get(), lopt_params);
+    llama_opt_init(ctx, model, lopt_params);
 
     const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split);
 
@@ -78,7 +79,7 @@ int main(int argc, char ** argv) {
     ggml_opt_result_t result_eval  = ggml_opt_result_init();
 
     for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) {
-        llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
+        llama_opt_epoch(ctx, dataset, result_train, result_eval, idata_split,
                         ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
         fprintf(stderr, "\n");
 
@@ -88,7 +89,7 @@ int main(int argc, char ** argv) {
     ggml_opt_result_free(result_train);
     ggml_opt_result_free(result_eval);
 
-    llama_model_save_to_file(model.get(), params.out_file.c_str());
+    llama_model_save_to_file(model, params.out_file.c_str());
 
     llama_backend_free();
 
diff --git a/include/llama.h b/include/llama.h
index 50a4cc7c139..080ac27f1f2 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -376,7 +376,7 @@ extern "C" {
                           // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix
                           // ref: https://github.com/ggml-org/llama.cpp/pull/14363
 
-        // backend sampler chain configuration
+        // backend sampler chain configuration (does not keep a reference, so make sure the caller keeps the samplers alive)
         struct llama_sampler_seq_config * samplers;
         size_t                            n_samplers;
     };
@@ -1209,10 +1209,6 @@ extern "C" {
 
         void                   (*init_ggml)(struct llama_sampler      * smpl,
                                             ggml_backend_buffer_type_t  buft);
-
-
-        // TODO: API for internal libllama usage for appending the sampling to an existing ggml_cgraph
-        //void (*apply_ggml) (struct llama_sampler * smpl, ...);
     };
 
     struct llama_sampler {
@@ -1231,18 +1227,17 @@ extern "C" {
     LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl);
     // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add)
     LLAMA_API void                   llama_sampler_free  (      struct llama_sampler * smpl);
-    LLAMA_API void                   llama_sampler_init_ggml(struct llama_sampler      * smpl,
-                                                             ggml_backend_buffer_type_t  buft);
-    LLAMA_API void                   llama_sampler_set_input_ggml(struct llama_sampler * smpl);
-    LLAMA_API void                   llama_sampler_apply_ggml(  struct llama_sampler           * smpl,
-                                                                struct ggml_context            * ctx,
-                                                                struct ggml_cgraph             * gf,
-                                                                struct llama_sampler_ggml_data * ggml_data);
-
-    LLAMA_API void                   llama_sampler_accept_ggml( struct llama_sampler * smpl,
-                                                                struct ggml_context  * ctx,
-                                                                struct  ggml_cgraph  * gf,
-                                                                struct ggml_tensor   * selected_token);
+
+    LLAMA_API void llama_sampler_init_ggml     (struct llama_sampler * smpl, ggml_backend_buffer_type_t buft);
+    LLAMA_API void llama_sampler_set_input_ggml(struct llama_sampler * smpl);
+    LLAMA_API void llama_sampler_apply_ggml    (struct llama_sampler * smpl,
+                                                struct ggml_context  * ctx,
+                                                struct ggml_cgraph   * gf,
+                                                struct llama_sampler_ggml_data * ggml_data);
+    LLAMA_API void llama_sampler_accept_ggml   (struct llama_sampler * smpl,
+                                                struct ggml_context  * ctx,
+                                                struct ggml_cgraph   * gf,
+                                                struct ggml_tensor   * selected_token);
 
     // llama_sampler_chain
     // a type of llama_sampler that can chain multiple samplers one after another
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index b8c5accff88..3e15789f289 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -66,7 +66,15 @@ llama_context::llama_context(
 
         for (size_t i = 0; i < params.n_samplers; ++i) {
             const auto & config = params.samplers[i];
+
+            const int n_samplers = llama_sampler_chain_n(config.sampler);
+            if (n_samplers <= 0) {
+                continue;
+            }
+
             sampling.samplers[config.seq_id] = config.sampler;
+
+            LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
         }
     }
 
@@ -438,8 +446,8 @@ llama_context::llama_context(
 
     // Initialize the full vocabulary token ids for backend samplers.
     {
-        const llama_vocab * vocab = llama_model_get_vocab(&model);
-        const int n_vocab = llama_vocab_n_tokens(vocab);
+        const int n_vocab = model.vocab.n_tokens();
+
         sampling.token_ids_full_vocab.resize(n_vocab);
         for (int i = 0; i < n_vocab; ++i) {
             sampling.token_ids_full_vocab[i] = i;
@@ -449,10 +457,6 @@ llama_context::llama_context(
 
 llama_context::~llama_context() {
     ggml_opt_free(opt_ctx);
-    // TODO: perhaps use a smart pointer for samplers
-    for (auto const& [seq_id, sampler] : sampling.samplers) {
-        llama_sampler_free(sampler);
-    }
 }
 
 void llama_context::synchronize() {
@@ -910,31 +914,10 @@ void llama_context::set_warmup(bool value) {
 void llama_context::set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
     LLAMA_LOG_DEBUG("%s: seq_id = %d, sampler = %p\n", __func__, (int) seq_id, (void *) sampler);
 
-    auto it = sampling.samplers.find(seq_id);
-    if (it != sampling.samplers.end()) {
-        // If the sampler to be set is the same that is already set, do nothing.
-        if (it->second == sampler) {
-            return;
-        }
-
-        llama_sampler_free(it->second);
-
-        // If sampler is nullptr, we remove the samppler chain for this seq_id.
-        // chain for this seq_id.
-        if (sampler == nullptr) {
-            sampling.samplers.erase(it);
-            return;
-        }
-
-        // Otherwise, we replace the existing sampler with the new one.
-        it->second = sampler;
-        return;
-    }
-
-    // If there is no sampler for this seq_id and the caller provides a non-null
-    // sampler, we set it.
-    if (sampler != nullptr) {
+    if (sampler != nullptr && llama_sampler_chain_n(sampler) > 0) {
         sampling.samplers[seq_id] = sampler;
+    } else {
+        sampling.samplers.erase(seq_id);
     }
 }
 
@@ -1700,8 +1683,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & ba
         sampling.sampled_size    = n_outputs_max;
         sampling.candidates_size = n_vocab*n_outputs_max;
 
-        backend_float_count      = sampling.logits_size + sampling.probs_size;
-        backend_token_count      = sampling.sampled_size + sampling.candidates_size;
+        backend_float_count = sampling.logits_size + sampling.probs_size;
+        backend_token_count = sampling.sampled_size + sampling.candidates_size;
     }
 
     if (output_ids.empty()) {
diff --git a/src/llama-context.h b/src/llama-context.h
index 1dcd3bf419b..2940d337a8e 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -258,7 +258,7 @@ struct llama_context {
     float * logits      = nullptr;
 
     struct sampling_info {
-        std::unordered_map<llama_seq_id, llama_sampler*> samplers;
+        std::unordered_map<llama_seq_id, llama_sampler *> samplers;
 
         float       * logits      = nullptr;
         size_t        logits_size = 0;
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 5c3214f029f..4d1760a629b 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -439,10 +439,10 @@ void llama_sampler_free(struct llama_sampler * smpl) {
 }
 
 llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx) {
-    const llama_token   sampled_token        = llama_get_backend_sampled_token_ith(ctx, idx);
-    const float *       sampled_probs        = llama_get_backend_sampled_probs_ith(ctx, idx);
-    const float *       sampled_logits       = llama_get_backend_sampled_logits_ith(ctx, idx);
-    const llama_token * sampled_ids          = llama_get_backend_sampled_candidates_ith(ctx, idx);
+    const llama_token   sampled_token  = llama_get_backend_sampled_token_ith     (ctx, idx);
+    const float *       sampled_probs  = llama_get_backend_sampled_probs_ith     (ctx, idx);
+    const float *       sampled_logits = llama_get_backend_sampled_logits_ith    (ctx, idx);
+    const llama_token * sampled_ids    = llama_get_backend_sampled_candidates_ith(ctx, idx);
 
     // If a backend sampler has already sampled a token, return it.
     if (sampled_token != LLAMA_TOKEN_NULL) {
diff --git a/tools/cvector-generator/cvector-generator.cpp b/tools/cvector-generator/cvector-generator.cpp
index d2d97e05ceb..3ba7c529506 100644
--- a/tools/cvector-generator/cvector-generator.cpp
+++ b/tools/cvector-generator/cvector-generator.cpp
@@ -419,10 +419,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model to get hparams
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     // int n_ctx = llama_n_ctx(ctx);
     int n_layers = llama_model_n_layer(model);
diff --git a/tools/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp
index f28a036deeb..669de55ddb9 100644
--- a/tools/imatrix/imatrix.cpp
+++ b/tools/imatrix/imatrix.cpp
@@ -1265,10 +1265,10 @@ int main(int argc, char ** argv) {
     params.warmup = false;
 
     // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == nullptr || ctx == nullptr) {
         LOG_ERR("%s : failed to init\n", __func__);
diff --git a/tools/main/main.cpp b/tools/main/main.cpp
index 263387f4175..6f64708dcd1 100644
--- a/tools/main/main.cpp
+++ b/tools/main/main.cpp
@@ -138,9 +138,11 @@ int main(int argc, char ** argv) {
     // load the model and apply lora adapter, if any
     LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
 
-    common_init_result llama_init = common_init_from_params(params);
-    ctx = llama_init.context.get();
-    model = llama_init.model.get(); // Update pointer (now managed by llama_init)
+    auto llama_init = common_init_from_params(params);
+
+    ctx   = llama_init->context();
+    model = llama_init->model();
+    smpl  = llama_init->sampler(0);
 
     if (ctx == NULL) {
         LOG_ERR("%s: error: unable to create context\n", __func__);
@@ -470,12 +472,6 @@ int main(int argc, char ** argv) {
         }
     }
 
-    smpl = common_sampler_init(model, sparams);
-    if (!smpl) {
-        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
-        return 1;
-    }
-
     LOG_INF("sampler seed: %u\n",     common_sampler_get_seed(smpl));
     LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
     LOG_INF("sampler chain: %s\n",    common_sampler_print(smpl).c_str());
@@ -989,8 +985,6 @@ int main(int argc, char ** argv) {
     LOG("\n\n");
     common_perf_print(ctx, smpl);
 
-    common_sampler_free(smpl);
-
     llama_backend_free();
 
     ggml_threadpool_free_fn(threadpool);
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index 6679de309b4..aaec65d8ff6 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -65,7 +65,7 @@ static void sigint_handler(int signo) {
 
 struct mtmd_cli_context {
     mtmd::context_ptr ctx_vision;
-    common_init_result llama_init;
+    common_init_result_ptr llama_init;
 
     llama_model       * model;
     llama_context     * lctx;
@@ -89,8 +89,8 @@ struct mtmd_cli_context {
     llama_pos n_past = 0;
 
     mtmd_cli_context(common_params & params) : llama_init(common_init_from_params(params)) {
-        model = llama_init.model.get();
-        lctx = llama_init.context.get();
+        model = llama_init->model();
+        lctx = llama_init->context();
         vocab = llama_model_get_vocab(model);
         smpl = common_sampler_init(model, params.sampling);
         n_threads = params.cpuparams.n_threads;
diff --git a/tools/perplexity/perplexity.cpp b/tools/perplexity/perplexity.cpp
index caf080e8d18..1ead9c871e9 100644
--- a/tools/perplexity/perplexity.cpp
+++ b/tools/perplexity/perplexity.cpp
@@ -2024,10 +2024,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model and apply lora adapter, if any
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index d083777fa7e..c38131b5870 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -207,8 +207,8 @@ task_params server_task::params_from_json_cmpl(
     params.sampling.min_keep           = json_value(data, "min_keep",            defaults.sampling.min_keep);
     params.post_sampling_probs         = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);
 
-    const bool request_backend_sampling    = json_value(data, "backend_sampling",        defaults.sampling.backend_sampling);
-    params.sampling.backend_sampling       = defaults.sampling.backend_sampling && request_backend_sampling;
+    const bool request_backend_sampling = json_value(data, "backend_sampling", defaults.sampling.backend_sampling);
+    params.sampling.backend_sampling = defaults.sampling.backend_sampling && request_backend_sampling;
 
     params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
     params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 48a7cf2c826..0517d21518a 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -151,8 +151,7 @@ struct server_slot {
     // sampling
     json json_schema;
 
-    struct common_sampler * smpl = nullptr;
-    llama_sampler * backend_sampler = nullptr;
+    common_sampler_ptr smpl;
 
     llama_token sampled;
 
@@ -198,13 +197,6 @@ struct server_slot {
         n_draft_total = 0;
         n_draft_accepted = 0;
 
-        if (backend_sampler != nullptr) {
-            if (ctx != nullptr) {
-                llama_set_backend_sampler(ctx, id, nullptr);
-            }
-            backend_sampler = nullptr;
-        }
-
         task.reset();
         task_prev.reset();
 
@@ -481,8 +473,8 @@ struct server_context {
     common_params params_base;
 
     // note: keep these alive - they determine the lifetime of the model, context, etc.
-    common_init_result llama_init;
-    common_init_result llama_init_dft;
+    common_init_result_ptr llama_init;
+    common_init_result_ptr llama_init_dft;
 
     llama_model * model = nullptr;
     llama_context * ctx = nullptr;
@@ -526,16 +518,6 @@ struct server_context {
 
         // Clear any sampling context
         for (server_slot & slot : slots) {
-            common_sampler_free(slot.smpl);
-            slot.smpl = nullptr;
-
-            if (slot.backend_sampler != nullptr) {
-                if (ctx != nullptr) {
-                    llama_set_backend_sampler(ctx, slot.id, nullptr);
-                }
-                slot.backend_sampler = nullptr;
-            }
-
             llama_free(slot.ctx_dft);
             slot.ctx_dft = nullptr;
 
@@ -556,8 +538,8 @@ struct server_context {
 
         llama_init = common_init_from_params(params_base);
 
-        model = llama_init.model.get();
-        ctx   = llama_init.context.get();
+        model = llama_init->model();
+        ctx   = llama_init->context();
 
         if (model == nullptr) {
             SRV_ERR("failed to load model, '%s'\n", params_base.model.path.c_str());
@@ -589,25 +571,25 @@ struct server_context {
 
             llama_init_dft = common_init_from_params(params_dft);
 
-            model_dft = llama_init_dft.model.get();
+            model_dft = llama_init_dft->model();
 
             if (model_dft == nullptr) {
                 SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.path.c_str());
                 return false;
             }
 
-            vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft.context.get());
+            vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft->context());
             if (!vocab_dft_compatible) {
                 SRV_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params_base.speculative.model.path.c_str(), params_base.model.path.c_str());
             }
 
-            const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get());
+            const int n_ctx_dft = llama_n_ctx(llama_init_dft->context());
 
             cparams_dft = common_context_params_to_llama(params_dft);
             cparams_dft.n_batch = n_ctx_dft;
 
             // the context is not needed - we will create one for each slot
-            llama_init_dft.context.reset();
+            llama_init_dft->free_context();
         }
 
         chat_templates = common_chat_templates_init(model, params_base.chat_template);
@@ -1001,23 +983,17 @@ struct server_context {
 
         // initialize samplers
         {
-            if (slot.smpl != nullptr) {
-                common_sampler_free(slot.smpl);
-            }
-
-            slot.smpl = common_sampler_init(model, task.params.sampling);
+            slot.smpl.reset(common_sampler_init(model, task.params.sampling));
             if (slot.smpl == nullptr) {
                 // for now, the only error that may happen here is invalid grammar
                 send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
 
-            SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl).c_str());
-        }
+            SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl.get()).c_str());
 
-        if (!configure_slot_backend_sampler(slot, task.params.sampling)) {
-            send_error(task, "Failed to configure backend samplers", ERROR_TYPE_SERVER);
-            return false;
+            llama_sampler * backend_chain = common_sampler_chain_backend(slot.smpl.get());
+            llama_set_backend_sampler(ctx, slot.id, backend_chain);
         }
 
         // initialize draft batch
@@ -1037,39 +1013,6 @@ struct server_context {
         return true;
     }
 
-    bool configure_slot_backend_sampler(server_slot & slot, const common_params_sampling & sampling) {
-        if (!sampling.backend_sampling) {
-            if (slot.backend_sampler != nullptr) {
-                llama_set_backend_sampler(ctx, slot.id, nullptr);
-                slot.backend_sampler = nullptr;
-            }
-            return true;
-        }
-
-        llama_sampler * backend_chain = common_sampler_backend_init(model, sampling);
-        // The sampler types configured with --samplers might not be supported
-        // by backend samplers in which case we disable backend sampling and
-        // fallback to CPU only sampling.
-        if (backend_chain == nullptr) {
-            if (slot.backend_sampler != nullptr) {
-                llama_set_backend_sampler(ctx, slot.id, nullptr);
-                slot.backend_sampler = nullptr;
-            }
-            SLT_INF(slot, "%s", "no backend samplers configured (sampler chain doesn't start with backend-supported samplers)\n");
-            return true;
-        }
-
-        if (slot.backend_sampler != nullptr) {
-            llama_set_backend_sampler(ctx, slot.id, nullptr);
-            slot.backend_sampler = nullptr;
-        }
-
-        slot.backend_sampler = backend_chain;
-        llama_set_backend_sampler(ctx, slot.id, backend_chain);
-        SLT_INF(slot, "%s", "configured backend samplers\n");
-        return true;
-    }
-
     bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
         const std::string token_str = result.text_to_send;
@@ -1206,7 +1149,7 @@ struct server_context {
         size_t n_vocab = llama_vocab_n_tokens(vocab);
 
         if (post_sampling) {
-            const auto * cur_p = common_sampler_get_candidates(slot.smpl, true);
+            const auto * cur_p = common_sampler_get_candidates(slot.smpl.get(), true);
             const size_t max_probs = cur_p->size;
 
             // set probability for sampled token
@@ -2185,13 +2128,13 @@ struct server_context {
 
                         GGML_ASSERT(batch.n_tokens > 0);
 
-                        common_sampler_reset(slot.smpl);
+                        common_sampler_reset(slot.smpl.get());
 
                         // Process all prompt tokens through sampler system
                         for (int i = 0; i < slot.task->n_tokens(); ++i) {
                             llama_token id = input_tokens[i];
                             if (id != LLAMA_TOKEN_NULL) {
-                                common_sampler_accept(slot.smpl, id, false);
+                                common_sampler_accept(slot.smpl.get(), id, false);
                             }
                         }
 
@@ -2381,11 +2324,11 @@ struct server_context {
 
                 const int tok_idx = slot.i_batch - i;
 
-                llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx);
+                llama_token id = common_sampler_sample(slot.smpl.get(), ctx, tok_idx);
 
                 slot.i_batch = -1;
 
-                common_sampler_accept(slot.smpl, id, true);
+                common_sampler_accept(slot.smpl.get(), id, true);
 
                 slot.n_decoded += 1;
 
@@ -2488,7 +2431,7 @@ struct server_context {
                 llama_decode(ctx, slot.batch_spec);
 
                 // the accepted tokens from the speculation
-                const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, draft);
+                const auto ids = common_sampler_sample_and_accept_n(slot.smpl.get(), ctx, draft);
 
                 slot.n_decoded += ids.size();
 
diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp
index eaf56591d9d..8c39fce8baa 100644
--- a/tools/tts/tts.cpp
+++ b/tools/tts/tts.cpp
@@ -568,10 +568,10 @@ int main(int argc, char ** argv) {
     llama_context * ctx_ttc = NULL;
     llama_context * ctx_cts = NULL;
 
-    common_init_result llama_init_ttc = common_init_from_params(params);
+    auto llama_init_ttc = common_init_from_params(params);
 
-    model_ttc = llama_init_ttc.model.get();
-    ctx_ttc   = llama_init_ttc.context.get();
+    model_ttc = llama_init_ttc->model();
+    ctx_ttc   = llama_init_ttc->context();
 
     if (model_ttc == nullptr || ctx_ttc == nullptr) {
         return ENOENT;
@@ -583,10 +583,10 @@ int main(int argc, char ** argv) {
     params.embedding = true;
     params.n_ubatch = params.n_batch;
 
-    common_init_result llama_init_cts = common_init_from_params(params);
+    auto llama_init_cts = common_init_from_params(params);
 
-    model_cts = llama_init_cts.model.get();
-    ctx_cts   = llama_init_cts.context.get();
+    model_cts = llama_init_cts->model();
+    ctx_cts   = llama_init_cts->context();
 
     if (model_cts == nullptr || ctx_cts == nullptr) {
         return ENOENT;

From 333da805feef6858def23ee091983e0c2827f6e7 Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Fri, 28 Nov 2025 15:08:20 +0100
Subject: [PATCH 52/59] Add initial version for top-p sampling

As we only support static graphs for the time and we don't know the size
of the output of top-p, we have to do value-scaling same as for min-p
operator.

Further improvements can be applied to the unit-test (i.e. check for
equivalence of top_p happening on backend with top_p happening on cpu)
and also by constructing candidates and sorting those as opposed to
reversing the sort of the logits (this would be arange +
get_rows instead of argsort + get_rows)
---
 common/sampling.cpp            |   4 ++
 include/llama.h                |   3 +
 src/llama-backend-sampler.cpp  | 122 +++++++++++++++++++++++++++++++++
 tests/test-backend-sampler.cpp |  56 +++++++++++++++
 4 files changed, 185 insertions(+)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index a831eac18b6..94367bd307f 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -169,6 +169,7 @@ static bool common_sampler_type_has_backend_support(enum common_sampler_type typ
         case COMMON_SAMPLER_TYPE_TOP_K:
         case COMMON_SAMPLER_TYPE_TEMPERATURE:
         case COMMON_SAMPLER_TYPE_MIN_P:
+        case COMMON_SAMPLER_TYPE_TOP_P:
             return true;
         default:
             return false;
@@ -382,6 +383,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
                 case COMMON_SAMPLER_TYPE_MIN_P:
                     llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_min_p(params.min_p));
                     break;
+                case COMMON_SAMPLER_TYPE_TOP_P:
+                    llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_top_p(params.top_p));
+                    break;
                 default:
                     GGML_ASSERT(false && "unsupported backend sampler");
             }
diff --git a/include/llama.h b/include/llama.h
index 080ac27f1f2..38178d919f8 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1403,6 +1403,9 @@ extern "C" {
     /// @details Min-P filtering on backend - filter tokens with a probability less than p times the maximum probability.
     LLAMA_API struct llama_sampler * llama_sampler_backend_init_min_p(float p);
 
+    /// @details Top-p filtering on backend - filter all tokens with cumulative pseudo-probability less than p.
+    LLAMA_API struct llama_sampler * llama_sampler_backend_init_top_p(float p);
+
     // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
     LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
 
diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
index 6a3893b1294..a4f22055e6a 100644
--- a/src/llama-backend-sampler.cpp
+++ b/src/llama-backend-sampler.cpp
@@ -596,3 +596,125 @@ struct llama_sampler * llama_sampler_backend_init_min_p(float p) {
 
     return sampler;
 }
+
+
+struct llama_sampler_backend_top_p_ctx {
+    float p;
+
+    // Only required for checking operation support and can be removed later.
+    ggml_backend_dev_t device;
+};
+
+
+static void llama_sampler_backend_top_p_init_ggml(
+        struct llama_sampler           * smpl,
+        ggml_backend_buffer_type_t       buft) {
+    auto * sctx = (llama_sampler_backend_top_p_ctx *) smpl->ctx;
+    sctx->device = ggml_backend_buft_get_device(buft);
+}
+
+static void llama_sampler_backend_top_p_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    GGML_UNUSED(gf);
+
+    auto * sctx = (llama_sampler_backend_top_p_ctx *) smpl->ctx;
+
+    struct ggml_tensor * softmax = ggml_soft_max(ctx, ggml_data->logits);
+    ggml_set_name(softmax, "top_p_softmax");
+
+    // Get the sorted indices of the softmax probabilities in descending order.
+    struct ggml_tensor * sorted_idx = ggml_argsort(ctx, softmax, GGML_SORT_ORDER_DESC);
+    ggml_set_name(sorted_idx, "top_p_sorted_idx");
+
+    // Do the sorting via reshape + get_rows
+    struct ggml_tensor * softmax_reshaped = ggml_reshape_2d(ctx, softmax, 1, softmax->ne[0]);
+    ggml_set_name(softmax_reshaped, "top_p_softmax_reshaped");
+
+    struct ggml_tensor * sorted_probs = ggml_get_rows(ctx, softmax_reshaped, sorted_idx);
+    ggml_set_name(sorted_probs, "top_p_sorted_probs");
+
+    struct ggml_tensor * sorted_probs_reshaped = ggml_reshape_2d(ctx, sorted_probs, softmax->ne[0], 1);
+    ggml_set_name(sorted_probs_reshaped, "top_p_sorted_probs_reshaped");
+    // Compute Cumulative Distribution Function (CDF) by means of GGML_OP_CUMSUM.
+    struct ggml_tensor * sorted_cdf = ggml_cumsum(ctx, sorted_probs_reshaped);
+    ggml_set_name(sorted_cdf, "top_p_sorted_cdf");
+
+    // Invert CDF and add top-p value so that ggml_step yields 1 for values we want to keep
+    struct ggml_tensor * sorted_cdf_scaled = ggml_scale_bias(ctx, sorted_cdf, -1.0f, sctx->p);
+    ggml_set_name(sorted_cdf_scaled, "top_p_sorted_cdf_scaled");
+
+    struct ggml_tensor * sorted_mask = ggml_step(ctx, sorted_cdf_scaled);
+    ggml_set_name(sorted_mask, "top_p_sorted_mask");
+
+    // reverse sorting by argsort(argsort)
+    // cast to F32 since cuda only supports float inputs
+    struct ggml_tensor * reverse_argsort = ggml_argsort(ctx, ggml_cast(ctx, sorted_idx, GGML_TYPE_F32), GGML_SORT_ORDER_ASC);
+    ggml_set_name(reverse_argsort, "top_p_reverse_argsort");
+    
+    // Do the sorting via reshape + get_rows
+    struct ggml_tensor * sorted_reshaped_mask = ggml_reshape_2d(ctx, sorted_mask, 1, sorted_mask->ne[0]);
+    ggml_set_name(sorted_reshaped_mask, "top_p_sorted_reshaped_mask");
+
+    struct ggml_tensor * reshaped_mask = ggml_get_rows(ctx, sorted_reshaped_mask, reverse_argsort);
+    ggml_set_name(reshaped_mask, "top_p_reshaped_mask");
+
+    struct ggml_tensor * mask = ggml_reshape_2d(ctx, reshaped_mask, sorted_mask->ne[0], 1);
+    ggml_set_name(mask, "top_p_mask");
+
+    // Use ggml_scale_bias (output = (a * s) + b) which in this case becomes:
+    // top_p_bias = (mask * 1e9f) - 1e9f.
+    // So entries in the mask that we want to discard will become -1e9f, and
+    // others will be 0 (meaning that will not effect the logits).
+    const float large_val = 1e9f;
+    struct ggml_tensor * top_p_bias = ggml_scale_bias(ctx, mask, large_val, -large_val);
+    ggml_set_name(top_p_bias, "top_p_bias");
+
+    ggml_data->logits = ggml_add(ctx, ggml_data->logits, top_p_bias);
+    ggml_set_name(ggml_data->logits, "top_p_logits");
+
+    ggml_build_forward_expand(gf, ggml_data->logits);
+}
+
+static const char * llama_sampler_backend_top_p_name(const struct llama_sampler *) {
+    return "backend-top-p";
+}
+
+static void llama_sampler_backend_top_p_free(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_top_p_ctx *) smpl->ctx;
+    delete sctx;
+}
+
+static struct llama_sampler * llama_sampler_backend_top_p_clone(const struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_backend_top_p_ctx *) smpl->ctx;
+    return llama_sampler_backend_init_top_p(sctx->p);
+}
+
+struct llama_sampler * llama_sampler_backend_init_top_p(float p) {
+    static const llama_sampler_i iface = {
+        /*.name                =*/ llama_sampler_backend_top_p_name,
+        /*.accept              =*/ nullptr,
+        /*.apply               =*/ nullptr,
+        /*.reset               =*/ nullptr,
+        /*.clone               =*/ llama_sampler_backend_top_p_clone,
+        /*.free                =*/ llama_sampler_backend_top_p_free,
+        /*.apply_ggml          =*/ llama_sampler_backend_top_p_apply_ggml,
+        /*.accept_ggml         =*/ nullptr,
+        /*.set_input_ggml      =*/ nullptr,
+        /*.init_ggml           =*/ llama_sampler_backend_top_p_init_ggml,
+    };
+
+    auto * sctx = new llama_sampler_backend_top_p_ctx {
+        /*.p       =*/ p,
+        /*.device  =*/ nullptr,
+    };
+
+    auto * sampler = new llama_sampler {
+        /*.iface =*/ &iface,
+        /*.ctx   =*/ sctx,
+    };
+
+    return sampler;
+}
\ No newline at end of file
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index cd9aa003b56..47d2a139ea6 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -481,6 +481,61 @@ static void test_backend_min_p_sampling(const char * model_path) {
     llama_sampler_free(chain);
 }
 
+static void test_backend_top_p_sampling(const char * model_path) {
+    test_model_context test_ctx;
+
+    const int seq_id = 0;
+    const float p = 0.9;
+    struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_top_p(p));
+    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
+
+    if (!test_ctx.setup(model_path, backend_sampler_configs)) {
+        return;
+    }
+
+    if (!test_ctx.decode({{seq_id, "Hello"}})) {
+        return;
+    }
+
+    int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
+
+    float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);
+    uint32_t n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+
+    // Print the logits that are above the min-p threshold
+    std::vector<float> filtered_logits;
+    for (size_t i = 0; i < n_logits; ++i) {
+        if (logits[i] > -1e9f) {
+            filtered_logits.push_back(logits[i]);
+        }
+    }
+    GGML_ASSERT(filtered_logits.size() < (size_t) test_ctx.n_vocab);
+
+    // Sample using CPU sampler for verification to inspect they are reasonable
+    struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
+    struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
+    llama_sampler_chain_add(chain, llama_sampler_init_dist(88));
+
+    llama_token token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
+    const std::string token_str = test_ctx.token_to_piece(token, false);
+    printf("top-p cpu sampled token id:%d, string: '%s'\n", token, token_str.c_str());
+    GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
+
+    // Decode and sampler 10 more tokens
+    for (int i = 0; i < 10; i++) {
+        int32_t loop_idx = test_ctx.idx_for_seq(seq_id);
+        llama_token token = llama_sampler_sample(chain, test_ctx.ctx, loop_idx);
+        printf("top-p gen step %d: token id :%5.d, string: %s\n", i, token, test_ctx.token_to_piece(token, false).c_str());
+        test_ctx.decode_token(token, 0);
+    }
+
+    printf("top-p sampling test PASSED\n");
+
+    llama_sampler_free(chain);
+}
+
 static void test_backend_multi_sequence_sampling(const char * model_path) {
     test_model_context test_ctx;
 
@@ -934,6 +989,7 @@ static const backend_test_case BACKEND_TESTS[] = {
     { "mixed",           test_backend_mixed_sampling,          true  },
     { "min_p",           test_backend_min_p_sampling,          true  },
     { "cpu_mixed",       test_backend_cpu_mixed_batch,         true  },
+    { "top_p",           test_backend_top_p_sampling,          true  },
 };
 
 struct backend_cli_args {

From 8cac9dee45fa9084e213a91a19e85ddba204ae0b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 28 Nov 2025 16:12:05 +0100
Subject: [PATCH 53/59] sampling : use logits directly for min-p filtering

---
 src/llama-backend-sampler.cpp | 26 +++++++++++---------------
 1 file changed, 11 insertions(+), 15 deletions(-)

diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
index a4f22055e6a..42f7cf9accd 100644
--- a/src/llama-backend-sampler.cpp
+++ b/src/llama-backend-sampler.cpp
@@ -512,31 +512,27 @@ static void llama_sampler_backend_min_p_apply_ggml(
 
     auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
 
-    struct ggml_tensor * softmax = ggml_soft_max(ctx, ggml_data->logits);
-    ggml_set_name(softmax, "softmax");
-
-    // Get the sorted indices of the softmax probabilities in descending order.
-    struct ggml_tensor * max_idx = ggml_argmax(ctx, softmax);
+    struct ggml_tensor * max_idx = ggml_argmax(ctx, ggml_data->logits);
     ggml_set_name(max_idx, "max_idx");
 
-    struct ggml_tensor * softmax_rows = ggml_reshape_2d(ctx, softmax, 1, softmax->ne[0]);
-    ggml_set_name(softmax_rows, "softmax_rows");
+    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
+    ggml_set_name(logits_rows, "logits_rows");
 
-    struct ggml_tensor * max_prob = ggml_get_rows(ctx, softmax_rows, max_idx);
-    ggml_set_name(max_prob, "max_prob");
+    struct ggml_tensor * max_logit = ggml_get_rows(ctx, logits_rows, max_idx);
+    ggml_set_name(max_logit, "max_logit");
 
     // Calculate the threshold value.
-    struct ggml_tensor * threshold = ggml_scale(ctx, max_prob, sctx->p);
+    struct ggml_tensor * threshold = ggml_scale_bias(ctx, max_logit, 1.0f, logf(sctx->p));
     ggml_set_name(threshold, "min_p_threshold");
 
-    // Broadcast the threshold to match the shape of softmax.
-    struct ggml_tensor * threshold_b = ggml_repeat(ctx, threshold, softmax);
+    // Broadcast the threshold to match the shape of logits.
+    struct ggml_tensor * threshold_b = ggml_repeat(ctx, threshold, ggml_data->logits);
     ggml_set_name(threshold_b, "min_p_threshold_b");
 
-    // Subtract the threshold from softmax probabilities.
-    struct ggml_tensor * sub = ggml_sub(ctx, softmax, threshold_b);
+    // Subtract the threshold from logits.
+    struct ggml_tensor * sub = ggml_sub(ctx, ggml_data->logits, threshold_b);
 
-    // Create a mask where probabilities below the threshold are 0 (discard),
+    // Create a mask where logits below the threshold are 0 (discard),
     // and others are 1 (keep).
     struct ggml_tensor * mask = ggml_step(ctx, sub);
     ggml_set_name(mask, "min_p_mask");

From 2464d1b3fc0f4600c1c0b10da6c4216f6533611b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 28 Nov 2025 17:21:12 +0200
Subject: [PATCH 54/59] sampling : simplify

---
 src/llama-backend-sampler.cpp | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
index 42f7cf9accd..20a52866db9 100644
--- a/src/llama-backend-sampler.cpp
+++ b/src/llama-backend-sampler.cpp
@@ -149,8 +149,7 @@ static void llama_sampler_backend_top_k_apply_ggml(
         fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
     }
 
-    // TODO: temporary cont until https://github.com/ggml-org/llama.cpp/pull/17365 is merged
-    ggml_data->candidates = ggml_cont(ctx, top_k);
+    ggml_data->candidates = top_k;
 
     struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
     struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);
@@ -525,12 +524,8 @@ static void llama_sampler_backend_min_p_apply_ggml(
     struct ggml_tensor * threshold = ggml_scale_bias(ctx, max_logit, 1.0f, logf(sctx->p));
     ggml_set_name(threshold, "min_p_threshold");
 
-    // Broadcast the threshold to match the shape of logits.
-    struct ggml_tensor * threshold_b = ggml_repeat(ctx, threshold, ggml_data->logits);
-    ggml_set_name(threshold_b, "min_p_threshold_b");
-
     // Subtract the threshold from logits.
-    struct ggml_tensor * sub = ggml_sub(ctx, ggml_data->logits, threshold_b);
+    struct ggml_tensor * sub = ggml_sub(ctx, ggml_data->logits, threshold);
 
     // Create a mask where logits below the threshold are 0 (discard),
     // and others are 1 (keep).
@@ -713,4 +708,4 @@ struct llama_sampler * llama_sampler_backend_init_top_p(float p) {
     };
 
     return sampler;
-}
\ No newline at end of file
+}

From fbc8f49f3c3267ee3dba657efac4825c107265a2 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 29 Nov 2025 15:58:59 +0200
Subject: [PATCH 55/59] llama : simplify

---
 common/sampling.cpp            |  32 +-
 examples/batched/batched.cpp   |   6 +-
 include/llama.h                |  22 -
 src/CMakeLists.txt             |   1 -
 src/llama-backend-sampler.cpp  | 711 -------------------------------
 src/llama-sampling.cpp         | 748 ++++++++++++++++++++++++---------
 tests/test-backend-sampler.cpp |  42 +-
 7 files changed, 595 insertions(+), 967 deletions(-)
 delete mode 100644 src/llama-backend-sampler.cpp

diff --git a/common/sampling.cpp b/common/sampling.cpp
index 94367bd307f..ca3a0f06911 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -347,19 +347,11 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
     is_backend = is_backend && (params.samplers.size() == 0 || common_sampler_type_has_backend_support(params.samplers[idx_smpl]));
 
     if (params.has_logit_bias()) {
-        if (is_backend) {
-            llama_sampler_chain_add(result->chain_backend,
-                    llama_sampler_backend_init_logit_bias(
-                        llama_vocab_n_tokens(vocab),
-                        params.logit_bias.size(),
-                        params.logit_bias.data()));
-        } else {
-            llama_sampler_chain_add(result->chain,
-                    llama_sampler_init_logit_bias(
-                        llama_vocab_n_tokens(vocab),
-                        params.logit_bias.size(),
-                        params.logit_bias.data()));
-        }
+        llama_sampler_chain_add(is_backend ? result->chain_backend : result->chain,
+                llama_sampler_init_logit_bias(
+                    llama_vocab_n_tokens(vocab),
+                    params.logit_bias.size(),
+                    params.logit_bias.data()));
     }
 
     if (params.mirostat == 0) {
@@ -375,16 +367,16 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
 
             switch (cnstr) {
                 case COMMON_SAMPLER_TYPE_TOP_K:
-                    llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_top_k(params.top_k));
+                    llama_sampler_chain_add(result->chain_backend, llama_sampler_init_top_k(params.top_k));
                     break;
                 case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_temp(params.temp));
+                    llama_sampler_chain_add(result->chain_backend, llama_sampler_init_temp(params.temp));
                     break;
                 case COMMON_SAMPLER_TYPE_MIN_P:
-                    llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_min_p(params.min_p));
+                    llama_sampler_chain_add(result->chain_backend, llama_sampler_init_min_p(params.min_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_P:
-                    llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_top_p(params.top_p));
+                    llama_sampler_chain_add(result->chain_backend, llama_sampler_init_top_p(params.top_p, params.min_keep));
                     break;
                 default:
                     GGML_ASSERT(false && "unsupported backend sampler");
@@ -439,11 +431,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
             }
         }
 
-        if (is_backend) {
-            llama_sampler_chain_add(result->chain_backend, llama_sampler_backend_init_dist(params.seed));
-        } else {
-            llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
-        }
+        llama_sampler_chain_add(is_backend ? result->chain_backend : result->chain, llama_sampler_init_dist(params.seed));
     } else if (params.mirostat == 1) {
         llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
         llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index e23a3bab215..97cad5d2605 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -74,9 +74,9 @@ int main(int argc, char ** argv) {
         llama_sampler * smpl = llama_sampler_chain_init(sparams);
 
         if (params.sampling.backend_sampling) {
-            llama_sampler_chain_add(smpl, llama_sampler_backend_init_top_k(params.sampling.top_k));
-            llama_sampler_chain_add(smpl, llama_sampler_backend_init_temp (params.sampling.temp));
-            llama_sampler_chain_add(smpl, llama_sampler_backend_init_dist (params.sampling.seed));
+            llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
+            llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
+            llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
         } else {
             llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
             llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
diff --git a/include/llama.h b/include/llama.h
index 38178d919f8..263733cf2c5 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1388,31 +1388,9 @@ extern "C" {
     // Backend samplers
     //
 
-    /// @details Greedy sampling on backend - always selects the token with the highest probability
-    LLAMA_API struct llama_sampler * llama_sampler_backend_init_greedy(void);
-
-    /// @details Temperature scaling on backend - scales logits by 1/temperature
-    LLAMA_API struct llama_sampler * llama_sampler_backend_init_temp(float temp);
-
-    /// @details Top-K filtering on backend - keeps only the k tokens with highest probabilities
-    LLAMA_API struct llama_sampler * llama_sampler_backend_init_top_k(int32_t k);
-
-    /// @details Distribution sampling on backend - final sampling step that selects a token
-    LLAMA_API struct llama_sampler * llama_sampler_backend_init_dist(uint32_t seed);
-
-    /// @details Min-P filtering on backend - filter tokens with a probability less than p times the maximum probability.
-    LLAMA_API struct llama_sampler * llama_sampler_backend_init_min_p(float p);
-
-    /// @details Top-p filtering on backend - filter all tokens with cumulative pseudo-probability less than p.
-    LLAMA_API struct llama_sampler * llama_sampler_backend_init_top_p(float p);
-
     // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
     LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
 
-    LLAMA_API struct llama_sampler * llama_sampler_backend_init_logit_bias(int32_t   n_vocab,
-                                                                 int32_t   n_logit_bias,
-                                                  const llama_logit_bias * logit_bias);
-
     /// @details Sample and accept a token from the idx-th output of the last evaluation
     //
     // Shorthand for:
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 97320fe97df..f7a8c9841ec 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -31,7 +31,6 @@ add_library(llama
             llama-model.cpp
             llama-quant.cpp
             llama-sampling.cpp
-            llama-backend-sampler.cpp
             llama-vocab.cpp
             unicode-data.cpp
             unicode.cpp
diff --git a/src/llama-backend-sampler.cpp b/src/llama-backend-sampler.cpp
deleted file mode 100644
index 20a52866db9..00000000000
--- a/src/llama-backend-sampler.cpp
+++ /dev/null
@@ -1,711 +0,0 @@
-#include "llama.h"
-#include "ggml.h"
-#include <cstdio>
-#include <chrono>
-#include <random>
-#include <unordered_map>
-#include <vector>
-
-static void llama_sampler_backend_greedy_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
-    GGML_UNUSED(gf);
-    GGML_UNUSED(smpl);
-    struct ggml_tensor * argmax_result = ggml_argmax(ctx, ggml_data->logits);
-    ggml_set_name(argmax_result, "argmax_result");
-    ggml_data->sampled = argmax_result;
-}
-
-static const char * llama_sampler_backend_greedy_sampler_name(const struct llama_sampler *) {
-    return "test-ggml";
-}
-
-static struct llama_sampler * llama_sampler_backend_greedy_clone(const struct llama_sampler * smpl) {
-    (void) smpl;
-    return llama_sampler_backend_init_greedy();
-}
-
-struct llama_sampler * llama_sampler_backend_init_greedy() {
-    static const llama_sampler_i iface = {
-        /*.name                =*/ llama_sampler_backend_greedy_sampler_name,
-        /*.accept              =*/ nullptr,
-        /*.apply               =*/ nullptr,
-        /*.reset               =*/ nullptr,
-        /*.clone               =*/ llama_sampler_backend_greedy_clone,
-        /*.free                =*/ nullptr,
-        /*.apply_ggml          =*/ llama_sampler_backend_greedy_apply_ggml,
-        /*.accept_ggml         =*/ nullptr,
-        /*.set_input_ggml      =*/ nullptr,
-        /*.init_ggml           =*/ nullptr,
-    };
-
-    auto * sampler = new llama_sampler {
-        /*.iface =*/ &iface,
-        /*.ctx   =*/ nullptr,
-    };
-
-    return sampler;
-}
-
-struct llama_sampler_backend_temp_ctx {
-    float temp;
-};
-
-
-static void llama_sampler_backend_temp_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
-
-    auto * ctx_data = (llama_sampler_backend_temp_ctx *) smpl->ctx;
-
-    if (ctx_data->temp <= 0.0f) {
-        return;
-    }
-
-    struct ggml_tensor * scaled = ggml_scale(ctx, ggml_data->logits, 1.0f / ctx_data->temp);
-    ggml_set_name(scaled, "temp_scaled");
-
-    // Make sure the scaled tensor is contiguous for subsequent operations
-    ggml_data->logits = ggml_cont(ctx, scaled);
-    ggml_set_name(ggml_data->logits, "temp_scaled_logits");
-
-    ggml_build_forward_expand(gf, ggml_data->logits);
-}
-
-static const char * llama_sampler_backend_temp_name(const struct llama_sampler *) {
-    return "backend-temp";
-}
-
-static void llama_sampler_backend_temp_free(struct llama_sampler * smpl) {
-    auto * ctx_data = (llama_sampler_backend_temp_ctx *) smpl->ctx;
-    delete ctx_data;
-}
-
-static struct llama_sampler * llama_sampler_backend_temp_clone(const struct llama_sampler * smpl) {
-    auto * ctx = (llama_sampler_backend_temp_ctx *) smpl->ctx;
-    return llama_sampler_backend_init_temp(ctx->temp);
-}
-
-struct llama_sampler * llama_sampler_backend_init_temp(float temp) {
-    static const llama_sampler_i iface = {
-        /*.name                =*/ llama_sampler_backend_temp_name,
-        /*.accept              =*/ nullptr,
-        /*.apply               =*/ nullptr,
-        /*.reset               =*/ nullptr,
-        /*.clone               =*/ llama_sampler_backend_temp_clone,
-        /*.free                =*/ llama_sampler_backend_temp_free,
-        /*.apply_ggml          =*/ llama_sampler_backend_temp_apply_ggml,
-        /*.accept_ggml         =*/ nullptr,
-        /*.set_input_ggml      =*/ nullptr,
-        /*.set_backend_context =*/ nullptr,
-    };
-
-    auto * ctx_data = new llama_sampler_backend_temp_ctx {
-        /*.temp    =*/ temp,
-    };
-
-    auto * sampler = new llama_sampler {
-        /*.iface =*/ &iface,
-        /*.ctx   =*/ ctx_data,
-    };
-
-    return sampler;
-}
-
-
-struct llama_sampler_backend_top_k_ctx {
-    int32_t k;
-
-    // Only required for checking operation support and can be removed later.
-    ggml_backend_dev_t device;
-};
-
-static void llama_sampler_backend_top_k_init_ggml(
-        struct llama_sampler           * smpl,
-        ggml_backend_buffer_type_t       buft) {
-    auto * ctx_data = (llama_sampler_backend_top_k_ctx *) smpl->ctx;
-    ctx_data->device = ggml_backend_buft_get_device(buft);
-}
-
-static void llama_sampler_backend_top_k_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
-
-    auto * ctx_data = (llama_sampler_backend_top_k_ctx *) smpl->ctx;
-
-    struct ggml_tensor * top_k = ggml_top_k(ctx, ggml_data->logits, ctx_data->k);
-    ggml_set_name(top_k, "top_k");
-
-    // top_k is a view of argsort - check if backend supports the underlying argsort operation
-    // by checking the source tensor (which is the argsort result)
-    if (ctx_data->device && top_k->src[0] && !ggml_backend_dev_supports_op(ctx_data->device, top_k->src[0])) {
-        fprintf(stderr, "Warning: backend does not support argsort operation required for top-k sampling\n");
-        fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
-    }
-
-    ggml_data->candidates = top_k;
-
-    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
-    struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);
-    ggml_set_name(top_k_rows, "top_k_rows");
-
-    ggml_data->logits = ggml_reshape_1d(ctx, top_k_rows, ctx_data->k);
-    ggml_build_forward_expand(gf, ggml_data->logits);
-}
-
-static const char * llama_sampler_backend_top_k_name(const struct llama_sampler *) {
-    return "backend-top-k";
-}
-
-static void llama_sampler_backend_top_k_free(struct llama_sampler * smpl) {
-    auto * ctx_data = (llama_sampler_backend_top_k_ctx *) smpl->ctx;
-    delete ctx_data;
-}
-
-static struct llama_sampler * llama_sampler_backend_top_k_clone(const struct llama_sampler * smpl) {
-    auto * ctx = (llama_sampler_backend_top_k_ctx *) smpl->ctx;
-    return llama_sampler_backend_init_top_k(ctx->k);
-}
-
-struct llama_sampler * llama_sampler_backend_init_top_k(int32_t k) {
-    static const llama_sampler_i iface = {
-        /*.name                =*/ llama_sampler_backend_top_k_name,
-        /*.accept              =*/ nullptr,
-        /*.apply               =*/ nullptr,
-        /*.reset               =*/ nullptr,
-        /*.clone               =*/ llama_sampler_backend_top_k_clone,
-        /*.free                =*/ llama_sampler_backend_top_k_free,
-        /*.apply_ggml          =*/ llama_sampler_backend_top_k_apply_ggml,
-        /*.accept_ggml         =*/ nullptr,
-        /*.set_input_ggml      =*/ nullptr,
-        /*.init_ggml           =*/ llama_sampler_backend_top_k_init_ggml,
-    };
-
-    auto * ctx_data = new llama_sampler_backend_top_k_ctx {
-        /*.k       =*/ k,
-        /*.device  =*/ nullptr,
-    };
-
-    auto * sampler = new llama_sampler {
-        /*.iface =*/ &iface,
-        /*.ctx   =*/ ctx_data,
-    };
-
-    return sampler;
-}
-
-
-static uint32_t get_rng_seed(uint32_t seed) {
-    if (seed == LLAMA_DEFAULT_SEED) {
-        // use system clock if std::random_device is not a true RNG
-        static bool is_rd_prng = std::random_device().entropy() == 0;
-        if (is_rd_prng) {
-            return (uint32_t) std::chrono::system_clock::now().time_since_epoch().count();
-        }
-        std::random_device rd;
-        return rd();
-    }
-    return seed;
-}
-
-struct llama_sampler_backend_dist_ctx {
-    const uint32_t seed;
-          uint32_t seed_cur;
-    std::mt19937   rng;
-
-    struct ggml_tensor   * uniform;
-    struct ggml_context  * ctx;
-    ggml_backend_buffer_t  buffer;
-
-    // Only required for checking operation support and can be removed later.
-    ggml_backend_dev_t device;
-};
-
-static void llama_sampler_backend_dist_init_ggml(
-        struct llama_sampler      * smpl,
-        ggml_backend_buffer_type_t  buft) {
-
-    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
-    sctx->device = ggml_backend_buft_get_device(buft);
-    ggml_init_params params = {
-        /*.mem_size   =*/ ggml_tensor_overhead(),
-        /*.mem_buffer =*/ nullptr,
-        /*.no_alloc   =*/ true,
-    };
-    sctx->ctx = ggml_init(params);
-
-    // Create the uniform random scalar input tensor. This will be set by
-    // llama_sampler_backend_dist_set_input_ggml after this graph is built.
-    sctx->uniform = ggml_new_tensor_1d(sctx->ctx, GGML_TYPE_F32, 1);
-    ggml_set_name(sctx->uniform, "uniform");
-    ggml_set_input(sctx->uniform);
-    ggml_set_output(sctx->uniform);
-
-    // Allocate all tensors from our context to the backend
-    sctx->buffer = ggml_backend_alloc_ctx_tensors_from_buft(sctx->ctx, buft);
-}
-
-static void llama_sampler_backend_dist_set_input_ggml(struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
-    GGML_ASSERT(sctx->uniform != nullptr);
-
-    std::uniform_real_distribution<float> dist(0.0f, 1.0f);
-    const float rnd = dist(sctx->rng);
-    ggml_backend_tensor_set(sctx->uniform, &rnd, 0, sizeof(float));
-}
-
-static void llama_sampler_backend_dist_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
-    GGML_UNUSED(gf);
-    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
-
-    struct ggml_tensor * probs = ggml_soft_max(ctx, ggml_data->logits);
-    ggml_set_name(probs, "dist_probs");
-
-    struct ggml_tensor * cumsum = ggml_cumsum(ctx, probs);
-    if (sctx->device && !ggml_backend_dev_supports_op(sctx->device, cumsum)) {
-        fprintf(stderr, "Warning: backend does not support cumsum operation required for dist sampling\n");
-        fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
-    }
-    ggml_set_name(cumsum, "cumsum");
-
-    // The uniform tensor has a random value and we subtract this tensor with
-    // the cumsum tensor (the uniform tensor will be broadcasted by ggml_sub).
-    // Recall that each entry in cumsum is the cumulative probability up to that
-    // index so values stay negative while the cumulative total is below the
-    // random value, and become zero/positive once the threshold is crossed.
-    struct ggml_tensor * diff = ggml_sub(ctx, cumsum, sctx->uniform);
-    ggml_set_name(diff, "dist_cumsum");
-
-    // The ggml_step function produces a tensor where entries are 1 if the
-    // corresponding entry in diff is > 0, and 0 otherwise. So all values up to
-    // the index where the cumulative probability exceeds the random value are 0,
-    // and all entries after that are 1.
-    struct ggml_tensor * mask = ggml_step(ctx, diff);
-    ggml_set_name(mask, "dist_mask");
-
-    // Taking the sum of the mask gives us the sum of elements after the threshold
-    // we are interested in.
-    struct ggml_tensor * idxf = ggml_sum(ctx, mask);
-    ggml_set_name(idxf, "dist_index_f32");
-
-    // Use ggml_scale_bias to scale the index value by -1 and then add the size
-    // of the mask to that value so we get the correct index ((-1 * idxf) + n).
-    struct ggml_tensor * idx = ggml_cast(ctx, ggml_scale_bias(ctx, idxf, -1.0f, mask->ne[0]), GGML_TYPE_I32);
-    ggml_set_name(idx, "dist_index_i32");
-
-    // Map back to original vocab ids if a candidates tensor is available.
-    struct ggml_tensor * sampled_token = idx;
-    if (ggml_data->candidates != nullptr) {
-        struct ggml_tensor * candidates = ggml_data->candidates;
-        struct ggml_tensor * candidates_reshaped = ggml_view_2d(ctx, candidates, 1, ggml_nelements(candidates),
-                ggml_type_size(candidates->type), 0);
-
-        sampled_token = ggml_get_rows(ctx, candidates_reshaped, idx);
-        ggml_set_name(sampled_token, "dist_sampled_token");
-    }
-
-    ggml_set_output(sampled_token);
-    ggml_data->sampled = sampled_token;
-}
-
-static const char * llama_sampler_backend_dist_name(const struct llama_sampler *) {
-    return "backend-dist";
-}
-
-static void llama_sampler_backend_dist_free(struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
-    ggml_backend_buffer_free(sctx->buffer);
-    ggml_free(sctx->ctx);
-    delete sctx;
-}
-
-static struct llama_sampler * llama_sampler_backend_dist_clone(const struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_dist_ctx *) smpl->ctx;
-    return llama_sampler_backend_init_dist(sctx->seed);
-}
-
-
-struct llama_sampler * llama_sampler_backend_init_dist(uint32_t seed) {
-    static const llama_sampler_i iface = {
-        /*.name           =*/ llama_sampler_backend_dist_name,
-        /*.accept         =*/ nullptr,
-        /*.apply          =*/ nullptr,
-        /*.reset          =*/ nullptr,
-        /*.clone          =*/ llama_sampler_backend_dist_clone,
-        /*.free           =*/ llama_sampler_backend_dist_free,
-        /*.apply_ggml     =*/ llama_sampler_backend_dist_apply_ggml,
-        /*.accept_ggml    =*/ nullptr,
-        /*.set_input_ggml =*/ llama_sampler_backend_dist_set_input_ggml,
-        /*.init_ggml      =*/ llama_sampler_backend_dist_init_ggml,
-    };
-
-    auto seed_cur = get_rng_seed(seed);
-    auto * ctx_data = new llama_sampler_backend_dist_ctx {
-        /*.seed     =*/ seed,
-        /*.seed_cur =*/ seed_cur,
-        /*.rng      =*/ std::mt19937(seed_cur),
-        /*.uniform  =*/ nullptr,
-        /*.ctx      =*/ nullptr,
-        /*.buffer   =*/ nullptr,
-        /*.device   =*/ nullptr,
-    };
-
-    auto * sampler = new llama_sampler {
-        /*.iface =*/ &iface,
-        /*.ctx   =*/ ctx_data,
-    };
-
-    return sampler;
-}
-
-struct llama_sampler_backend_logit_bias_ctx {
-    const int32_t n_vocab;
-
-    const std::vector<llama_logit_bias> logit_bias;
-
-    struct ggml_tensor * logit_bias_t;
-    struct ggml_context * ctx;
-    ggml_backend_buffer_t buffer;
-};
-
-static void llama_sampler_backend_logit_bias_init_ggml(
-        struct llama_sampler      * smpl,
-        ggml_backend_buffer_type_t  buft) {
-    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
-    if (sctx->logit_bias.empty()) {
-        return;
-    }
-    ggml_init_params params = {
-        /*.mem_size   =*/ ggml_tensor_overhead() * sctx->n_vocab * sizeof(float),
-        /*.mem_buffer =*/ nullptr,
-        /*.no_alloc   =*/ true,
-    };
-    sctx->ctx = ggml_init(params);
-
-    struct ggml_tensor * logit_bias = ggml_new_tensor_1d(sctx->ctx, GGML_TYPE_F32, sctx->n_vocab);
-    sctx->logit_bias_t = logit_bias;
-    ggml_set_name(sctx->logit_bias_t, "logit_bias");
-    ggml_set_input(sctx->logit_bias_t);
-    ggml_set_output(sctx->logit_bias_t);
-
-    // Allocate all tensors from our context to the backend
-    sctx->buffer = ggml_backend_alloc_ctx_tensors_from_buft(sctx->ctx, buft);
-}
-
-static void llama_sampler_backend_logit_bias_set_input_ggml(struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
-    if (sctx->logit_bias.empty()) {
-        return;
-    }
-    GGML_ASSERT(sctx->logit_bias_t != nullptr);
-
-    // Create a sparse logit_bias vector from the logit_bias entries.
-    std::vector<float> logit_bias_sparse(sctx->n_vocab, 0.0f);
-    for (const auto & lb : sctx->logit_bias) {
-        GGML_ASSERT(lb.token >= 0 && lb.token < (int32_t) sctx->n_vocab);
-        logit_bias_sparse[lb.token] = lb.bias;
-    }
-
-    ggml_backend_tensor_set(sctx->logit_bias_t, logit_bias_sparse.data(), 0, ggml_nbytes(sctx->logit_bias_t));
-}
-
-static void llama_sampler_backend_logit_bias_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
-    GGML_UNUSED(gf);
-    GGML_UNUSED(ctx);
-
-    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
-    if (sctx->logit_bias_t == nullptr) {
-        return;
-    }
-
-    // Add the sparse logit logit_bias to the logits
-    struct ggml_tensor * logit_biased = ggml_add_inplace(sctx->ctx, ggml_data->logits, sctx->logit_bias_t);
-    ggml_build_forward_expand(gf, logit_biased);
-}
-
-static const char * llama_sampler_backend_logit_bias_name(const struct llama_sampler *) {
-    return "backend-logit_bias";
-}
-
-static void llama_sampler_backend_logit_bias_free(struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
-    ggml_backend_buffer_free(sctx->buffer);
-    ggml_free(sctx->ctx);
-    delete sctx;
-}
-
-static struct llama_sampler * llama_sampler_backend_logit_bias_clone(const struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_logit_bias_ctx *) smpl->ctx;
-    return llama_sampler_backend_init_logit_bias(sctx->n_vocab,
-                                      sctx->logit_bias.size(),
-                                      sctx->logit_bias.data());
-}
-
-
-struct llama_sampler * llama_sampler_backend_init_logit_bias(int32_t   n_vocab,
-                                                   int32_t   n_logit_bias,
-                                    const llama_logit_bias * logit_bias) {
-    static const llama_sampler_i iface = {
-        /*.name           =*/ llama_sampler_backend_logit_bias_name,
-        /*.accept         =*/ nullptr,
-        /*.apply          =*/ nullptr,
-        /*.reset          =*/ nullptr,
-        /*.clone          =*/ llama_sampler_backend_logit_bias_clone,
-        /*.free           =*/ llama_sampler_backend_logit_bias_free,
-        /*.apply_ggml     =*/ llama_sampler_backend_logit_bias_apply_ggml,
-        /*.accept_ggml    =*/ nullptr,
-        /*.set_input_ggml =*/ llama_sampler_backend_logit_bias_set_input_ggml,
-        /*.init_ggml      =*/ llama_sampler_backend_logit_bias_init_ggml,
-    };
-
-    auto * ctx_data = new llama_sampler_backend_logit_bias_ctx {
-        /*.n_vocab      =*/ n_vocab,
-        /*.logit_bias   =*/ std::vector<llama_logit_bias>(logit_bias, logit_bias + n_logit_bias),
-        /*.logit_bias_t =*/ nullptr,
-        /*.ctx          =*/ nullptr,
-        /*.buffer       =*/ nullptr,
-    };
-
-    auto * sampler = new llama_sampler {
-        /*.iface =*/ &iface,
-        /*.ctx   =*/ ctx_data,
-    };
-
-    return sampler;
-}
-
-struct llama_sampler_backend_min_p_ctx {
-    float p;
-
-    // Only required for checking operation support and can be removed later.
-    ggml_backend_dev_t device;
-};
-
-static void llama_sampler_backend_min_p_init_ggml(
-        struct llama_sampler           * smpl,
-        ggml_backend_buffer_type_t       buft) {
-    auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
-    sctx->device = ggml_backend_buft_get_device(buft);
-}
-
-static void llama_sampler_backend_min_p_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
-    GGML_UNUSED(gf);
-
-    auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
-
-    struct ggml_tensor * max_idx = ggml_argmax(ctx, ggml_data->logits);
-    ggml_set_name(max_idx, "max_idx");
-
-    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
-    ggml_set_name(logits_rows, "logits_rows");
-
-    struct ggml_tensor * max_logit = ggml_get_rows(ctx, logits_rows, max_idx);
-    ggml_set_name(max_logit, "max_logit");
-
-    // Calculate the threshold value.
-    struct ggml_tensor * threshold = ggml_scale_bias(ctx, max_logit, 1.0f, logf(sctx->p));
-    ggml_set_name(threshold, "min_p_threshold");
-
-    // Subtract the threshold from logits.
-    struct ggml_tensor * sub = ggml_sub(ctx, ggml_data->logits, threshold);
-
-    // Create a mask where logits below the threshold are 0 (discard),
-    // and others are 1 (keep).
-    struct ggml_tensor * mask = ggml_step(ctx, sub);
-    ggml_set_name(mask, "min_p_mask");
-
-    // Use ggml_scale_bias (output = (a * s) + b) which in this case becomes:
-    // min_p_bias = (mask * 1e9f) - 1e9f.
-    // So entries in the mask that we want to discard will become -1e9f, and
-    // others will be 0 (meaning that will not effect the logits).
-    const float large_val = 1e9f;
-    struct ggml_tensor * min_p_bias = ggml_scale_bias(ctx, mask, large_val, -large_val);
-    ggml_set_name(min_p_bias, "min_p_bias");
-
-    // Add the min_p bias to the logits.
-    ggml_data->logits = ggml_add(ctx, ggml_data->logits, min_p_bias);
-    ggml_set_name(ggml_data->logits, "min_p_logits");
-
-    ggml_build_forward_expand(gf, ggml_data->logits);
-}
-
-static const char * llama_sampler_backend_min_p_name(const struct llama_sampler *) {
-    return "backend-min-p";
-}
-
-static void llama_sampler_backend_min_p_free(struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
-    delete sctx;
-}
-
-static struct llama_sampler * llama_sampler_backend_min_p_clone(const struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_min_p_ctx *) smpl->ctx;
-    return llama_sampler_backend_init_min_p(sctx->p);
-}
-
-struct llama_sampler * llama_sampler_backend_init_min_p(float p) {
-    static const llama_sampler_i iface = {
-        /*.name                =*/ llama_sampler_backend_min_p_name,
-        /*.accept              =*/ nullptr,
-        /*.apply               =*/ nullptr,
-        /*.reset               =*/ nullptr,
-        /*.clone               =*/ llama_sampler_backend_min_p_clone,
-        /*.free                =*/ llama_sampler_backend_min_p_free,
-        /*.apply_ggml          =*/ llama_sampler_backend_min_p_apply_ggml,
-        /*.accept_ggml         =*/ nullptr,
-        /*.set_input_ggml      =*/ nullptr,
-        /*.init_ggml           =*/ llama_sampler_backend_min_p_init_ggml,
-    };
-
-    auto * sctx = new llama_sampler_backend_min_p_ctx {
-        /*.p       =*/ p,
-        /*.device  =*/ nullptr,
-    };
-
-    auto * sampler = new llama_sampler {
-        /*.iface =*/ &iface,
-        /*.ctx   =*/ sctx,
-    };
-
-    return sampler;
-}
-
-
-struct llama_sampler_backend_top_p_ctx {
-    float p;
-
-    // Only required for checking operation support and can be removed later.
-    ggml_backend_dev_t device;
-};
-
-
-static void llama_sampler_backend_top_p_init_ggml(
-        struct llama_sampler           * smpl,
-        ggml_backend_buffer_type_t       buft) {
-    auto * sctx = (llama_sampler_backend_top_p_ctx *) smpl->ctx;
-    sctx->device = ggml_backend_buft_get_device(buft);
-}
-
-static void llama_sampler_backend_top_p_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
-    GGML_UNUSED(gf);
-
-    auto * sctx = (llama_sampler_backend_top_p_ctx *) smpl->ctx;
-
-    struct ggml_tensor * softmax = ggml_soft_max(ctx, ggml_data->logits);
-    ggml_set_name(softmax, "top_p_softmax");
-
-    // Get the sorted indices of the softmax probabilities in descending order.
-    struct ggml_tensor * sorted_idx = ggml_argsort(ctx, softmax, GGML_SORT_ORDER_DESC);
-    ggml_set_name(sorted_idx, "top_p_sorted_idx");
-
-    // Do the sorting via reshape + get_rows
-    struct ggml_tensor * softmax_reshaped = ggml_reshape_2d(ctx, softmax, 1, softmax->ne[0]);
-    ggml_set_name(softmax_reshaped, "top_p_softmax_reshaped");
-
-    struct ggml_tensor * sorted_probs = ggml_get_rows(ctx, softmax_reshaped, sorted_idx);
-    ggml_set_name(sorted_probs, "top_p_sorted_probs");
-
-    struct ggml_tensor * sorted_probs_reshaped = ggml_reshape_2d(ctx, sorted_probs, softmax->ne[0], 1);
-    ggml_set_name(sorted_probs_reshaped, "top_p_sorted_probs_reshaped");
-    // Compute Cumulative Distribution Function (CDF) by means of GGML_OP_CUMSUM.
-    struct ggml_tensor * sorted_cdf = ggml_cumsum(ctx, sorted_probs_reshaped);
-    ggml_set_name(sorted_cdf, "top_p_sorted_cdf");
-
-    // Invert CDF and add top-p value so that ggml_step yields 1 for values we want to keep
-    struct ggml_tensor * sorted_cdf_scaled = ggml_scale_bias(ctx, sorted_cdf, -1.0f, sctx->p);
-    ggml_set_name(sorted_cdf_scaled, "top_p_sorted_cdf_scaled");
-
-    struct ggml_tensor * sorted_mask = ggml_step(ctx, sorted_cdf_scaled);
-    ggml_set_name(sorted_mask, "top_p_sorted_mask");
-
-    // reverse sorting by argsort(argsort)
-    // cast to F32 since cuda only supports float inputs
-    struct ggml_tensor * reverse_argsort = ggml_argsort(ctx, ggml_cast(ctx, sorted_idx, GGML_TYPE_F32), GGML_SORT_ORDER_ASC);
-    ggml_set_name(reverse_argsort, "top_p_reverse_argsort");
-    
-    // Do the sorting via reshape + get_rows
-    struct ggml_tensor * sorted_reshaped_mask = ggml_reshape_2d(ctx, sorted_mask, 1, sorted_mask->ne[0]);
-    ggml_set_name(sorted_reshaped_mask, "top_p_sorted_reshaped_mask");
-
-    struct ggml_tensor * reshaped_mask = ggml_get_rows(ctx, sorted_reshaped_mask, reverse_argsort);
-    ggml_set_name(reshaped_mask, "top_p_reshaped_mask");
-
-    struct ggml_tensor * mask = ggml_reshape_2d(ctx, reshaped_mask, sorted_mask->ne[0], 1);
-    ggml_set_name(mask, "top_p_mask");
-
-    // Use ggml_scale_bias (output = (a * s) + b) which in this case becomes:
-    // top_p_bias = (mask * 1e9f) - 1e9f.
-    // So entries in the mask that we want to discard will become -1e9f, and
-    // others will be 0 (meaning that will not effect the logits).
-    const float large_val = 1e9f;
-    struct ggml_tensor * top_p_bias = ggml_scale_bias(ctx, mask, large_val, -large_val);
-    ggml_set_name(top_p_bias, "top_p_bias");
-
-    ggml_data->logits = ggml_add(ctx, ggml_data->logits, top_p_bias);
-    ggml_set_name(ggml_data->logits, "top_p_logits");
-
-    ggml_build_forward_expand(gf, ggml_data->logits);
-}
-
-static const char * llama_sampler_backend_top_p_name(const struct llama_sampler *) {
-    return "backend-top-p";
-}
-
-static void llama_sampler_backend_top_p_free(struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_top_p_ctx *) smpl->ctx;
-    delete sctx;
-}
-
-static struct llama_sampler * llama_sampler_backend_top_p_clone(const struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_backend_top_p_ctx *) smpl->ctx;
-    return llama_sampler_backend_init_top_p(sctx->p);
-}
-
-struct llama_sampler * llama_sampler_backend_init_top_p(float p) {
-    static const llama_sampler_i iface = {
-        /*.name                =*/ llama_sampler_backend_top_p_name,
-        /*.accept              =*/ nullptr,
-        /*.apply               =*/ nullptr,
-        /*.reset               =*/ nullptr,
-        /*.clone               =*/ llama_sampler_backend_top_p_clone,
-        /*.free                =*/ llama_sampler_backend_top_p_free,
-        /*.apply_ggml          =*/ llama_sampler_backend_top_p_apply_ggml,
-        /*.accept_ggml         =*/ nullptr,
-        /*.set_input_ggml      =*/ nullptr,
-        /*.init_ggml           =*/ llama_sampler_backend_top_p_init_ggml,
-    };
-
-    auto * sctx = new llama_sampler_backend_top_p_ctx {
-        /*.p       =*/ p,
-        /*.device  =*/ nullptr,
-    };
-
-    auto * sampler = new llama_sampler {
-        /*.iface =*/ &iface,
-        /*.ctx   =*/ sctx,
-    };
-
-    return sampler;
-}
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 4d1760a629b..a13be032402 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -4,6 +4,8 @@
 #include "llama-vocab.h"
 #include "llama-grammar.h"
 
+#include "ggml-cpp.h"
+
 #include <array>
 #include <algorithm>
 #include <cassert>
@@ -610,16 +612,16 @@ static void llama_sampler_chain_set_backend_context(
 }
 
 static struct llama_sampler_i llama_sampler_chain_i = {
-    /* .name                = */ llama_sampler_chain_name,
-    /* .accept              = */ llama_sampler_chain_accept,
-    /* .apply               = */ llama_sampler_chain_apply,
-    /* .reset               = */ llama_sampler_chain_reset,
-    /* .clone               = */ llama_sampler_chain_clone,
-    /* .free                = */ llama_sampler_chain_free,
-    /* .apply_ggml          = */ llama_sampler_chain_apply_ggml,
-    /* .accept_ggml         = */ llama_sampler_chain_accept_ggml,
-    /* .set_input_ggml      = */ llama_sampler_chain_set_input_ggml,
-    /* .set_backend_context = */ llama_sampler_chain_set_backend_context,
+    /* .name           = */ llama_sampler_chain_name,
+    /* .accept         = */ llama_sampler_chain_accept,
+    /* .apply          = */ llama_sampler_chain_apply,
+    /* .reset          = */ llama_sampler_chain_reset,
+    /* .clone          = */ llama_sampler_chain_clone,
+    /* .free           = */ llama_sampler_chain_free,
+    /* .apply_ggml     = */ llama_sampler_chain_apply_ggml,
+    /* .accept_ggml    = */ llama_sampler_chain_accept_ggml,
+    /* .set_input_ggml = */ llama_sampler_chain_set_input_ggml,
+    /* .init_ggml      = */ llama_sampler_chain_set_backend_context,
 };
 
 struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params) {
@@ -687,17 +689,29 @@ static void llama_sampler_greedy_apply(struct llama_sampler * /*smpl*/, llama_to
     }
 }
 
+static void llama_sampler_greedy_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    GGML_UNUSED(gf);
+    GGML_UNUSED(smpl);
+    struct ggml_tensor * argmax_result = ggml_argmax(ctx, ggml_data->logits);
+    ggml_set_name(argmax_result, "argmax_result");
+    ggml_data->sampled = argmax_result;
+}
+
 static struct llama_sampler_i llama_sampler_greedy_i = {
-    /* .name                = */ llama_sampler_greedy_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_greedy_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ nullptr,
-    /* .free                = */ nullptr,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_greedy_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_greedy_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ nullptr,
+    /* .free           = */ nullptr,
+    /* .apply_ggml     = */ llama_sampler_greedy_apply_ggml,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_greedy() {
@@ -714,6 +728,14 @@ struct llama_sampler_dist {
           uint32_t seed_cur;
 
     std::mt19937 rng;
+
+    // Only required for checking operation support and can be removed later.
+    ggml_backend_dev_t device;
+
+    struct ggml_tensor * inp_uniform;
+
+    ggml_context_ptr inp_ctx;
+    ggml_backend_buffer_ptr inp_buf;
 };
 
 static const char * llama_sampler_dist_name(const struct llama_sampler * /*smpl*/) {
@@ -816,17 +838,109 @@ static void llama_sampler_dist_free(struct llama_sampler * smpl) {
     delete (llama_sampler_dist *) smpl->ctx;
 }
 
+static void llama_sampler_dist_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    GGML_UNUSED(gf);
+    auto * sctx = (llama_sampler_dist *) smpl->ctx;
+
+    struct ggml_tensor * probs = ggml_soft_max(ctx, ggml_data->logits);
+    ggml_set_name(probs, "dist_probs");
+
+    struct ggml_tensor * cumsum = ggml_cumsum(ctx, probs);
+    if (sctx->device && !ggml_backend_dev_supports_op(sctx->device, cumsum)) {
+        fprintf(stderr, "Warning: backend does not support cumsum operation required for dist sampling\n");
+        fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
+    }
+    ggml_set_name(cumsum, "cumsum");
+
+    // The uniform tensor has a random value and we subtract this tensor with
+    // the cumsum tensor (the uniform tensor will be broadcasted by ggml_sub).
+    // Recall that each entry in cumsum is the cumulative probability up to that
+    // index so values stay negative while the cumulative total is below the
+    // random value, and become zero/positive once the threshold is crossed.
+    struct ggml_tensor * diff = ggml_sub(ctx, cumsum, sctx->inp_uniform);
+    ggml_set_name(diff, "dist_cumsum");
+
+    // The ggml_step function produces a tensor where entries are 1 if the
+    // corresponding entry in diff is > 0, and 0 otherwise. So all values up to
+    // the index where the cumulative probability exceeds the random value are 0,
+    // and all entries after that are 1.
+    struct ggml_tensor * mask = ggml_step(ctx, diff);
+    ggml_set_name(mask, "dist_mask");
+
+    // Taking the sum of the mask gives us the sum of elements after the threshold
+    // we are interested in.
+    struct ggml_tensor * idxf = ggml_sum(ctx, mask);
+    ggml_set_name(idxf, "dist_index_f32");
+
+    // Use ggml_scale_bias to scale the index value by -1 and then add the size
+    // of the mask to that value so we get the correct index ((-1 * idxf) + n).
+    struct ggml_tensor * idx = ggml_cast(ctx, ggml_scale_bias(ctx, idxf, -1.0f, mask->ne[0]), GGML_TYPE_I32);
+    ggml_set_name(idx, "dist_index_i32");
+
+    // Map back to original vocab ids if a candidates tensor is available.
+    struct ggml_tensor * sampled_token = idx;
+    if (ggml_data->candidates != nullptr) {
+        struct ggml_tensor * candidates = ggml_data->candidates;
+        struct ggml_tensor * candidates_reshaped = ggml_view_2d(ctx, candidates, 1, ggml_nelements(candidates),
+                ggml_type_size(candidates->type), 0);
+
+        sampled_token = ggml_get_rows(ctx, candidates_reshaped, idx);
+        ggml_set_name(sampled_token, "dist_sampled_token");
+    }
+
+    ggml_set_output(sampled_token);
+    ggml_data->sampled = sampled_token;
+}
+
+static void llama_sampler_dist_set_input_ggml(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_dist *) smpl->ctx;
+    GGML_ASSERT(sctx->inp_uniform != nullptr);
+
+    std::uniform_real_distribution<float> dist(0.0f, 1.0f);
+    const float rnd = dist(sctx->rng);
+    ggml_backend_tensor_set(sctx->inp_uniform, &rnd, 0, sizeof(float));
+}
+
+static void llama_sampler_dist_init_ggml(
+        struct llama_sampler      * smpl,
+        ggml_backend_buffer_type_t  buft) {
+    auto * sctx = (llama_sampler_dist *) smpl->ctx;
+
+    sctx->device = ggml_backend_buft_get_device(buft);
+
+    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_set_input_ggml 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));
+}
+
 static struct llama_sampler_i llama_sampler_dist_i = {
-    /* .name                = */ llama_sampler_dist_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_dist_apply,
-    /* .reset               = */ llama_sampler_dist_reset,
-    /* .clone               = */ llama_sampler_dist_clone,
-    /* .free                = */ llama_sampler_dist_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_dist_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_dist_apply,
+    /* .reset          = */ llama_sampler_dist_reset,
+    /* .clone          = */ llama_sampler_dist_clone,
+    /* .free           = */ llama_sampler_dist_free,
+    /* .apply_ggml     = */ llama_sampler_dist_apply_ggml,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ llama_sampler_dist_set_input_ggml,
+    /* .init_ggml      = */ llama_sampler_dist_init_ggml,
 };
 
 struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
@@ -834,9 +948,13 @@ struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
     return llama_sampler_init(
         /* .iface = */ &llama_sampler_dist_i,
         /* .ctx   = */ new llama_sampler_dist {
-            /* .seed     = */ seed,
-            /* .seed_cur = */ seed_cur,
-            /* .rng      = */ std::mt19937(seed_cur),
+            /* .seed        = */ seed,
+            /* .seed_cur    = */ seed_cur,
+            /* .rng         = */ std::mt19937(seed_cur),
+            /* .device      = */ nullptr,
+            /* .inp_uniform = */ nullptr,
+            /* .inp_ctx     = */ nullptr,
+            /* .inp_buf     = */ nullptr,
         }
     );
 }
@@ -845,6 +963,9 @@ struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
 
 struct llama_sampler_top_k {
     const int32_t k;
+
+    // Only required for checking operation support and can be removed later.
+    ggml_backend_dev_t device;
 };
 
 static const char * llama_sampler_top_k_name(const struct llama_sampler * /*smpl*/) {
@@ -865,24 +986,60 @@ static void llama_sampler_top_k_free(struct llama_sampler * smpl) {
     delete (llama_sampler_top_k *) smpl->ctx;
 }
 
+static void llama_sampler_top_k_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+
+    auto * ctx_data = (llama_sampler_top_k *) smpl->ctx;
+
+    struct ggml_tensor * top_k = ggml_top_k(ctx, ggml_data->logits, ctx_data->k);
+    ggml_set_name(top_k, "top_k");
+
+    // top_k is a view of argsort - check if backend supports the underlying argsort operation
+    // by checking the source tensor (which is the argsort result)
+    if (ctx_data->device && top_k->src[0] && !ggml_backend_dev_supports_op(ctx_data->device, top_k->src[0])) {
+        fprintf(stderr, "Warning: backend does not support argsort operation required for top-k sampling\n");
+        fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
+    }
+
+    ggml_data->candidates = top_k;
+
+    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
+    struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);
+    ggml_set_name(top_k_rows, "top_k_rows");
+
+    ggml_data->logits = ggml_reshape_1d(ctx, top_k_rows, ctx_data->k);
+    ggml_build_forward_expand(gf, ggml_data->logits);
+}
+
+static void llama_sampler_top_k_init_ggml(
+        struct llama_sampler           * smpl,
+        ggml_backend_buffer_type_t       buft) {
+    auto * ctx_data = (llama_sampler_top_k *) smpl->ctx;
+    ctx_data->device = ggml_backend_buft_get_device(buft);
+}
+
 static struct llama_sampler_i llama_sampler_top_k_i = {
-    /* .name                = */ llama_sampler_top_k_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_top_k_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_top_k_clone,
-    /* .free                = */ llama_sampler_top_k_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_top_k_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_top_k_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_top_k_clone,
+    /* .free           = */ llama_sampler_top_k_free,
+    /* .apply_ggml     = */ llama_sampler_top_k_apply_ggml,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ llama_sampler_top_k_init_ggml,
 };
 
 struct llama_sampler * llama_sampler_init_top_k(int32_t k) {
     return llama_sampler_init(
         /* .iface = */ &llama_sampler_top_k_i,
         /* .ctx   = */ new llama_sampler_top_k {
-            /* .k = */ k,
+            /* .k      = */ k,
+            /* .device = */ nullptr,
         }
     );
 }
@@ -894,6 +1051,9 @@ struct llama_sampler_top_p {
     const size_t min_keep;
 
     std::vector<llama_token_data> buf_sort;
+
+    // Only required for checking operation support and can be removed later.
+    ggml_backend_dev_t device;
 };
 
 static const char * llama_sampler_top_p_name(const struct llama_sampler * /*smpl*/) {
@@ -964,17 +1124,87 @@ static void llama_sampler_top_p_free(struct llama_sampler * smpl) {
     delete (llama_sampler_top_p *) smpl->ctx;
 }
 
+static void llama_sampler_top_p_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    auto * sctx = (llama_sampler_top_p *) smpl->ctx;
+
+    struct ggml_tensor * softmax = ggml_soft_max(ctx, ggml_data->logits);
+    ggml_set_name(softmax, "top_p_softmax");
+
+    // Get the sorted indices of the softmax probabilities in descending order.
+    struct ggml_tensor * sorted_idx = ggml_argsort(ctx, softmax, GGML_SORT_ORDER_DESC);
+    ggml_set_name(sorted_idx, "top_p_sorted_idx");
+
+    // Do the sorting via reshape + get_rows
+    struct ggml_tensor * softmax_reshaped = ggml_reshape_2d(ctx, softmax, 1, softmax->ne[0]);
+    ggml_set_name(softmax_reshaped, "top_p_softmax_reshaped");
+
+    struct ggml_tensor * sorted_probs = ggml_get_rows(ctx, softmax_reshaped, sorted_idx);
+    ggml_set_name(sorted_probs, "top_p_sorted_probs");
+
+    struct ggml_tensor * sorted_probs_reshaped = ggml_reshape_2d(ctx, sorted_probs, softmax->ne[0], 1);
+    ggml_set_name(sorted_probs_reshaped, "top_p_sorted_probs_reshaped");
+    // Compute Cumulative Distribution Function (CDF) by means of GGML_OP_CUMSUM.
+    struct ggml_tensor * sorted_cdf = ggml_cumsum(ctx, sorted_probs_reshaped);
+    ggml_set_name(sorted_cdf, "top_p_sorted_cdf");
+
+    // Invert CDF and add top-p value so that ggml_step yields 1 for values we want to keep
+    struct ggml_tensor * sorted_cdf_scaled = ggml_scale_bias(ctx, sorted_cdf, -1.0f, sctx->p);
+    ggml_set_name(sorted_cdf_scaled, "top_p_sorted_cdf_scaled");
+
+    struct ggml_tensor * sorted_mask = ggml_step(ctx, sorted_cdf_scaled);
+    ggml_set_name(sorted_mask, "top_p_sorted_mask");
+
+    // reverse sorting by argsort(argsort)
+    // cast to F32 since cuda only supports float inputs
+    struct ggml_tensor * reverse_argsort = ggml_argsort(ctx, ggml_cast(ctx, sorted_idx, GGML_TYPE_F32), GGML_SORT_ORDER_ASC);
+    ggml_set_name(reverse_argsort, "top_p_reverse_argsort");
+
+    // Do the sorting via reshape + get_rows
+    struct ggml_tensor * sorted_reshaped_mask = ggml_reshape_2d(ctx, sorted_mask, 1, sorted_mask->ne[0]);
+    ggml_set_name(sorted_reshaped_mask, "top_p_sorted_reshaped_mask");
+
+    struct ggml_tensor * reshaped_mask = ggml_get_rows(ctx, sorted_reshaped_mask, reverse_argsort);
+    ggml_set_name(reshaped_mask, "top_p_reshaped_mask");
+
+    struct ggml_tensor * mask = ggml_reshape_2d(ctx, reshaped_mask, sorted_mask->ne[0], 1);
+    ggml_set_name(mask, "top_p_mask");
+
+    // Use ggml_scale_bias (output = (a * s) + b) which in this case becomes:
+    // top_p_bias = (mask * 1e9f) - 1e9f.
+    // So entries in the mask that we want to discard will become -1e9f, and
+    // others will be 0 (meaning that will not effect the logits).
+    const float large_val = 1e9f;
+    struct ggml_tensor * top_p_bias = ggml_scale_bias(ctx, mask, large_val, -large_val);
+    ggml_set_name(top_p_bias, "top_p_bias");
+
+    ggml_data->logits = ggml_add(ctx, ggml_data->logits, top_p_bias);
+    ggml_set_name(ggml_data->logits, "top_p_logits");
+
+    ggml_build_forward_expand(gf, ggml_data->logits);
+}
+
+static void llama_sampler_top_p_init_ggml(
+        struct llama_sampler       * smpl,
+        ggml_backend_buffer_type_t   buft) {
+    auto * sctx = (llama_sampler_top_p *) smpl->ctx;
+    sctx->device = ggml_backend_buft_get_device(buft);
+}
+
 static struct llama_sampler_i llama_sampler_top_p_i = {
-    /* .name                = */ llama_sampler_top_p_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_top_p_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_top_p_clone,
-    /* .free                = */ llama_sampler_top_p_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_top_p_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_top_p_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_top_p_clone,
+    /* .free           = */ llama_sampler_top_p_free,
+    /* .apply_ggml     = */ llama_sampler_top_p_apply_ggml,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ llama_sampler_top_p_init_ggml,
 };
 
 struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
@@ -984,6 +1214,7 @@ struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
             /* .p        = */ p,
             /* .min_keep = */ min_keep,
             /* .buf_sort = */ {},
+            /* .device   = */ nullptr,
         }
     );
 }
@@ -993,6 +1224,9 @@ struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
 struct llama_sampler_min_p {
     const float  p;
     const size_t min_keep;
+
+    // Only required for checking operation support and can be removed later.
+    ggml_backend_dev_t device;
 };
 
 static const char * llama_sampler_min_p_name(const struct llama_sampler * /*smpl*/) {
@@ -1062,17 +1296,67 @@ static void llama_sampler_min_p_free(struct llama_sampler * smpl) {
     delete (llama_sampler_min_p *) smpl->ctx;
 }
 
+static void llama_sampler_min_p_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    auto * sctx = (llama_sampler_min_p *) smpl->ctx;
+
+    struct ggml_tensor * max_idx = ggml_argmax(ctx, ggml_data->logits);
+    ggml_set_name(max_idx, "max_idx");
+
+    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
+    ggml_set_name(logits_rows, "logits_rows");
+
+    struct ggml_tensor * max_logit = ggml_get_rows(ctx, logits_rows, max_idx);
+    ggml_set_name(max_logit, "max_logit");
+
+    // Calculate the threshold value.
+    struct ggml_tensor * threshold = ggml_scale_bias(ctx, max_logit, 1.0f, logf(sctx->p));
+    ggml_set_name(threshold, "min_p_threshold");
+
+    // Subtract the threshold from logits.
+    struct ggml_tensor * sub = ggml_sub(ctx, ggml_data->logits, threshold);
+
+    // Create a mask where logits below the threshold are 0 (discard),
+    // and others are 1 (keep).
+    struct ggml_tensor * mask = ggml_step(ctx, sub);
+    ggml_set_name(mask, "min_p_mask");
+
+    // Use ggml_scale_bias (output = (a * s) + b) which in this case becomes:
+    // min_p_bias = (mask * 1e9f) - 1e9f.
+    // So entries in the mask that we want to discard will become -1e9f, and
+    // others will be 0 (meaning that will not effect the logits).
+    const float large_val = 1e9f;
+    struct ggml_tensor * min_p_bias = ggml_scale_bias(ctx, mask, large_val, -large_val);
+    ggml_set_name(min_p_bias, "min_p_bias");
+
+    // Add the min_p bias to the logits.
+    ggml_data->logits = ggml_add(ctx, ggml_data->logits, min_p_bias);
+    ggml_set_name(ggml_data->logits, "min_p_logits");
+
+    ggml_build_forward_expand(gf, ggml_data->logits);
+}
+
+static void llama_sampler_min_p_init_ggml(
+        struct llama_sampler       * smpl,
+        ggml_backend_buffer_type_t   buft) {
+    auto * sctx = (llama_sampler_min_p *) smpl->ctx;
+    sctx->device = ggml_backend_buft_get_device(buft);
+}
+
 static struct llama_sampler_i llama_sampler_min_p_i = {
-    /* .name                = */ llama_sampler_min_p_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_min_p_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_min_p_clone,
-    /* .free                = */ llama_sampler_min_p_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_min_p_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_min_p_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_min_p_clone,
+    /* .free           = */ llama_sampler_min_p_free,
+    /* .apply_ggml     = */ llama_sampler_min_p_apply_ggml,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ llama_sampler_min_p_init_ggml,
 };
 
 struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) {
@@ -1081,6 +1365,7 @@ struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) {
         /* .ctx   = */ new llama_sampler_min_p {
             /* .p        = */ p,
             /* .min_keep = */ min_keep,
+            /* .device   = */ nullptr,
         }
     );
 }
@@ -1166,16 +1451,16 @@ static void llama_sampler_typical_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_typical_i = {
-    /* .name                = */ llama_sampler_typical_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_typical_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_typical_clone,
-    /* .free                = */ llama_sampler_typical_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_typical_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_typical_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_typical_clone,
+    /* .free           = */ llama_sampler_typical_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_typical(float p, size_t min_keep) {
@@ -1213,17 +1498,38 @@ static void llama_sampler_temp_free(struct llama_sampler * smpl) {
     delete (llama_sampler_temp *) smpl->ctx;
 }
 
+static void llama_sampler_temp_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    auto * ctx_data = (llama_sampler_temp *) smpl->ctx;
+
+    if (ctx_data->temp <= 0.0f) {
+        return;
+    }
+
+    struct ggml_tensor * scaled = ggml_scale(ctx, ggml_data->logits, 1.0f / ctx_data->temp);
+    ggml_set_name(scaled, "temp_scaled");
+
+    // Make sure the scaled tensor is contiguous for subsequent operations
+    ggml_data->logits = ggml_cont(ctx, scaled);
+    ggml_set_name(ggml_data->logits, "temp_scaled_logits");
+
+    ggml_build_forward_expand(gf, ggml_data->logits);
+}
+
 static struct llama_sampler_i llama_sampler_temp_i = {
-    /* .name                = */ llama_sampler_temp_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_temp_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_temp_clone,
-    /* .free                = */ llama_sampler_temp_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_temp_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_temp_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_temp_clone,
+    /* .free           = */ llama_sampler_temp_free,
+    /* .apply_ggml     = */ llama_sampler_temp_apply_ggml,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_temp(float temp) {
@@ -1328,16 +1634,16 @@ static void llama_sampler_temp_ext_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_temp_ext_i = {
-    /* .name                = */ llama_sampler_temp_ext_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_temp_ext_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_temp_ext_clone,
-    /* .free                = */ llama_sampler_temp_ext_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_temp_ext_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_temp_ext_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_temp_ext_clone,
+    /* .free           = */ llama_sampler_temp_ext_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, float exponent) {
@@ -1426,16 +1732,16 @@ static void llama_sampler_xtc_reset(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_xtc_i = {
-    /* .name                = */ llama_sampler_xtc_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sample_xtc_apply,
-    /* .reset               = */ llama_sampler_xtc_reset,
-    /* .clone               = */ llama_sampler_xtc_clone,
-    /* .free                = */ llama_sampler_xtc_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_xtc_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sample_xtc_apply,
+    /* .reset          = */ llama_sampler_xtc_reset,
+    /* .clone          = */ llama_sampler_xtc_clone,
+    /* .free           = */ llama_sampler_xtc_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
@@ -1538,16 +1844,16 @@ static void llama_sampler_mirostat_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_mirostat_i = {
-    /* .name                = */ llama_sampler_mirostat_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_mirostat_apply,
-    /* .reset               = */ llama_sampler_mirostat_reset,
-    /* .clone               = */ llama_sampler_mirostat_clone,
-    /* .free                = */ llama_sampler_mirostat_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_mirostat_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_mirostat_apply,
+    /* .reset          = */ llama_sampler_mirostat_reset,
+    /* .clone          = */ llama_sampler_mirostat_clone,
+    /* .free           = */ llama_sampler_mirostat_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t seed, float tau, float eta, int32_t m) {
@@ -1641,16 +1947,16 @@ static void llama_sampler_mirostat_v2_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_mirostat_v2_i = {
-    /* .name                = */ llama_sampler_mirostat_v2_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_mirostat_v2_apply,
-    /* .reset               = */ llama_sampler_mirostat_v2_reset,
-    /* .clone               = */ llama_sampler_mirostat_v2_clone,
-    /* .free                = */ llama_sampler_mirostat_v2_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_mirostat_v2_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_mirostat_v2_apply,
+    /* .reset          = */ llama_sampler_mirostat_v2_reset,
+    /* .clone          = */ llama_sampler_mirostat_v2_clone,
+    /* .free           = */ llama_sampler_mirostat_v2_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau, float eta) {
@@ -1762,16 +2068,16 @@ static void llama_sampler_grammar_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_grammar_i = {
-    /* .name                = */ llama_sampler_grammar_name,
-    /* .accept              = */ llama_sampler_grammar_accept_impl,
-    /* .apply               = */ llama_sampler_grammar_apply,
-    /* .reset               = */ llama_sampler_grammar_reset,
-    /* .clone               = */ llama_sampler_grammar_clone,
-    /* .free                = */ llama_sampler_grammar_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_grammar_name,
+    /* .accept         = */ llama_sampler_grammar_accept_impl,
+    /* .apply          = */ llama_sampler_grammar_apply,
+    /* .reset          = */ llama_sampler_grammar_reset,
+    /* .clone          = */ llama_sampler_grammar_clone,
+    /* .free           = */ llama_sampler_grammar_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 static struct llama_sampler * llama_sampler_init_grammar_impl(
@@ -1973,16 +2279,16 @@ static void llama_sampler_penalties_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_penalties_i = {
-    /* .name                = */ llama_sampler_penalties_name,
-    /* .accept              = */ llama_sampler_penalties_accept,
-    /* .apply               = */ llama_sampler_penalties_apply,
-    /* .reset               = */ llama_sampler_penalties_reset,
-    /* .clone               = */ llama_sampler_penalties_clone,
-    /* .free                = */ llama_sampler_penalties_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_penalties_name,
+    /* .accept         = */ llama_sampler_penalties_accept,
+    /* .apply          = */ llama_sampler_penalties_apply,
+    /* .reset          = */ llama_sampler_penalties_reset,
+    /* .clone          = */ llama_sampler_penalties_clone,
+    /* .free           = */ llama_sampler_penalties_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_penalties(
@@ -2068,16 +2374,16 @@ static void llama_sampler_top_n_sigma_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_top_n_sigma_i = {
-    /* .name                = */ llama_sampler_top_n_sigma_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_top_n_sigma_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_top_n_sigma_clone,
-    /* .free                = */ llama_sampler_top_n_sigma_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_top_n_sigma_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_top_n_sigma_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_top_n_sigma_clone,
+    /* .free           = */ llama_sampler_top_n_sigma_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_top_n_sigma(float n) {
@@ -2402,16 +2708,16 @@ static void llama_sampler_dry_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_dry_i = {
-    /* .name                = */ llama_sampler_dry_name,
-    /* .accept              = */ llama_sampler_dry_accept,
-    /* .apply               = */ llama_sampler_dry_apply,
-    /* .reset               = */ llama_sampler_dry_reset,
-    /* .clone               = */ llama_sampler_dry_clone,
-    /* .free                = */ llama_sampler_dry_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_dry_name,
+    /* .accept         = */ llama_sampler_dry_accept,
+    /* .apply          = */ llama_sampler_dry_apply,
+    /* .reset          = */ llama_sampler_dry_reset,
+    /* .clone          = */ llama_sampler_dry_clone,
+    /* .free           = */ llama_sampler_dry_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, int32_t n_ctx_train, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
@@ -2498,6 +2804,11 @@ struct llama_sampler_logit_bias {
     const std::vector<llama_logit_bias> logit_bias;
 
     std::vector<llama_logit_bias> to_search;
+
+    struct ggml_tensor * inp_logit_bias;
+
+    ggml_context_ptr inp_ctx;
+    ggml_backend_buffer_ptr inp_buf;
 };
 
 static const char * llama_sampler_logit_bias_name(const struct llama_sampler * /*smpl*/) {
@@ -2546,17 +2857,77 @@ static void llama_sampler_logit_bias_free(struct llama_sampler * smpl) {
     delete (llama_sampler_logit_bias *) smpl->ctx;
 }
 
+static void llama_sampler_logit_bias_apply_ggml(
+        struct llama_sampler           * smpl,
+        struct ggml_context            * ctx,
+        struct ggml_cgraph             * gf,
+        struct llama_sampler_ggml_data * ggml_data) {
+    GGML_UNUSED(gf);
+    GGML_UNUSED(ctx);
+
+    auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
+    if (sctx->logit_bias.empty()) {
+        return;
+    }
+
+    // Add the sparse logit logit_bias to the logits
+    struct ggml_tensor * logit_biased = ggml_add_inplace(ctx, ggml_data->logits, sctx->inp_logit_bias);
+    ggml_build_forward_expand(gf, logit_biased);
+}
+
+static void llama_sampler_logit_bias_set_input_ggml(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
+    if (sctx->logit_bias.empty()) {
+        return;
+    }
+    GGML_ASSERT(sctx->inp_logit_bias != nullptr);
+
+    // Create a sparse logit_bias vector from the logit_bias entries.
+    std::vector<float> logit_bias_sparse(sctx->n_vocab, 0.0f);
+    for (const auto & lb : sctx->logit_bias) {
+        GGML_ASSERT(lb.token >= 0 && lb.token < (int32_t) sctx->n_vocab);
+        logit_bias_sparse[lb.token] = lb.bias;
+    }
+
+    ggml_backend_tensor_set(sctx->inp_logit_bias, logit_bias_sparse.data(), 0, ggml_nbytes(sctx->inp_logit_bias));
+}
+
+static void llama_sampler_logit_bias_init_ggml(
+        struct llama_sampler      * smpl,
+        ggml_backend_buffer_type_t  buft) {
+    auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
+
+    if (sctx->logit_bias.empty()) {
+        return;
+    }
+
+    ggml_init_params params = {
+        /*.mem_size   =*/ ggml_tensor_overhead(),
+        /*.mem_buffer =*/ nullptr,
+        /*.no_alloc   =*/ true,
+    };
+
+    sctx->inp_ctx.reset(ggml_init(params));
+
+    sctx->inp_logit_bias = ggml_new_tensor_1d(sctx->inp_ctx.get(), GGML_TYPE_F32, sctx->n_vocab);
+    ggml_set_name(sctx->inp_logit_bias, "logit_bias");
+    ggml_set_input(sctx->inp_logit_bias);
+
+    // 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));
+}
+
 static struct llama_sampler_i llama_sampler_logit_bias_i = {
-    /* .name                = */ llama_sampler_logit_bias_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_logit_bias_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_logit_bias_clone,
-    /* .free                = */ llama_sampler_logit_bias_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_logit_bias_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_logit_bias_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_logit_bias_clone,
+    /* .free           = */ llama_sampler_logit_bias_free,
+    /* .apply_ggml     = */ llama_sampler_logit_bias_apply_ggml,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ llama_sampler_logit_bias_set_input_ggml,
+    /* .init_ggml      = */ llama_sampler_logit_bias_init_ggml,
 };
 
 struct llama_sampler * llama_sampler_init_logit_bias(
@@ -2566,9 +2937,12 @@ struct llama_sampler * llama_sampler_init_logit_bias(
     return llama_sampler_init(
         /* .iface = */ &llama_sampler_logit_bias_i,
         /* .ctx   = */ new llama_sampler_logit_bias {
-            /* .n_vocab    = */ n_vocab,
-            /* .logit_bias = */ std::vector<llama_logit_bias>(logit_bias, logit_bias + n_logit_bias),
-            /* .to_search  = */ {},
+            /* .n_vocab        = */ n_vocab,
+            /* .logit_bias     = */ std::vector<llama_logit_bias>(logit_bias, logit_bias + n_logit_bias),
+            /* .to_search      = */ {},
+            /* .inp_logit_bias = */ nullptr,
+            /* .inp_ctx        = */ nullptr,
+            /* .inp_buf        = */ nullptr,
         }
     );
 }
@@ -2781,16 +3155,16 @@ static void llama_sampler_infill_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_infill_i = {
-    /* .name                = */ llama_sampler_infill_name,
-    /* .accept              = */ nullptr,
-    /* .apply               = */ llama_sampler_infill_apply,
-    /* .reset               = */ nullptr,
-    /* .clone               = */ llama_sampler_infill_clone,
-    /* .free                = */ llama_sampler_infill_free,
-    /* .apply_ggml          = */ nullptr,
-    /* .accept_ggml         = */ nullptr,
-    /* .set_input_ggml      = */ nullptr,
-    /* .set_backend_context = */ nullptr,
+    /* .name           = */ llama_sampler_infill_name,
+    /* .accept         = */ nullptr,
+    /* .apply          = */ llama_sampler_infill_apply,
+    /* .reset          = */ nullptr,
+    /* .clone          = */ llama_sampler_infill_clone,
+    /* .free           = */ llama_sampler_infill_free,
+    /* .apply_ggml     = */ nullptr,
+    /* .accept_ggml    = */ nullptr,
+    /* .set_input_ggml = */ nullptr,
+    /* .init_ggml      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab) {
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index 47d2a139ea6..d6839c88050 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -277,7 +277,7 @@ static void test_backend_greedy_sampling(const char * model_path) {
     struct llama_sampler_chain_params backend_sampler_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_sampler_params);
 
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_greedy());
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_greedy());
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {
@@ -315,7 +315,7 @@ static void test_backend_top_k_sampling(const char * model_path) {
     const int32_t k = 8;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_top_k(k));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_top_k(k));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {
@@ -363,12 +363,12 @@ static void test_backend_temp_sampling(const char * model_path) {
     const float temp_0 = 0.8f;
     struct llama_sampler_chain_params backend_chain_params_0 = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain_0 = llama_sampler_chain_init(backend_chain_params_0);
-    llama_sampler_chain_add(backend_sampler_chain_0, llama_sampler_backend_init_temp(temp_0));
+    llama_sampler_chain_add(backend_sampler_chain_0, llama_sampler_init_temp(temp_0));
 
     const float temp_1 = 0.1f;
     struct llama_sampler_chain_params backend_chain_params_1 = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain_1 = llama_sampler_chain_init(backend_chain_params_1);
-    llama_sampler_chain_add(backend_sampler_chain_1, llama_sampler_backend_init_temp(temp_1));
+    llama_sampler_chain_add(backend_sampler_chain_1, llama_sampler_init_temp(temp_1));
 
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {
         { 0, backend_sampler_chain_0 },
@@ -430,7 +430,7 @@ static void test_backend_min_p_sampling(const char * model_path) {
     const float p = 0.1;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_min_p(p));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_min_p(p, 0));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {
@@ -488,7 +488,7 @@ static void test_backend_top_p_sampling(const char * model_path) {
     const float p = 0.9;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_top_p(p));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_top_p(p, 0));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {
@@ -541,12 +541,12 @@ static void test_backend_multi_sequence_sampling(const char * model_path) {
 
     struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
     struct llama_sampler * sampler_chain_0 = llama_sampler_chain_init(chain_params_0);
-    llama_sampler_chain_add(sampler_chain_0, llama_sampler_backend_init_greedy());
+    llama_sampler_chain_add(sampler_chain_0, llama_sampler_init_greedy());
 
     struct llama_sampler_chain_params chain_params_1 = llama_sampler_chain_default_params();
     struct llama_sampler * sampler_chain_1 = llama_sampler_chain_init(chain_params_1);
-    llama_sampler_chain_add(sampler_chain_1, llama_sampler_backend_init_temp(0.8f));
-    llama_sampler_chain_add(sampler_chain_1, llama_sampler_backend_init_greedy());
+    llama_sampler_chain_add(sampler_chain_1, llama_sampler_init_temp(0.8f));
+    llama_sampler_chain_add(sampler_chain_1, llama_sampler_init_greedy());
 
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {
         { 0, sampler_chain_0 },
@@ -613,7 +613,7 @@ static void test_backend_dist_sampling(const char * model_path) {
     const int32_t seed = 88;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_dist(seed));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {
@@ -642,7 +642,7 @@ static void test_backend_dist_sampling_and_cpu(const char * model_path) {
     const int32_t seed = 88;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_dist(seed));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {
@@ -689,11 +689,11 @@ static void test_backend_logit_bias_sampling(const char * model_path) {
 
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_logit_bias(
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_logit_bias(
                 llama_vocab_n_tokens(test_ctx.vocab),
                 logit_bias.size(),
                 logit_bias.data()));
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(88));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_dist(88));
 
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {
         { seq_id, backend_sampler_chain },
@@ -720,12 +720,12 @@ static void test_backend_mixed_sampling(const char * model_path) {
 
     struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
     struct llama_sampler * sampler_chain_0 = llama_sampler_chain_init(chain_params_0);
-    llama_sampler_chain_add(sampler_chain_0, llama_sampler_backend_init_dist(88));
+    llama_sampler_chain_add(sampler_chain_0, llama_sampler_init_dist(88));
 
     int k = 40;
     struct llama_sampler_chain_params chain_params_1 = llama_sampler_chain_default_params();
     struct llama_sampler * sampler_chain_1 = llama_sampler_chain_init(chain_params_1);
-    llama_sampler_chain_add(sampler_chain_1, llama_sampler_backend_init_top_k(k));
+    llama_sampler_chain_add(sampler_chain_1, llama_sampler_init_top_k(k));
 
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {
         { 0, sampler_chain_0 },
@@ -776,7 +776,7 @@ static void test_backend_set_sampler(const char * model_path) {
     const int seq_id = 0;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_dist(seed));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {
@@ -822,8 +822,8 @@ static void test_backend_set_sampler(const char * model_path) {
     // Set a new backend sampler for the sequence.
     struct llama_sampler_chain_params new_backend_chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * new_backend_sampler_chain = llama_sampler_chain_init(new_backend_chain_params);
-    llama_sampler_chain_add(new_backend_sampler_chain, llama_sampler_backend_init_top_k(20));
-    llama_sampler_chain_add(new_backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    llama_sampler_chain_add(new_backend_sampler_chain, llama_sampler_init_top_k(20));
+    llama_sampler_chain_add(new_backend_sampler_chain, llama_sampler_init_dist(seed));
     llama_set_backend_sampler(test_ctx.ctx, seq_id, new_backend_sampler_chain);
 
     if (!test_ctx.decode_tokens(tokens2)) {
@@ -841,7 +841,7 @@ static void test_backend_cpu_mixed_batch(const char * model_path) {
     // Sequence 0 uses backend sampling
     struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
     struct llama_sampler * sampler_chain_0 = llama_sampler_chain_init(chain_params_0);
-    llama_sampler_chain_add(sampler_chain_0, llama_sampler_backend_init_dist(88));
+    llama_sampler_chain_add(sampler_chain_0, llama_sampler_init_dist(88));
 
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {
         { 0, sampler_chain_0 },
@@ -912,7 +912,7 @@ static void test_backend_cpu_mixed_batch(const char * model_path) {
     {
         struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
         struct llama_sampler * sampler_chain= llama_sampler_chain_init(chain_params);
-        llama_sampler_chain_add(sampler_chain, llama_sampler_backend_init_dist(88));
+        llama_sampler_chain_add(sampler_chain, llama_sampler_init_dist(88));
 
         llama_set_backend_sampler(test_ctx.ctx, 0, sampler_chain);
 
@@ -937,7 +937,7 @@ static void test_backend_max_outputs(const char * model_path) {
     const int32_t seed = 88;
     llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     llama_sampler * backend_sampler_chain = llama_sampler_chain_init(backend_chain_params);
-    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_backend_init_dist(seed));
+    llama_sampler_chain_add(backend_sampler_chain, llama_sampler_init_dist(seed));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain }};
 
     if (!test_ctx.setup(model_path, backend_sampler_configs)) {

From 9028ebfea8c3a2f8edeb4a21e406f28f722a88b2 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 29 Nov 2025 22:37:07 +0200
Subject: [PATCH 56/59] llama : cleanup + naming

---
 include/llama.h                |  54 ++-
 src/llama-graph.cpp            |  36 +-
 src/llama-sampling.cpp         | 605 ++++++++++++++++-----------------
 src/llama-sampling.h           |  12 +-
 tests/test-backend-sampler.cpp |   2 +-
 5 files changed, 335 insertions(+), 374 deletions(-)

diff --git a/include/llama.h b/include/llama.h
index 263733cf2c5..24cd5be4a5f 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -210,13 +210,6 @@ extern "C" {
         bool sorted;      // note: do not assume the data is sorted - always check this flag
     } llama_token_data_array;
 
-    struct llama_sampler_ggml_data {
-        struct ggml_tensor * logits;
-        struct ggml_tensor * probs;
-        struct ggml_tensor * sampled;
-        struct ggml_tensor * candidates;
-    };
-
     typedef bool (*llama_progress_callback)(float progress, void * user_data);
 
     // Input data for llama_encode/llama_decode
@@ -1181,11 +1174,16 @@ extern "C" {
     //
     //    llama_sampler_free(smpl);
     //
-    // TODO: In the future, llama_sampler will be utilized to offload the sampling to the backends (e.g. GPU).
-    //
 
     typedef void * llama_sampler_context_t;
 
+    struct llama_sampler_backend_data {
+        struct ggml_tensor * logits;
+        struct ggml_tensor * probs;
+        struct ggml_tensor * sampled;
+        struct ggml_tensor * candidates;
+    };
+
     // user code can implement the interface below in order to create custom llama_sampler
     struct llama_sampler_i {
         const char *           (*name)  (const struct llama_sampler * smpl);                                 // can be NULL
@@ -1195,25 +1193,28 @@ extern "C" {
         struct llama_sampler * (*clone) (const struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
         void                   (*free)  (      struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
 
-        void                   (*apply_ggml)(  struct llama_sampler           * smpl,
-                                               struct ggml_context            * ctx,
-                                               struct  ggml_cgraph            * gf,
-                                               struct llama_sampler_ggml_data * ggml_data);
+        // backend sampling interface
+        void (*backend_init)(struct llama_sampler * smpl, ggml_backend_buffer_type_t buft);
 
-        void                   (*accept_ggml)( struct llama_sampler * smpl,
-                                               struct ggml_context  * ctx,
-                                               struct ggml_cgraph   * gf,
-                                               struct ggml_tensor   * selected_token);
+        void (*backend_accept)(
+                struct llama_sampler * smpl,
+                struct ggml_context  * ctx,
+                struct ggml_cgraph   * gf,
+                struct ggml_tensor   * selected_token);
 
-        void                   (*set_input_ggml)(struct llama_sampler * smpl);
+        void (*backend_apply)(
+                struct llama_sampler              * smpl,
+                struct ggml_context               * ctx,
+                struct ggml_cgraph                * gf,
+                struct llama_sampler_backend_data * ggml_data);
 
-        void                   (*init_ggml)(struct llama_sampler      * smpl,
-                                            ggml_backend_buffer_type_t  buft);
+        void (*backend_set_input)(struct llama_sampler * smpl);
     };
 
     struct llama_sampler {
         const struct llama_sampler_i * iface;
-        llama_sampler_context_t        ctx;
+
+        llama_sampler_context_t ctx;
     };
 
     LLAMA_API void llama_set_backend_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
@@ -1228,17 +1229,6 @@ extern "C" {
     // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add)
     LLAMA_API void                   llama_sampler_free  (      struct llama_sampler * smpl);
 
-    LLAMA_API void llama_sampler_init_ggml     (struct llama_sampler * smpl, ggml_backend_buffer_type_t buft);
-    LLAMA_API void llama_sampler_set_input_ggml(struct llama_sampler * smpl);
-    LLAMA_API void llama_sampler_apply_ggml    (struct llama_sampler * smpl,
-                                                struct ggml_context  * ctx,
-                                                struct ggml_cgraph   * gf,
-                                                struct llama_sampler_ggml_data * ggml_data);
-    LLAMA_API void llama_sampler_accept_ggml   (struct llama_sampler * smpl,
-                                                struct ggml_context  * ctx,
-                                                struct ggml_cgraph   * gf,
-                                                struct ggml_tensor   * selected_token);
-
     // llama_sampler_chain
     // a type of llama_sampler that can chain multiple samplers one after another
 
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 73a37773f78..3d88dcd2966 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -465,8 +465,8 @@ void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
 void llm_graph_input_sampling::set_input(const llama_ubatch * ubatch) {
     GGML_UNUSED(ubatch);
     for (const auto & [seq_id, sampler] : samplers) {
-        if (sampler->iface->set_input_ggml) {
-            sampler->iface->set_input_ggml(sampler);
+        if (sampler->iface->backend_set_input) {
+            sampler->iface->backend_set_input(sampler);
         }
     }
 }
@@ -2088,8 +2088,9 @@ void llm_graph_context::build_sampling() const {
         const int32_t row_idx = it->second;
 
         // Allow GPU sampler to create input tensors by implementing init_ggml.
-        if (sampler->iface->init_ggml != nullptr) {
-            sampler->iface->init_ggml(sampler, buft);
+        // TODO: this should not be done here
+        if (sampler->iface->backend_init != nullptr) {
+            sampler->iface->backend_init(sampler, buft);
         }
 
         active_samplers[seq_id] = sampler;
@@ -2097,33 +2098,34 @@ void llm_graph_context::build_sampling() const {
         ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, n_vocab, row_idx * logits_t->nb[1]);
         ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
 
-        struct llama_sampler_ggml_data ggml_data = {
+        struct llama_sampler_backend_data data = {
             /*.logits      =*/ logits_seq,
             /*.probs       =*/ nullptr,
             /*.sampled     =*/ nullptr,
             /*.candidates  =*/ nullptr,
         };
 
-        llama_sampler_apply_ggml(sampler, ctx0, gf, &ggml_data);
+        assert(sampler->iface->backend_apply);
+        sampler->iface->backend_apply(sampler, ctx0, gf, &data);
 
-        if (ggml_data.sampled != nullptr) {
-            res->t_sampled[seq_id] = ggml_data.sampled;
-            ggml_build_forward_expand(gf, ggml_data.sampled);
+        if (data.sampled != nullptr) {
+            res->t_sampled[seq_id] = data.sampled;
+            ggml_build_forward_expand(gf, data.sampled);
         }
 
-        if  (ggml_data.probs != nullptr) {
-            res->t_sampled_probs[seq_id] = ggml_data.probs;
-            ggml_build_forward_expand(gf, ggml_data.probs);
+        if  (data.probs != nullptr) {
+            res->t_sampled_probs[seq_id] = data.probs;
+            ggml_build_forward_expand(gf, data.probs);
         }
 
-        if (ggml_data.logits != logits_seq) {
-            res->t_sampled_logits[seq_id] = ggml_data.logits;
+        if (data.logits != logits_seq) {
+            res->t_sampled_logits[seq_id] = data.logits;
             ggml_build_forward_expand(gf, res->t_sampled_logits[seq_id]);
         }
 
-        if (ggml_data.candidates != nullptr) {
-            res->t_candidates[seq_id] = ggml_data.candidates;
-            ggml_build_forward_expand(gf, ggml_data.candidates);
+        if (data.candidates != nullptr) {
+            res->t_candidates[seq_id] = data.candidates;
+            ggml_build_forward_expand(gf, data.candidates);
         }
     }
 
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index a13be032402..8069aa6802a 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -348,7 +348,9 @@ static uint32_t get_rng_seed(uint32_t seed) {
 
 // llama_sampler API
 
-struct llama_sampler * llama_sampler_init(const struct llama_sampler_i * iface, llama_sampler_context_t ctx) {
+struct llama_sampler * llama_sampler_init(
+        const struct llama_sampler_i * iface,
+        llama_sampler_context_t ctx) {
     return new llama_sampler {
         /* .iface = */ iface,
         /* .ctx   = */ ctx,
@@ -374,39 +376,6 @@ void llama_sampler_apply(struct llama_sampler * smpl, struct llama_token_data_ar
     smpl->iface->apply(smpl, cur_p);
 }
 
-void llama_sampler_apply_ggml(
-          struct llama_sampler           * smpl,
-          struct ggml_context            * ctx,
-          struct ggml_cgraph             * gf,
-          struct llama_sampler_ggml_data * ggml_data) {
-    GGML_ASSERT(smpl->iface->apply_ggml);
-    smpl->iface->apply_ggml(smpl, ctx, gf, ggml_data);
-}
-
-void llama_sampler_accept_ggml(
-        struct llama_sampler * smpl,
-        ggml_context * ctx,
-        ggml_cgraph * gf,
-        struct ggml_tensor * selected_token) {
-    if (smpl->iface->accept_ggml) {
-        smpl->iface->accept_ggml(smpl, ctx, gf, selected_token);
-    }
-}
-
-void llama_sampler_set_input_ggml(struct llama_sampler * smpl) {
-    if (smpl->iface->set_input_ggml) {
-        smpl->iface->set_input_ggml(smpl);
-    }
-}
-
-void llama_sampler_init_ggml(
-        struct llama_sampler      * smpl,
-        ggml_backend_buffer_type_t  buft) {
-    if (smpl->iface->init_ggml) {
-        smpl->iface->init_ggml(smpl, buft);
-    }
-}
-
 void llama_sampler_reset(struct llama_sampler * smpl) {
     if (smpl->iface->reset) {
         smpl->iface->reset(smpl);
@@ -523,10 +492,10 @@ static void llama_sampler_chain_apply(struct llama_sampler * smpl, llama_token_d
     time_meas tm(chain->t_sample_us, chain->params.no_perf);
 
     for (auto * smpl : chain->samplers) {
-        // Skip GPU samplers - they have apply_ggml but no apply
         if (smpl->iface->apply == nullptr) {
             continue;
         }
+
         llama_sampler_apply(smpl, cur_p);
     }
 }
@@ -561,21 +530,19 @@ static void llama_sampler_chain_free(struct llama_sampler * smpl) {
     delete chain;
 }
 
-static void llama_sampler_chain_apply_ggml(
-          struct llama_sampler * smpl,
-          struct ggml_context * ctx,
-          struct ggml_cgraph * gf,
-          struct llama_sampler_ggml_data * ggml_data) {
+static void llama_sampler_chain_backend_init(
+        struct llama_sampler       * smpl,
+        ggml_backend_buffer_type_t   buft) {
     auto * chain = (llama_sampler_chain *) smpl->ctx;
 
     for (auto * smpl : chain->samplers) {
-        if (smpl->iface->apply_ggml) {
-            smpl->iface->apply_ggml(smpl, ctx, gf, ggml_data);
+        if (smpl->iface->backend_init) {
+            smpl->iface->backend_init(smpl,buft);
         }
     }
 }
 
-static void llama_sampler_chain_accept_ggml(
+static void llama_sampler_chain_backend_accept(
         struct llama_sampler * smpl,
         ggml_context * ctx,
         ggml_cgraph * gf,
@@ -583,45 +550,47 @@ static void llama_sampler_chain_accept_ggml(
     auto * chain = (llama_sampler_chain *) smpl->ctx;
 
     for (auto * smpl : chain->samplers) {
-        if (smpl->iface->accept_ggml) {
-            smpl->iface->accept_ggml(smpl, ctx, gf, selected_token);
+        if (smpl->iface->backend_accept) {
+            smpl->iface->backend_accept(smpl, ctx, gf, selected_token);
         }
     }
 }
 
-static void llama_sampler_chain_set_input_ggml(struct llama_sampler * smpl) {
+static void llama_sampler_chain_backend_apply(
+          struct llama_sampler * smpl,
+          struct ggml_context * ctx,
+          struct ggml_cgraph * gf,
+          struct llama_sampler_backend_data * data) {
     auto * chain = (llama_sampler_chain *) smpl->ctx;
 
     for (auto * smpl : chain->samplers) {
-        if (smpl->iface->set_input_ggml) {
-            smpl->iface->set_input_ggml(smpl);
+        if (smpl->iface->backend_apply) {
+            smpl->iface->backend_apply(smpl, ctx, gf, data);
         }
     }
 }
 
-static void llama_sampler_chain_set_backend_context(
-        struct llama_sampler      * smpl,
-        ggml_backend_buffer_type_t  buft) {
+static void llama_sampler_chain_backend_set_input(struct llama_sampler * smpl) {
     auto * chain = (llama_sampler_chain *) smpl->ctx;
 
     for (auto * smpl : chain->samplers) {
-        if (smpl->iface->init_ggml) {
-            smpl->iface->init_ggml(smpl,buft);
+        if (smpl->iface->backend_set_input) {
+            smpl->iface->backend_set_input(smpl);
         }
     }
 }
 
 static struct llama_sampler_i llama_sampler_chain_i = {
-    /* .name           = */ llama_sampler_chain_name,
-    /* .accept         = */ llama_sampler_chain_accept,
-    /* .apply          = */ llama_sampler_chain_apply,
-    /* .reset          = */ llama_sampler_chain_reset,
-    /* .clone          = */ llama_sampler_chain_clone,
-    /* .free           = */ llama_sampler_chain_free,
-    /* .apply_ggml     = */ llama_sampler_chain_apply_ggml,
-    /* .accept_ggml    = */ llama_sampler_chain_accept_ggml,
-    /* .set_input_ggml = */ llama_sampler_chain_set_input_ggml,
-    /* .init_ggml      = */ llama_sampler_chain_set_backend_context,
+    /* .name              = */ llama_sampler_chain_name,
+    /* .accept            = */ llama_sampler_chain_accept,
+    /* .apply             = */ llama_sampler_chain_apply,
+    /* .reset             = */ llama_sampler_chain_reset,
+    /* .clone             = */ llama_sampler_chain_clone,
+    /* .free              = */ llama_sampler_chain_free,
+    /* .backend_init      = */ llama_sampler_chain_backend_init,
+    /* .backend_accept    = */ llama_sampler_chain_backend_accept,
+    /* .backend_apply     = */ llama_sampler_chain_backend_apply,
+    /* .backend_set_input = */ llama_sampler_chain_backend_set_input,
 };
 
 struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params) {
@@ -689,29 +658,29 @@ static void llama_sampler_greedy_apply(struct llama_sampler * /*smpl*/, llama_to
     }
 }
 
-static void llama_sampler_greedy_apply_ggml(
+static void llama_sampler_greedy_backend_apply(
         struct llama_sampler           * smpl,
         struct ggml_context            * ctx,
         struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
+        struct llama_sampler_backend_data * data) {
     GGML_UNUSED(gf);
     GGML_UNUSED(smpl);
-    struct ggml_tensor * argmax_result = ggml_argmax(ctx, ggml_data->logits);
+    struct ggml_tensor * argmax_result = ggml_argmax(ctx, data->logits);
     ggml_set_name(argmax_result, "argmax_result");
-    ggml_data->sampled = argmax_result;
+    data->sampled = argmax_result;
 }
 
 static struct llama_sampler_i llama_sampler_greedy_i = {
-    /* .name           = */ llama_sampler_greedy_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_greedy_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ nullptr,
-    /* .free           = */ nullptr,
-    /* .apply_ggml     = */ llama_sampler_greedy_apply_ggml,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_greedy_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_greedy_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ nullptr,
+    /* .free              = */ nullptr,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ llama_sampler_greedy_backend_apply,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_greedy() {
@@ -838,15 +807,24 @@ static void llama_sampler_dist_free(struct llama_sampler * smpl) {
     delete (llama_sampler_dist *) smpl->ctx;
 }
 
-static void llama_sampler_dist_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
+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);
+
+    std::uniform_real_distribution<float> dist(0.0f, 1.0f);
+    const float rnd = dist(sctx->rng);
+    ggml_backend_tensor_set(sctx->inp_uniform, &rnd, 0, sizeof(float));
+}
+
+static void llama_sampler_dist_backend_apply(
+        struct llama_sampler              * smpl,
+        struct ggml_context               * ctx,
+        struct ggml_cgraph                * gf,
+        struct llama_sampler_backend_data * data) {
     GGML_UNUSED(gf);
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
-    struct ggml_tensor * probs = ggml_soft_max(ctx, ggml_data->logits);
+    struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits);
     ggml_set_name(probs, "dist_probs");
 
     struct ggml_tensor * cumsum = ggml_cumsum(ctx, probs);
@@ -883,8 +861,8 @@ static void llama_sampler_dist_apply_ggml(
 
     // Map back to original vocab ids if a candidates tensor is available.
     struct ggml_tensor * sampled_token = idx;
-    if (ggml_data->candidates != nullptr) {
-        struct ggml_tensor * candidates = ggml_data->candidates;
+    if (data->candidates != nullptr) {
+        struct ggml_tensor * candidates = data->candidates;
         struct ggml_tensor * candidates_reshaped = ggml_view_2d(ctx, candidates, 1, ggml_nelements(candidates),
                 ggml_type_size(candidates->type), 0);
 
@@ -893,19 +871,10 @@ static void llama_sampler_dist_apply_ggml(
     }
 
     ggml_set_output(sampled_token);
-    ggml_data->sampled = sampled_token;
-}
-
-static void llama_sampler_dist_set_input_ggml(struct llama_sampler * smpl) {
-    auto * sctx = (llama_sampler_dist *) smpl->ctx;
-    GGML_ASSERT(sctx->inp_uniform != nullptr);
-
-    std::uniform_real_distribution<float> dist(0.0f, 1.0f);
-    const float rnd = dist(sctx->rng);
-    ggml_backend_tensor_set(sctx->inp_uniform, &rnd, 0, sizeof(float));
+    data->sampled = sampled_token;
 }
 
-static void llama_sampler_dist_init_ggml(
+static void llama_sampler_dist_backend_init(
         struct llama_sampler      * smpl,
         ggml_backend_buffer_type_t  buft) {
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
@@ -921,7 +890,7 @@ static void llama_sampler_dist_init_ggml(
     sctx->inp_ctx.reset(ggml_init(params));
 
     // Create the uniform random scalar input tensor. This will be set by
-    // llama_sampler_dist_set_input_ggml after this graph is built.
+    // 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);
@@ -931,16 +900,16 @@ static void llama_sampler_dist_init_ggml(
 }
 
 static struct llama_sampler_i llama_sampler_dist_i = {
-    /* .name           = */ llama_sampler_dist_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_dist_apply,
-    /* .reset          = */ llama_sampler_dist_reset,
-    /* .clone          = */ llama_sampler_dist_clone,
-    /* .free           = */ llama_sampler_dist_free,
-    /* .apply_ggml     = */ llama_sampler_dist_apply_ggml,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ llama_sampler_dist_set_input_ggml,
-    /* .init_ggml      = */ llama_sampler_dist_init_ggml,
+    /* .name              = */ llama_sampler_dist_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_dist_apply,
+    /* .reset             = */ llama_sampler_dist_reset,
+    /* .clone             = */ llama_sampler_dist_clone,
+    /* .free              = */ llama_sampler_dist_free,
+    /* .backend_init      = */ llama_sampler_dist_backend_init,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ llama_sampler_dist_backend_apply,
+    /* .backend_set_input = */ llama_sampler_dist_backend_set_input,
 };
 
 struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
@@ -986,15 +955,22 @@ static void llama_sampler_top_k_free(struct llama_sampler * smpl) {
     delete (llama_sampler_top_k *) smpl->ctx;
 }
 
-static void llama_sampler_top_k_apply_ggml(
+static void llama_sampler_top_k_backend_init(
         struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
+        ggml_backend_buffer_type_t       buft) {
+    auto * ctx_data = (llama_sampler_top_k *) smpl->ctx;
+    ctx_data->device = ggml_backend_buft_get_device(buft);
+}
+
+static void llama_sampler_top_k_backend_apply(
+        struct llama_sampler              * smpl,
+        struct ggml_context               * ctx,
+        struct ggml_cgraph                * gf,
+        struct llama_sampler_backend_data * data) {
 
     auto * ctx_data = (llama_sampler_top_k *) smpl->ctx;
 
-    struct ggml_tensor * top_k = ggml_top_k(ctx, ggml_data->logits, ctx_data->k);
+    struct ggml_tensor * top_k = ggml_top_k(ctx, data->logits, ctx_data->k);
     ggml_set_name(top_k, "top_k");
 
     // top_k is a view of argsort - check if backend supports the underlying argsort operation
@@ -1004,34 +980,27 @@ static void llama_sampler_top_k_apply_ggml(
         fprintf(stderr, "CPU backend will be used instead which defeats the purpose of having backend samplers\n");
     }
 
-    ggml_data->candidates = top_k;
+    data->candidates = top_k;
 
-    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
+    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
     struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);
     ggml_set_name(top_k_rows, "top_k_rows");
 
-    ggml_data->logits = ggml_reshape_1d(ctx, top_k_rows, ctx_data->k);
-    ggml_build_forward_expand(gf, ggml_data->logits);
-}
-
-static void llama_sampler_top_k_init_ggml(
-        struct llama_sampler           * smpl,
-        ggml_backend_buffer_type_t       buft) {
-    auto * ctx_data = (llama_sampler_top_k *) smpl->ctx;
-    ctx_data->device = ggml_backend_buft_get_device(buft);
+    data->logits = ggml_reshape_1d(ctx, top_k_rows, ctx_data->k);
+    ggml_build_forward_expand(gf, data->logits);
 }
 
 static struct llama_sampler_i llama_sampler_top_k_i = {
-    /* .name           = */ llama_sampler_top_k_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_top_k_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_top_k_clone,
-    /* .free           = */ llama_sampler_top_k_free,
-    /* .apply_ggml     = */ llama_sampler_top_k_apply_ggml,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ llama_sampler_top_k_init_ggml,
+    /* .name              = */ llama_sampler_top_k_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_top_k_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_top_k_clone,
+    /* .free              = */ llama_sampler_top_k_free,
+    /* .backend_init      = */ llama_sampler_top_k_backend_init,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ llama_sampler_top_k_backend_apply,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_top_k(int32_t k) {
@@ -1124,14 +1093,21 @@ static void llama_sampler_top_p_free(struct llama_sampler * smpl) {
     delete (llama_sampler_top_p *) smpl->ctx;
 }
 
-static void llama_sampler_top_p_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
+static void llama_sampler_top_p_backend_init(
+        struct llama_sampler       * smpl,
+        ggml_backend_buffer_type_t   buft) {
     auto * sctx = (llama_sampler_top_p *) smpl->ctx;
+    sctx->device = ggml_backend_buft_get_device(buft);
+}
 
-    struct ggml_tensor * softmax = ggml_soft_max(ctx, ggml_data->logits);
+static void llama_sampler_top_p_backend_apply(
+        struct llama_sampler              * smpl,
+        struct ggml_context               * ctx,
+        struct ggml_cgraph                * gf,
+        struct llama_sampler_backend_data * data) {
+    auto * sctx = (llama_sampler_top_p *) smpl->ctx;
+
+    struct ggml_tensor * softmax = ggml_soft_max(ctx, data->logits);
     ggml_set_name(softmax, "top_p_softmax");
 
     // Get the sorted indices of the softmax probabilities in descending order.
@@ -1181,30 +1157,23 @@ static void llama_sampler_top_p_apply_ggml(
     struct ggml_tensor * top_p_bias = ggml_scale_bias(ctx, mask, large_val, -large_val);
     ggml_set_name(top_p_bias, "top_p_bias");
 
-    ggml_data->logits = ggml_add(ctx, ggml_data->logits, top_p_bias);
-    ggml_set_name(ggml_data->logits, "top_p_logits");
+    data->logits = ggml_add(ctx, data->logits, top_p_bias);
+    ggml_set_name(data->logits, "top_p_logits");
 
-    ggml_build_forward_expand(gf, ggml_data->logits);
-}
-
-static void llama_sampler_top_p_init_ggml(
-        struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
-    auto * sctx = (llama_sampler_top_p *) smpl->ctx;
-    sctx->device = ggml_backend_buft_get_device(buft);
+    ggml_build_forward_expand(gf, data->logits);
 }
 
 static struct llama_sampler_i llama_sampler_top_p_i = {
-    /* .name           = */ llama_sampler_top_p_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_top_p_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_top_p_clone,
-    /* .free           = */ llama_sampler_top_p_free,
-    /* .apply_ggml     = */ llama_sampler_top_p_apply_ggml,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ llama_sampler_top_p_init_ggml,
+    /* .name              = */ llama_sampler_top_p_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_top_p_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_top_p_clone,
+    /* .free              = */ llama_sampler_top_p_free,
+    /* .backend_init      = */ llama_sampler_top_p_backend_init,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ llama_sampler_top_p_backend_apply,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
@@ -1296,17 +1265,24 @@ static void llama_sampler_min_p_free(struct llama_sampler * smpl) {
     delete (llama_sampler_min_p *) smpl->ctx;
 }
 
-static void llama_sampler_min_p_apply_ggml(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
+static void llama_sampler_min_p_backend_init(
+        struct llama_sampler       * smpl,
+        ggml_backend_buffer_type_t   buft) {
+    auto * sctx = (llama_sampler_min_p *) smpl->ctx;
+    sctx->device = ggml_backend_buft_get_device(buft);
+}
+
+static void llama_sampler_min_p_backend_apply(
+        struct llama_sampler              * smpl,
+        struct ggml_context               * ctx,
+        struct ggml_cgraph                * gf,
+        struct llama_sampler_backend_data * data) {
     auto * sctx = (llama_sampler_min_p *) smpl->ctx;
 
-    struct ggml_tensor * max_idx = ggml_argmax(ctx, ggml_data->logits);
+    struct ggml_tensor * max_idx = ggml_argmax(ctx, data->logits);
     ggml_set_name(max_idx, "max_idx");
 
-    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, ggml_data->logits, 1, ggml_data->logits->ne[0]);
+    struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
     ggml_set_name(logits_rows, "logits_rows");
 
     struct ggml_tensor * max_logit = ggml_get_rows(ctx, logits_rows, max_idx);
@@ -1317,7 +1293,7 @@ static void llama_sampler_min_p_apply_ggml(
     ggml_set_name(threshold, "min_p_threshold");
 
     // Subtract the threshold from logits.
-    struct ggml_tensor * sub = ggml_sub(ctx, ggml_data->logits, threshold);
+    struct ggml_tensor * sub = ggml_sub(ctx, data->logits, threshold);
 
     // Create a mask where logits below the threshold are 0 (discard),
     // and others are 1 (keep).
@@ -1333,30 +1309,23 @@ static void llama_sampler_min_p_apply_ggml(
     ggml_set_name(min_p_bias, "min_p_bias");
 
     // Add the min_p bias to the logits.
-    ggml_data->logits = ggml_add(ctx, ggml_data->logits, min_p_bias);
-    ggml_set_name(ggml_data->logits, "min_p_logits");
+    data->logits = ggml_add(ctx, data->logits, min_p_bias);
+    ggml_set_name(data->logits, "min_p_logits");
 
-    ggml_build_forward_expand(gf, ggml_data->logits);
-}
-
-static void llama_sampler_min_p_init_ggml(
-        struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
-    auto * sctx = (llama_sampler_min_p *) smpl->ctx;
-    sctx->device = ggml_backend_buft_get_device(buft);
+    ggml_build_forward_expand(gf, data->logits);
 }
 
 static struct llama_sampler_i llama_sampler_min_p_i = {
-    /* .name           = */ llama_sampler_min_p_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_min_p_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_min_p_clone,
-    /* .free           = */ llama_sampler_min_p_free,
-    /* .apply_ggml     = */ llama_sampler_min_p_apply_ggml,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ llama_sampler_min_p_init_ggml,
+    /* .name              = */ llama_sampler_min_p_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_min_p_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_min_p_clone,
+    /* .free              = */ llama_sampler_min_p_free,
+    /* .backend_init      = */ llama_sampler_min_p_backend_init,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ llama_sampler_min_p_backend_apply,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) {
@@ -1451,16 +1420,16 @@ static void llama_sampler_typical_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_typical_i = {
-    /* .name           = */ llama_sampler_typical_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_typical_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_typical_clone,
-    /* .free           = */ llama_sampler_typical_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_typical_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_typical_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_typical_clone,
+    /* .free              = */ llama_sampler_typical_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_typical(float p, size_t min_keep) {
@@ -1498,38 +1467,38 @@ static void llama_sampler_temp_free(struct llama_sampler * smpl) {
     delete (llama_sampler_temp *) smpl->ctx;
 }
 
-static void llama_sampler_temp_apply_ggml(
+static void llama_sampler_temp_backend_apply(
         struct llama_sampler           * smpl,
         struct ggml_context            * ctx,
         struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
+        struct llama_sampler_backend_data * data) {
     auto * ctx_data = (llama_sampler_temp *) smpl->ctx;
 
     if (ctx_data->temp <= 0.0f) {
         return;
     }
 
-    struct ggml_tensor * scaled = ggml_scale(ctx, ggml_data->logits, 1.0f / ctx_data->temp);
+    struct ggml_tensor * scaled = ggml_scale(ctx, data->logits, 1.0f / ctx_data->temp);
     ggml_set_name(scaled, "temp_scaled");
 
     // Make sure the scaled tensor is contiguous for subsequent operations
-    ggml_data->logits = ggml_cont(ctx, scaled);
-    ggml_set_name(ggml_data->logits, "temp_scaled_logits");
+    data->logits = ggml_cont(ctx, scaled);
+    ggml_set_name(data->logits, "temp_scaled_logits");
 
-    ggml_build_forward_expand(gf, ggml_data->logits);
+    ggml_build_forward_expand(gf, data->logits);
 }
 
 static struct llama_sampler_i llama_sampler_temp_i = {
-    /* .name           = */ llama_sampler_temp_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_temp_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_temp_clone,
-    /* .free           = */ llama_sampler_temp_free,
-    /* .apply_ggml     = */ llama_sampler_temp_apply_ggml,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_temp_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_temp_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_temp_clone,
+    /* .free              = */ llama_sampler_temp_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ llama_sampler_temp_backend_apply,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_temp(float temp) {
@@ -1634,16 +1603,16 @@ static void llama_sampler_temp_ext_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_temp_ext_i = {
-    /* .name           = */ llama_sampler_temp_ext_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_temp_ext_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_temp_ext_clone,
-    /* .free           = */ llama_sampler_temp_ext_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_temp_ext_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_temp_ext_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_temp_ext_clone,
+    /* .free              = */ llama_sampler_temp_ext_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, float exponent) {
@@ -1732,16 +1701,16 @@ static void llama_sampler_xtc_reset(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_xtc_i = {
-    /* .name           = */ llama_sampler_xtc_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sample_xtc_apply,
-    /* .reset          = */ llama_sampler_xtc_reset,
-    /* .clone          = */ llama_sampler_xtc_clone,
-    /* .free           = */ llama_sampler_xtc_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_xtc_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sample_xtc_apply,
+    /* .reset             = */ llama_sampler_xtc_reset,
+    /* .clone             = */ llama_sampler_xtc_clone,
+    /* .free              = */ llama_sampler_xtc_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
@@ -1844,16 +1813,16 @@ static void llama_sampler_mirostat_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_mirostat_i = {
-    /* .name           = */ llama_sampler_mirostat_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_mirostat_apply,
-    /* .reset          = */ llama_sampler_mirostat_reset,
-    /* .clone          = */ llama_sampler_mirostat_clone,
-    /* .free           = */ llama_sampler_mirostat_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_mirostat_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_mirostat_apply,
+    /* .reset             = */ llama_sampler_mirostat_reset,
+    /* .clone             = */ llama_sampler_mirostat_clone,
+    /* .free              = */ llama_sampler_mirostat_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t seed, float tau, float eta, int32_t m) {
@@ -1947,16 +1916,16 @@ static void llama_sampler_mirostat_v2_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_mirostat_v2_i = {
-    /* .name           = */ llama_sampler_mirostat_v2_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_mirostat_v2_apply,
-    /* .reset          = */ llama_sampler_mirostat_v2_reset,
-    /* .clone          = */ llama_sampler_mirostat_v2_clone,
-    /* .free           = */ llama_sampler_mirostat_v2_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_mirostat_v2_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_mirostat_v2_apply,
+    /* .reset             = */ llama_sampler_mirostat_v2_reset,
+    /* .clone             = */ llama_sampler_mirostat_v2_clone,
+    /* .free              = */ llama_sampler_mirostat_v2_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau, float eta) {
@@ -2068,16 +2037,16 @@ static void llama_sampler_grammar_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_grammar_i = {
-    /* .name           = */ llama_sampler_grammar_name,
-    /* .accept         = */ llama_sampler_grammar_accept_impl,
-    /* .apply          = */ llama_sampler_grammar_apply,
-    /* .reset          = */ llama_sampler_grammar_reset,
-    /* .clone          = */ llama_sampler_grammar_clone,
-    /* .free           = */ llama_sampler_grammar_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_grammar_name,
+    /* .accept            = */ llama_sampler_grammar_accept_impl,
+    /* .apply             = */ llama_sampler_grammar_apply,
+    /* .reset             = */ llama_sampler_grammar_reset,
+    /* .clone             = */ llama_sampler_grammar_clone,
+    /* .free              = */ llama_sampler_grammar_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 static struct llama_sampler * llama_sampler_init_grammar_impl(
@@ -2279,16 +2248,16 @@ static void llama_sampler_penalties_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_penalties_i = {
-    /* .name           = */ llama_sampler_penalties_name,
-    /* .accept         = */ llama_sampler_penalties_accept,
-    /* .apply          = */ llama_sampler_penalties_apply,
-    /* .reset          = */ llama_sampler_penalties_reset,
-    /* .clone          = */ llama_sampler_penalties_clone,
-    /* .free           = */ llama_sampler_penalties_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_penalties_name,
+    /* .accept            = */ llama_sampler_penalties_accept,
+    /* .apply             = */ llama_sampler_penalties_apply,
+    /* .reset             = */ llama_sampler_penalties_reset,
+    /* .clone             = */ llama_sampler_penalties_clone,
+    /* .free              = */ llama_sampler_penalties_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_penalties(
@@ -2374,16 +2343,16 @@ static void llama_sampler_top_n_sigma_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_top_n_sigma_i = {
-    /* .name           = */ llama_sampler_top_n_sigma_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_top_n_sigma_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_top_n_sigma_clone,
-    /* .free           = */ llama_sampler_top_n_sigma_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_top_n_sigma_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_top_n_sigma_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_top_n_sigma_clone,
+    /* .free              = */ llama_sampler_top_n_sigma_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_top_n_sigma(float n) {
@@ -2708,16 +2677,16 @@ static void llama_sampler_dry_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_dry_i = {
-    /* .name           = */ llama_sampler_dry_name,
-    /* .accept         = */ llama_sampler_dry_accept,
-    /* .apply          = */ llama_sampler_dry_apply,
-    /* .reset          = */ llama_sampler_dry_reset,
-    /* .clone          = */ llama_sampler_dry_clone,
-    /* .free           = */ llama_sampler_dry_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_dry_name,
+    /* .accept            = */ llama_sampler_dry_accept,
+    /* .apply             = */ llama_sampler_dry_apply,
+    /* .reset             = */ llama_sampler_dry_reset,
+    /* .clone             = */ llama_sampler_dry_clone,
+    /* .free              = */ llama_sampler_dry_free,
+    /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, int32_t n_ctx_train, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
@@ -2857,11 +2826,11 @@ static void llama_sampler_logit_bias_free(struct llama_sampler * smpl) {
     delete (llama_sampler_logit_bias *) smpl->ctx;
 }
 
-static void llama_sampler_logit_bias_apply_ggml(
+static void llama_sampler_logit_bias_backend_apply(
         struct llama_sampler           * smpl,
         struct ggml_context            * ctx,
         struct ggml_cgraph             * gf,
-        struct llama_sampler_ggml_data * ggml_data) {
+        struct llama_sampler_backend_data * data) {
     GGML_UNUSED(gf);
     GGML_UNUSED(ctx);
 
@@ -2871,11 +2840,11 @@ static void llama_sampler_logit_bias_apply_ggml(
     }
 
     // Add the sparse logit logit_bias to the logits
-    struct ggml_tensor * logit_biased = ggml_add_inplace(ctx, ggml_data->logits, sctx->inp_logit_bias);
+    struct ggml_tensor * logit_biased = ggml_add_inplace(ctx, data->logits, sctx->inp_logit_bias);
     ggml_build_forward_expand(gf, logit_biased);
 }
 
-static void llama_sampler_logit_bias_set_input_ggml(struct llama_sampler * smpl) {
+static void llama_sampler_logit_bias_backend_set_input(struct llama_sampler * smpl) {
     auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
     if (sctx->logit_bias.empty()) {
         return;
@@ -2892,7 +2861,7 @@ static void llama_sampler_logit_bias_set_input_ggml(struct llama_sampler * smpl)
     ggml_backend_tensor_set(sctx->inp_logit_bias, logit_bias_sparse.data(), 0, ggml_nbytes(sctx->inp_logit_bias));
 }
 
-static void llama_sampler_logit_bias_init_ggml(
+static void llama_sampler_logit_bias_backend_init(
         struct llama_sampler      * smpl,
         ggml_backend_buffer_type_t  buft) {
     auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
@@ -2918,16 +2887,16 @@ static void llama_sampler_logit_bias_init_ggml(
 }
 
 static struct llama_sampler_i llama_sampler_logit_bias_i = {
-    /* .name           = */ llama_sampler_logit_bias_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_logit_bias_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_logit_bias_clone,
-    /* .free           = */ llama_sampler_logit_bias_free,
-    /* .apply_ggml     = */ llama_sampler_logit_bias_apply_ggml,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ llama_sampler_logit_bias_set_input_ggml,
-    /* .init_ggml      = */ llama_sampler_logit_bias_init_ggml,
+    /* .name              = */ llama_sampler_logit_bias_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_logit_bias_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_logit_bias_clone,
+    /* .free              = */ llama_sampler_logit_bias_free,
+    /* .backend_init      = */ llama_sampler_logit_bias_backend_init,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ llama_sampler_logit_bias_backend_apply,
+    /* .backend_set_input = */ llama_sampler_logit_bias_backend_set_input,
 };
 
 struct llama_sampler * llama_sampler_init_logit_bias(
@@ -3155,16 +3124,16 @@ static void llama_sampler_infill_free(struct llama_sampler * smpl) {
 }
 
 static struct llama_sampler_i llama_sampler_infill_i = {
-    /* .name           = */ llama_sampler_infill_name,
-    /* .accept         = */ nullptr,
-    /* .apply          = */ llama_sampler_infill_apply,
-    /* .reset          = */ nullptr,
-    /* .clone          = */ llama_sampler_infill_clone,
-    /* .free           = */ llama_sampler_infill_free,
-    /* .apply_ggml     = */ nullptr,
-    /* .accept_ggml    = */ nullptr,
-    /* .set_input_ggml = */ nullptr,
-    /* .init_ggml      = */ nullptr,
+    /* .name              = */ llama_sampler_infill_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ llama_sampler_infill_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ llama_sampler_infill_clone,
+    /* .free              = */ llama_sampler_infill_free,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_set_input = */ nullptr,
+    /* .backend_init      = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab) {
diff --git a/src/llama-sampling.h b/src/llama-sampling.h
index 759dd7dcb70..80ea22ac356 100644
--- a/src/llama-sampling.h
+++ b/src/llama-sampling.h
@@ -24,9 +24,9 @@ struct llama_sampler_chain {
 };
 
 struct llama_sampler * llama_sampler_init_dry_testing(
-                         int32_t   context_size,
-                           float   dry_multiplier,
-                           float   dry_base,
-                         int32_t   dry_allowed_length,
-                         int32_t   dry_penalty_last_n,
-  const std::vector<std::vector<llama_token>>& seq_breakers);
+        int32_t context_size,
+        float   dry_multiplier,
+        float   dry_base,
+        int32_t dry_allowed_length,
+        int32_t dry_penalty_last_n,
+        const std::vector<std::vector<llama_token>> & seq_breakers);
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index d6839c88050..918766994b4 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -345,7 +345,7 @@ static void test_backend_top_k_sampling(const char * model_path) {
     // sampling, first top_k on the backend and then dist on the CPU.
     struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
     struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
-    GGML_ASSERT(chain->iface->apply_ggml != nullptr);
+    GGML_ASSERT(chain->iface->backend_apply != nullptr);
 
     llama_sampler_chain_add(chain, llama_sampler_init_dist(18));
     llama_token token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);

From ff7b0bf6320db65df659a13784844d0dd380c6b6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 29 Nov 2025 23:09:53 +0200
Subject: [PATCH 57/59] llama : call backend_init once

---
 include/llama.h       |  2 +-
 src/llama-context.cpp | 45 +++++++++++++++++++++++++++++--------------
 src/llama-context.h   |  2 +-
 src/llama-graph.cpp   | 10 +---------
 src/llama-graph.h     |  3 ---
 5 files changed, 34 insertions(+), 28 deletions(-)

diff --git a/include/llama.h b/include/llama.h
index 24cd5be4a5f..57fe4bd127b 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1217,7 +1217,7 @@ extern "C" {
         llama_sampler_context_t ctx;
     };
 
-    LLAMA_API void llama_set_backend_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
+    LLAMA_API bool llama_set_backend_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
 
     // mirror of llama_sampler_i:
     LLAMA_API struct llama_sampler * llama_sampler_init  (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 2b4dc58a43c..79d1d633d1c 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -68,14 +68,11 @@ llama_context::llama_context(
         for (size_t i = 0; i < params.n_samplers; ++i) {
             const auto & config = params.samplers[i];
 
-            const int n_samplers = llama_sampler_chain_n(config.sampler);
-            if (n_samplers <= 0) {
-                continue;
-            }
-
-            sampling.samplers[config.seq_id] = config.sampler;
+            if (set_backend_sampler(config.seq_id, config.sampler)) {
+                const int n_samplers = llama_sampler_chain_n(config.sampler);
 
-            LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
+                LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
+            }
         }
     }
 
@@ -912,14 +909,35 @@ void llama_context::set_warmup(bool value) {
     cparams.warmup = value;
 }
 
-void llama_context::set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
+bool llama_context::set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
     LLAMA_LOG_DEBUG("%s: seq_id = %d, sampler = %p\n", __func__, (int) seq_id, (void *) sampler);
 
-    if (sampler != nullptr && llama_sampler_chain_n(sampler) > 0) {
+    const bool can_offload =
+        sampler &&
+        sampler->iface->backend_init &&
+        sampler->iface->backend_apply &&
+        llama_sampler_chain_n(sampler) > 0;
+
+    if (sampler && can_offload) {
+        ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(model.dev_output());
+        sampler->iface->backend_init(sampler, buft);
+
         sampling.samplers[seq_id] = sampler;
-    } else {
+
+        return true;
+    }
+
+    if (sampler && !can_offload) {
+        LLAMA_LOG_WARN("%s: sampler '%s' cannot be offloaded to the backend\n", __func__, llama_sampler_name(sampler));
+
         sampling.samplers.erase(seq_id);
+
+        return false;
     }
+
+    sampling.samplers.erase(seq_id);
+
+    return true;
 }
 
 void llama_context::set_adapter_lora(
@@ -1910,7 +1928,7 @@ llm_graph_params llama_context::graph_params(
                         llm_graph_result * res,
                       const llama_ubatch & ubatch,
             const llama_memory_context_i * mctx,
-            llm_graph_type   gtype) const {
+                          llm_graph_type   gtype) const {
     return {
         /*.arch        =*/ model.arch,
         /*.hparams     =*/ model.hparams,
@@ -1919,7 +1937,6 @@ llm_graph_params llama_context::graph_params(
         /*.gtype       =*/ gtype,
         /*.sched       =*/ sched.get(),
         /*.backend_cpu =*/ backend_cpu,
-        /*.dev_out     =*/ model.dev_output(),
         /*.cvec        =*/ &cvec,
         /*.loras       =*/ &loras,
         /*.mctx        =*/ mctx,
@@ -2980,8 +2997,8 @@ float * llama_get_embeddings_seq(llama_context * ctx, llama_seq_id seq_id) {
     return ctx->get_embeddings_seq(seq_id);
 }
 
-void llama_set_backend_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
-    ctx->set_backend_sampler(seq_id, smpl);
+bool llama_set_backend_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
+    return ctx->set_backend_sampler(seq_id, smpl);
 }
 
 llama_token llama_get_backend_sampled_token_ith(llama_context * ctx, int32_t i) {
diff --git a/src/llama-context.h b/src/llama-context.h
index 2940d337a8e..9b568d98b86 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -221,7 +221,7 @@ struct llama_context {
     // reserve a graph with a dummy ubatch of the specified size
     ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false);
 
-    void set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler);
+    bool set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler);
 
 private:
     llm_graph_params graph_params(
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 3d88dcd2966..f396feededc 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -609,7 +609,6 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     rope_type        (hparams.rope_type),
     sched            (params.sched),
     backend_cpu      (params.backend_cpu),
-    dev_out          (params.dev_out),
     cvec             (params.cvec),
     loras            (params.loras),
     mctx             (params.mctx),
@@ -2075,8 +2074,6 @@ void llm_graph_context::build_sampling() const {
 
     const int64_t n_vocab = logits_t->ne[0];
 
-    ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(dev_out);
-
     std::unordered_map<llama_seq_id, llama_sampler*> active_samplers;
 
     for (const auto & [seq_id, sampler] : samplers) {
@@ -2085,13 +2082,8 @@ void llm_graph_context::build_sampling() const {
         if (it == seq_to_logit_row.end()) {
             continue;
         }
-        const int32_t row_idx = it->second;
 
-        // Allow GPU sampler to create input tensors by implementing init_ggml.
-        // TODO: this should not be done here
-        if (sampler->iface->backend_init != nullptr) {
-            sampler->iface->backend_init(sampler, buft);
-        }
+        const int32_t row_idx = it->second;
 
         active_samplers[seq_id] = sampler;
 
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 6797d78a20e..9090eca0282 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -428,7 +428,6 @@ struct llm_graph_params {
 
     ggml_backend_sched_t sched;
     ggml_backend_t backend_cpu;
-    ggml_backend_dev_t dev_out;
 
     const llama_adapter_cvec     * cvec;
     const llama_adapter_loras    * loras;
@@ -617,8 +616,6 @@ struct llm_graph_context {
 
     ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
 
-    ggml_backend_dev_t dev_out;
-
     const llama_adapter_cvec     * cvec;
     const llama_adapter_loras    * loras;
     const llama_memory_context_i * mctx;

From 1760bd69b3f07fa45d6aa23f6cce3ff3904816b6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 29 Nov 2025 23:57:25 +0200
Subject: [PATCH 58/59] llama : reserve graphs with samplers

---
 src/llama-context.cpp | 9 +++++++++
 src/llama-graph.cpp   | 6 +++---
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 79d1d633d1c..4639be985c0 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1903,6 +1903,15 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
     llama_batch_allocr balloc(model.hparams.n_pos_per_embd());
     llama_ubatch ubatch = balloc.ubatch_reserve(n_tokens/n_seqs, n_seqs);
 
+    // set one output token per sequence in order to activate all backend samplers
+    std::vector<llama_seq_id> seq_ids(n_seqs);
+    for (uint32_t i = 0; i < n_seqs; ++i) {
+        seq_ids[i] = i;
+        ubatch.n_seq_id[i] = 1;
+        ubatch.seq_id[i] = &seq_ids[i];
+        ubatch.output[i] = true;
+    }
+
     auto * res = gf_res_reserve.get();
 
     const auto gparams = graph_params(res, ubatch, mctx, LLM_GRAPH_TYPE_DEFAULT);
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index f396feededc..0abdab1637e 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -2063,18 +2063,18 @@ void llm_graph_context::build_sampling() const {
             logit_row_idx++;
         }
     }
+
     if (seq_to_logit_row.empty()) {
         return;
     }
 
-    // res->t_logits will contain logits for all tokens that specied that want
-    // logits calculated (logits=1 or output=1)
+    // res->t_logits will contain logits for all tokens that want the logits calculated (logits=1 or output=1)
     ggml_tensor * logits_t = res->t_logits;
     GGML_ASSERT(res->t_logits != nullptr && "missing t_logits tensor");
 
     const int64_t n_vocab = logits_t->ne[0];
 
-    std::unordered_map<llama_seq_id, llama_sampler*> active_samplers;
+    std::unordered_map<llama_seq_id, llama_sampler *> active_samplers;
 
     for (const auto & [seq_id, sampler] : samplers) {
         // Only process samplers for sequences that are in the current batch

From c187003d81043789ad08f82b01f3c032a00e316e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 30 Nov 2025 00:05:47 +0200
Subject: [PATCH 59/59] llama : naming

---
 common/sampling.cpp             | 12 ++---
 include/llama.h                 | 28 ++++++------
 src/llama-context.cpp           | 56 +++++++++++------------
 src/llama-context.h             | 18 ++++----
 src/llama-graph.cpp             |  2 +-
 src/llama-sampling.cpp          | 80 ++++++++++++++++-----------------
 tests/test-backend-sampler.cpp  | 74 +++++++++++++++---------------
 tools/server/server-context.cpp |  2 +-
 8 files changed, 136 insertions(+), 136 deletions(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index ca3a0f06911..2a6f57cd745 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -123,9 +123,9 @@ struct common_sampler {
     }
 
     void set_logits(struct llama_context * ctx, int idx) {
-        const float *       sampled_probs  = llama_get_backend_sampled_probs_ith     (ctx, idx);
-        const float *       sampled_logits = llama_get_backend_sampled_logits_ith    (ctx, idx);
-        const llama_token * sampled_ids    = llama_get_backend_sampled_candidates_ith(ctx, idx);
+        const float *       sampled_probs  = llama_get_sampled_probs_ith     (ctx, idx);
+        const float *       sampled_logits = llama_get_sampled_logits_ith    (ctx, idx);
+        const llama_token * sampled_ids    = llama_get_sampled_candidates_ith(ctx, idx);
 
         const llama_model * model = llama_get_model(ctx);
         const llama_vocab * vocab = llama_model_get_vocab(model);
@@ -133,13 +133,13 @@ struct common_sampler {
         const int n_vocab = llama_vocab_n_tokens(vocab);
 
         if (sampled_probs) {
-            const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
+            const uint32_t sampled_probs_count = llama_get_sampled_probs_count_ith(ctx, idx);
             cur.resize(sampled_probs_count);
             for (uint32_t i = 0; i < sampled_probs_count; ++i) {
                 cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]};
             }
         } else if (sampled_logits) {
-            const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
+            const uint32_t sampled_logits_count = llama_get_sampled_logits_count_ith(ctx, idx);
             cur.resize(sampled_logits_count);
             for (uint32_t i = 0; i < sampled_logits_count; i++) {
                 cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f};
@@ -536,7 +536,7 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     // Check if a backend sampler has already sampled a token in which case we
     // return that token id directly.
     {
-        const llama_token id = llama_get_backend_sampled_token_ith(ctx, idx);
+        const llama_token id = llama_get_sampled_token_ith(ctx, idx);
         if (id != LLAMA_TOKEN_NULL) {
             LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, id);
             return id;
diff --git a/include/llama.h b/include/llama.h
index 57fe4bd127b..6a1aca76330 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -979,29 +979,29 @@ extern "C" {
 
     // Get the backend sampled token for the ith token.
     // Returns LLAMA_TOKEN_NULL if no token was sampled.
-    LLAMA_API llama_token llama_get_backend_sampled_token_ith(struct llama_context * ctx, int32_t i);
+    LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i);
 
     // Get the backend sampled probabilites for the ith token
-    // The index matches llama_get_backend_sampled_token_ith().
+    // The index matches llama_get_sampled_token_ith().
     // Returns NULL if no probabilites were generated.
-    LLAMA_API float * llama_get_backend_sampled_probs_ith(struct llama_context * ctx, int32_t i);
+    LLAMA_API float * llama_get_sampled_probs_ith(struct llama_context * ctx, int32_t i);
     //
     // Get the number of backend sampled probabilites for the ith token.
-    LLAMA_API uint32_t llama_get_backend_sampled_probs_count_ith(struct llama_context * ctx, int32_t i);
+    LLAMA_API uint32_t llama_get_sampled_probs_count_ith(struct llama_context * ctx, int32_t i);
 
     // Get the backend sampled logits for the ith token
     // Returns NULL if no logits were sampled.
-    LLAMA_API float * llama_get_backend_sampled_logits_ith(struct llama_context * ctx, int32_t i);
+    LLAMA_API float * llama_get_sampled_logits_ith(struct llama_context * ctx, int32_t i);
     //
     // Get the number of backend sampled logits for the ith token.
-    LLAMA_API uint32_t llama_get_backend_sampled_logits_count_ith(struct llama_context * ctx, int32_t i);
+    LLAMA_API uint32_t llama_get_sampled_logits_count_ith(struct llama_context * ctx, int32_t i);
 
     // Get the backend sampled candidates (token ids) for the ith token
     // Returns NULL if no candidates were sampled.
-    LLAMA_API llama_token * llama_get_backend_sampled_candidates_ith(struct llama_context * ctx, int32_t i);
+    LLAMA_API llama_token * llama_get_sampled_candidates_ith(struct llama_context * ctx, int32_t i);
     //
     // Get the number of backend sampled candidates for the ith token.
-    LLAMA_API uint32_t llama_get_backend_sampled_candidates_count_ith(struct llama_context * ctx, int32_t i);
+    LLAMA_API uint32_t llama_get_sampled_candidates_count_ith(struct llama_context * ctx, int32_t i);
 
     //
     // Vocab
@@ -1177,7 +1177,7 @@ extern "C" {
 
     typedef void * llama_sampler_context_t;
 
-    struct llama_sampler_backend_data {
+    struct llama_sampler_data {
         struct ggml_tensor * logits;
         struct ggml_tensor * probs;
         struct ggml_tensor * sampled;
@@ -1203,10 +1203,10 @@ extern "C" {
                 struct ggml_tensor   * selected_token);
 
         void (*backend_apply)(
-                struct llama_sampler              * smpl,
-                struct ggml_context               * ctx,
-                struct ggml_cgraph                * gf,
-                struct llama_sampler_backend_data * ggml_data);
+                struct llama_sampler      * smpl,
+                struct ggml_context       * ctx,
+                struct ggml_cgraph        * gf,
+                struct llama_sampler_data * data);
 
         void (*backend_set_input)(struct llama_sampler * smpl);
     };
@@ -1217,7 +1217,7 @@ extern "C" {
         llama_sampler_context_t ctx;
     };
 
-    LLAMA_API bool llama_set_backend_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
+    LLAMA_API bool llama_set_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
 
     // mirror of llama_sampler_i:
     LLAMA_API struct llama_sampler * llama_sampler_init  (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 4639be985c0..50a31f11684 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -68,7 +68,7 @@ llama_context::llama_context(
         for (size_t i = 0; i < params.n_samplers; ++i) {
             const auto & config = params.samplers[i];
 
-            if (set_backend_sampler(config.seq_id, config.sampler)) {
+            if (set_sampler(config.seq_id, config.sampler)) {
                 const int n_samplers = llama_sampler_chain_n(config.sampler);
 
                 LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
@@ -670,7 +670,7 @@ float * llama_context::get_embeddings() {
     return embd;
 }
 
-llama_token * llama_context::get_backend_sampled_tokens() {
+llama_token * llama_context::get_sampled_tokens() {
     return sampling.sampled;
 }
 
@@ -723,7 +723,7 @@ float * llama_context::get_embeddings_seq(llama_seq_id seq_id) {
     return it->second.data();
 }
 
-llama_token llama_context::get_backend_sampled_token_ith(int32_t idx) {
+llama_token llama_context::get_sampled_token_ith(int32_t idx) {
     output_reorder();
 
     if (sampling.sampled == nullptr) {
@@ -740,7 +740,7 @@ llama_token llama_context::get_backend_sampled_token_ith(int32_t idx) {
     }
 }
 
-float * llama_context::get_backend_sampled_probs_ith(int32_t idx) {
+float * llama_context::get_sampled_probs_ith(int32_t idx) {
     output_reorder();
 
     if (sampling.probs == nullptr) {
@@ -759,7 +759,7 @@ float * llama_context::get_backend_sampled_probs_ith(int32_t idx) {
     }
 }
 
-float * llama_context::get_backend_sampled_logits_ith(int32_t idx) {
+float * llama_context::get_sampled_logits_ith(int32_t idx) {
     output_reorder();
 
     if (sampling.logits == nullptr) {
@@ -778,7 +778,7 @@ float * llama_context::get_backend_sampled_logits_ith(int32_t idx) {
     }
 }
 
-const llama_token * llama_context::get_backend_sampled_candidates_ith(int32_t idx) {
+const llama_token * llama_context::get_sampled_candidates_ith(int32_t idx) {
     output_reorder();
 
     try {
@@ -795,7 +795,7 @@ const llama_token * llama_context::get_backend_sampled_candidates_ith(int32_t id
     return sampling.token_ids_full_vocab.data();
 }
 
-size_t llama_context::get_backend_sampled_candidates_count(int32_t idx) {
+size_t llama_context::get_sampled_candidates_count(int32_t idx) {
     output_reorder();
 
     if (sampling.candidates == nullptr) {
@@ -814,7 +814,7 @@ size_t llama_context::get_backend_sampled_candidates_count(int32_t idx) {
     }
 }
 
-size_t llama_context::get_backend_sampled_logits_count(int32_t idx) {
+size_t llama_context::get_sampled_logits_count(int32_t idx) {
     output_reorder();
 
     if (sampling.logits == nullptr) {
@@ -833,7 +833,7 @@ size_t llama_context::get_backend_sampled_logits_count(int32_t idx) {
     }
 }
 
-size_t llama_context::get_backend_sampled_probs_count(int32_t idx) {
+size_t llama_context::get_sampled_probs_count(int32_t idx) {
     output_reorder();
 
     if (sampling.probs == nullptr) {
@@ -909,7 +909,7 @@ void llama_context::set_warmup(bool value) {
     cparams.warmup = value;
 }
 
-bool llama_context::set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
+bool llama_context::set_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
     LLAMA_LOG_DEBUG("%s: seq_id = %d, sampler = %p\n", __func__, (int) seq_id, (void *) sampler);
 
     const bool can_offload =
@@ -2978,10 +2978,10 @@ float * llama_get_logits(llama_context * ctx) {
 float * llama_get_logits_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    if (ctx->get_backend_sampled_token_ith(i) != LLAMA_TOKEN_NULL) {
+    if (ctx->get_sampled_token_ith(i) != LLAMA_TOKEN_NULL) {
         return nullptr;
     }
-    if (ctx->get_backend_sampled_probs_ith(i) != nullptr) {
+    if (ctx->get_sampled_probs_ith(i) != nullptr) {
         return nullptr;
     }
 
@@ -3006,50 +3006,50 @@ float * llama_get_embeddings_seq(llama_context * ctx, llama_seq_id seq_id) {
     return ctx->get_embeddings_seq(seq_id);
 }
 
-bool llama_set_backend_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
-    return ctx->set_backend_sampler(seq_id, smpl);
+bool llama_set_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
+    return ctx->set_sampler(seq_id, smpl);
 }
 
-llama_token llama_get_backend_sampled_token_ith(llama_context * ctx, int32_t i) {
+llama_token llama_get_sampled_token_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    return ctx->get_backend_sampled_token_ith(i);
+    return ctx->get_sampled_token_ith(i);
 }
 
-float * llama_get_backend_sampled_probs_ith(llama_context * ctx, int32_t i) {
+float * llama_get_sampled_probs_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    return ctx->get_backend_sampled_probs_ith(i);
+    return ctx->get_sampled_probs_ith(i);
 }
 
-float * llama_get_backend_sampled_logits_ith(llama_context * ctx, int32_t i) {
+float * llama_get_sampled_logits_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    return ctx->get_backend_sampled_logits_ith(i);
+    return ctx->get_sampled_logits_ith(i);
 }
 
-llama_token * llama_get_backend_sampled_candidates_ith(llama_context * ctx, int32_t i) {
+llama_token * llama_get_sampled_candidates_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    return const_cast<llama_token *>(ctx->get_backend_sampled_candidates_ith(i));
+    return const_cast<llama_token *>(ctx->get_sampled_candidates_ith(i));
 }
 
-uint32_t llama_get_backend_sampled_candidates_count_ith(llama_context * ctx, int32_t i) {
+uint32_t llama_get_sampled_candidates_count_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    return static_cast<uint32_t>(ctx->get_backend_sampled_candidates_count(i));
+    return static_cast<uint32_t>(ctx->get_sampled_candidates_count(i));
 }
 
-uint32_t llama_get_backend_sampled_logits_count_ith(llama_context * ctx, int32_t i) {
+uint32_t llama_get_sampled_logits_count_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    return static_cast<uint32_t>(ctx->get_backend_sampled_logits_count(i));
+    return static_cast<uint32_t>(ctx->get_sampled_logits_count(i));
 }
 
-uint32_t llama_get_backend_sampled_probs_count_ith(llama_context * ctx, int32_t i) {
+uint32_t llama_get_sampled_probs_count_ith(llama_context * ctx, int32_t i) {
     ctx->synchronize();
 
-    return static_cast<uint32_t>(ctx->get_backend_sampled_probs_count(i));
+    return static_cast<uint32_t>(ctx->get_sampled_probs_count(i));
 }
 
 // llama adapter API
diff --git a/src/llama-context.h b/src/llama-context.h
index 9b568d98b86..c2f0e3e5707 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -66,17 +66,17 @@ struct llama_context {
     float * get_embeddings_ith(int32_t i);
     float * get_embeddings_seq(llama_seq_id seq_id);
 
-    llama_token       * get_backend_sampled_tokens();
-    llama_token         get_backend_sampled_token_ith(int32_t idx);
+    llama_token       * get_sampled_tokens();
+    llama_token         get_sampled_token_ith(int32_t idx);
 
-    float             * get_backend_sampled_logits_ith(int32_t idx);
-    size_t              get_backend_sampled_logits_count(int32_t idx);
+    float             * get_sampled_logits_ith(int32_t idx);
+    size_t              get_sampled_logits_count(int32_t idx);
 
-    float             * get_backend_sampled_probs_ith(int32_t idx);
-    size_t              get_backend_sampled_probs_count(int32_t idx);
+    float             * get_sampled_probs_ith(int32_t idx);
+    size_t              get_sampled_probs_count(int32_t idx);
 
-    const llama_token * get_backend_sampled_candidates_ith(int32_t idx);
-    size_t              get_backend_sampled_candidates_count(int32_t idx);
+    const llama_token * get_sampled_candidates_ith(int32_t idx);
+    size_t              get_sampled_candidates_count(int32_t idx);
 
     void attach_threadpool(
             ggml_threadpool_t threadpool,
@@ -221,7 +221,7 @@ struct llama_context {
     // reserve a graph with a dummy ubatch of the specified size
     ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false);
 
-    bool set_backend_sampler(llama_seq_id seq_id, llama_sampler * sampler);
+    bool set_sampler(llama_seq_id seq_id, llama_sampler * sampler);
 
 private:
     llm_graph_params graph_params(
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 0abdab1637e..fcbad001575 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -2090,7 +2090,7 @@ void llm_graph_context::build_sampling() const {
         ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, n_vocab, row_idx * logits_t->nb[1]);
         ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
 
-        struct llama_sampler_backend_data data = {
+        struct llama_sampler_data data = {
             /*.logits      =*/ logits_seq,
             /*.probs       =*/ nullptr,
             /*.sampled     =*/ nullptr,
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 8069aa6802a..eb924bd5032 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -410,10 +410,10 @@ void llama_sampler_free(struct llama_sampler * smpl) {
 }
 
 llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx) {
-    const llama_token   sampled_token  = llama_get_backend_sampled_token_ith     (ctx, idx);
-    const float *       sampled_probs  = llama_get_backend_sampled_probs_ith     (ctx, idx);
-    const float *       sampled_logits = llama_get_backend_sampled_logits_ith    (ctx, idx);
-    const llama_token * sampled_ids    = llama_get_backend_sampled_candidates_ith(ctx, idx);
+    const llama_token   sampled_token  = llama_get_sampled_token_ith     (ctx, idx);
+    const float *       sampled_probs  = llama_get_sampled_probs_ith     (ctx, idx);
+    const float *       sampled_logits = llama_get_sampled_logits_ith    (ctx, idx);
+    const llama_token * sampled_ids    = llama_get_sampled_candidates_ith(ctx, idx);
 
     // If a backend sampler has already sampled a token, return it.
     if (sampled_token != LLAMA_TOKEN_NULL) {
@@ -430,13 +430,13 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
     std::vector<llama_token_data> cur;
 
     if (sampled_probs) {
-        const uint32_t sampled_probs_count = llama_get_backend_sampled_probs_count_ith(ctx, idx);
+        const uint32_t sampled_probs_count = llama_get_sampled_probs_count_ith(ctx, idx);
         cur.resize(sampled_probs_count);
         for (uint32_t i = 0; i < sampled_probs_count; ++i) {
             cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]};
         }
     } else if (sampled_logits) {
-        const uint32_t sampled_logits_count = llama_get_backend_sampled_logits_count_ith(ctx, idx);
+        const uint32_t sampled_logits_count = llama_get_sampled_logits_count_ith(ctx, idx);
         cur.resize(sampled_logits_count);
         for (llama_token i = 0; i < (int)sampled_logits_count; i++) {
             cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f};
@@ -557,10 +557,10 @@ static void llama_sampler_chain_backend_accept(
 }
 
 static void llama_sampler_chain_backend_apply(
-          struct llama_sampler * smpl,
-          struct ggml_context * ctx,
-          struct ggml_cgraph * gf,
-          struct llama_sampler_backend_data * data) {
+          struct llama_sampler      * smpl,
+          struct ggml_context       * ctx,
+          struct ggml_cgraph        * gf,
+          struct llama_sampler_data * data) {
     auto * chain = (llama_sampler_chain *) smpl->ctx;
 
     for (auto * smpl : chain->samplers) {
@@ -659,10 +659,10 @@ static void llama_sampler_greedy_apply(struct llama_sampler * /*smpl*/, llama_to
 }
 
 static void llama_sampler_greedy_backend_apply(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_backend_data * data) {
+        struct llama_sampler      * smpl,
+        struct ggml_context       * ctx,
+        struct ggml_cgraph        * gf,
+        struct llama_sampler_data * data) {
     GGML_UNUSED(gf);
     GGML_UNUSED(smpl);
     struct ggml_tensor * argmax_result = ggml_argmax(ctx, data->logits);
@@ -817,10 +817,10 @@ static void llama_sampler_dist_backend_set_input(struct llama_sampler * smpl) {
 }
 
 static void llama_sampler_dist_backend_apply(
-        struct llama_sampler              * smpl,
-        struct ggml_context               * ctx,
-        struct ggml_cgraph                * gf,
-        struct llama_sampler_backend_data * data) {
+        struct llama_sampler      * smpl,
+        struct ggml_context       * ctx,
+        struct ggml_cgraph        * gf,
+        struct llama_sampler_data * data) {
     GGML_UNUSED(gf);
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
@@ -875,8 +875,8 @@ static void llama_sampler_dist_backend_apply(
 }
 
 static void llama_sampler_dist_backend_init(
-        struct llama_sampler      * smpl,
-        ggml_backend_buffer_type_t  buft) {
+        struct llama_sampler       * smpl,
+        ggml_backend_buffer_type_t   buft) {
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
     sctx->device = ggml_backend_buft_get_device(buft);
@@ -963,10 +963,10 @@ static void llama_sampler_top_k_backend_init(
 }
 
 static void llama_sampler_top_k_backend_apply(
-        struct llama_sampler              * smpl,
-        struct ggml_context               * ctx,
-        struct ggml_cgraph                * gf,
-        struct llama_sampler_backend_data * data) {
+        struct llama_sampler      * smpl,
+        struct ggml_context       * ctx,
+        struct ggml_cgraph        * gf,
+        struct llama_sampler_data * data) {
 
     auto * ctx_data = (llama_sampler_top_k *) smpl->ctx;
 
@@ -1101,10 +1101,10 @@ static void llama_sampler_top_p_backend_init(
 }
 
 static void llama_sampler_top_p_backend_apply(
-        struct llama_sampler              * smpl,
-        struct ggml_context               * ctx,
-        struct ggml_cgraph                * gf,
-        struct llama_sampler_backend_data * data) {
+        struct llama_sampler      * smpl,
+        struct ggml_context       * ctx,
+        struct ggml_cgraph        * gf,
+        struct llama_sampler_data * data) {
     auto * sctx = (llama_sampler_top_p *) smpl->ctx;
 
     struct ggml_tensor * softmax = ggml_soft_max(ctx, data->logits);
@@ -1273,10 +1273,10 @@ static void llama_sampler_min_p_backend_init(
 }
 
 static void llama_sampler_min_p_backend_apply(
-        struct llama_sampler              * smpl,
-        struct ggml_context               * ctx,
-        struct ggml_cgraph                * gf,
-        struct llama_sampler_backend_data * data) {
+        struct llama_sampler      * smpl,
+        struct ggml_context       * ctx,
+        struct ggml_cgraph        * gf,
+        struct llama_sampler_data * data) {
     auto * sctx = (llama_sampler_min_p *) smpl->ctx;
 
     struct ggml_tensor * max_idx = ggml_argmax(ctx, data->logits);
@@ -1468,10 +1468,10 @@ static void llama_sampler_temp_free(struct llama_sampler * smpl) {
 }
 
 static void llama_sampler_temp_backend_apply(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_backend_data * data) {
+        struct llama_sampler      * smpl,
+        struct ggml_context       * ctx,
+        struct ggml_cgraph        * gf,
+        struct llama_sampler_data * data) {
     auto * ctx_data = (llama_sampler_temp *) smpl->ctx;
 
     if (ctx_data->temp <= 0.0f) {
@@ -2827,10 +2827,10 @@ static void llama_sampler_logit_bias_free(struct llama_sampler * smpl) {
 }
 
 static void llama_sampler_logit_bias_backend_apply(
-        struct llama_sampler           * smpl,
-        struct ggml_context            * ctx,
-        struct ggml_cgraph             * gf,
-        struct llama_sampler_backend_data * data) {
+        struct llama_sampler      * smpl,
+        struct ggml_context       * ctx,
+        struct ggml_cgraph        * gf,
+        struct llama_sampler_data * data) {
     GGML_UNUSED(gf);
     GGML_UNUSED(ctx);
 
diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp
index 918766994b4..6251a5ab1c7 100644
--- a/tests/test-backend-sampler.cpp
+++ b/tests/test-backend-sampler.cpp
@@ -290,17 +290,17 @@ static void test_backend_greedy_sampling(const char * model_path) {
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
 
-    llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+    llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
     printf("greedy sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
     GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
 
-    token = llama_get_backend_sampled_token_ith(test_ctx.ctx, -1);
+    token = llama_get_sampled_token_ith(test_ctx.ctx, -1);
     printf("greedy sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
     GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
 
     for (int i = 0; i < 10; i++) {
         int32_t loop_idx = test_ctx.idx_for_seq(seq_id);
-        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, loop_idx);
+        llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, loop_idx);
         printf("Generation step %d: token id:%d, string: %s\n", i, token, test_ctx.token_to_piece(token, false).c_str());
         if (!test_ctx.decode_token(token, 0)) {
             GGML_ASSERT(false && "Failed to decode token");
@@ -328,14 +328,14 @@ static void test_backend_top_k_sampling(const char * model_path) {
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
 
-    float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);
-    uint32_t n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+    float * logits = llama_get_sampled_logits_ith(test_ctx.ctx, batch_idx);
+    uint32_t n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
     for (size_t i = 0; i < n_logits; ++i) {
         printf("top_k logit[%zu] = %.6f\n", i, logits[i]);
     }
 
-    llama_token * candidates = llama_get_backend_sampled_candidates_ith(test_ctx.ctx, batch_idx);
-    uint32_t n_candidates = llama_get_backend_sampled_candidates_count_ith(test_ctx.ctx, batch_idx);
+    llama_token * candidates = llama_get_sampled_candidates_ith(test_ctx.ctx, batch_idx);
+    uint32_t n_candidates = llama_get_sampled_candidates_count_ith(test_ctx.ctx, batch_idx);
     for (size_t i = 0; i < n_candidates; ++i) {
         printf("top_k candidate[%zu] = %d : %s\n", i, candidates[i],
                test_ctx.token_to_piece(candidates[i], false).c_str());
@@ -386,7 +386,7 @@ static void test_backend_temp_sampling(const char * model_path) {
     // Verfify sequence 0
     {
         int32_t batch_idx = test_ctx.idx_for_seq(0);
-        int n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+        int n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
         GGML_ASSERT(n_logits == test_ctx.n_vocab);
 
         // Sample from sequence 0 using CPU sampler
@@ -443,8 +443,8 @@ static void test_backend_min_p_sampling(const char * model_path) {
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
 
-    float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);
-    uint32_t n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+    float * logits = llama_get_sampled_logits_ith(test_ctx.ctx, batch_idx);
+    uint32_t n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
 
     // Print the logits that are above the min-p threshold
     std::vector<float> filtered_logits;
@@ -501,8 +501,8 @@ static void test_backend_top_p_sampling(const char * model_path) {
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
 
-    float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);
-    uint32_t n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+    float * logits = llama_get_sampled_logits_ith(test_ctx.ctx, batch_idx);
+    uint32_t n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
 
     // Print the logits that are above the min-p threshold
     std::vector<float> filtered_logits;
@@ -569,7 +569,7 @@ static void test_backend_multi_sequence_sampling(const char * model_path) {
     // Verfiy sequence 0
     {
         int32_t batch_idx = test_ctx.idx_for_seq(0);
-        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
         const std::string token_str = test_ctx.token_to_piece(token, false);
         printf("Seq 0 sampled token id=%d, string='%s'\n", token, token_str.c_str());
         GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
@@ -578,7 +578,7 @@ static void test_backend_multi_sequence_sampling(const char * model_path) {
     // Verify sequence 1
     {
         int32_t batch_idx= test_ctx.idx_for_seq(1);
-        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
         const std::string token_str = test_ctx.token_to_piece(token, false);
         printf("Seq 1 sampled token id=%d, string='%s'\n", token, token_str.c_str());
         GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
@@ -591,7 +591,7 @@ static void test_backend_multi_sequence_sampling(const char * model_path) {
 
         for (llama_seq_id seq_id : {0, 1}) {
             int32_t idx = test_ctx.idx_for_seq(seq_id);
-            llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, idx);
+            llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, idx);
             const std::string token_str = test_ctx.token_to_piece(token, false);
             printf("  Seq %d, step %d: token id=%d, string='%s'\n", seq_id, step, token, token_str.c_str());
             tokens[seq_id] = token;
@@ -625,12 +625,12 @@ static void test_backend_dist_sampling(const char * model_path) {
     }
 
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
-    llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+    llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
     printf("dist sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
     GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
-    GGML_ASSERT(llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx) == nullptr);
+    GGML_ASSERT(llama_get_sampled_logits_ith(test_ctx.ctx, batch_idx) == nullptr);
 
-    token = llama_get_backend_sampled_token_ith(test_ctx.ctx, -1);
+    token = llama_get_sampled_token_ith(test_ctx.ctx, -1);
     printf("dist sampled id:%d, string:'%s'\n", token, test_ctx.token_to_piece(token, false).c_str());
     GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
 }
@@ -660,7 +660,7 @@ static void test_backend_dist_sampling_and_cpu(const char * model_path) {
     struct llama_sampler * chain = llama_sampler_chain_init(chain_params);
     llama_sampler_chain_add(chain, llama_sampler_init_dist(18));
 
-    llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+    llama_token backend_token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
     llama_token cpu_token = llama_sampler_sample(chain, test_ctx.ctx, batch_idx);
     printf("dist & cpu sampled id:%d, string:'%s'\n", cpu_token, test_ctx.token_to_piece(cpu_token, false).c_str());
     GGML_ASSERT(backend_token == cpu_token);
@@ -707,7 +707,7 @@ static void test_backend_logit_bias_sampling(const char * model_path) {
         GGML_ASSERT(false && "Failed to decode token");
     }
 
-    llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(seq_id));
+    llama_token backend_token = llama_get_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(seq_id));
     const std::string backend_token_str = test_ctx.token_to_piece(backend_token, false);
     printf("logit bias sampled token = %d, string='%s'\n", backend_token, backend_token_str.c_str());
     GGML_ASSERT(backend_token == bias_token);
@@ -748,22 +748,22 @@ static void test_backend_mixed_sampling(const char * model_path) {
     // Verfiy sequence 0 that used the dist backend sampler.
     {
         int32_t batch_idx = test_ctx.idx_for_seq(0);
-        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
         const std::string token_str = test_ctx.token_to_piece(token, false);
         printf("sampled token id=%d, string='%s'\n", token, token_str.c_str());
         GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
-        GGML_ASSERT(llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx) == nullptr);
-        GGML_ASSERT(llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx) == 0);
+        GGML_ASSERT(llama_get_sampled_logits_ith(test_ctx.ctx, batch_idx) == nullptr);
+        GGML_ASSERT(llama_get_sampled_logits_count_ith(test_ctx.ctx, batch_idx) == 0);
     }
 
     // Verfiy sequence 1 that used the top-k backend sampler.
     {
         int32_t batch_idx = test_ctx.idx_for_seq(1);
-        float * logits = llama_get_backend_sampled_logits_ith(test_ctx.ctx, batch_idx);
+        float * logits = llama_get_sampled_logits_ith(test_ctx.ctx, batch_idx);
         GGML_ASSERT(logits != nullptr);
-        size_t n_logits = llama_get_backend_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
+        size_t n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx, batch_idx);
         GGML_ASSERT(n_logits == (size_t) k);
-        GGML_ASSERT(llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx) == LLAMA_TOKEN_NULL);
+        GGML_ASSERT(llama_get_sampled_token_ith(test_ctx.ctx, batch_idx) == LLAMA_TOKEN_NULL);
     }
 
     printf("backend mixed sampling test PASSED\n");
@@ -790,12 +790,12 @@ static void test_backend_set_sampler(const char * model_path) {
     int32_t batch_idx = test_ctx.idx_for_seq(seq_id);
 
     // Sample using backend sampler configured above
-    llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+    llama_token backend_token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
     const std::string backend_token_str = test_ctx.token_to_piece(backend_token, false);
     printf("dist sampled token = %d, string='%s'\n", backend_token, backend_token_str.c_str());
 
     // Now clear the backend sampler for this sequence.
-    llama_set_backend_sampler(test_ctx.ctx, seq_id, nullptr);
+    llama_set_sampler(test_ctx.ctx, seq_id, nullptr);
     printf("Cleared backend sampler for seq_id %d\n", seq_id);
 
     // Sample using CPU sampler
@@ -810,8 +810,8 @@ static void test_backend_set_sampler(const char * model_path) {
 
     // Should not have any sampled token or probs after clearing the backend sampler.
     const int32_t idx = test_ctx.idx_for_seq(seq_id);
-    GGML_ASSERT(llama_get_backend_sampled_token_ith(test_ctx.ctx, idx) == LLAMA_TOKEN_NULL);
-    GGML_ASSERT(llama_get_backend_sampled_probs_ith(test_ctx.ctx, idx) == nullptr);
+    GGML_ASSERT(llama_get_sampled_token_ith(test_ctx.ctx, idx) == LLAMA_TOKEN_NULL);
+    GGML_ASSERT(llama_get_sampled_probs_ith(test_ctx.ctx, idx) == nullptr);
 
     // Sample the token using the CPU sampler chain.
     llama_token token2 = llama_sampler_sample(chain, test_ctx.ctx, seq_id);
@@ -824,13 +824,13 @@ static void test_backend_set_sampler(const char * model_path) {
     struct llama_sampler * new_backend_sampler_chain = llama_sampler_chain_init(new_backend_chain_params);
     llama_sampler_chain_add(new_backend_sampler_chain, llama_sampler_init_top_k(20));
     llama_sampler_chain_add(new_backend_sampler_chain, llama_sampler_init_dist(seed));
-    llama_set_backend_sampler(test_ctx.ctx, seq_id, new_backend_sampler_chain);
+    llama_set_sampler(test_ctx.ctx, seq_id, new_backend_sampler_chain);
 
     if (!test_ctx.decode_tokens(tokens2)) {
         GGML_ASSERT(false && "Failed to decode token");
     }
 
-    llama_token new_backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(seq_id));
+    llama_token new_backend_token = llama_get_sampled_token_ith(test_ctx.ctx, test_ctx.idx_for_seq(seq_id));
     const std::string new_backend_token_str = test_ctx.token_to_piece(new_backend_token, false);
     printf("dist sampled token = %d, string='%s'\n", new_backend_token, new_backend_token_str.c_str());
 }
@@ -864,7 +864,7 @@ static void test_backend_cpu_mixed_batch(const char * model_path) {
     // Verify sequence 0 (backend sampled)
     {
         int32_t batch_idx = test_ctx.idx_for_seq(0);
-        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
         const std::string token_str = test_ctx.token_to_piece(token, false);
         printf("Seq 0 (backend) sampled token id=%d, string='%s'\n", token, token_str.c_str());
         GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
@@ -874,7 +874,7 @@ static void test_backend_cpu_mixed_batch(const char * model_path) {
     {
         int32_t batch_idx = test_ctx.idx_for_seq(1);
 
-        llama_token backend_token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        llama_token backend_token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
         GGML_ASSERT(backend_token == LLAMA_TOKEN_NULL);
 
         struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
@@ -892,7 +892,7 @@ static void test_backend_cpu_mixed_batch(const char * model_path) {
     {
         // clear the backend sampler for seq 0 so that there are no backend
         // samplers.
-        llama_set_backend_sampler(test_ctx.ctx, 0, nullptr);
+        llama_set_sampler(test_ctx.ctx, 0, nullptr);
 
         // Create a CPU sampler and verify we can sampler from it.
         struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
@@ -914,14 +914,14 @@ static void test_backend_cpu_mixed_batch(const char * model_path) {
         struct llama_sampler * sampler_chain= llama_sampler_chain_init(chain_params);
         llama_sampler_chain_add(sampler_chain, llama_sampler_init_dist(88));
 
-        llama_set_backend_sampler(test_ctx.ctx, 0, sampler_chain);
+        llama_set_sampler(test_ctx.ctx, 0, sampler_chain);
 
         if (!test_ctx.decode_token(3834, 0)) {
             GGML_ASSERT(false && "Failed to decode token");
         }
 
         int32_t batch_idx = test_ctx.idx_for_seq(0);
-        llama_token token = llama_get_backend_sampled_token_ith(test_ctx.ctx, batch_idx);
+        llama_token token = llama_get_sampled_token_ith(test_ctx.ctx, batch_idx);
         const std::string token_str = test_ctx.token_to_piece(token, false);
         printf("re-added backend sampled token id=%d, string='%s'\n", token, token_str.c_str());
         GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index b1af1b109fd..a97977defde 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -1014,7 +1014,7 @@ struct server_context_impl {
             SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl.get()).c_str());
 
             llama_sampler * backend_chain = common_sampler_chain_backend(slot.smpl.get());
-            llama_set_backend_sampler(ctx, slot.id, backend_chain);
+            llama_set_sampler(ctx, slot.id, backend_chain);
         }
 
         // initialize draft batch