Merge commit '88d23ad515' into concedo_experimental

# Conflicts:
#	CODEOWNERS
#	docs/build.md
#	ggml/CMakeLists.txt
#	ggml/src/CMakeLists.txt
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	ggml/src/ggml-zendnn/CMakeLists.txt
#	tests/test-chat-template.cpp

src/llama-context.cpp

@@ -256,11 +256,7 @@ llama_context::llama_context(
 
         // graph outputs buffer
         {
-            // resized during inference when a batch uses more outputs
-            // 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) {
+            if (output_reserve(params.n_seq_max) < params.n_seq_max) {
                 throw std::runtime_error("failed to reserve initial output buffer");
             }
 
@@ -1235,7 +1231,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
     n_queued_tokens += n_tokens;
 
     // reserve output buffer
-    if (output_reserve(n_tokens, batch_inp) < n_tokens) {
+    if (output_reserve(n_tokens) < n_tokens) {
         LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
         return -2;
     };
@@ -1466,6 +1462,23 @@ static void copy_tensor_async_candidates(
     }
 }
 
+static bool needs_raw_logits(const llama_ubatch & ubatch, const std::map<llama_seq_id, llama_sampler *> & samplers) {
+    for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
+        if (!ubatch.output[i]) {
+            continue;
+        }
+
+        // Check if the output token has at least one sequence without a backend sampler.
+        for (int32_t j = 0; j < ubatch.n_seq_id[i]; ++j) {
+            llama_seq_id seq_id = ubatch.seq_id[i][j];
+            if (samplers.find(seq_id) == samplers.end()) {
+                return true;
+            }
+        }
+    }
+    return false; // all sequences use backend sampling
+}
+
 int llama_context::decode(const llama_batch & batch_inp) {
     GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT
 
@@ -1598,7 +1611,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
     }
 
     // reserve output buffer
-    if (output_reserve(n_outputs_all, balloc->get_batch()) < n_outputs_all) {
+    if (output_reserve(n_outputs_all) < n_outputs_all) {
         LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all);
         return -2;
     };
@@ -1671,10 +1684,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
         }
 
         // extract logits
-        // For multi-sequence 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) {
+        if (logits && t_logits && n_outputs > 0 && needs_raw_logits(ubatch, sampling.samplers)) {
             ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
             GGML_ASSERT(backend_res != nullptr);
             GGML_ASSERT(logits != nullptr);
@@ -1744,11 +1754,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
             }
         }
 
-        // This flag indicates whether a backend sampler has actually sampled a specific
-        // token, or if it has produced probabilites. If true, we can skip the normal copying of logits and embeddings.
-        const bool has_sampled = !res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty();
-
-        if (has_samplers && has_sampled) {
+        // Copy backend sampling output if this ubatch produced any sampling tensors.
+        if (has_samplers && (!res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty())) {
             const auto seq_to_output_row = build_seq_to_output_row(ubatch, n_outputs_prev);
             const auto stride = n_vocab;
 
@@ -1823,7 +1830,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
 // output
 //
 
-uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & batch) {
+uint32_t llama_context::output_reserve(int32_t n_outputs) {
+
     const auto & hparams = model.hparams;
     const auto & vocab   = model.vocab;
 
@@ -1842,45 +1850,16 @@ uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & ba
         has_embd   = true;
     }
 
-    // Check which sampling modes are needed for the current batch.
-    // TODO: avoid this branching by working with the worst-case
-    bool has_sampling = false;
-    bool cpu_logits   = false;
-
-    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()) {
-                    has_sampling = true;
-                } else {
-                    cpu_logits = true;
-                }
-            }
-        }
-    } else {
-        // When batch.logits is nullptr (when loading state with a dummy batch),
-        // allocate CPU logits.
-        cpu_logits = true;
-    }
 
     size_t backend_float_count = 0;
     size_t backend_token_count = 0;
 
-    // Allocate CPU logits buffer only if needed by sequences in this batch
-    logits_size = (has_logits && cpu_logits) ? n_vocab*n_outputs_max : 0;
+    logits_size = has_logits ? n_vocab*n_outputs_max : 0;
     embd_size   = has_embd ? n_embd_out*n_outputs_max : 0;
 
-    // TODO: avoid this branching by working with the worst-case
-    if (!has_sampling) {
-        sampling.logits_size     = 0;
-        sampling.probs_size      = 0;
-        sampling.sampled_size    = 0;
-        sampling.candidates_size = 0;
-    } else {
+    // Allocate backend sampling output buffers if there are backend samplers configured.
+    const bool has_sampling = !sampling.samplers.empty();
+    if (has_sampling) {
         sampling.logits_size     = n_vocab*n_outputs_max;
         sampling.probs_size      = n_vocab*n_outputs_max;
         sampling.sampled_size    =         n_outputs_max;
@@ -1938,7 +1917,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & ba
     size_t offset = 0;
     uint8_t * base = (uint8_t *) output_base;
 
-    logits = (has_logits && cpu_logits) ? output_base : nullptr;
+    logits = has_logits ? output_base : nullptr;
     offset += logits_size * sizeof(float);
 
     embd = has_embd ? (float *) (base + offset) : nullptr;
@@ -2624,10 +2603,7 @@ 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));
 
-        // 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)) {
+        if (n_outputs > output_reserve(n_outputs)) {
             throw std::runtime_error("could not reserve outputs");
         }
 
@@ -2872,7 +2848,7 @@ void llama_context::opt_epoch_iter(
         }
 
         // reserve output buffer
-        if (output_reserve(n_outputs_all, balloc->get_batch()) < n_outputs_all) {
+        if (output_reserve(n_outputs_all) < 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");
         };