TP: round up granularity to 128 (#24180)

* TP: round up granularity to 128

* remove assert

src/llama-model.cpp

@@ -553,10 +553,12 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
     };
 
     auto get_split_granularity = [&](int64_t blck_size, uint32_t il, const std::vector<std::pair<int64_t, uint32_t>> & segments) -> std::vector<int64_t> {
+        // for better performance it may make sense to round up blck_size to a higher power of 2 so that more efficient kernels can be used
         if (hparams.is_recr(il)) {
             // linear attention
-            const int64_t head_dim  = hparams.ssm_d_state;
-            const int64_t granularity_qkv = std::lcm(blck_size, head_dim);
+            const int64_t head_dim        = hparams.ssm_d_state;
+            const int64_t blck_size_perf  = std::lcm(blck_size, 128);
+            const int64_t granularity_qkv = std::lcm(blck_size_perf, head_dim);
             if (std::regex_match(tensor_name, pattern_qkv_weight) || std::regex_match(tensor_name, pattern_attn_gate_weight) ||
                     std::regex_match(tensor_name, pattern_ssm_conv1d) || std::regex_match(tensor_name, pattern_ssm_out_weight)) {
                 return std::vector<int64_t>(segments.size(), granularity_qkv);
@@ -578,17 +580,24 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
             // regular attention
             const uint32_t n_gqa    = hparams.n_gqa(il);
             const uint32_t n_embd_q = n_gqa * hparams.n_embd_head_k(il);
-            if (std::regex_match(tensor_name, pattern_attn_sinks)) {
-                GGML_ASSERT(segments.size() == 1);
-                return {std::lcm(n_embd_q, blck_size)/n_embd_q * n_gqa};
+
+            // to handle head sizes like 80, only increase granularity while it doesn't cause underutilization
+            int64_t blck_size_perf = blck_size;
+            while (blck_size_perf < 128 && blck_size_perf*ud->n_devices < n_embd_q) {
+                blck_size_perf *= 2;
             }
 
-            const int64_t granularity_q = std::lcm(n_embd_q, blck_size);
+            if (std::regex_match(tensor_name, pattern_attn_sinks)) {
+                GGML_ASSERT(segments.size() == 1);
+                return {std::lcm(n_embd_q, blck_size_perf)/n_embd_q * n_gqa};
+            }
+
+            const int64_t granularity_q = std::lcm(n_embd_q, blck_size_perf);
             if (std::regex_match(tensor_name, pattern_q_weight) || std::regex_match(tensor_name, pattern_q_bias)) {
                 GGML_ASSERT(segments.size() == 1);
                 // some models have Q gate tensors, for those cases the granularity needs to be doubled:
                 if (ud->model->arch == LLM_ARCH_QWEN3NEXT || ud->model->arch == LLM_ARCH_QWEN35 || ud->model->arch == LLM_ARCH_QWEN35MOE) {
-                    return {std::lcm(2*n_embd_q, blck_size)};
+                    return {std::lcm(2*n_embd_q, blck_size_perf)};
                 }
                 return {granularity_q};
             }
@@ -613,8 +622,9 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
         // FFN
         if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight) || std::regex_match(tensor_name, pattern_ffn_up_gate_bias) ||
                 std::regex_match(tensor_name, pattern_ffn_gate_up_weight) || std::regex_match(tensor_name, pattern_ffn_down_weight)) {
+            const int64_t blck_size_perf = std::lcm(blck_size, 128);
             GGML_ASSERT(segments.size() == 1);
-            return {blck_size};
+            return {blck_size_perf};
         }
 
         // everything else
@@ -627,7 +637,6 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
     tensor_config tc = get_tensor_config();
     split_state.axis = tc.axis;
     if (split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS) {
-        const int64_t ne_full = tensor->ne[split_state.axis];
         const int64_t blck_size = ggml_blck_size(tc.tensor_axis_0->type);
         const float * tensor_split = ud->model->tensor_split();
         std::vector<float> tensor_split_scan;
@@ -644,7 +653,6 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
             const int64_t  ne_s = segments[is].first;
             const uint32_t nr_s = segments[is].second;
             const int64_t  g_s  = granularity[is];
-            GGML_ASSERT(ne_full % g_s == 0);
             int64_t low = 0;
             size_t j = 0;
             for (; j < ud->n_devices - 1; j++) {

tools/cli/cli.cpp

@@ -397,6 +397,8 @@ int llama_cli(int argc, char ** argv) {
         return 1;
     }
 
+    ctx_cli.defaults.sampling = params.sampling;
+
     console::spinner::stop();
     console::log("\n");