sampling : remove redundant calls to ggml_build_forward_expand

Author: ggerganov

src/llama-sampling.cpp

@@ -1078,7 +1078,8 @@ static void llama_sampler_top_k_backend_apply(
     ggml_set_name(top_k_rows, "top_k_rows");
 
     data->logits = ggml_reshape_1d(ctx, top_k_rows, ctx_data->k);
-    ggml_build_forward_expand(gf, data->logits);
+
+    GGML_UNUSED(gf);
 }
 
 static struct llama_sampler_i llama_sampler_top_k_i = {
@@ -1264,10 +1265,9 @@ static void llama_sampler_top_p_backend_apply(
     ggml_set_name(data->logits, "top_p_logits");
 
     ggml_set_output(data->candidates);
-    ggml_build_forward_expand(gf, data->candidates);
-
     ggml_set_output(data->logits);
-    ggml_build_forward_expand(gf, data->logits);
+
+    GGML_UNUSED(gf);
 }
 
 static struct llama_sampler_i llama_sampler_top_p_i = {
@@ -1421,7 +1421,7 @@ static void llama_sampler_min_p_backend_apply(
     data->logits = ggml_add(ctx, data->logits, min_p_bias);
     ggml_set_name(data->logits, "min_p_logits");
 
-    ggml_build_forward_expand(gf, data->logits);
+    GGML_UNUSED(gf);
 }
 
 static struct llama_sampler_i llama_sampler_min_p_i = {
@@ -1602,7 +1602,6 @@ static void llama_sampler_backend_temp_sampling(
         struct ggml_tensor * logit = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
 
         data->logits = ggml_get_rows(ctx, logit, max_idx);
-        ggml_build_forward_expand(gf, data->logits);
 
         return;
     }
@@ -1614,7 +1613,7 @@ static void llama_sampler_backend_temp_sampling(
     data->logits = ggml_cont(ctx, scaled);
     ggml_set_name(data->logits, "temp_scaled_logits");
 
-    ggml_build_forward_expand(gf, data->logits);
+    GGML_UNUSED(gf);
 }
 
 static void llama_sampler_temp_backend_apply(
@@ -1807,7 +1806,6 @@ static void llama_sampler_temp_ext_backend_apply(
     ggml_set_name(scaled_logits, "temp_ext_scaled_logits");
 
     data->logits = scaled_logits;
-    ggml_build_forward_expand(gf, data->logits);
 }
 
 static struct llama_sampler_i llama_sampler_temp_ext_i = {
@@ -3080,8 +3078,6 @@ static void llama_sampler_logit_bias_backend_apply(
     // Add the sparse logit logit_bias to the logits
     struct ggml_tensor * logit_biased = ggml_add_inplace(ctx, data->logits, sctx->inp_logit_bias);
     data->logits = logit_biased;
-
-    ggml_build_forward_expand(gf, logit_biased);
 }
 
 static void llama_sampler_logit_bias_backend_set_input(struct llama_sampler * smpl) {