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ollama/llama/patches/0019-ggml-Add-batch-size-hint.patch
Jesse Gross afaf7ce8c3 ggml: Enable op_offload to improve partial offload performance 
When a model is partially offloaded to system RAM, we can either
do the calculations on the CPU or we can temporarily transfer the
data to the GPU to do the calculations there. Small batches tend
to be better on the CPU, large batches on the GPU.

The llamarunner used the GPU in most cases and the ollamarunner
used the CPU. Although the ollamarunner saw an improvement in
token generation performance, there was a large performance hit
in prompt processing (3-10x).

There is an existing heuristic to dynamically switch between these
two modes but in practice it doesn't have enough information to
accurately make that decision. This adds authoritative data to make
the check work to get the best of both worlds.

Fixes #12037
2025-10-30 13:53:10 -07:00

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From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: Jesse Gross <jesse@ollama.com>
Date: Tue, 28 Oct 2025 17:36:54 -0700
Subject: [PATCH] ggml: Add batch size hint
Some operations use heuristics to determine the batch size, which
affects offloading decisions. However, these are not always
accurate when looking at single operations. This provides an
explicit signal on the batch size from higher layers to ensure
consistent performance.
---
ggml/include/ggml-backend.h | 5 ++-
ggml/src/ggml-backend-impl.h | 4 +--
ggml/src/ggml-backend.cpp | 19 +++++++----
ggml/src/ggml-blas/ggml-blas.cpp | 3 +-
ggml/src/ggml-cpu/ggml-cpu.cpp | 4 ++-
ggml/src/ggml-cuda/ggml-cuda.cu | 48 +++++++++++++++++-----------
ggml/src/ggml-metal/ggml-metal.cpp | 4 ++-
ggml/src/ggml-vulkan/ggml-vulkan.cpp | 3 +-
8 files changed, 58 insertions(+), 32 deletions(-)
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index 229bf387b..2763f2bd6 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -98,7 +98,7 @@ extern "C" {
GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
- GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+ GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph, int batch_size);
// NOTE: will be removed, use device version instead
GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
@@ -307,6 +307,9 @@ extern "C" {
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel, bool op_offload);
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
+ // Provide a hint on the batch size to optimize processing (uses heuristics if unset)
+ GGML_API void ggml_backend_sched_set_batch_size(ggml_backend_sched_t sched, int batch_size);
+
// Initialize backend buffers from a measure graph
GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success
diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h
index 6792ba986..0f5b03cef 100644
--- a/ggml/src/ggml-backend-impl.h
+++ b/ggml/src/ggml-backend-impl.h
@@ -106,8 +106,8 @@ extern "C" {
// compute the graph with the plan
enum ggml_status (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
- // compute graph (always async if supported by the backend)
- enum ggml_status (*graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+ // compute graph (always async if supported by the backend). batch_size may be -1 if unknown
+ enum ggml_status (*graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph, int batch_size);
// (optional) event synchronization
// record an event on this stream
diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index cb2b99562..41eef3b5f 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -348,14 +348,14 @@ enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_ba
}
enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
- enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph);
+ enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph, -1);
ggml_backend_synchronize(backend);
return err;
}
-enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph, int batch_size) {
GGML_ASSERT(backend);
- return backend->iface.graph_compute(backend, cgraph);
+ return backend->iface.graph_compute(backend, cgraph, batch_size);
}
bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
@@ -722,6 +722,8 @@ struct ggml_backend_sched {
bool op_offload;
+ int batch_size; // a hint on the batch size to optimize processing, -1 to use heuristics
+
int debug;
};
@@ -814,7 +816,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
// check if a backend with higher prio wants to offload the op
- if (sched->op_offload && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) {
+ if (sched->op_offload && (sched->batch_size < 0 || sched->batch_size >= 32) && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) {
for (int b = 0; b < src_backend_id; b++) {
if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
SET_CAUSE(tensor, "1.off");
@@ -1550,7 +1552,7 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
}
if (!sched->callback_eval) {
- enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
+ enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph, sched->batch_size);
if (ec != GGML_STATUS_SUCCESS) {
return ec;
}
@@ -1572,7 +1574,7 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
- enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv);
+ enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv, sched->batch_size);
if (ec != GGML_STATUS_SUCCESS) {
return ec;
}
@@ -1651,6 +1653,7 @@ ggml_backend_sched_t ggml_backend_sched_new(
sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
sched->op_offload = op_offload;
+ sched->batch_size = -1;
ggml_backend_sched_reset(sched);
@@ -1682,6 +1685,10 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
free(sched);
}
+void ggml_backend_sched_set_batch_size(ggml_backend_sched_t sched, int batch_size) {
+ sched->batch_size = batch_size;
+}
+
void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
GGML_ASSERT(sched);
// reset state for the next run
diff --git a/ggml/src/ggml-blas/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp
index 5b888cdd8..88d088952 100644
--- a/ggml/src/ggml-blas/ggml-blas.cpp
+++ b/ggml/src/ggml-blas/ggml-blas.cpp
@@ -224,7 +224,7 @@ static void ggml_backend_blas_free(ggml_backend_t backend) {
delete backend;
}
-static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph, int batch_size) {
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
for (int i = 0; i < cgraph->n_nodes; i++) {
@@ -254,6 +254,7 @@ static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend,
return GGML_STATUS_SUCCESS;
GGML_UNUSED(backend);
+ GGML_UNUSED(batch_size);
}
static struct ggml_backend_i blas_backend_i = {
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index 3191faaa4..32f14c811 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -164,7 +164,7 @@ static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backe
GGML_UNUSED(backend);
}
-static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph, int batch_size) {
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool);
@@ -184,6 +184,8 @@ static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, s
cplan.abort_callback_data = cpu_ctx->abort_callback_data;
return ggml_graph_compute(cgraph, &cplan);
+
+ GGML_UNUSED(batch_size);
}
static const struct ggml_backend_i ggml_backend_cpu_i = {
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 5b852f690..c555cd30f 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2684,7 +2684,7 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
#ifdef USE_CUDA_GRAPH
static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
- bool use_cuda_graph) {
+ int batch_size, bool use_cuda_graph) {
// Loop over nodes in GGML graph to obtain info needed for CUDA graph
cuda_ctx->cuda_graph->cpy_dest_ptrs.clear();
@@ -2718,24 +2718,34 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
#endif
}
- if (node->op == GGML_OP_ADD &&
- node->src[1] && node->src[1]->ne[1] > 1 &&
- (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) &&
- (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true) &&
- strncmp(node->name, ffn_moe_gate_bias_prefix.c_str(), ffn_moe_gate_bias_prefix.size()) != 0 &&
- strncmp(node->name, ffn_moe_up_bias_prefix.c_str(), ffn_moe_up_bias_prefix.size()) != 0 &&
- strncmp(node->name, ffn_moe_down_bias_prefix.c_str(), ffn_moe_down_bias_prefix.size()) != 0 &&
- strncmp(node->name, nemotron_h_block_out_prefix.c_str(), nemotron_h_block_out_prefix.size()) != 0 &&
- strncmp(node->name, mamba2_y_add_d_prefix.c_str(), mamba2_y_add_d_prefix.size()) != 0) {
- // disable CUDA graphs for batch size > 1 for now while excluding the matrix-matrix addition as part of Gemma3n's `project_per_layer_input` operation
- // by means of matching node names. See
- // https://github.com/ggml-org/llama.cpp/blob/f9a31eea06a859e34cecb88b4d020c7f03d86cc4/src/llama-model.cpp#L10199-L10241 and
- // https://github.com/huggingface/transformers/blob/bda75b4011239d065de84aa3e744b67ebfa7b245/src/transformers/models/gemma3n/modeling_gemma3n.py#L1773,
- // Generally, changes in batch size or context size can cause changes to the grid size of some kernels.
- use_cuda_graph = false;
+ // If we have an explicit batch size hint then we don't need to use the tensor name heuristics
+ if (batch_size >= 0) {
+ if (batch_size > 1) {
+ use_cuda_graph = false;
#ifndef NDEBUG
- GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
+ GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%d]\n", __func__, batch_size);
#endif
+ }
+ } else {
+ if (node->op == GGML_OP_ADD &&
+ node->src[1] && node->src[1]->ne[1] > 1 &&
+ (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) &&
+ (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true) &&
+ strncmp(node->name, ffn_moe_gate_bias_prefix.c_str(), ffn_moe_gate_bias_prefix.size()) != 0 &&
+ strncmp(node->name, ffn_moe_up_bias_prefix.c_str(), ffn_moe_up_bias_prefix.size()) != 0 &&
+ strncmp(node->name, ffn_moe_down_bias_prefix.c_str(), ffn_moe_down_bias_prefix.size()) != 0 &&
+ strncmp(node->name, nemotron_h_block_out_prefix.c_str(), nemotron_h_block_out_prefix.size()) != 0 &&
+ strncmp(node->name, mamba2_y_add_d_prefix.c_str(), mamba2_y_add_d_prefix.size()) != 0) {
+ // disable CUDA graphs for batch size > 1 for now while excluding the matrix-matrix addition as part of Gemma3n's `project_per_layer_input` operation
+ // by means of matching node names. See
+ // https://github.com/ggml-org/llama.cpp/blob/f9a31eea06a859e34cecb88b4d020c7f03d86cc4/src/llama-model.cpp#L10199-L10241 and
+ // https://github.com/huggingface/transformers/blob/bda75b4011239d065de84aa3e744b67ebfa7b245/src/transformers/models/gemma3n/modeling_gemma3n.py#L1773,
+ // Generally, changes in batch size or context size can cause changes to the grid size of some kernels.
+ use_cuda_graph = false;
+#ifndef NDEBUG
+ GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
+#endif
+ }
}
if (node->op == GGML_OP_CPY) {
@@ -3132,7 +3142,7 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
}
}
-static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph, int batch_size) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
ggml_cuda_set_device(cuda_ctx->device);
@@ -3170,7 +3180,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (use_cuda_graph) {
cuda_graph_update_required = is_cuda_graph_update_required(cuda_ctx, cgraph);
- use_cuda_graph = check_node_graph_compatibility_and_refresh_copy_ops(cuda_ctx, cgraph, use_cuda_graph);
+ use_cuda_graph = check_node_graph_compatibility_and_refresh_copy_ops(cuda_ctx, cgraph, batch_size, use_cuda_graph);
// Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
if (use_cuda_graph && cuda_graph_update_required) {
diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp
index f2ff9f322..05ff6a5a6 100644
--- a/ggml/src/ggml-metal/ggml-metal.cpp
+++ b/ggml/src/ggml-metal/ggml-metal.cpp
@@ -410,10 +410,12 @@ static bool ggml_backend_metal_cpy_tensor_async(ggml_backend_t backend_src, ggml
GGML_UNUSED(dst);
}
-static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph, int batch_size) {
ggml_metal_t ctx = (ggml_metal_t)backend->context;
return ggml_metal_graph_compute(ctx, cgraph);
+
+ GGML_UNUSED(batch_size);
}
static void ggml_backend_metal_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) {
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index ed83236f4..bd3ece516 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -12015,7 +12015,7 @@ static uint32_t ggml_vk_fuse_multi_add(ggml_backend_vk_context * ctx, const stru
return num_adds;
}
-static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph, int batch_size) {
VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)");
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
@@ -12211,6 +12211,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
return GGML_STATUS_SUCCESS;
UNUSED(backend);
+ UNUSED(batch_size);
}
// Sort the graph for improved parallelism.
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