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llama.cpp
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Merge 7d0be2c483 into 2ba9adc093

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Georgi Gerganov 2026-02-16 18:49:49 +05:00 committed by GitHub
parent 2ba9adc093 7d0be2c483
commit ad993ca286
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1 changed files with 9 additions and 32 deletions
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41
ggml/src/ggml-cuda/ggml-cuda.cu
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@ -2278,6 +2278,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
// [TAG_MUL_MAT_ID_CUDA_GRAPHS]
if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
static_assert(MMVQ_MAX_BATCH_SIZE == MMVF_MAX_BATCH_SIZE);
if (ne2 <= MMVQ_MAX_BATCH_SIZE) {
@ -2305,6 +2306,8 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
}
}
// note: this path should not be reached when recording CUDA graphs, because it requires stream synchronization
cudaStream_t stream = ctx.stream();
GGML_ASSERT(nb12 % nb11 == 0);
@ -2865,15 +2868,6 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
bool use_cuda_graph = true;
// Loop over nodes in GGML graph to obtain info needed for CUDA graph
const std::string gemma3n_per_layer_proj_src0_name = "inp_per_layer_selected";
const std::string gemma3n_per_layer_proj_src1_name = "per_layer_proj";
const std::string ffn_moe_gate_bias_prefix = "ffn_moe_gate_biased";
const std::string ffn_moe_up_bias_prefix = "ffn_moe_up_biased";
const std::string ffn_moe_down_bias_prefix = "ffn_moe_down_biased";
const std::string nemotron_h_block_out_prefix = "nemotron_h_block_out";
const std::string mamba2_y_add_d_prefix = "mamba2_y_add_d";
const std::string delta_net_prefix = "dnet_add";
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
@ -2888,31 +2882,14 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
#endif
}
if (node->op == GGML_OP_MUL_MAT_ID && node->ne[2] != 1) {
use_cuda_graph = false; // This node type is not supported by CUDA graph capture
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported node type\n", __func__);
#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 &&
strncmp(node->name, delta_net_prefix.c_str(), delta_net_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.
// [TAG_MUL_MAT_ID_CUDA_GRAPHS]
if (node->op == GGML_OP_MUL_MAT_ID && (!ggml_is_quantized(node->src[0]->type) || node->ne[2] > 4)) {
// under these conditions, the mul_mat_id operation will need to synchronize the stream, so we cannot use CUDA graphs
// TODO: figure out a way to enable for larger batch sizes, without hurting performance
// ref: https://github.com/ggml-org/llama.cpp/pull/18958
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 unsupported node type\n", __func__);
#endif
}