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CUDA: skip fusion for repeating adds in bias #17080

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Merged
merged 1 commit into from
Nov 8, 2025
Merged

CUDA: skip fusion for repeating adds in bias #17080

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1 change: 1 addition & 0 deletions ggml/src/ggml-cuda/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -124,6 +124,7 @@ if (CUDAToolkit_FOUND)

if (GGML_CUDA_DEBUG)
list(APPEND CUDA_FLAGS -lineinfo)
add_compile_definitions(GGML_CUDA_DEBUG)
endif()

if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.8")
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13 changes: 11 additions & 2 deletions ggml/src/ggml-cuda/ggml-cuda.cu
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Expand Up @@ -3152,8 +3152,6 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx

for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];


#ifdef GGML_CUDA_DEBUG
const int nodes_fused = i - prev_i - 1;
prev_i = i;
Expand Down Expand Up @@ -3302,6 +3300,13 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
continue;
}

// we don't support repeating adds
if (bias_op == GGML_OP_ADD &&
(!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
!ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
continue;
}

const ggml_tensor * src0 = up_n->src[0];
const ggml_tensor * src1 = up_n->src[1];
const ggml_tensor * ids = up_n->src[2];
Expand Down Expand Up @@ -3411,6 +3416,10 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
continue;
}

if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
continue;
}

ggml_cuda_mm_fusion_args_host fusion_data{};
fusion_data.x_bias = bias_tensor;

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10 changes: 6 additions & 4 deletions tests/test-backend-ops.cpp
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Expand Up @@ -4911,23 +4911,25 @@ struct test_mul_mat_vec_fusion : public test_case {

ggml_tensor * build_graph(ggml_context * ctx) override {
if (!use_id) {
std::array<int64_t, 4> ne = {k, m, 1, 1};
std::array<int64_t, 4> ne0 = {k, n, 1, 1};
const int channels = 4;
const int samples = 2;
std::array<int64_t, 4> ne = { k, m, channels, samples };
std::array<int64_t, 4> ne0 = { k, n, channels, samples };

ggml_tensor * cur = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne.data());
ggml_tensor * gate = with_gate ? ggml_new_tensor(ctx, type, 4, ne0.data()) : nullptr;
ggml_tensor * up = ggml_new_tensor(ctx, type, 4, ne0.data());

ggml_tensor * ffn_up = ggml_mul_mat(ctx, up, cur);
if (with_bias) {
std::array<int64_t, 4> bias_ne = {ffn_up->ne[0], 1, 1, 1};
std::array<int64_t, 4> bias_ne = { ffn_up->ne[0], 1, channels, samples };
ggml_tensor * up_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
ffn_up = ggml_add(ctx, ffn_up, up_bias);
}

ggml_tensor * ffn_gate = with_gate ? ggml_mul_mat(ctx, gate, cur) : nullptr;
if (with_bias && with_gate) {
std::array<int64_t, 4> bias_ne = {ffn_gate->ne[0], 1, 1, 1};
std::array<int64_t, 4> bias_ne = { ffn_gate->ne[0], 1, channels, samples };
ggml_tensor * gate_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
ffn_gate = ggml_add(ctx, ffn_gate, gate_bias);
}
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