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CUDA: skip fusion for repeating adds in bias (#17080)

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This commit is contained in:
Aman Gupta
2025-11-08 16:58:05 +08:00
committed by GitHub
parent b8a5cfd11a
commit c1b187688d
3 changed files with 18 additions and 6 deletions
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1
ggml/src/ggml-cuda/CMakeLists.txt
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@@ -124,6 +124,7 @@ if (CUDAToolkit_FOUND)
if (GGML_CUDA_DEBUG) if (GGML_CUDA_DEBUG)
list(APPEND CUDA_FLAGS -lineinfo) list(APPEND CUDA_FLAGS -lineinfo)
add_compile_definitions(GGML_CUDA_DEBUG)
endif() endif()
if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.8") if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.8")

13
ggml/src/ggml-cuda/ggml-cuda.cu
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@@ -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++) { for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i]; ggml_tensor * node = cgraph->nodes[i];
#ifdef GGML_CUDA_DEBUG #ifdef GGML_CUDA_DEBUG
const int nodes_fused = i - prev_i - 1; const int nodes_fused = i - prev_i - 1;
prev_i = i; prev_i = i;
@@ -3302,6 +3300,13 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
continue; 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 * src0 = up_n->src[0];
const ggml_tensor * src1 = up_n->src[1]; const ggml_tensor * src1 = up_n->src[1];
const ggml_tensor * ids = up_n->src[2]; const ggml_tensor * ids = up_n->src[2];
@@ -3411,6 +3416,10 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
continue; 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{}; ggml_cuda_mm_fusion_args_host fusion_data{};
fusion_data.x_bias = bias_tensor; fusion_data.x_bias = bias_tensor;

10
tests/test-backend-ops.cpp
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@@ -4984,8 +4984,10 @@ struct test_mul_mat_vec_fusion : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * build_graph(ggml_context * ctx) override {
if (!use_id) { if (!use_id) {
std::array<int64_t, 4> ne = {k, m, 1, 1}; const int channels = 4;
std::array<int64_t, 4> ne0 = {k, n, 1, 1}; 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 * 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 * gate = with_gate ? ggml_new_tensor(ctx, type, 4, ne0.data()) : nullptr;
@@ -4993,14 +4995,14 @@ struct test_mul_mat_vec_fusion : public test_case {
ggml_tensor * ffn_up = ggml_mul_mat(ctx, up, cur); ggml_tensor * ffn_up = ggml_mul_mat(ctx, up, cur);
if (with_bias) { 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()); ggml_tensor * up_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
ffn_up = ggml_add(ctx, ffn_up, up_bias); ffn_up = ggml_add(ctx, ffn_up, up_bias);
} }
ggml_tensor * ffn_gate = with_gate ? ggml_mul_mat(ctx, gate, cur) : nullptr; ggml_tensor * ffn_gate = with_gate ? ggml_mul_mat(ctx, gate, cur) : nullptr;
if (with_bias && with_gate) { 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()); ggml_tensor * gate_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
ffn_gate = ggml_add(ctx, ffn_gate, gate_bias); ffn_gate = ggml_add(ctx, ffn_gate, gate_bias);
} }