ikawrakow/ik_llama.cpp
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More gemv+add fusing

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Iwan Kawrakow
2025-10-26 09:32:02 +02:00
parent 7da6fb0979
commit a5b16b82bb
1 changed files with 26 additions and 25 deletions
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51
ggml/src/ggml-cuda.cu
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@@ -2064,26 +2064,8 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const ggml_cgraph * cgraph, int node_n, bool is_gemv) {
//if (cgraph && node_n + 6 < cgraph->n_nodes) {
// printf("=== %s\n", __func__);
// for (int i = 0; i <= 6; ++i) printf("%d: %s(%s)\n", i, ggml_op_name(cgraph->nodes[node_n+i]->op), cgraph->nodes[node_n+i]->name);
//}
auto stream = ctx.stream();
//if (cgraph && node_n + 5 < cgraph->n_nodes &&
// cgraph->nodes[node_n+1]->op == GGML_OP_MUL_MAT &&
// cgraph->nodes[node_n+2]->op == GGML_OP_MUL_MAT &&
// cgraph->nodes[node_n+3]->op == GGML_OP_ADD &&
// cgraph->nodes[node_n+4]->op == GGML_OP_ADD &&
// cgraph->nodes[node_n+5]->op == GGML_OP_ADD &&
// cgraph->nodes[node_n+0] == cgraph->nodes[node_n+3]->src[0] &&
// cgraph->nodes[node_n+1] == cgraph->nodes[node_n+4]->src[0] &&
// cgraph->nodes[node_n+2] == cgraph->nodes[node_n+5]->src[0]) {
// printf("Could process mulmat(%s) + mulmat(%s) + mulmat(%s) + add(%s) + add(%s) + add(%s)\n",
// cgraph->nodes[node_n+0]->name, cgraph->nodes[node_n+1]->name, cgraph->nodes[node_n+2]->name,
// cgraph->nodes[node_n+3]->name, cgraph->nodes[node_n+4]->name, cgraph->nodes[node_n+5]->name);
//}
auto ne10_padded = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
auto nb10_padded = ne10_padded*sizeof(block_q8_1)/QK8_1;
auto quantized_size = nb10_padded*ggml_nrows(src1);
@@ -2096,6 +2078,8 @@ static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tenso
src0->type, stream);
CUDA_CHECK(cudaGetLastError());
// The code below handles the case when Q, K, V have a bias applied after the resepctive matrix multiplication.
// In that case the graph contains mul_mat(Q) -> mul_mat(K) -> mul_mat(V) -> add(Q) -> add(K) -> add(V)
if (cgraph && node_n + 5 < cgraph->n_nodes &&
cgraph->nodes[node_n+1]->op == GGML_OP_MUL_MAT &&
cgraph->nodes[node_n+2]->op == GGML_OP_MUL_MAT &&
@@ -2107,19 +2091,24 @@ static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tenso
cgraph->nodes[node_n+0] == cgraph->nodes[node_n+3]->src[0] &&
cgraph->nodes[node_n+1] == cgraph->nodes[node_n+4]->src[0] &&
cgraph->nodes[node_n+2] == cgraph->nodes[node_n+5]->src[0]) {
//printf("Processing mulmat(%s) + mulmat(%s) + mulmat(%s) + add(%s) + add(%s) + add(%s)\n",
// cgraph->nodes[node_n+0]->name, cgraph->nodes[node_n+1]->name, cgraph->nodes[node_n+2]->name,
// cgraph->nodes[node_n+3]->name, cgraph->nodes[node_n+4]->name, cgraph->nodes[node_n+5]->name);
for (int i = 0; i < 3; ++i) {
auto src0_i = cgraph->nodes[node_n+i]->src[0];
//printf(" using %s(%s) with %s, %s\n", ggml_op_name(cgraph->nodes[node_n+i+3]->op), cgraph->nodes[node_n+i+3]->name,
// cgraph->nodes[node_n+i+3]->src[0]->name, cgraph->nodes[node_n+i+3]->src[1]->name);
ggml_cuda_op_mul_mat_vec_q_biased(ctx, src0_i, src1, cgraph->nodes[node_n+i], cgraph->nodes[node_n+i+3]->src[1],
(const char *)src0_i->data, nullptr, src1_quantized.get(), (float *)cgraph->nodes[node_n+i]->data,
0, src0_i->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
}
node_n += 5;
} else if (cgraph && node_n + 1 < cgraph->n_nodes &&
cgraph->nodes[node_n+1]->op == GGML_OP_ADD &&
dst == cgraph->nodes[node_n+1]->src[0] &&
dst->ne[0] == cgraph->nodes[node_n+1]->src[1]->ne[0] &&
cgraph->nodes[node_n+1]->src[1]->type == GGML_TYPE_F32) {
// We have a bias applied after the matrix multiplication and we can fuse it
ggml_cuda_op_mul_mat_vec_q_biased(ctx, dst->src[0], src1, cgraph->nodes[node_n+1], cgraph->nodes[node_n+1]->src[1],
(const char *)dst->src[0]->data, nullptr, src1_quantized.get(), (float *)cgraph->nodes[node_n+1]->data,
0, dst->src[0]->ne[1], src1->ne[1], ne10_padded, stream);
++node_n;
} else {
ggml_cuda_op_mul_mat_vec_q(ctx, src0, src1, dst, (const char *)src0->data, nullptr, src1_quantized.get(), (float *)dst->data,
0, src0->ne[1], src1->ne[1], ne10_padded, stream);
@@ -2145,8 +2134,20 @@ static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tenso
if (dst->op != GGML_OP_MUL_MAT || dst->src[1] != src1 || !ggml_is_quantized(dst->src[0]->type)) break;
if (!is_gemv && mmq_get_q8_1_ds_layout(src0->type) != mmq_get_q8_1_ds_layout(dst->src[0]->type)) break;
if (is_gemv) {
ggml_cuda_op_mul_mat_vec_q(ctx, dst->src[0], src1, dst, (const char *)dst->src[0]->data, nullptr, src1_quantized.get(),
(float *)dst->data, 0, dst->src[0]->ne[1], src1->ne[1], ne10_padded, stream);
if (node_n + 1 < cgraph->n_nodes &&
cgraph->nodes[node_n+1]->op == GGML_OP_ADD &&
dst == cgraph->nodes[node_n+1]->src[0] &&
dst->ne[0] == cgraph->nodes[node_n+1]->src[1]->ne[0] &&
cgraph->nodes[node_n+1]->src[1]->type == GGML_TYPE_F32) {
// We have a bias applied after the matrix multiplication and we can fuse it
ggml_cuda_op_mul_mat_vec_q_biased(ctx, dst->src[0], src1, cgraph->nodes[node_n+1], cgraph->nodes[node_n+1]->src[1],
(const char *)dst->src[0]->data, nullptr, src1_quantized.get(), (float *)cgraph->nodes[node_n+1]->data,
0, dst->src[0]->ne[1], src1->ne[1], ne10_padded, stream);
++node_n;
} else {
ggml_cuda_op_mul_mat_vec_q(ctx, dst->src[0], src1, dst, (const char *)dst->src[0]->data, nullptr, src1_quantized.get(),
(float *)dst->data, 0, dst->src[0]->ne[1], src1->ne[1], ne10_padded, stream);
}
} else {
ggml_cuda_op_mul_mat_q(ctx, dst->src[0], src1, dst, (const char *)dst->src[0]->data, nullptr, src1_quantized.get(),
(float *)dst->data, 0, dst->src[0]->ne[1], src1->ne[1], ne10_padded, stream);