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https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-05-01 03:41:53 +00:00
Make biased gemv fusion optional
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@@ -2067,6 +2067,8 @@ static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tenso
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auto stream = ctx.stream();
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auto stream = ctx.stream();
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auto fusion = ctx.fusion;
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auto ne10_padded = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
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auto ne10_padded = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
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auto nb10_padded = ne10_padded*sizeof(block_q8_1)/QK8_1;
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auto nb10_padded = ne10_padded*sizeof(block_q8_1)/QK8_1;
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auto quantized_size = nb10_padded*ggml_nrows(src1);
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auto quantized_size = nb10_padded*ggml_nrows(src1);
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@@ -2081,7 +2083,7 @@ static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tenso
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// The code below handles the case when Q, K, V have a bias applied after the resepctive matrix multiplication.
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// The code below handles the case when Q, K, V have a bias applied after the resepctive matrix multiplication.
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// In that case the graph contains mul_mat(Q) -> mul_mat(K) -> mul_mat(V) -> add(Q) -> add(K) -> add(V)
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// In that case the graph contains mul_mat(Q) -> mul_mat(K) -> mul_mat(V) -> add(Q) -> add(K) -> add(V)
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if (cgraph && node_n + 5 < cgraph->n_nodes &&
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if (fusion && cgraph && node_n + 5 < cgraph->n_nodes &&
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cgraph->nodes[node_n+1]->op == GGML_OP_MUL_MAT &&
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cgraph->nodes[node_n+1]->op == GGML_OP_MUL_MAT &&
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cgraph->nodes[node_n+2]->op == GGML_OP_MUL_MAT &&
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cgraph->nodes[node_n+2]->op == GGML_OP_MUL_MAT &&
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ggml_is_quantized(cgraph->nodes[node_n+1]->src[0]->type) &&
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ggml_is_quantized(cgraph->nodes[node_n+1]->src[0]->type) &&
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@@ -2100,7 +2102,7 @@ static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tenso
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CUDA_CHECK(cudaGetLastError());
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CUDA_CHECK(cudaGetLastError());
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}
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}
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node_n += 5;
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node_n += 5;
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} else if (cgraph && node_n + 1 < cgraph->n_nodes &&
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} else if (fusion && cgraph && node_n + 1 < cgraph->n_nodes &&
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cgraph->nodes[node_n+1]->op == GGML_OP_ADD &&
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cgraph->nodes[node_n+1]->op == GGML_OP_ADD &&
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dst == cgraph->nodes[node_n+1]->src[0] &&
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dst == cgraph->nodes[node_n+1]->src[0] &&
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dst->ne[0] == cgraph->nodes[node_n+1]->src[1]->ne[0] &&
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dst->ne[0] == cgraph->nodes[node_n+1]->src[1]->ne[0] &&
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@@ -2136,7 +2138,7 @@ static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tenso
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if (dst->op != GGML_OP_MUL_MAT || dst->src[1] != src1 || !ggml_is_quantized(dst->src[0]->type)) break;
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if (dst->op != GGML_OP_MUL_MAT || dst->src[1] != src1 || !ggml_is_quantized(dst->src[0]->type)) break;
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if (!is_gemv && mmq_get_q8_1_ds_layout(src0->type) != mmq_get_q8_1_ds_layout(dst->src[0]->type)) break;
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if (!is_gemv && mmq_get_q8_1_ds_layout(src0->type) != mmq_get_q8_1_ds_layout(dst->src[0]->type)) break;
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if (is_gemv) {
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if (is_gemv) {
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if (node_n + 1 < cgraph->n_nodes &&
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if (fusion && node_n + 1 < cgraph->n_nodes &&
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cgraph->nodes[node_n+1]->op == GGML_OP_ADD &&
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cgraph->nodes[node_n+1]->op == GGML_OP_ADD &&
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dst == cgraph->nodes[node_n+1]->src[0] &&
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dst == cgraph->nodes[node_n+1]->src[0] &&
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dst->ne[0] == cgraph->nodes[node_n+1]->src[1]->ne[0] &&
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dst->ne[0] == cgraph->nodes[node_n+1]->src[1]->ne[0] &&
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