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https://github.com/ggml-org/llama.cpp.git
synced 2026-10-08 22:10:37 +02:00
@@ -1846,11 +1846,14 @@ static bool ggml_cuda_match_shared_expert(const ggml_cgraph * graph, int routed_
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return (a == graph->nodes[idx] && b == graph->nodes[idx + 1]) ||
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(b == graph->nodes[idx] && a == graph->nodes[idx + 1]);
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};
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// only batch-size independent checks here: graph_optimize must produce the same graph topology for every ubatch
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// size, otherwise ggml-alloc has to re-reserve (and the scheduler to synchronize) at runtime.
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// the MMVQ batch size check is done in ggml_cuda_try_fuse
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if (!is_pair(gate, up, routed_idx) || !is_pair(shared_gate, shared_up, shared_idx) ||
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!ggml_cuda_should_fuse_mul_mat(up, gate, routed) ||
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!ggml_cuda_should_fuse_mul_mat(shared_up, shared_gate, shared) ||
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!up->src[0]->buffer ||
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!ggml_cuda_should_fuse_mul_mat_vec_q(up)) {
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!ggml_is_quantized(up->src[0]->type)) {
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return false;
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}
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const ggml_tensor * input = up->src[1];
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@@ -3510,7 +3513,8 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph
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ggml_tensor * node = cgraph->nodes[i];
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if (node->op == GGML_OP_MUL_MAT_ID && cuda_ctx->stream_context().concurrent_events.empty() &&
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ggml_cuda_match_shared_expert(cgraph, i, i + 3)) {
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ggml_cuda_match_shared_expert(cgraph, i, i + 3) &&
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ggml_cuda_should_fuse_mul_mat_vec_q(cgraph->nodes[i + 2]->src[1])) {
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const int outputs[] = { i + 2, i + 5 };
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if (ggml_cuda_check_fusion_memory_ranges(cgraph, i, 6, outputs, 2)) {
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ggml_tensor * routed = cgraph->nodes[i + 2];
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