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model : more uniform output id handling (#14275)
* model : more uniform output id handling ggml-ci * cont : revert n_outputs < n_tokens optimization ggml-ci * cont : fix out_ids initialization ggml-ci
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@ -92,12 +92,8 @@ void llm_graph_input_pos_bucket_kv::set_input(const llama_ubatch * ubatch) {
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}
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void llm_graph_input_out_ids::set_input(const llama_ubatch * ubatch) {
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if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
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//GGML_ASSERT(out_ids && "every model that can must skip unused outputs");
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GGML_ASSERT(out_ids);
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if (!out_ids) {
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LLAMA_LOG_WARN("%s: 'out_ids' is not created\n", __func__);
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} else {
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const int64_t n_tokens = ubatch->n_tokens;
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GGML_ASSERT(ggml_backend_buffer_is_host(out_ids->buffer));
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@ -107,23 +103,19 @@ void llm_graph_input_out_ids::set_input(const llama_ubatch * ubatch) {
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for (int i = 0; i < n_tokens; ++i) {
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data[i] = i;
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}
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} else if (ubatch->output) {
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int32_t n_outputs = 0;
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return;
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}
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GGML_ASSERT(ubatch->output);
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int n_outputs = 0;
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for (int i = 0; i < n_tokens; ++i) {
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if (ubatch->output[i]) {
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data[n_outputs++] = i;
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}
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}
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// the graph needs to have been passed the correct number of outputs
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GGML_ASSERT(n_outputs == n_outputs);
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} else if (n_outputs == 1) {
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// only keep last output
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data[0] = n_tokens - 1;
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} else {
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GGML_ASSERT(n_outputs == 0);
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}
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}
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}
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}
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void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) {
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@ -874,6 +866,14 @@ ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
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}
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ggml_tensor * llm_graph_context::build_inp_out_ids() const {
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// note: when all tokens are output, we could skip this optimization to spare the ggml_get_rows() calls,
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// but this would make the graph topology depend on the number of output tokens, which can interere with
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// features that require constant topology such as pipline parallelism
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// ref: https://github.com/ggml-org/llama.cpp/pull/14275#issuecomment-2987424471
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//if (n_outputs < n_tokens) {
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// return nullptr;
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//}
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auto inp = std::make_unique<llm_graph_input_out_ids>(hparams, cparams, n_outputs);
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auto & cur = inp->out_ids;
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