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@@ -285,8 +285,8 @@ llama_context::llama_context(
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const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
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const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
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const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
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const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
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// avoid reserving graphs with zero outputs
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// avoid reserving graphs with zero outputs - assume one output per sequence
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n_outputs = 1;
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n_outputs = n_seqs;
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LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
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LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
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@@ -1431,7 +1431,8 @@ ggml_tensor * llm_graph_context::build_attn(
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// [TAG_NO_CACHE_PAD]
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// [TAG_NO_CACHE_PAD]
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// TODO: if ubatch.equal_seqs() == true, we can split the three tensors below into ubatch.n_seqs_unq streams
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// TODO: if ubatch.equal_seqs() == true, we can split the three tensors below into ubatch.n_seqs_unq streams
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assert(!ubatch.equal_seqs() || (k_cur->ne[3] == 1 && k_cur->ne[3] == ubatch.n_seqs_unq));
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// but it might not be worth it: https://github.com/ggml-org/llama.cpp/pull/15636
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//assert(!ubatch.equal_seqs() || (k_cur->ne[3] == 1 && k_cur->ne[3] == ubatch.n_seqs_unq));
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ggml_tensor * q = q_cur;
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ggml_tensor * q = q_cur;
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ggml_tensor * k = k_cur;
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ggml_tensor * k = k_cur;
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