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@ -7773,6 +7773,7 @@ static int llama_decode_impl(
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llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : lctx.pos_max() + 1);
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const llama_batch & batch = batch_allocr.batch;
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const uint32_t n_tokens_all = batch.n_tokens;
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const auto & model = lctx.model;
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@ -7800,9 +7801,6 @@ static int llama_decode_impl(
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}
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lctx.n_queued_tokens += n_tokens_all;
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auto & kv_self = lctx.kv_self;
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llama_kv_slot_restorer kv_slot_restorer(kv_self);
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_vocab = vocab.n_tokens();
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@ -7828,16 +7826,19 @@ static int llama_decode_impl(
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n_outputs = 1;
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}
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lctx.sbatch.from_batch(batch, n_embd,
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/* simple_split */ !kv_self.recurrent,
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/* logits_all */ n_outputs == n_tokens_all);
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// reserve output buffer
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if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
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LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
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return -2;
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};
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auto & kv_self = lctx.kv_self;
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llama_kv_slot_restorer kv_slot_restorer(kv_self);
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lctx.sbatch.from_batch(batch, n_embd,
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/* simple_split */ !kv_self.recurrent,
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/* logits_all */ n_outputs == n_tokens_all);
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while (lctx.sbatch.n_tokens > 0) {
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llama_ubatch ubatch;
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if (kv_self.recurrent) {
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@ -8635,7 +8636,6 @@ struct llama_context * llama_init_from_model(
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cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base;
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cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
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// this is necessary due to kv_self.n being padded later during inference
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cparams.n_ctx = GGML_PAD(cparams.n_ctx, ctx->get_ctx_padding(cparams));
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// with causal attention, the batch size is limited by the context size
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