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https://github.com/ggerganov/llama.cpp.git
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llama : sanitize invalid tokens (#9357)
* common : do not add null tokens during warmup ggml-ci * llama : check that the input tokens are valid ggml-ci * tests : fix batch size of bert model ggml-ci
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@ -2690,10 +2690,15 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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llama_token bos = llama_token_bos(model);
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llama_token eos = llama_token_eos(model);
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// some models (e.g. T5) don't have a BOS token
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if (bos != -1) {
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if (bos != LLAMA_TOKEN_NULL) {
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tmp.push_back(bos);
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}
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tmp.push_back(eos);
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if (eos != LLAMA_TOKEN_NULL) {
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tmp.push_back(eos);
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}
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if (tmp.empty()) {
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tmp.push_back(0);
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}
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if (llama_model_has_encoder(model)) {
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llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
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@ -9,8 +9,11 @@ Feature: llama.cpp server
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And a model alias bert-bge-small
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And 42 as server seed
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And 2 slots
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And 1024 as batch size
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And 1024 as ubatch size
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# the bert-bge-small model has context size of 512
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# since the generated prompts are as big as the batch size, we need to set the batch size to 512
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# ref: https://huggingface.co/BAAI/bge-small-en-v1.5/blob/5c38ec7c405ec4b44b94cc5a9bb96e735b38267a/config.json#L20
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And 512 as batch size
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And 512 as ubatch size
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And 2048 KV cache size
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And embeddings extraction
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Then the server is starting
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@ -16066,6 +16066,13 @@ static int llama_decode_internal(
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return -1;
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}
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for (uint32_t i = 0; i < n_tokens_all; ++i) {
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if (batch_all.token[i] < 0) {
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LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch_all.token[i]);
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return -1;
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}
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}
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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const auto & cparams = lctx.cparams;
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@ -16358,6 +16365,13 @@ static int llama_encode_internal(
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return -1;
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}
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for (uint32_t i = 0; i < n_tokens; ++i) {
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if (batch.token[i] < 0) {
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LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch.token[i]);
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return -1;
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}
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}
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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const auto & cparams = lctx.cparams;
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