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https://github.com/ggerganov/llama.cpp.git
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server : add --no-context-shift option (#9607)
* server : add --no-context-shift option * small fix * Update examples/server/tests/features/embeddings.feature Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * tests : minor fix * revert usage of GGML_ASSERT * update server documentation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Xuan Son Nguyen
GitHub
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f0c7b5edf8
commit
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@ -691,7 +691,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
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[](gpt_params & params) {
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params.ctx_shift = false;
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}
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).set_examples({LLAMA_EXAMPLE_MAIN}));
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).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
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add_opt(llama_arg(
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{"--chunks"}, "N",
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format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
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@ -21,8 +21,6 @@ The project is under active development, and we are [looking for feedback and co
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| -------- | ----------- |
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| `-h, --help, --usage` | print usage and exit |
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| `--version` | show version and build info |
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| `-v, --verbose` | print verbose information |
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| `--verbosity N` | set specific verbosity level (default: 0) |
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| `-t, --threads N` | number of threads to use during generation (default: -1)<br/>(env: LLAMA_ARG_THREADS) |
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| `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) |
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| `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") |
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@ -40,15 +38,18 @@ The project is under active development, and we are [looking for feedback and co
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| `-b, --batch-size N` | logical maximum batch size (default: 2048)<br/>(env: LLAMA_ARG_BATCH) |
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| `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
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| `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
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| `--no-context-shift` | disables context shift on inifinite text generation (default: disabled) |
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| `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
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| `-p, --prompt PROMPT` | prompt to start generation with |
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| `--no-perf` | disable internal libllama performance timings (default: false)<br/>(env: LLAMA_ARG_NO_PERF) |
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| `-f, --file FNAME` | a file containing the prompt (default: none) |
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| `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) |
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| `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
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| `--no-escape` | do not process escape sequences |
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| `-sp, --special` | special tokens output enabled (default: false) |
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| `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
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| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) |
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| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
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| `-s, --seed SEED` | RNG seed (default: 4294967295, use random seed for 4294967295) |
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| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) |
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| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
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| `--penalize-nl` | penalize newline tokens (default: false) |
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@ -128,12 +129,13 @@ The project is under active development, and we are [looking for feedback and co
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| `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
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| `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
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| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) |
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| `--log-test` | Log test |
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| `--log-disable` | Log disable |
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| `--log-enable` | Log enable |
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| `--log-new` | Log new |
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| `--log-append` | Log append |
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| `--log-file FNAME` | Log file |
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| `--log-file FNAME` | Log to file |
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| `--log-colors` | Enable colored logging<br/>(env: LLAMA_LOG_COLORS) |
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| `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) |
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| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored.<br/>(env: LLAMA_LOG_VERBOSITY) |
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| `--log-prefix` | Enable prefx in log messages<br/>(env: LLAMA_LOG_PREFIX) |
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| `--log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_LOG_TIMESTAMPS) |
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Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
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@ -1180,6 +1180,15 @@ struct server_context {
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SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.params.n_predict);
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}
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// if context shift is disabled, we stop when it reaches the context limit
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if (slot.n_decoded >= slot.n_ctx) {
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slot.truncated = true;
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slot.stopped_limit = true;
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slot.has_next_token = false;
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SLT_DBG(slot, "stopped due to running out of context capacity, n_decoded = %d, n_ctx = %d\n", slot.n_decoded, slot.n_ctx);
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}
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if (llama_token_is_eog(model, result.tok)) {
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slot.stopped_eos = true;
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slot.has_next_token = false;
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@ -1480,7 +1489,7 @@ struct server_context {
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if (result.error) {
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error_handler(result.data);
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cancel_tasks(id_tasks);
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break;
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return;
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}
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size_t idx = result.data["index"];
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@ -1827,6 +1836,14 @@ struct server_context {
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for (server_slot & slot : slots) {
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if (slot.ga_n == 1) {
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if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
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if (!params.ctx_shift) {
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// this check is redundant (for good)
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// we should never get here, because generation should already stopped in process_token()
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slot.release();
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send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER);
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continue;
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}
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// Shift context
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const int n_keep = slot.params.n_keep + add_bos_token;
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const int n_left = (int) system_tokens.size() + slot.n_past - n_keep;
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@ -1961,6 +1978,14 @@ struct server_context {
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continue;
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}
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} else {
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if (!params.ctx_shift) {
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// if context shift is disabled, we make sure prompt size is smaller than KV size
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if ((int) system_tokens.size() + slot.n_prompt_tokens >= slot.n_ctx) {
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slot.release();
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send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_INVALID_REQUEST);
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continue;
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}
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}
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if (slot.params.n_keep < 0) {
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slot.params.n_keep = slot.n_prompt_tokens;
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}
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62
examples/server/tests/features/ctx_shift.feature
Normal file
62
examples/server/tests/features/ctx_shift.feature
Normal file
@ -0,0 +1,62 @@
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@llama.cpp
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@ctx_shift
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Feature: llama.cpp server
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Background: Server startup
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Given a server listening on localhost:8080
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And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
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And a model file test-model.gguf
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And a model alias tinyllama-2
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And BOS token is 1
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And 42 as server seed
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And 256 KV cache size
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And 32 as batch size
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And 2 slots
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Scenario: Inference with context shift
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And 64 server max tokens to predict
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Then the server is starting
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Then the server is healthy
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Given a prompt:
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"""
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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
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Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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"""
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And a completion request with no api error
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Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl
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And the completion is truncated
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And 109 prompt tokens are processed
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Scenario Outline: Inference without context shift
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And <n_predict> server max tokens to predict
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And disable context shifting
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Then the server is starting
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Then the server is healthy
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Given a prompt:
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"""
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Hi how are you
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"""
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And a completion request with no api error
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Then <n_token_output> tokens are predicted matching twind|Anna
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And the completion is <truncated> truncated
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And 8 prompt tokens are processed
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Examples:
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| n_predict | n_token_output | truncated |
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| 64 | 64 | not |
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| -1 | 120 | |
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Scenario: Inference without context shift (expected error: prompt too long)
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And disable context shifting
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Then the server is starting
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Then the server is healthy
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Given a prompt:
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"""
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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
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Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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"""
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And a completion request with 400 api error
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@ -10,11 +10,11 @@ Feature: llama.cpp server
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And 42 as server seed
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And 2 slots
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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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# 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 128 as batch size
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And 128 as ubatch size
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And 512 KV cache size
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And embeddings extraction
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Then the server is starting
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Then the server is healthy
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@ -26,6 +26,20 @@ Feature: llama.cpp server
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"""
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Then embeddings are generated
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Scenario: Embedding (error: prompt too long)
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When embeddings are computed for:
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"""
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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
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Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
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Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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"""
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And embeddings request with 500 api error
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Scenario: OAI Embeddings compatibility
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Given a model bert-bge-small
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When an OAI compatible embeddings computation request for:
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@ -77,6 +77,7 @@ def step_server_config(context, server_fqdn: str, server_port: str):
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context.response_format = None
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context.temperature = None
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context.lora_file = None
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context.disable_ctx_shift = False
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context.tasks_result = []
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context.concurrent_tasks = []
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@ -148,7 +149,7 @@ def step_n_slots(context, n_slots: int):
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@step('{n_predict:d} server max tokens to predict')
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def step_server_n_predict(context, n_predict: int):
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context.n_server_predict = n_predict
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context.n_server_predict = n_predict if n_predict > 0 else None
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@step('{slot_save_path} as slot save path')
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@ -180,6 +181,9 @@ def step_server_embeddings(context):
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def step_server_metrics(context):
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context.server_metrics = True
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@step('disable context shifting')
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def step_server_disable_ctx_shift(context):
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context.disable_ctx_shift = True
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@step("the server is starting")
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def step_start_server(context):
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@ -257,7 +261,7 @@ async def step_all_slots_status(context, expected_slot_status_string: Literal['i
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@step('a completion request with {api_error} api error')
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@async_run_until_complete
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async def step_request_completion(context, api_error: Literal['raised'] | str):
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expect_api_error = api_error == 'raised'
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expect_api_error = api_error == 'raised' or api_error != 'no'
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seeds = await completions_seed(context, num_seeds=1)
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completion = await request_completion(context.prompts.pop(),
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seeds[0] if seeds is not None else seeds,
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@ -272,8 +276,11 @@ async def step_request_completion(context, api_error: Literal['raised'] | str):
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context.tasks_result.append(completion)
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if context.debug:
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print(f"Completion response: {completion}")
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if expect_api_error:
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if api_error == 'raised':
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assert completion == 401, f"completion must be an 401 status code: {completion}"
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elif api_error.isdigit():
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api_error_code = int(api_error)
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assert completion == api_error_code, f"completion must be an {api_error_code} status code: {completion}"
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@step('{predicted_n:d} tokens are predicted matching {re_content}')
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@ -645,6 +652,9 @@ def step_assert_embeddings(context):
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for embedding in context.embeddings:
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assert_embeddings(embedding)
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@step('embeddings request with {api_error_code:d} api error')
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def step_assert_embeddings(context, api_error_code: int):
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assert context.embeddings == api_error_code, f"embeddings request must return code {api_error_code}, but got {context.embeddings}"
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@step('an OAI compatible embeddings computation request for')
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@async_run_until_complete
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@ -1089,15 +1099,17 @@ async def oai_chat_completions(user_prompt,
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return completion_response
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async def request_embedding(content, seed, base_url=None) -> list[list[float]]:
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async def request_embedding(content, seed, base_url=None) -> list[list[float]] | int:
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async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
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async with session.post(f'{base_url}/embedding',
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json={
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"content": content,
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}) as response:
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assert response.status == 200
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if response.status == 200:
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response_json = await response.json()
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return [response_json['embedding']]
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else:
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return response.status
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async def request_oai_embeddings(input, seed,
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@ -1372,6 +1384,8 @@ def start_server_background(context):
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server_args.append('--verbose')
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if context.lora_file:
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server_args.extend(['--lora', context.lora_file])
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if context.disable_ctx_shift:
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server_args.extend(['--no-context-shift'])
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args = [str(arg) for arg in [context.server_path, *server_args]]
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print(f"bench: starting server with: {' '.join(args)}")
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