mirror of
https://github.com/ggerganov/llama.cpp.git
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930b178026
* server: logs - always use JSON logger, add add thread_id in message, log task_id and slot_id * server : skip GH copilot requests from logging * server : change message format of server_log() * server : no need to repeat log in comment * server : log style consistency * server : fix compile warning * server : fix tests regex patterns on M2 Ultra * server: logs: PR feedback on log level * server: logs: allow to choose log format in json or plain text * server: tests: output server logs in text * server: logs switch init logs to server logs macro * server: logs ensure value json value does not raised error * server: logs reduce level VERBOSE to VERB to max 4 chars * server: logs lower case as other log messages * server: logs avoid static in general Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * server: logs PR feedback: change text log format to: LEVEL [function_name] message | additional=data --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
85 lines
3.0 KiB
Gherkin
85 lines
3.0 KiB
Gherkin
@llama.cpp
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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 stories260K.gguf
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And a model alias tinyllama-2
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And 42 as server seed
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# KV Cache corresponds to the total amount of tokens
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# that can be stored across all independent sequences: #4130
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# see --ctx-size and #5568
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And 32 KV cache size
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And 1 slots
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And embeddings extraction
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And 32 server max tokens to predict
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And prometheus compatible metrics exposed
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Then the server is starting
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Then the server is healthy
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Scenario: Health
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Then the server is ready
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And all slots are idle
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Scenario Outline: Completion
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Given a prompt <prompt>
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And <n_predict> max tokens to predict
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And a completion request with no api error
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Then <n_predicted> tokens are predicted matching <re_content>
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And prometheus metrics are exposed
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Examples: Prompts
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| prompt | n_predict | re_content | n_predicted |
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| I believe the meaning of life is | 8 | (read<or>going)+ | 8 |
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| Write a joke about AI | 64 | (park<or>friends<or>scared<or>always)+ | 32 |
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Scenario Outline: OAI Compatibility
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Given a model <model>
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And a system prompt <system_prompt>
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And a user prompt <user_prompt>
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And <max_tokens> max tokens to predict
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And streaming is <enable_streaming>
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Given an OAI compatible chat completions request with no api error
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Then <n_predicted> tokens are predicted matching <re_content>
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Examples: Prompts
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| model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming |
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| llama-2 | Book | What is the best book | 8 | (Mom<or>what)+ | 8 | disabled |
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| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks<or>happy<or>bird)+ | 32 | enabled |
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Scenario: Embedding
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When embeddings are computed for:
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"""
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What is the capital of Bulgaria ?
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"""
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Then embeddings are generated
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Scenario: OAI Embeddings compatibility
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Given a model tinyllama-2
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When an OAI compatible embeddings computation request for:
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"""
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What is the capital of Spain ?
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"""
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Then embeddings are generated
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Scenario: OAI Embeddings compatibility with multiple inputs
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Given a model tinyllama-2
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Given a prompt:
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"""
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In which country Paris is located ?
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"""
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And a prompt:
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"""
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Is Madrid the capital of Spain ?
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"""
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When an OAI compatible embeddings computation request for multiple inputs
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Then embeddings are generated
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Scenario: Tokenize / Detokenize
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When tokenizing:
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"""
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What is the capital of France ?
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"""
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Then tokens can be detokenize
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