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https://github.com/ggml-org/llama.cpp.git
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9731134296
* server: tests: add models endpoint scenario * server: /v1/models add some metadata * server: tests: add debug field in context before scenario * server: tests: download model from HF, add batch size * server: tests: add passkey test * server: tests: add group attention params * server: do not truncate prompt tokens if self-extend through group attention is enabled * server: logs: do not truncate log values * server: tests - passkey - first good working value of nga * server: tests: fix server timeout * server: tests: fix passkey, add doc, fix regex content matching, fix timeout * server: tests: fix regex content matching * server: tests: schedule slow tests on master * server: metrics: fix when no prompt processed * server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1 * server: tests: increase timeout for completion * server: tests: keep only the PHI-2 test * server: tests: passkey add a negative test
147 lines
3.6 KiB
Gherkin
147 lines
3.6 KiB
Gherkin
@llama.cpp
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@parallel
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Feature: Parallel
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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 42 as server seed
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And 512 as batch size
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And 64 KV cache size
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And 2 slots
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And embeddings extraction
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And continuous batching
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Then the server is starting
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Then the server is healthy
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Scenario Outline: Multi users completion
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Given a prompt:
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"""
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Write a very long story about AI.
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"""
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And a prompt:
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"""
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Write another very long music lyrics.
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"""
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And <n_predict> max tokens to predict
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Given concurrent completion requests
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Then the server is busy
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Then the server is idle
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And all slots are idle
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Then all prompts are predicted with <n_predict> tokens
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Examples:
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| n_predict |
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| 128 |
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Scenario Outline: Multi users OAI completions compatibility
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Given a system prompt You are a writer.
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And a model tinyllama-2
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Given a prompt:
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"""
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Write a very long book.
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"""
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And a prompt:
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"""
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Write another a poem.
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"""
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And <n_predict> max tokens to predict
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And streaming is <streaming>
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Given concurrent OAI completions requests
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Then the server is busy
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Then the server is idle
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Then all prompts are predicted with <n_predict> tokens
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Examples:
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| streaming | n_predict |
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| disabled | 128 |
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| enabled | 64 |
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Scenario Outline: Multi users OAI completions compatibility no v1
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Given a system prompt You are a writer.
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And a model tinyllama-2
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Given a prompt:
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"""
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Write a very long book.
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"""
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And a prompt:
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"""
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Write another a poem.
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"""
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And <n_predict> max tokens to predict
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And streaming is <streaming>
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Given concurrent OAI completions requests no v1
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Then the server is busy
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Then the server is idle
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Then all prompts are predicted with <n_predict> tokens
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Examples:
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| streaming | n_predict |
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| disabled | 128 |
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| enabled | 64 |
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Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969
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Given a prompt:
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"""
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Write a very long story about AI.
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"""
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And a prompt:
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"""
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Write another very long music lyrics.
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"""
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And a prompt:
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"""
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Write a very long poem.
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"""
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And a prompt:
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"""
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Write a very long joke.
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"""
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And 128 max tokens to predict
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Given concurrent completion requests
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Then the server is busy
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Then the server is idle
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Then all prompts are predicted
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Scenario: Multi users embeddings
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Given a prompt:
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"""
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Write a very long story about AI.
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"""
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And a prompt:
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"""
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Write another very long music lyrics.
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"""
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And a prompt:
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"""
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Write a very long poem.
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"""
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And a prompt:
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"""
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Write a very long joke.
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"""
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Given concurrent embedding requests
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Then the server is busy
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Then the server is idle
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Then all embeddings are generated
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Scenario: Multi users OAI compatibility embeddings
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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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And a prompt:
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"""
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What is the biggest US city ?
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"""
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And a prompt:
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"""
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What is the capital of Bulgaria ?
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"""
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And a model tinyllama-2
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Given concurrent OAI embedding requests
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Then the server is busy
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Then the server is idle
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Then all embeddings are generated
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