mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
f30ea47a87
* llama : add pipeline parallelism support for batch processing with multiple CUDA GPUs ggml-ci * server : add -ub, --ubatch-size parameter * fix server embedding test * llama : fix Mamba inference for pipeline parallelism Tested to work correctly with both `main` and `parallel` examples. * llama : limit max batch size to n_batch * add LLAMA_SCHED_MAX_COPIES to configure the number of input copies for pipeline parallelism default increase to 4 (from 2) changing this value may improve performance for some systems, but increases memory usage * fix hip build * fix sycl build (disable cpy_tensor_async) * fix hip build * llama : limit n_batch and n_ubatch to n_ctx during context creation * llama : fix norm backend * batched-bench : sync after decode * swiftui : sync after decode * ggml : allow ggml_get_rows to use multiple threads if they are available * check n_ubatch >= n_tokens with non-casual attention * llama : do not limit n_batch to n_ctx with non-casual attn * server : construct batch with size of llama_n_batch * ggml_backend_cpu_graph_compute : fix return value when alloc fails * llama : better n_batch and n_ubatch comment * fix merge * small fix * reduce default n_batch to 2048 --------- Co-authored-by: Francis Couture-Harpin <git@compilade.net> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
96 lines
2.4 KiB
Gherkin
96 lines
2.4 KiB
Gherkin
@llama.cpp
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@embeddings
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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 bert-bge-small/ggml-model-f16.gguf from HF repo ggml-org/models
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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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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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Then the server is healthy
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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 bert-bge-small
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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 bert-bge-small
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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: 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 bert-bge-small
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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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Scenario: All embeddings should be the same
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Given 10 fixed prompts
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And a model bert-bge-small
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Given concurrent OAI embedding requests
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Then all embeddings are the same
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