llama.cpp/examples/batched

llama.cpp/example/batched

The example demonstrates batched generation from a given prompt

./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4 --kv-unified

...

main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113

 Hello my name is

main: generating 4 sequences ...

main: stream 0 finished
main: stream 1 finished
main: stream 2 finished
main: stream 3 finished

sequence 0:

Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b

sequence 1:

Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between

sequence 2:

Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am

sequence 3:

Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and

main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s

llama_print_timings:        load time =   587.00 ms
llama_print_timings:      sample time =     2.56 ms /   112 runs   (    0.02 ms per token, 43664.72 tokens per second)
llama_print_timings: prompt eval time =  4089.11 ms /   118 tokens (   34.65 ms per token,    28.86 tokens per second)
llama_print_timings:        eval time =     0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
llama_print_timings:       total time =  4156.04 ms

Using backend samplers

It is possible to run this example using backend samplers so that sampling is performed on a backend device, like a GPU.

./llama-batched \
    -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf -p "Hello my name is" \
    -np 4 \
    -kvu \
    --backend_sampling \
    --samplers 'top_k;temperature' \
    --top-k 80

The samplers specified with --samplers must be supported by the backend and this is why we are explicitly specifying only top_k and temperature here as at the time of writing these are supported.

The --verbose flag can be added to see more detailed output and also show that the backend samplers are being used.

With --backend_sampling enabled, the sampler chain is automatically analyzed to determine which samplers can run on the backend. The system finds the longest contiguous chain of backend-supported samplers from the start of the sampler sequence. For example:

• If the chain is top-k -> temperature -> top-p, and both top-k and temperature are backend-supported but top-p is not, then top-k and temperature will run on the backend, while top-p and subsequent samplers run on the CPU.
• If all configured samplers are supported, the final distribution sampling will also happen on the backend, transferring only the sampled token IDs back to the host.
• If the sampler chain starts with an unsupported sampler (e.g., penalties), all sampling runs on the CPU.

Note: The default sampler chain includes penalties as the first sampler, which is not backend-supported yet. To use backend sampling, you must explicitly configure a sampler chain that starts with backend-supported samplers using --samplers like shown above.