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llama : fix pooling assertion crash in chunked GDN detection path (#20468)
* llama : fix pooling assertion crash in chunked GDN detection path

The chunked fused Gated Delta Net detection in sched_reserve() calls
graph_reserve(16*n_seqs, n_seqs, n_outputs, ...) where n_outputs = n_seqs.
This creates a dimension mismatch in build_pooling() for embedding models
with mean/rank pooling: build_inp_mean() creates a tensor with shape
[n_tokens=16*n_seqs, ...] while t_embd is reduced to [n_outputs=n_seqs, ...]
via out_ids, causing ggml_mul_mat to assert on ggml_can_mul_mat(a, b).

Fix: pass n_tokens as n_outputs in the chunked GDN graph reservation,
matching the pattern used by the pp/tg worst-case reservations.

Regression introduced by #20340 (d28961d).
Same class of bug as #12517, fixed by #12545.

* server : add mean pooling tests to embedding test suite

Add test_embedding_pooling_mean and test_embedding_pooling_mean_multiple
to cover the --pooling mean codepath, which was previously untested.

These tests would have caught the regression introduced by #20340 where
build_pooling() crashes with a ggml_mul_mat assertion due to mismatched
dimensions in the chunked GDN detection path.

---------

Co-authored-by: Domenico Crupi <domenico@zerovolt.it>
2026-03-13 20:53:42 +02:00
..

Server tests

Python based server tests scenario using pytest.

Tests target GitHub workflows job runners with 4 vCPU.

Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in n_predict, kv_size.

Install dependencies

pip install -r requirements.txt

Run tests

  1. Build the server
cd ../../..
cmake -B build
cmake --build build --target llama-server
  1. Start the test: ./tests.sh

It's possible to override some scenario steps values with environment variables:

variable description
PORT context.server_port to set the listening port of the server during scenario, default: 8080
LLAMA_SERVER_BIN_PATH to change the server binary path, default: ../../../build/bin/llama-server
DEBUG to enable steps and server verbose mode --verbose
N_GPU_LAYERS number of model layers to offload to VRAM -ngl --n-gpu-layers
LLAMA_CACHE by default server tests re-download models to the tmp subfolder. Set this to your cache (e.g. $HOME/Library/Caches/llama.cpp on Mac or $HOME/.cache/llama.cpp on Unix) to avoid this

To run slow tests (will download many models, make sure to set LLAMA_CACHE if needed):

SLOW_TESTS=1 ./tests.sh

To run with stdout/stderr display in real time (verbose output, but useful for debugging):

DEBUG=1 ./tests.sh -s -v -x

To run all the tests in a file:

./tests.sh unit/test_chat_completion.py -v -x

To run a single test:

./tests.sh unit/test_chat_completion.py::test_invalid_chat_completion_req

Hint: You can compile and run test in single command, useful for local development:

cmake --build build -j --target llama-server && ./tools/server/tests/tests.sh

To see all available arguments, please refer to pytest documentation

Debugging external llama-server

It can sometimes be useful to run the server in a debugger when invesigating test failures. To do this, the environment variable DEBUG_EXTERNAL=1 can be set which will cause the test to skip starting a llama-server itself. Instead, the server can be started in a debugger.

Example using gdb:

$ gdb --args ../../../build/bin/llama-server \
    --host 127.0.0.1 --port 8080 \
    --temp 0.8 --seed 42 \
    --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf \
    --batch-size 32 --no-slots --alias tinyllama-2 --ctx-size 512 \
    --parallel 2 --n-predict 64

And a break point can be set in before running:

(gdb) br server.cpp:4604
(gdb) r
main: server is listening on http://127.0.0.1:8080 - starting the main loop
srv  update_slots: all slots are idle

And then the test in question can be run in another terminal:

(venv) $ env DEBUG_EXTERNAL=1 ./tests.sh unit/test_chat_completion.py -v -x

And this should trigger the breakpoint and allow inspection of the server state in the debugger terminal.