[WIP] Test on oneAPI 2024.1.1

.buildkite/check-wheel-size.py

@@ -1,7 +1,7 @@
 import os
 import zipfile
 
-MAX_SIZE_MB = 200
+MAX_SIZE_MB = 250
 
 
 def print_top_10_largest_files(zip_file):

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
+model_name: "HandH1998/QQQ-Llama-3-8b-g128"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.409
+  - name: "exact_match,flexible-extract"
+    value: 0.406
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Minitron-4B-Base.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nvidia/Minitron-4B-Base -b auto -l 1000 -f 5 -t 1
+model_name: "nvidia/Minitron-4B-Base"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.252
+  - name: "exact_match,flexible-extract"
+    value: 0.252
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2-1.5B-Instruct-FP8W8.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-FP8W8 -b auto -l 1000 -f 5 -t 1
+model_name: "nm-testing/Qwen2-1.5B-Instruct-FP8W8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.578
+  - name: "exact_match,flexible-extract"
+    value: 0.585
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/models-small.txt

@@ -4,4 +4,7 @@ Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
+Minitron-4B-Base.yaml
 Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml
+Qwen2-1.5B-Instruct-FP8W8.yaml
+Meta-Llama-3-8B-QQQ.yaml

.buildkite/nightly-benchmarks/README.md

@@ -3,30 +3,51 @@
 
 ## Introduction
 
-This directory contains the performance benchmarking CI for vllm.
-The goal is to help developers know the impact of their PRs on the performance of vllm.
+This directory contains two sets of benchmark for vllm.
+- Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
+- Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.
 
-This benchmark will be *triggered* upon:
+
+See  [vLLM performance dashboard](https://perf.vllm.ai) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
+
+
+## Performance benchmark quick overview
+
+**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!), with different models.
+
+**Benchmarking Duration**: about 1hr.
+
+**For benchmarking developers**: please try your best to constraint the duration of benchmarking to about 1 hr so that it won't take forever to run.
+
+
+## Nightly benchmark quick overview
+
+**Benchmarking Coverage**: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B. 
+
+**Benchmarking engines**: vllm, TGI, trt-llm and lmdeploy.
+
+**Benchmarking Duration**: about 3.5hrs.
+
+
+
+## Trigger the benchmark
+
+Performance benchmark will be triggered when:
 - A PR being merged into vllm.
 - Every commit for those PRs with `perf-benchmarks` label.
 
-**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for more GPUs is comming later), with different models.
+Nightly benchmark will be triggered when:
+- Every commit for those PRs with `nightly-benchmarks` label.
 
-**Benchmarking Duration**: about 1hr.
 
-**For benchmarking developers**: please try your best to constraint the duration of benchmarking to less than 1.5 hr so that it won't take forever to run.
 
 
-## Configuring the workload
+## Performance benchmark details
 
-The benchmarking workload contains three parts:
-- Latency tests in `latency-tests.json`.
-- Throughput tests in `throughput-tests.json`.
-- Serving tests in `serving-tests.json`.
+See [descriptions.md](tests/descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
 
-See [descriptions.md](tests/descriptions.md) for detailed descriptions. 
 
-### Latency test
+#### Latency test
 
 Here is an example of one test inside `latency-tests.json`:
 
@@ -54,12 +75,12 @@ Note that the performance numbers are highly sensitive to the value of the param
 WARNING: The benchmarking script will save json results by itself, so please do not configure `--output-json` parameter in the json file.
 
 
-### Throughput test
+#### Throughput test
 The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `benchmark_throughput.py`.
 
 The number of this test is also stable -- a slight change on the value of this number might vary the performance numbers by a lot.
 
-### Serving test
+#### Serving test
 We test the throughput by using `benchmark_serving.py` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
 
 ```
@@ -96,9 +117,36 @@ The number of this test is less stable compared to the delay and latency benchma
 
 WARNING: The benchmarking script will save json results by itself, so please do not configure `--save-results` or other results-saving-related parameters in `serving-tests.json`.
 
-## Visualizing the results
+#### Visualizing the results
 The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](tests/descriptions.md) with real benchmarking results.
 You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
 If you do not see the table, please wait till the benchmark finish running.
 The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
 The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
+
+
+
+## Nightly test details
+
+See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
+
+
+#### Workflow
+
+- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines. 
+- Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.sh), which will probe the serving engine of the current container.
+- The `run-nightly-suite.sh` will redirect the request to `tests/run-[llm serving engine name]-nightly.sh`, which parses the workload described in [nightly-tests.json](tests/nightly-tests.json) and performs the benchmark.
+- At last, we run [scripts/plot-nightly-results.py](scripts/plot-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
+
+#### Nightly tests
+
+In [nightly-tests.json](tests/nightly-tests.json), we include the command line arguments for benchmarking commands, together with the benchmarking test cases. The format is highly similar to performance benchmark.
+
+#### Docker containers
+
+The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
+
+WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `tests/run-[llm serving engine name]-nightly.sh`.
+
+WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).
+

.buildkite/nightly-benchmarks/benchmark-pipeline.yaml

@@ -42,20 +42,20 @@ steps:
           - name: devshm
             emptyDir:
               medium: Memory
-  - label: "H100"
-    agents:
-      queue: H100
-    plugins:
-    - docker#v5.11.0:
-        image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-        command:
-        - bash
-        - .buildkite/nightly-benchmarks/run-benchmarks-suite.sh
-        mount-buildkite-agent: true
-        propagate-environment: true
-        ipc: host
-        gpus: all
-        environment:
-        - VLLM_USAGE_SOURCE
-        - HF_TOKEN
+  # - label: "H100"
+  #   agents:
+  #     queue: H100
+  #   plugins:
+  #   - docker#v5.11.0:
+  #       image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+  #       command:
+  #       - bash
+  #       - .buildkite/nightly-benchmarks/run-benchmarks-suite.sh
+  #       mount-buildkite-agent: true
+  #       propagate-environment: true
+  #       ipc: host
+  #       gpus: all
+  #       environment:
+  #       - VLLM_USAGE_SOURCE
+  #       - HF_TOKEN
 

.buildkite/nightly-benchmarks/run-benchmarks-suite.sh

@@ -34,6 +34,15 @@ check_hf_token() {
   fi
 }
 
+ensure_sharegpt_downloaded() {
+  local FILE=ShareGPT_V3_unfiltered_cleaned_split.json
+  if [ ! -f "$FILE" ]; then
+      wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/$FILE
+  else
+      echo "$FILE already exists."
+  fi
+}
+
 json2args() {
   # transforms the JSON string to command line args, and '_' is replaced to '-'
   # example:
@@ -73,11 +82,6 @@ kill_gpu_processes() {
       echo "All GPU processes have been killed."
   fi
 
-  # Sometimes kill with pid doesn't work properly, we can also kill all process running python or python3
-  # since we are in container anyway
-  pkill -9 -f python
-  pkill -9 -f python3
-
   # waiting for GPU processes to be fully killed
   # loop while nvidia-smi returns any processes
   while [ -n "$(nvidia-smi --query-compute-apps=pid --format=csv,noheader)" ]; do
@@ -355,7 +359,7 @@ main() {
 
   # prepare for benchmarking
   cd benchmarks || exit 1
-  wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+  ensure_sharegpt_downloaded
   declare -g RESULTS_FOLDER=results/
   mkdir -p $RESULTS_FOLDER
   QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/

.buildkite/nightly-benchmarks/tests/serving-tests.json

@@ -55,5 +55,26 @@
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
             "num_prompts": 200
         }
+    },
+    {
+        "test_name": "serving_llama70B_tp4_sharegpt_specdecode",
+        "qps_list": [2],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3-70B-Instruct",
+            "disable_log_requests": "", 
+            "tensor_parallel_size": 4,
+            "swap_space": 16, 
+            "speculative_model": "turboderp/Qwama-0.5B-Instruct",
+            "num_speculative_tokens": 4,
+            "speculative_draft_tensor_parallel_size": 1,
+            "use_v2_block_manager": ""
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3-70B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200 
+        }
     }
-]
+]

.buildkite/release-pipeline.yaml

@@ -3,13 +3,15 @@ steps:
     agents:
       queue: cpu_queue
     commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg CUDA_VERSION={{matrix.cuda_version}} --tag vllm-ci:build-image --target build --progress plain ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg buildkite_commit=$BUILDKITE_COMMIT --build-arg USE_SCCACHE=1 --build-arg CUDA_VERSION={{matrix.cuda_version}} --tag vllm-ci:build-image --target build --progress plain ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
       # rename the files to change linux -> manylinux1
       - "for f in artifacts/dist/*.whl; do mv -- \"$$f\" \"$${f/linux/manylinux1}\"; done"
       - "aws s3 cp --recursive artifacts/dist s3://vllm-wheels/$BUILDKITE_COMMIT/"
       - "aws s3 cp --recursive artifacts/dist s3://vllm-wheels/nightly/"
+    env:
+      DOCKER_BUILDKIT: "1"
     matrix:
       setup:
         cuda_version:

.buildkite/run-amd-test.sh

@@ -55,7 +55,7 @@ while true; do
 done
 
 echo "--- Pulling container" 
-image_name="rocmshared/vllm-ci:${BUILDKITE_COMMIT}"
+image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 docker pull ${image_name}
 

.buildkite/run-cpu-test.sh

@@ -3,26 +3,38 @@
 set -ex
 
 # Try building the docker image
-docker build -t cpu-test -f Dockerfile.cpu .
-docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" -t cpu-test-avx2 -f Dockerfile.cpu .
+numactl -C 48-95 -N 1 docker build -t cpu-test -f Dockerfile.cpu .
+numactl -C 48-95 -N 1 docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" -t cpu-test-avx2 -f Dockerfile.cpu .
 
 # Setup cleanup
 remove_docker_container() { docker rm -f cpu-test cpu-test-avx2 || true; }
 trap remove_docker_container EXIT
 remove_docker_container
 
-# Run the image
+# Run the image, setting --shm-size=4g for tensor parallel.
 docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus=48-95 \
-  --cpuset-mems=1 --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --name cpu-test cpu-test
+ --cpuset-mems=1 --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test cpu-test
 docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus=48-95 \
-  --cpuset-mems=1 --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --name cpu-test-avx2 cpu-test-avx2
+ --cpuset-mems=1 --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-avx2 cpu-test-avx2
 
 # offline inference
-docker exec cpu-test bash -c "python3 examples/offline_inference.py"
 docker exec cpu-test-avx2 bash -c "python3 examples/offline_inference.py"
 
 # Run basic model test
-docker exec cpu-test bash -c "cd tests;
+docker exec cpu-test bash -c "
   pip install pytest Pillow protobuf
-  cd ../
-  pytest -v -s tests/models -m \"not vlm\" --ignore=tests/models/test_embedding.py --ignore=tests/models/test_registry.py --ignore=tests/models/test_jamba.py" # Mamba on CPU is not supported
+  pytest -v -s tests/models -m \"not vlm\" --ignore=tests/models/test_embedding.py --ignore=tests/models/test_registry.py --ignore=tests/models/test_jamba.py --ignore=tests/models/test_danube3_4b.py" # Mamba and Danube3-4B on CPU is not supported
+
+# online inference
+docker exec cpu-test bash -c "
+  export VLLM_CPU_KVCACHE_SPACE=10 
+  export VLLM_CPU_OMP_THREADS_BIND=48-92 
+  python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m & 
+  timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
+  python3 benchmarks/benchmark_serving.py \
+    --backend vllm \
+    --dataset-name random \
+    --model facebook/opt-125m \
+    --num-prompts 20 \
+    --endpoint /v1/completions \
+    --tokenizer facebook/opt-125m"