[CI/Build][TPU] Add TPU CI test (vllm-project#6277)

Co-authored-by: kevin <[email protected]>

.buildkite/run-tpu-test.sh

@@ -0,0 +1,16 @@
+set -e
+
+# Build the docker image.
+docker build -f Dockerfile.tpu -t vllm-tpu .
+
+# Set up cleanup.
+remove_docker_container() { docker rm -f tpu-test || true; }
+trap remove_docker_container EXIT
+# Remove the container that might not be cleaned up in the previous run.
+remove_docker_container
+
+# For HF_TOKEN.
+source /etc/environment
+# Run a simple end-to-end example.
+docker run --privileged --net host --shm-size=16G -it -e HF_TOKEN=$HF_TOKEN --name tpu-test vllm-tpu \
+    python3 /workspace/vllm/examples/offline_inference_tpu.py

Dockerfile.tpu

@@ -15,9 +15,4 @@ COPY . /workspace/vllm
 ENV VLLM_TARGET_DEVICE="tpu"
 RUN cd /workspace/vllm && python setup.py develop
 
-# Re-install outlines to avoid dependency errors.
-# The outlines version must follow requirements-common.txt.
-RUN pip uninstall outlines -y
-RUN pip install "outlines>=0.0.43"
-
 CMD ["/bin/bash"]

examples/offline_inference_tpu.py

@@ -0,0 +1,28 @@
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "A robot may not injure a human being",
+    "It is only with the heart that one can see rightly;",
+    "The greatest glory in living lies not in never falling,",
+]
+answers = [
+    " or, through inaction, allow a human being to come to harm.",
+    " what is essential is invisible to the eye.",
+    " but in rising every time we fall.",
+]
+N = 1
+# Currently, top-p sampling is disabled. `top_p` should be 1.0.
+sampling_params = SamplingParams(temperature=0.7,
+                                 top_p=1.0,
+                                 n=N,
+                                 max_tokens=16)
+
+# Set `enforce_eager=True` to avoid ahead-of-time compilation.
+# In real workloads, `enforace_eager` should be `False`.
+llm = LLM(model="google/gemma-2b", enforce_eager=True)
+outputs = llm.generate(prompts, sampling_params)
+for output, answer in zip(outputs, answers):
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    assert generated_text.startswith(answer)