A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 15, 18:30 UTC
[V1] TPU - Fix CI/CD runner (#14974)
Alexander Matveev committed
Mar 17, 2025 at 17:07 UTC
18551e820c10f2e834050b94dc41b384232b10e2
4 files changed
+69
-65
.buildkite/run-tpu-test.sh
deleted
-25
@@ -1,25 +0,0 @@
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-#!/bin/bash
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-
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-set -e
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-
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-# Build the docker image.
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-docker build -f Dockerfile.tpu -t vllm-tpu .
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-
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-# Set up cleanup.
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-remove_docker_container() { docker rm -f tpu-test || true; }
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-trap remove_docker_container EXIT
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-# Remove the container that might not be cleaned up in the previous run.
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-remove_docker_container
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-
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-# For HF_TOKEN.
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-source /etc/environment
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-# Run a simple end-to-end example.
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-docker run --privileged --net host --shm-size=16G -it \
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- -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
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- vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
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- && python3 -m pip install pytest \
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- && python3 -m pip install lm_eval[api]==0.4.4 \
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- && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
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- && python3 /workspace/vllm/tests/tpu/test_compilation.py \
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- && python3 /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
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- && python3 /workspace/vllm/examples/offline_inference/tpu.py"
.buildkite/run-tpu-v1-test.sh
+16
-7
@@ -15,13 +15,22 @@ remove_docker_container
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source /etc/environment
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# Run a simple end-to-end example.
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docker run --privileged --net host --shm-size=16G -it \
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- -e "HF_TOKEN=$HF_TOKEN" -e "VLLM_USE_V1=1" --name tpu-test \
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+ -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
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vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
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&& python3 -m pip install pytest \
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&& python3 -m pip install lm_eval[api]==0.4.4 \
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- && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
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- && pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
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- && pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
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- && python3 /workspace/vllm/tests/tpu/test_compilation.py \
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- && python3 /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
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- && python3 /workspace/vllm/examples/offline_inference/tpu.py"
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+ && echo TEST_1 \
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+ && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
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+ && echo TEST_2 \
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+ && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
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+ && echo TEST_3 \
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+ && VLLM_USE_V1=1 pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
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+ && echo TEST_4 \
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+ && VLLM_USE_V1=1 python3 /workspace/vllm/examples/offline_inference/tpu.py" \
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+ && echo TEST_5 \
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+ && VLLM_USE_V1=1 python3 /workspace/vllm/tests/tpu/test_compilation.py \
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+
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+
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+# TODO: Fix these tests
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+# && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
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+
tests/tpu/test_compilation.py
+39
-24
@@ -34,7 +34,9 @@ with depyf.prepare_debug(temp_dir):
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# disable custom dispatcher, let Dynamo takes over
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# all the control
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- llm = LLM(model="google/gemma-2b",
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+ llm = LLM(model="Qwen/Qwen2.5-1.5B-Instruct",
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+ max_model_len=512,
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+ max_num_seqs=64,
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enforce_eager=True,
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compilation_config={"level": CompilationLevel.DYNAMO_AS_IS})
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outputs = llm.generate(prompts, sampling_params)
@@ -44,38 +46,51 @@ with depyf.prepare_debug(temp_dir):
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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assert generated_text.startswith(answer)
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-compiled_code = sorted(
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+compiled_codes = sorted(
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glob.glob(os.path.join(temp_dir, "__transformed_code*.py")))
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-# we should only trigger Dynamo compilation three times:
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-# one for the profiling phase without kv cache
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-# one for the prefill phase with symbolic shapes
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-# one for the decode phase with symbolic shapes
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+for i, compiled_code in enumerate(compiled_codes):
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+ print("{} file: {}".format(i + 1, compiled_code))
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+
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+# We should only trigger Dynamo compilation 4 times:
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+# 1. forward pass (symbolic)
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+# 2. compute_logits (symbolic)
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+# 3. forward pass (shape 16)
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+# 4. forward pass (shape 32)
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# and later calls should not trigger Dynamo compilation again.
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-# NOTE: it might still trigger XLA compilation.
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+# NOTE: It might still trigger XLA compilation.
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+
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+# Check we have 4 compiled codes
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+assert len(compiled_codes) == 4
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-# check we have three compiled code
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-# this is the assumption when we use the custom dispatcher
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-assert len(compiled_code) == 3
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+kv_cache_prefix = "kv_cache"
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+attn_prefix = "ragged_paged_attention"
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-# check all the compilations are as expected
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-compiled_fn = sorted(
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+# Check all the compilations are as expected
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+compiled_fns = sorted(
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glob.glob(os.path.join(temp_dir, "__compiled_fn*Captured*.py")))
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-# the first compilation is the profiling phase,
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-# it should not have any kv cache
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-with open(compiled_fn[0]) as f:
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+for i, compiled_fn in enumerate(compiled_fns):
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+ print("{} file: {}".format(i + 1, compiled_fn))
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+
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+# The first compilation is symbolic, so it should not have any kv_caches
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+with open(compiled_fns[0]) as f:
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+ content = f.read()
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+ assert kv_cache_prefix not in content
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+
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+# The second compilation is symbolic, so it should not have any kv_caches
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+with open(compiled_fns[1]) as f:
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content = f.read()
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- assert "kv_caches" not in content
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+ assert kv_cache_prefix not in content
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-# the second compilation is the prefill phase,
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-# it should have kv cache and the flash_attention op
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-with open(compiled_fn[1]) as f:
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+# The third compilation is shape 16, so it should have kv_caches and the
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+# ragged_paged_attention
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+with open(compiled_fns[2]) as f:
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content = f.read()
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- assert "kv_caches" in content and "torch.ops.xla.flash_attention" in content
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+ assert (kv_cache_prefix in content and attn_prefix in content)
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-# the third compilation is the decode phase,
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-# it should have kv cache and the paged_attention op
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-with open(compiled_fn[2]) as f:
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+# The forth compilation is shape 32, so it should have kv_caches and the
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+# ragged_paged_attention
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+with open(compiled_fns[3]) as f:
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content = f.read()
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- assert "kv_caches" in content and "torch.ops.xla.paged_attention" in content
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+ assert (kv_cache_prefix in content and attn_prefix in content)
tests/tpu/test_custom_dispatcher.py
+14
-9
@@ -14,12 +14,17 @@ from ..utils import compare_two_settings
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def test_custom_dispatcher(monkeypatch: pytest.MonkeyPatch):
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with monkeypatch.context() as m:
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m.setenv("VLLM_RPC_TIMEOUT", "30000")
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- compare_two_settings(
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- "google/gemma-2b",
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- arg1=[
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- "--enforce-eager",
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- f"-O{CompilationLevel.DYNAMO_ONCE}",
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- ],
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- arg2=["--enforce-eager", f"-O{CompilationLevel.DYNAMO_AS_IS}"],
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- env1={},
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- env2={})
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+ compare_two_settings("Qwen/Qwen2.5-1.5B-Instruct",
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+ arg1=[
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+ "--max-model-len=256",
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+ "--max-num-seqs=32",
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+ "--enforce-eager",
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+ f"-O{CompilationLevel.DYNAMO_ONCE}",
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+ ],
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+ arg2=[
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+ "--max-model-len=256", "--max-num-seqs=32",
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+ "--enforce-eager",
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+ f"-O{CompilationLevel.DYNAMO_AS_IS}"
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+ ],
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+ env1={},
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+ env2={})