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e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
325 lines
11 KiB
Python
325 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Compare the outputs of HF and vLLM when using greedy sampling.
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It tests chunked prefill. Chunked prefill can be enabled by
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enable_chunked_prefill=True. If prefill size exceeds max_num_batched_tokens,
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prefill requests are chunked.
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Run `pytest tests/models/test_chunked_prefill.py`.
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"""
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import os
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from contextlib import nullcontext
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import pytest
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from tests.kernels.utils import override_backend_env_variable
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from vllm.platforms import current_platform
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from ..models.utils import check_logprobs_close, check_outputs_equal
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from ..utils import multi_gpu_test
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MODELS = [
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"facebook/opt-125m",
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"meta-llama/Llama-3.2-1B",
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]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16])
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@pytest.mark.parametrize("enforce_eager", [False, True])
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# NOTE: Increasing this in this suite will fail CI because we currently cannot
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# reset distributed env properly. Use a value > 1 just when you test.
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@pytest.mark.parametrize("tensor_parallel_size", [1])
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@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"])
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def test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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chunked_prefill_token_size: int,
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enforce_eager: bool,
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tensor_parallel_size: int,
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attention_backend: str,
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monkeypatch,
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) -> None:
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"""
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Checks exact match decode between huggingface model and vllm runner with
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chunked prefill.
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"""
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override_backend_env_variable(monkeypatch, attention_backend)
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max_num_seqs = chunked_prefill_token_size
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max_num_batched_tokens = chunked_prefill_token_size
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with hf_runner(model, dtype=dtype) as hf_model:
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hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
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with vllm_runner(
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model,
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dtype=dtype,
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max_num_batched_tokens=max_num_batched_tokens,
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enable_chunked_prefill=True,
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tensor_parallel_size=tensor_parallel_size,
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enforce_eager=enforce_eager,
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max_num_seqs=max_num_seqs,
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) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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)
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@multi_gpu_test(num_gpus=2)
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@pytest.mark.parametrize("distributed_executor_backend", ["ray", "mp"])
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"])
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def test_models_distributed(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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distributed_executor_backend: str,
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attention_backend: str,
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monkeypatch,
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) -> None:
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override_backend_env_variable(monkeypatch, attention_backend)
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if (model == "meta-llama/Llama-2-7b-hf"
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and distributed_executor_backend == "ray"):
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# test ray adag
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os.environ['VLLM_USE_RAY_SPMD_WORKER'] = "1"
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os.environ['VLLM_USE_RAY_COMPILED_DAG'] = "1"
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dtype = "half"
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max_tokens = 5
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chunked_prefill_token_size = 16
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# Add a chunked prefill config.
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max_num_seqs = min(chunked_prefill_token_size, 256)
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assert chunked_prefill_token_size != -1
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enable_chunked_prefill = True
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max_num_batched_tokens = chunked_prefill_token_size
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# NOTE: take care of the order. run vLLM first, and then run HF.
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# vLLM needs a fresh new process without cuda initialization.
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# if we run HF first, the cuda initialization will be done and it
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# will hurt multiprocessing backend with fork method (the default method).
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with vllm_runner(
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model,
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dtype=dtype,
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tensor_parallel_size=2,
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max_num_seqs=max_num_seqs,
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enable_chunked_prefill=enable_chunked_prefill,
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max_num_batched_tokens=max_num_batched_tokens,
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distributed_executor_backend=distributed_executor_backend,
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) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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with hf_runner(model, dtype=dtype) as hf_model:
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hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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)
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@pytest.mark.parametrize(
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"kv_cache_dtype,model",
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[("fp8_e4m3",
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"nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme")])
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# Due to low-precision numerical divergence, we only test logprob of 4 tokens
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@pytest.mark.parametrize("max_tokens", [4])
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@pytest.mark.parametrize("chunked_prefill_token_size", [4, 16])
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@pytest.mark.parametrize("enforce_eager", [False, True])
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# NOTE: Increasing this in this suite will fail CI because we currently cannot
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# reset distributed env properly. Use a value > 1 just when you test.
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@pytest.mark.parametrize("tensor_parallel_size", [1])
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# Due to low-precision numerical divergence, this test is too sensitive to
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# the async postprocessor
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@pytest.mark.parametrize("disable_async_output_proc", [True])
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def test_models_with_fp8_kv_cache(
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vllm_runner,
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example_prompts,
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kv_cache_dtype: str,
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model: str,
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max_tokens: int,
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chunked_prefill_token_size: int,
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enforce_eager: bool,
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tensor_parallel_size: int,
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disable_async_output_proc: bool,
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) -> None:
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"""
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Check output logprobs match between no_chunked_prefill and chunked_prefill
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with fp8 kv cache. General fp8 kv-cache tests are covered in test_fp8.py,
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so here we only check chunked prefill.
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"""
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NUM_LOG_PROBS = 8
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max_num_seqs = chunked_prefill_token_size
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max_num_batched_tokens = chunked_prefill_token_size
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with vllm_runner(
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model,
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tensor_parallel_size=tensor_parallel_size,
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enforce_eager=enforce_eager,
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max_num_seqs=max_num_seqs,
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kv_cache_dtype=kv_cache_dtype,
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disable_async_output_proc=disable_async_output_proc,
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) as vllm_model:
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no_chunked_prefill_outputs = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, NUM_LOG_PROBS)
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with vllm_runner(
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model,
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max_num_batched_tokens=max_num_batched_tokens,
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enable_chunked_prefill=True,
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tensor_parallel_size=tensor_parallel_size,
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enforce_eager=enforce_eager,
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max_num_seqs=max_num_seqs,
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kv_cache_dtype=kv_cache_dtype,
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disable_async_output_proc=disable_async_output_proc,
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) as vllm_model:
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chunked_prefill_outputs = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, NUM_LOG_PROBS)
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check_logprobs_close(
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outputs_0_lst=no_chunked_prefill_outputs,
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outputs_1_lst=chunked_prefill_outputs,
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name_0="no_chunked_prefill",
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name_1="chunked_prefill",
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)
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@pytest.mark.parametrize("max_tokens", [16])
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@pytest.mark.parametrize("enforce_eager", [False])
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@pytest.mark.parametrize("chunk_size", [30, 32])
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# NOTE: Increasing this in this suite will fail CI because we currently cannot
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# reset distributed env properly. Use a value > 1 just when you test.
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@pytest.mark.parametrize("tensor_parallel_size", [1])
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@pytest.mark.parametrize("dtype", ["half"])
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def test_with_prefix_caching(
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vllm_runner,
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max_tokens: int,
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enforce_eager: bool,
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chunk_size: int,
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tensor_parallel_size: int,
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dtype: str,
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) -> None:
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"""
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Checks exact match decode with and without prefix caching
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with chunked prefill enabled.
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"""
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model = "meta-llama/Llama-2-7b-chat-hf"
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# The common prompt has 142 tokens with Llama-2 tokenizer.
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common_prompt = "You are a helpful AI assistant " * 20
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unique_prompts = [
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"Question", # Warmup
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"Question", # Fully cached
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"Another question", # Partial cached
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]
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full_prompts = [f"{common_prompt}\n{p}" for p in unique_prompts]
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max_num_batched_tokens = max_num_seqs = chunk_size
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outputs = {} # type: ignore
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check_result = True
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for enable in (True, False):
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with vllm_runner(
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model,
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dtype=dtype,
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max_num_batched_tokens=max_num_batched_tokens,
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enable_chunked_prefill=True,
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enable_prefix_caching=enable,
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tensor_parallel_size=tensor_parallel_size,
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enforce_eager=enforce_eager,
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max_num_seqs=max_num_seqs,
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) as vllm_model:
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# It should fail when prefix caching is enable and chunk
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# size is not a multiple of block size (16).
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should_fail = chunk_size % 16 != 0 and enable
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check_result &= not should_fail
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outputs[enable] = []
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# Send the request one-by-one to ensure the cache is populated.
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with pytest.raises(ValueError) if should_fail else nullcontext():
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for prompt in full_prompts:
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outputs[enable] += vllm_model.generate_greedy([prompt],
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max_tokens)
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# Check results only if we did not expect a failure.
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if check_result:
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check_outputs_equal(
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outputs_0_lst=outputs[False],
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outputs_1_lst=outputs[True],
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name_0="w/o prefix caching",
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name_1="with prefix caching",
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)
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@pytest.mark.parametrize("model", ["facebook/opt-125m"])
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16])
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@pytest.mark.parametrize("enforce_eager", [False])
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@pytest.mark.parametrize("attention_backend", ["TORCH_SDPA"])
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@pytest.mark.cpu_model
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@pytest.mark.skipif(not current_platform.is_cpu(), reason="CPU only")
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def test_models_cpu(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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chunked_prefill_token_size: int,
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enforce_eager: bool,
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attention_backend: str,
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monkeypatch,
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) -> None:
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test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model,
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dtype,
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max_tokens,
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chunked_prefill_token_size,
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enforce_eager,
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1,
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attention_backend,
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monkeypatch,
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)
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@pytest.mark.parametrize("max_tokens", [16])
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@pytest.mark.parametrize("enforce_eager", [False])
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@pytest.mark.parametrize("chunk_size", [30, 32])
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.cpu_model
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@pytest.mark.skipif(not current_platform.is_cpu(), reason="CPU only")
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def test_with_prefix_caching_cpu(
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vllm_runner,
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max_tokens: int,
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enforce_eager: bool,
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chunk_size: int,
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dtype: str,
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) -> None:
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test_with_prefix_caching(
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vllm_runner,
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max_tokens,
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enforce_eager,
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chunk_size,
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1,
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dtype,
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)
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