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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>
345 lines
12 KiB
Python
345 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import pytest
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from tests.utils import multi_gpu_test
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from vllm.engine.arg_utils import EngineArgs
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from vllm.sampling_params import SamplingParams
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from ...utils import check_outputs_equal
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MODELS = ["ai21labs/Jamba-tiny-dev"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [96])
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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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) -> None:
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with hf_runner(
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model,
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dtype=dtype,
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model_kwargs={
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"use_mamba_kernels":
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False, # mamba kernels are not installed so HF
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# don't use them
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}) as hf_model:
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hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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# This test is for verifying whether the model's extra_repr
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# can be printed correctly.
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def print_model(model):
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print(model)
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vllm_model.apply_model(print_model)
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for i in range(len(example_prompts)):
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hf_output_ids, hf_output_str = hf_outputs[i]
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vllm_output_ids, vllm_output_str = vllm_outputs[i]
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assert hf_output_str == vllm_output_str, (
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f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
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assert hf_output_ids == vllm_output_ids, (
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f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [96])
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def test_batching(
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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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) -> None:
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# To pass the small model tests, we need full precision.
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for_loop_outputs = []
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with vllm_runner(model, dtype=dtype) as vllm_model:
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for prompt in example_prompts:
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for_loop_outputs.append(
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vllm_model.generate_greedy([prompt], max_tokens)[0])
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batched_outputs = vllm_model.generate_greedy(example_prompts,
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max_tokens)
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check_outputs_equal(
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outputs_0_lst=for_loop_outputs,
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outputs_1_lst=batched_outputs,
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name_0="for_loop_vllm",
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name_1="batched_vllm",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float16"])
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@pytest.mark.parametrize("max_tokens", [10])
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def test_mamba_prefill_chunking_with_parallel_sampling(
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hf_runner, vllm_runner, example_prompts, model: str, dtype: str,
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max_tokens: int) -> None:
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# Tests prefill chunking in conjunction with n>1, in this case,
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# prefill is populated with decoding tokens and we test that it
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# doesn't fail This test might fail if cache is not allocated
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# correctly for n > 1 decoding steps inside a
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# chunked prefill forward pass (where we have both prefills
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# and decoding together )
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sampling_params = SamplingParams(n=3,
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temperature=1,
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seed=0,
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max_tokens=max_tokens)
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with vllm_runner(
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model,
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dtype=dtype,
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enable_chunked_prefill=True,
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max_num_batched_tokens=30,
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max_num_seqs=10 # forces prefill chunks with decoding
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) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.parametrize("max_tokens", [10])
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def test_mamba_prefill_chunking(hf_runner, vllm_runner, example_prompts,
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model: str, dtype: str,
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max_tokens: int) -> None:
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# numeric error during prefill chucking produces different generation
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# compared to w/o prefill chunking for those examples, removed them for now
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example_prompts.pop(7)
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example_prompts.pop(2)
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example_prompts.pop(1)
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with hf_runner(
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model,
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dtype=dtype,
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model_kwargs={
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"use_mamba_kernels":
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False, # mamba kernels are not installed so HF
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# don't use them
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}) as hf_model:
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non_chunked = hf_model.generate_greedy(example_prompts, max_tokens)
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with vllm_runner(model,
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dtype=dtype,
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enable_chunked_prefill=True,
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max_num_batched_tokens=5,
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max_num_seqs=2) as vllm_model:
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chunked = vllm_model.generate_greedy(example_prompts,
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max_tokens=max_tokens)
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check_outputs_equal(
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outputs_0_lst=chunked,
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outputs_1_lst=non_chunked,
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name_0="chunked",
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name_1="non_chunked",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.parametrize("max_tokens", [15])
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def test_parallel_sampling(
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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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) -> None:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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for_loop_outputs = []
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for _ in range(10):
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for_loop_outputs.append(
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# using example_prompts index 1 instead of 0 since with 0 the
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# logprobs get really close and the test doesn't pass
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vllm_model.generate_greedy([example_prompts[1]], max_tokens)
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[0])
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sampling_params = SamplingParams(n=10,
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temperature=0.001,
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seed=0,
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max_tokens=max_tokens)
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n_lt_1_outputs = vllm_model.generate([example_prompts[1]],
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sampling_params)
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token_ids, texts = n_lt_1_outputs[0]
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n_lt_1_outputs = [(token_id, text)
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for token_id, text in zip(token_ids, texts)]
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check_outputs_equal(
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outputs_0_lst=n_lt_1_outputs,
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outputs_1_lst=for_loop_outputs,
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name_0="vllm_n_lt_1_outputs",
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name_1="vllm",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.parametrize("max_tokens", [20])
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def test_mamba_cache_cg_padding(
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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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) -> None:
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# This test is for verifying that mamba cache is padded to CG captured
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# batch size. If it's not, a torch RuntimeError will be raised because
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# tensor dimensions aren't compatible
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vllm_config = EngineArgs(model=model).create_engine_config()
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while len(example_prompts) == vllm_config.pad_for_cudagraph(
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len(example_prompts)):
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example_prompts.append(example_prompts[0])
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try:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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except RuntimeError:
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pytest.fail(
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"Couldn't run batch size which is not equal to a Cuda Graph "
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"captured batch size. "
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"Could be related to mamba cache not padded correctly")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [20])
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def test_models_preemption_recompute(
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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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) -> None:
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# Tests that outputs are identical with and w/o preemtions (recompute)
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assert dtype == "float"
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_model.model.llm_engine.scheduler[
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0].ENABLE_ARTIFICIAL_PREEMPT = True
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preempt_vllm_outputs = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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vllm_model.model.llm_engine.scheduler[
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0].ENABLE_ARTIFICIAL_PREEMPT = False
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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=preempt_vllm_outputs,
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outputs_1_lst=vllm_outputs,
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name_0="vllm_preepmtions",
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name_1="vllm",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_fail_upon_inc_requests_and_finished_requests_lt_available_blocks(
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vllm_runner,
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model: str,
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dtype: str,
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example_prompts,
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) -> None:
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# This test is for verifying that the Jamba inner state management doesn't
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# collapse in case where the number of incoming requests and
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# finished_requests_ids is larger than the maximum mamba block capacity.
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# This could generally happen due to the fact that Jamba does support
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# statelessness mechanism where it can cleanup new incoming requests in
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# a single step.
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try:
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with vllm_runner(model, dtype=dtype, max_num_seqs=10) as vllm_model:
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vllm_model.generate_greedy([example_prompts[0]] * 100, 10)
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except ValueError:
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pytest.fail("Jamba inner state wasn't cleaned up properly between"
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"steps finished requests registered unnecessarily ")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_state_cleanup(
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vllm_runner,
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model: str,
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dtype: str,
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example_prompts,
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) -> None:
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# This test is for verifying that the Jamba state is cleaned up between
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# steps, If its not cleaned, an error would be expected.
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try:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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for _ in range(10):
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vllm_model.generate_greedy([example_prompts[0]] * 100, 1)
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except ValueError:
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pytest.fail("Jamba inner state wasn't cleaned up between states, "
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"could be related to finished_requests_ids")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_multistep(
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vllm_runner,
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model: str,
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dtype: str,
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example_prompts,
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) -> None:
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# This test is verifying that multistep works correctly
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#on mamba-like models
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with vllm_runner(model, num_scheduler_steps=8,
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max_num_seqs=2) as vllm_model:
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vllm_model.generate_greedy([example_prompts[0]] * 10, 1)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [64])
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def test_multistep_correctness(vllm_runner, model: str, dtype: str,
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max_tokens: int, example_prompts) -> None:
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with vllm_runner(model, num_scheduler_steps=8,
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max_num_seqs=2) as vllm_model:
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vllm_outputs_multistep = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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with vllm_runner(model, num_scheduler_steps=1,
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max_num_seqs=2) as vllm_model:
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vllm_outputs_single_step = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_outputs_multistep,
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outputs_1_lst=vllm_outputs_single_step,
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name_0="vllm_outputs_multistep",
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name_1="vllm_outputs_single_step",
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)
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@multi_gpu_test(num_gpus=2)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [64])
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def test_jamba_distributed_produces_identical_generation(
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vllm_runner, model: str, dtype: str, max_tokens: int,
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example_prompts) -> None:
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with vllm_runner(model, dtype=dtype, tensor_parallel_size=2) as vllm_model:
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vllm_outputs_tp_2 = vllm_model.generate_greedy(example_prompts,
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max_tokens)
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with vllm_runner(model, dtype=dtype, tensor_parallel_size=1) as vllm_model:
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vllm_outputs_tp_1 = vllm_model.generate_greedy(example_prompts,
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max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_outputs_tp_1,
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outputs_1_lst=vllm_outputs_tp_2,
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name_0="vllm_tp_1",
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name_1="vllm_tp_2",
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)
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