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>
356 lines
11 KiB
Python
356 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from dataclasses import asdict
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import pytest
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from vllm.config import ModelConfig, PoolerConfig
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from vllm.model_executor.layers.pooler import PoolingType
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from vllm.platforms import current_platform
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@pytest.mark.parametrize(
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("model_id", "expected_runner_type", "expected_task"),
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[
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("facebook/opt-125m", "generate", "generate"),
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("intfloat/e5-mistral-7b-instruct", "pooling", "embed"),
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("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify"),
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("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "score"),
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("Qwen/Qwen2.5-Math-RM-72B", "pooling", "reward"),
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],
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)
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def test_auto_task(model_id, expected_runner_type, expected_task):
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config = ModelConfig(
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model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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)
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assert config.runner_type == expected_runner_type
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assert config.task == expected_task
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@pytest.mark.parametrize(("model_id", "bad_task"), [
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("Qwen/Qwen2.5-Math-RM-72B", "generate"),
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])
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def test_incorrect_task(model_id, bad_task):
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with pytest.raises(ValueError, match=r"does not support the .* task"):
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ModelConfig(
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model_id,
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task=bad_task,
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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)
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MODEL_IDS_EXPECTED = [
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("Qwen/Qwen1.5-7B", 32768),
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("mistralai/Mistral-7B-v0.1", 4096),
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("mistralai/Mistral-7B-Instruct-v0.2", 32768),
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]
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@pytest.mark.parametrize("model_id_expected", MODEL_IDS_EXPECTED)
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def test_disable_sliding_window(model_id_expected):
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model_id, expected = model_id_expected
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model_config = ModelConfig(
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model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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revision=None,
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disable_sliding_window=True,
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)
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assert model_config.max_model_len == expected
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def test_get_sliding_window():
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TEST_SLIDING_WINDOW = 4096
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# Test that the sliding window is correctly computed.
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# For Qwen1.5/Qwen2, get_sliding_window() should be None
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# when use_sliding_window is False.
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qwen2_model_config = ModelConfig(
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"Qwen/Qwen1.5-7B",
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task="auto",
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tokenizer="Qwen/Qwen1.5-7B",
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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revision=None,
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)
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qwen2_model_config.hf_config.use_sliding_window = False
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qwen2_model_config.hf_config.sliding_window = TEST_SLIDING_WINDOW
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assert qwen2_model_config.get_sliding_window() is None
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qwen2_model_config.hf_config.use_sliding_window = True
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assert qwen2_model_config.get_sliding_window() == TEST_SLIDING_WINDOW
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mistral_model_config = ModelConfig(
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"mistralai/Mistral-7B-v0.1",
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task="auto",
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tokenizer="mistralai/Mistral-7B-v0.1",
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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revision=None,
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)
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mistral_model_config.hf_config.sliding_window = None
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assert mistral_model_config.get_sliding_window() is None
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mistral_model_config.hf_config.sliding_window = TEST_SLIDING_WINDOW
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assert mistral_model_config.get_sliding_window() == TEST_SLIDING_WINDOW
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@pytest.mark.skipif(current_platform.is_rocm(),
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reason="Xformers backend is not supported on ROCm.")
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def test_get_pooling_config():
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model_id = "sentence-transformers/all-MiniLM-L12-v2"
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model_config = ModelConfig(
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model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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revision=None,
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)
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pooling_config = model_config._init_pooler_config(None)
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assert pooling_config is not None
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assert pooling_config.normalize
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assert pooling_config.pooling_type == PoolingType.MEAN.name
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@pytest.mark.skipif(current_platform.is_rocm(),
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reason="Xformers backend is not supported on ROCm.")
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def test_get_pooling_config_from_args():
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model_id = "sentence-transformers/all-MiniLM-L12-v2"
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model_config = ModelConfig(model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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revision=None)
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override_config = PoolerConfig(pooling_type='CLS', normalize=True)
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pooling_config = model_config._init_pooler_config(override_config)
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assert pooling_config is not None
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assert asdict(pooling_config) == asdict(override_config)
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@pytest.mark.skipif(current_platform.is_rocm(),
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reason="Xformers backend is not supported on ROCm.")
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def test_get_bert_tokenization_sentence_transformer_config():
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bge_model_config = ModelConfig(
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model="BAAI/bge-base-en-v1.5",
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task="auto",
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tokenizer="BAAI/bge-base-en-v1.5",
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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revision=None,
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)
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bert_bge_model_config = bge_model_config._get_encoder_config()
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assert bert_bge_model_config["max_seq_length"] == 512
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assert bert_bge_model_config["do_lower_case"]
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def test_rope_customization():
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TEST_ROPE_SCALING = {"rope_type": "dynamic", "factor": 2.0}
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TEST_ROPE_THETA = 16_000_000.0
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LONGCHAT_ROPE_SCALING = {"rope_type": "linear", "factor": 8.0}
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llama_model_config = ModelConfig(
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"meta-llama/Meta-Llama-3-8B-Instruct",
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task="auto",
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tokenizer="meta-llama/Meta-Llama-3-8B-Instruct",
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tokenizer_mode="auto",
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trust_remote_code=False,
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dtype="float16",
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seed=0,
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)
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assert getattr(llama_model_config.hf_config, "rope_scaling", None) is None
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assert getattr(llama_model_config.hf_config, "rope_theta", None) == 500_000
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assert llama_model_config.max_model_len == 8192
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llama_model_config = ModelConfig(
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"meta-llama/Meta-Llama-3-8B-Instruct",
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task="auto",
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tokenizer="meta-llama/Meta-Llama-3-8B-Instruct",
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tokenizer_mode="auto",
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trust_remote_code=False,
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dtype="float16",
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seed=0,
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hf_overrides={
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"rope_scaling": TEST_ROPE_SCALING,
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"rope_theta": TEST_ROPE_THETA,
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},
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)
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assert getattr(llama_model_config.hf_config, "rope_scaling",
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None) == TEST_ROPE_SCALING
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assert getattr(llama_model_config.hf_config, "rope_theta",
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None) == TEST_ROPE_THETA
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assert llama_model_config.max_model_len == 16384
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longchat_model_config = ModelConfig(
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"lmsys/longchat-13b-16k",
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task="auto",
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tokenizer="lmsys/longchat-13b-16k",
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tokenizer_mode="auto",
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trust_remote_code=False,
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dtype="float16",
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seed=0,
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)
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# Check if LONGCHAT_ROPE_SCALING entries are in longchat_model_config
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assert all(
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longchat_model_config.hf_config.rope_scaling.get(key) == value
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for key, value in LONGCHAT_ROPE_SCALING.items())
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assert longchat_model_config.max_model_len == 16384
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longchat_model_config = ModelConfig(
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"lmsys/longchat-13b-16k",
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task="auto",
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tokenizer="lmsys/longchat-13b-16k",
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tokenizer_mode="auto",
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trust_remote_code=False,
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dtype="float16",
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seed=0,
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hf_overrides={
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"rope_scaling": TEST_ROPE_SCALING,
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},
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)
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assert getattr(longchat_model_config.hf_config, "rope_scaling",
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None) == TEST_ROPE_SCALING
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assert longchat_model_config.max_model_len == 4096
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@pytest.mark.skipif(current_platform.is_rocm(),
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reason="Encoder Decoder models not supported on ROCm.")
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@pytest.mark.parametrize(("model_id", "is_encoder_decoder"), [
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("facebook/opt-125m", False),
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("facebook/bart-base", True),
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("meta-llama/Llama-3.2-1B", False),
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("meta-llama/Llama-3.2-11B-Vision", True),
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])
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def test_is_encoder_decoder(model_id, is_encoder_decoder):
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config = ModelConfig(
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model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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dtype="float16",
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seed=0,
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)
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assert config.is_encoder_decoder == is_encoder_decoder
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@pytest.mark.parametrize(("model_id", "uses_mrope"), [
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("facebook/opt-125m", False),
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("Qwen/Qwen2-VL-2B-Instruct", True),
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])
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def test_uses_mrope(model_id, uses_mrope):
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config = ModelConfig(
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model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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dtype="float16",
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seed=0,
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)
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assert config.uses_mrope == uses_mrope
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def test_generation_config_loading():
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model_id = "Qwen/Qwen2.5-1.5B-Instruct"
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# When set generation_config to None, the default generation config
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# will not be loaded.
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model_config = ModelConfig(model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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generation_config=None)
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assert model_config.get_diff_sampling_param() == {}
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# When set generation_config to "auto", the default generation config
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# should be loaded.
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model_config = ModelConfig(model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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generation_config="auto")
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correct_generation_config = {
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_p": 0.8,
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"top_k": 20,
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}
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assert model_config.get_diff_sampling_param() == correct_generation_config
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# The generation config could be overridden by the user.
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override_generation_config = {"temperature": 0.5, "top_k": 5}
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model_config = ModelConfig(
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model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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generation_config="auto",
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override_generation_config=override_generation_config)
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override_result = correct_generation_config.copy()
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override_result.update(override_generation_config)
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assert model_config.get_diff_sampling_param() == override_result
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# When generation_config is set to None and override_generation_config
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# is set, the override_generation_config should be used directly.
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model_config = ModelConfig(
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model_id,
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task="auto",
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tokenizer=model_id,
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tokenizer_mode="auto",
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trust_remote_code=False,
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seed=0,
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dtype="float16",
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generation_config=None,
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override_generation_config=override_generation_config)
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assert model_config.get_diff_sampling_param() == override_generation_config
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