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86ae693f20
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# ruff: noqa: SIM117
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import pytest
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from ...utils import EmbedModelInfo
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MODELS = [
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EmbedModelInfo("nomic-ai/nomic-embed-text-v1"),
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#EmbedModelInfo("nomic-ai/nomic-embed-text-v1.5"),
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#EmbedModelInfo("nomic-ai/CodeRankEmbed"),
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EmbedModelInfo("nomic-ai/nomic-embed-text-v2-moe"),
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#EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-long"),
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]
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rope_theta = 1000
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factor = 4.0
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original_max_position_embeddings = 2048
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max_model_len = int(original_max_position_embeddings * factor)
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@pytest.mark.parametrize("model_info", MODELS)
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def test_default(model_info, vllm_runner):
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with vllm_runner(model_info.name, runner="pooling",
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max_model_len=None) as vllm_model:
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model_config = vllm_model.llm.llm_engine.model_config
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if model_info.name == "nomic-ai/nomic-embed-text-v2-moe":
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# For nomic-embed-text-v2-moe the length is set to 512
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# by sentence_bert_config.json.
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assert model_config.max_model_len == 512
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else:
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assert (
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model_config.max_model_len == original_max_position_embeddings)
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@pytest.mark.parametrize("model_info", MODELS)
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def test_set_max_model_len_legal(model_info, vllm_runner):
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# set max_model_len <= 512
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with vllm_runner(model_info.name, runner="pooling",
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max_model_len=256) as vllm_model:
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model_config = vllm_model.llm.llm_engine.model_config
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assert model_config.max_model_len == 256
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# set 512 < max_model_len <= 2048
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if model_info.name == "nomic-ai/nomic-embed-text-v2-moe":
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# For nomic-embed-text-v2-moe the length is set to 512
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# by sentence_bert_config.json.
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with pytest.raises(ValueError):
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with vllm_runner(model_info.name,
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runner="pooling",
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max_model_len=1024):
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pass
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else:
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with vllm_runner(model_info.name, runner="pooling",
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max_model_len=1024) as vllm_model:
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model_config = vllm_model.llm.llm_engine.model_config
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assert model_config.max_model_len == 1024
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@pytest.mark.parametrize("model_info", MODELS)
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def test_set_max_model_len_illegal(model_info, vllm_runner):
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# set max_model_len > 2048
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with pytest.raises(ValueError):
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with vllm_runner(model_info.name, runner="pooling",
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max_model_len=4096):
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pass
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# set max_model_len > 2048 by hf_overrides
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hf_overrides = {"max_model_len": 4096}
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with pytest.raises(ValueError):
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with vllm_runner(model_info.name,
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runner="pooling",
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max_model_len=None,
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hf_overrides=hf_overrides):
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pass
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@pytest.mark.parametrize("model_info", MODELS)
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def test_use_rope_scaling_legal(model_info, vllm_runner):
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hf_overrides = {
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"rope_theta": rope_theta,
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"rope_scaling": {
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"rope_type": "yarn",
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"factor": factor,
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"original_max_position_embeddings":
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original_max_position_embeddings
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},
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"max_model_len": max_model_len
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}
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with vllm_runner(model_info.name,
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runner="pooling",
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max_model_len=None,
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hf_overrides=hf_overrides):
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pass
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@pytest.mark.parametrize("model_info", MODELS)
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def test_use_rope_scaling_illegal(model_info, vllm_runner):
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hf_overrides = {
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"rope_theta": rope_theta,
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"rope_scaling": {
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"rope_type": "yarn",
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"factor": factor,
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"original_max_position_embeddings":
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original_max_position_embeddings
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}
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}
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# illegal max_model_len
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with pytest.raises(ValueError):
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with vllm_runner(model_info.name,
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runner="pooling",
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max_model_len=max_model_len + 1,
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hf_overrides=hf_overrides):
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pass
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hf_overrides = {
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"rope_theta": rope_theta,
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"rope_scaling": {
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"rope_type": "yarn",
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"factor": factor,
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"original_max_position_embeddings":
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original_max_position_embeddings
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},
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"max_model_len": max_model_len + 1
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}
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# illegal max_model_len by hf_overrides
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with pytest.raises(ValueError):
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with vllm_runner(model_info.name,
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runner="pooling",
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max_model_len=None,
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hf_overrides=hf_overrides):
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pass
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