112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import multiprocessing as mp
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import unittest
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from typing import Dict, List, Tuple
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import torch
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from sglang.test.runners import SRTRunner
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from sglang.test.test_utils import CustomTestCase
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PROMPTS = [
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"AI is a field of computer science focused on",
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"""
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### Instruction:
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Compose a SQL query that uses the following table: users, and returns the user_id and name of all users whose name that does not have a duplicate in the table.
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### Response:
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SELECT user_id, name FROM users WHERE name LIKE 'A%';
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""",
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]
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ADAPTERS = [
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"faridlazuarda/valadapt-llama-3.1-8B-it-chinese", # target_modules = q, v
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"philschmid/code-llama-3-1-8b-text-to-sql-lora", # target_modules = q, k, v, o, gate, up, down
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]
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
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class TestLoRAEviction(CustomTestCase):
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def test_lora_eviction_with_different_target_modules(self):
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"""
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Test LoRA eviction with different target modules.
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This test runs inference against two LoRA adapters in different orders to force eviction behavior, and ensures
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that the outputs of the same (adapter, prompt) pair are consistent across runs.
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"""
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output_history = {}
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self._run_test(ADAPTERS, output_history, reverse=False)
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self._run_test(ADAPTERS, output_history, reverse=True)
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def _run_test(
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self,
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lora_paths: List[str],
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output_history: Dict[Tuple[str, str], str],
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reverse: bool,
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repeat: int = 2,
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):
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max_new_tokens = 256
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backend = "triton"
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torch_dtype = torch.float16
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base_path = BASE_MODEL
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assert len(lora_paths) >= 2
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# Initialize runners
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with SRTRunner(
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base_path,
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torch_dtype=torch_dtype,
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model_type="generation",
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lora_paths=lora_paths,
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max_loras_per_batch=1,
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lora_backend=backend,
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disable_radix_cache=True,
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) as srt_runner:
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adapter_sequence = lora_paths if not reverse else lora_paths[::-1]
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for i in range(repeat):
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for j, adapter in enumerate(adapter_sequence):
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print(
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f"\n========== Testing LoRA eviction with adapter '{adapter}' (#{j+1}/{len(adapter_sequence)}), reversed: {reverse}, repeat: {i+1}/{repeat} ---"
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)
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for prompt in PROMPTS:
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print("\nprompt:\n", prompt)
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srt_outputs = srt_runner.forward(
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[prompt],
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max_new_tokens=max_new_tokens,
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lora_paths=[adapter],
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)
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output = srt_outputs.output_strs[0].strip()
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print("\noutput:\n", output)
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prev_output = output_history.get((adapter, prompt))
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if prev_output is not None:
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self.assertEqual(
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prev_output,
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output,
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f"Output mismatch for adapter {adapter} and prompt '{prompt}' on repeat {j + 1}, previous: '{prev_output}', current: '{output}'.",
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)
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else:
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output_history[(adapter, prompt)] = output
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if __name__ == "__main__":
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try:
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mp.set_start_method("spawn")
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except RuntimeError:
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pass
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unittest.main(warnings="ignore")
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