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110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Tests whether vllm correctly load and run gptq_v2 format checkpoints.
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Run `pytest tests/quantization/test_gptq_v2.py --forked`.
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"""
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import pytest
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import torch
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from transformers import AutoTokenizer
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from vllm import SamplingParams
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from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
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# A dummy small model quantized by GPTQModel, stored in GPTQ v2 format
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MODELS = ["XXXXyu/Qwen3-1.7B-w2g64-gptq_v2"]
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# Generate multiple sequences for testing, because an 1.7B 2-bit model
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# cannot always generate normal texts.
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N_SEQ = 5
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@pytest.mark.parametrize("model_id", MODELS)
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def test_model_load(vllm_runner, model_id, monkeypatch):
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# `LLM.apply_model` requires pickling a function.
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monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
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# Only check the default GPTQ linear method (used for 2/3-bit models).
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# 4/8-bit linear methods like Marlin already support gptq_v2.
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linear_method_cls = GPTQLinearMethod
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with vllm_runner(model_id, dtype=torch.float16, max_model_len=512) as llm:
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def check_model(model_id):
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for name, submodule in model_id.named_modules():
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# Could check more modules if necessary
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if name == "model_id.layers.0.self_attn.qkv_proj":
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assert isinstance(submodule.quant_method, linear_method_cls)
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config = submodule.quant_method.quant_config
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assert config.checkpoint_format == "gptq_v2"
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assert submodule.quant_method.use_v2_format
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# Just break since currently we only check 1 module
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break
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# Check if gptq_v2 format is correctly loaded
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llm.apply_model(check_model)
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@pytest.mark.parametrize("model_id", MODELS)
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def test_model_inference(vllm_runner, model_id):
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# Prepare prompt to test the model's generation result.
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prompt = "What is the meaning of life?"
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt},
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]
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False, # If thinking model, set it to false
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)
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sampling_params = SamplingParams(
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n=N_SEQ,
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max_tokens=128,
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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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min_p=0,
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presence_penalty=2,
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)
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with vllm_runner(model_id, dtype=torch.float16, max_model_len=512) as llm:
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# Generate a response to verify inference correctness
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output = llm.generate(text, sampling_params)
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# Make sure the output exists
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assert output
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assert output[0][1]
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assert len(output[0][1]) == N_SEQ
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def has_normal_char_distribution(texts, min_len):
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for text in texts:
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# Response too short
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if len(text) < min_len:
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return False
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# Basic ratio checks
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letters = sum(c.isalpha() for c in text)
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spaces = sum(c.isspace() for c in text)
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total = len(text)
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letter_ratio = letters / total
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space_ratio = spaces / total
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# At least 1 normal text should exist within output sequences
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# Normal text should be mostly letters with reasonable spacing
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# Some magic numbers, could be adjusted
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if 0.5 <= letter_ratio <= 0.9 and 0.01 <= space_ratio <= 0.3:
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return True
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# No sequence contains normal text, output might be broken
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return False
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# Apply some simple checks for giberish output
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# Print the output sequences if failed
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assert has_normal_char_distribution(output[0][1], 5), output[0][1]
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