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51 lines
1.7 KiB
Python
51 lines
1.7 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 gptq models with quantized lm_head can be loaded.
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Run `pytest tests/quantization/test_quant_lm_head_true.py --forked`.
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"""
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import pytest
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import torch
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from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
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from vllm.model_executor.layers.quantization.gptq_marlin import (
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GPTQMarlinLinearMethod)
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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UnquantizedEmbeddingMethod)
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PROMPT = "On the surface of Mars, we found"
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MODELS_QUANT = [
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("ModelCloud/Qwen1.5-1.8B-Chat-GPTQ-4bits-dynamic-cfg-with-lm_head", True),
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("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", False),
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]
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@pytest.mark.parametrize("model_id, lm_head_quantized", MODELS_QUANT)
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def test_lm_head(
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vllm_runner,
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model_id: str,
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lm_head_quantized: bool,
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monkeypatch,
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) -> None:
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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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with vllm_runner(model_id, dtype=torch.float16,
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max_model_len=2048) as vllm_model:
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def check_model(model):
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lm_head_layer = model.lm_head
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if lm_head_quantized:
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assert isinstance(lm_head_layer.quant_method,
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(GPTQLinearMethod, GPTQMarlinLinearMethod))
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else:
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assert isinstance(lm_head_layer.quant_method,
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UnquantizedEmbeddingMethod)
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vllm_model.apply_model(check_model)
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print(
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vllm_model.generate_greedy(["Hello my name is"],
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max_tokens=10)[0][1])
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