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https://github.com/hiyouga/LLaMA-Factory.git
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52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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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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import pytest
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from llamafactory.v1.config.model_args import ModelArguments
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from llamafactory.v1.core.model_engine import ModelEngine
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bitsandbytes = pytest.importorskip("bitsandbytes")
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def check_quantization_status(model):
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quantized_info = {"bnb": []}
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for name, module in model.named_modules():
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# check BitsAndBytes quantization
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if isinstance(module, bitsandbytes.nn.modules.Linear8bitLt) or isinstance(
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module, bitsandbytes.nn.modules.Linear4bit
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):
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quantized_info["bnb"].append(name)
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return quantized_info
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@pytest.mark.runs_on(["cuda"])
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@pytest.mark.parametrize("name, quantization_bit", [("bnb", 4), ("auto", 4)])
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def test_quantization_plugin(name, quantization_bit):
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model_args = ModelArguments(
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model="llamafactory/tiny-random-qwen3",
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quant_config={
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"name": name,
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"quantization_bit": quantization_bit,
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},
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)
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model_engine = ModelEngine(model_args=model_args)
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quantized_info = check_quantization_status(model_engine.model)
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print(f"Quantized weights for method {name} with {quantization_bit} bit: {quantized_info}")
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assert any(v for v in quantized_info.values()), "model is not quantized properly."
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