mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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9829ae0a77
Co-authored-by: frozenleaves <frozen@Mac.local> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>
75 lines
3.0 KiB
Python
75 lines
3.0 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 sys
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from unittest.mock import MagicMock, patch
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import pytest
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import torch.multiprocessing as mp
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from transformers import AutoModelForCausalLM
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def _apply_kernel(rank) -> None:
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with patch("torch.accelerator.current_accelerator") as mock_get_accelerator:
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mock_device = MagicMock()
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setattr(mock_device, "type", "npu")
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mock_get_accelerator.return_value = mock_device
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# reload kernel modules to respect mocked accelerator
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for k in list(sys.modules.keys()):
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if k.startswith("llamafactory.v1.plugins.model_plugins.kernels"):
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del sys.modules[k]
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from llamafactory.v1.plugins.model_plugins.kernels.interface import apply_default_kernels
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model = AutoModelForCausalLM.from_pretrained("llamafactory/tiny-random-qwen3")
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original_rmsnorm_forward = model.model.layers[0].input_layernorm.forward
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original_swiglu_forward = model.model.layers[0].mlp.forward
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model = apply_default_kernels(model=model, include_kernels="npu_fused_rmsnorm")
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assert model.model.layers[0].input_layernorm.forward.__func__ is not original_rmsnorm_forward.__func__
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assert model.model.layers[0].mlp.forward.__func__ is original_swiglu_forward.__func__
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def _apply_all_kernels(rank) -> None:
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with patch("torch.accelerator.current_accelerator") as mock_get_accelerator:
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mock_device = MagicMock()
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setattr(mock_device, "type", "npu")
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mock_get_accelerator.return_value = mock_device
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# reload kernel modules to respect mocked accelerator
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for k in list(sys.modules.keys()):
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if k.startswith("llamafactory.v1.plugins.model_plugins.kernels"):
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del sys.modules[k]
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from llamafactory.v1.plugins.model_plugins.kernels.interface import apply_default_kernels
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model = AutoModelForCausalLM.from_pretrained("llamafactory/tiny-random-qwen3")
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original_rmsnorm_forward = model.model.layers[0].input_layernorm.forward
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original_swiglu_forward = model.model.layers[0].mlp.forward
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model = apply_default_kernels(model=model, include_kernels=True)
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assert model.model.layers[0].input_layernorm.forward.__func__ is not original_rmsnorm_forward.__func__
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assert model.model.layers[0].mlp.forward.__func__ is not original_swiglu_forward.__func__
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def test_apply_kernel():
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mp.spawn(_apply_kernel)
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def test_apply_all_kernels():
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mp.spawn(_apply_all_kernels)
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