File size: 1,899 Bytes
b0c0df0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
# Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from unittest.mock import MagicMock, patch
from transformers import AutoModelForCausalLM
class TestKernelPlugin(unittest.TestCase):
@patch('torch.accelerator.current_accelerator')
def test_apply_kernel(self, mock_get_accelerator):
mock_device = MagicMock()
mock_device.type = 'npu'
mock_get_accelerator.return_value = mock_device
model = AutoModelForCausalLM.from_pretrained("llamafactory/tiny-random-qwen2.5")
original_rmsnorm_forward = model.model.layers[0].input_layernorm.forward
original_swiglu_forward = model.model.layers[0].mlp.forward
from llamafactory.v1.plugins.model_plugins.kernels.mlp import npu_swiglu
from llamafactory.v1.plugins.model_plugins.kernels.registry import apply_kernel
from llamafactory.v1.plugins.model_plugins.kernels.rms_norm import npu_rms_norm
from llamafactory.v1.plugins.model_plugins.kernels.rope import npu_rope
apply_kernel(model, npu_rope.NpuRoPEKernel)
model = apply_kernel(model, npu_rms_norm.NpuRMSNormKernel)
assert model.model.layers[0].input_layernorm is not original_rmsnorm_forward
model = apply_kernel(model, npu_swiglu.NpuSwiGluKernel)
assert model.model.layers[0].mlp.forward is not original_swiglu_forward
|