tests_v1/plugins/model_plugins/test_kernel_plugin.py

# 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 sys
from unittest.mock import MagicMock, patch

import pytest
from transformers import AutoModelForCausalLM

from llamafactory.v1.accelerator.helper import get_current_accelerator


@pytest.fixture(autouse=True)
def clear_accelerator_cache():
    get_current_accelerator.cache_clear()


def reload_kernels():
    """Helper to reload kernel modules to respect mocked accelerator."""
    # Unload kernel interface and registry
    keys_to_remove = [k for k in sys.modules if k.startswith("llamafactory.v1.plugins.model_plugins.kernels")]
    for k in keys_to_remove:
        del sys.modules[k]


@patch("torch.accelerator.current_accelerator")
def test_apply_kernel(mock_get_accelerator: MagicMock):
    mock_device = MagicMock()
    setattr(mock_device, "type", "npu")
    mock_get_accelerator.return_value = mock_device
    # Force reload of kernels with mocked accelerator
    reload_kernels()
    from llamafactory.v1.plugins.model_plugins.kernels.interface import apply_default_kernels

    # NOTE: use a special model to avoid contamination by other tests
    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
    model = apply_default_kernels(model=model, include_kernels="npu_fused_rmsnorm")
    assert model.model.layers[0].input_layernorm.forward.__func__ is not original_rmsnorm_forward.__func__
    assert model.model.layers[0].mlp.forward.__func__ is original_swiglu_forward.__func__


@patch("torch.accelerator.current_accelerator")
def test_apply_all_kernels(mock_get_accelerator: MagicMock):
    get_current_accelerator.cache_clear()
    mock_device = MagicMock()
    setattr(mock_device, "type", "npu")
    mock_get_accelerator.return_value = mock_device

    # Force reload of kernels with mocked accelerator
    reload_kernels()
    from llamafactory.v1.plugins.model_plugins.kernels.interface import apply_default_kernels

    # NOTE: use a special model to avoid contamination by other tests
    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

    model = apply_default_kernels(model=model, include_kernels=True)
    assert model.model.layers[0].input_layernorm.forward.__func__ is not original_rmsnorm_forward.__func__
    assert model.model.layers[0].mlp.forward.__func__ is not original_swiglu_forward.__func__