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[misc] fix accelerator (#9661)

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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
Yaowei Zheng
2025-12-25 02:11:04 +08:00
committed by GitHub
parent 6a2eafbae3
commit a754604c11
44 changed files with 396 additions and 448 deletions
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79
tests_v1/plugins/model_plugins/test_kernel_plugin.py
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@@ -12,57 +12,56 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from unittest.mock import MagicMock, patch
import pytest
from transformers import AutoModelForCausalLM
from llamafactory.v1.accelerator.helper import get_current_accelerator
from llamafactory.v1.plugins.model_plugins.kernels.mlp import npu_swiglu
from llamafactory.v1.plugins.model_plugins.kernels.registry import apply_available_kernels, 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
class TestKernelPlugin(unittest.TestCase):
@patch("torch.accelerator.current_accelerator")
def test_apply_kernel(self, mock_get_accelerator):
get_current_accelerator.cache_clear()
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
@pytest.fixture(autouse=True)
def clear_accelerator_cache():
get_current_accelerator.cache_clear()
class Test_Use_V1_Kernels(unittest.TestCase):
@patch("torch.accelerator.current_accelerator")
def test_use_v1_kernels(self, mock_get_accelerator):
get_current_accelerator.cache_clear()
mock_device = MagicMock()
mock_device.type = "npu"
mock_get_accelerator.return_value = mock_device
@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
model = AutoModelForCausalLM.from_pretrained("llamafactory/tiny-random-qwen2.5")
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
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.registry import apply_available_kernels
apply_kernel(model, npu_rope.NpuRoPEKernel)
model = apply_available_kernels(model)
model = apply_kernel(model, npu_rms_norm.NpuRMSNormKernel)
assert model.model.layers[0].input_layernorm is not original_rmsnorm_forward
assert model.model.layers[0].input_layernorm is not original_rmsnorm_forward
assert model.model.layers[0].mlp.forward is not original_swiglu_forward
model = apply_kernel(model, npu_swiglu.NpuSwiGluKernel)
assert model.model.layers[0].mlp.forward is not original_swiglu_forward
@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
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_available_kernels(model)
assert model.model.layers[0].input_layernorm is not original_rmsnorm_forward
assert model.model.layers[0].mlp.forward is not original_swiglu_forward