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[CustomOp] Register RMSNorm instead of overwrite forward_oot (#2284)

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### What this PR does / why we need it?
Use function CustomOp.register_oot to achieve the customop registery
```
from vllm.model_executor.custom_op import CustomOp
CustomOp.register_oot(_decorated_op_cls=AscendRMSNorm, name="RMSNorm")
```

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.10.0
- vLLM main:
afa5b7ca0b

---------

Signed-off-by: Icey <1790571317@qq.com>
This commit is contained in:
Icey
2025-08-14 17:18:30 +08:00
committed by GitHub
parent e14f2ef669
commit c721ae6042
4 changed files with 85 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files

53
tests/ut/ops/test_layernorm.py Normal file
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@@ -0,0 +1,53 @@
from unittest.mock import patch
import pytest
import torch
from vllm.model_executor.layers.layernorm import RMSNorm
@pytest.fixture
def dummy_tensor():
return torch.randn(4, 8, dtype=torch.float16)
def mock_rms_norm(x, weight, eps):
return x + 1, None
def mock_add_rms_norm(x, residual, weight, eps):
return 2 * x, None, 2 * residual
@pytest.mark.parametrize("is_310p_return", [True, False])
@pytest.mark.parametrize("residual",
[None, torch.randn(4, 8, dtype=torch.float32)])
@patch("torch_npu.npu_rms_norm", side_effect=mock_rms_norm)
@patch("torch_npu.npu_add_rms_norm", side_effect=mock_add_rms_norm)
def test_RMSNorm_forward(mock_add_rmsnorm, mock_rmsnorm, is_310p_return,
residual, dummy_tensor):
with patch("vllm_ascend.utils.is_310p", return_value=is_310p_return):
layer = RMSNorm(hidden_size=32, eps=1e-05)
if residual is not None:
out_x, out_residual = layer.forward_oot(dummy_tensor, residual)
if is_310p_return:
expected_arg_x = dummy_tensor + residual.to(dummy_tensor.dtype)
expected_out_x = expected_arg_x + 1
expected_out_residual = expected_arg_x.to(residual.dtype)
mock_rmsnorm.assert_called_once()
assert torch.allclose(out_x, expected_out_x)
assert torch.allclose(out_residual, expected_out_residual)
else:
expected_out_x = 2 * dummy_tensor
expected_out_residual = 2 * residual
mock_add_rmsnorm.assert_called_once()
assert torch.allclose(out_x, expected_out_x)
assert torch.allclose(out_residual, expected_out_residual)
else:
out_x = layer.forward(dummy_tensor, residual)
expected_out_x = dummy_tensor + 1
mock_rmsnorm.assert_called_once()
assert torch.allclose(out_x, expected_out_x)

12
tests/ut/test_utils.py
View File

@@ -347,20 +347,22 @@ class TestUtils(TestBase):
@mock.patch("vllm.model_executor.custom_op.CustomOp")
@mock.patch("vllm_ascend.ops.activation.AscendQuickGELU")
@mock.patch("vllm_ascend.ops.activation.AscendSiluAndMul")
def test_register_ascend_customop(self, mock_ascend_silu_and_mul,
@mock.patch("vllm_ascend.ops.layernorm.AscendRMSNorm")
def test_register_ascend_customop(self, mock_ascend_rmsnorm,
mock_ascend_silu_and_mul,
mock_ascend_quick_gelu, mock_customop):
utils._ASCEND_CUSTOMOP_IS_REIGISTERED = False
# ascend custom op is not registered
utils.register_ascend_customop()
# should call register_oot twice
self.assertEqual(mock_customop.register_oot.call_count, 2)
# should call register_oot three
self.assertEqual(mock_customop.register_oot.call_count, 3)
self.assertTrue(utils._ASCEND_CUSTOMOP_IS_REIGISTERED)
# ascend custom op is already registered
utils.register_ascend_customop()
# should not register_oot again, thus only called twice in this ut
self.assertEqual(mock_customop.register_oot.call_count, 2)
# should not register_oot again, thus only called three in this ut
self.assertEqual(mock_customop.register_oot.call_count, 3)
class TestProfileExecuteDuration(TestBase):

45
vllm_ascend/ops/layernorm.py
View File

@@ -20,8 +20,6 @@ from typing import Optional, Tuple, Union
import torch
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm_ascend.utils import is_310p
class AddRMSNormW8A8Quant(RMSNorm):
# Fuse AddRmsNorm and W8A8 quantization ops together
@@ -60,27 +58,28 @@ class AddRMSNormW8A8Quant(RMSNorm):
return x
def forward_oot(
self,
x: torch.Tensor,
residual: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
import torch_npu
class AscendRMSNorm(RMSNorm):
if residual is not None:
if is_310p():
orig_dtype = residual.dtype
x = x + residual.to(x.dtype)
residual = x.to(orig_dtype)
x, _ = torch_npu.npu_rms_norm(x, self.weight,
self.variance_epsilon)
else:
x, _, residual = torch_npu.npu_add_rms_norm(
x, residual, self.weight, self.variance_epsilon)
return x, residual
def forward_oot(
self,
x: torch.Tensor,
residual: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
import torch_npu
x, residual = torch_npu.npu_rms_norm(x, self.weight, self.variance_epsilon)
return x
from vllm_ascend.utils import is_310p
if residual is not None:
if is_310p():
orig_dtype = residual.dtype
x = x + residual.to(x.dtype)
residual = x.to(orig_dtype)
x, _ = torch_npu.npu_rms_norm(x, self.weight,
self.variance_epsilon)
else:
x, _, residual = torch_npu.npu_add_rms_norm(
x, residual, self.weight, self.variance_epsilon)
return x, residual
RMSNorm.forward_oot = forward_oot
x, residual = torch_npu.npu_rms_norm(x, self.weight,
self.variance_epsilon)
return x

3
vllm_ascend/utils.py
View File

@@ -479,6 +479,9 @@ def register_ascend_customop():
CustomOp.register_oot(_decorated_op_cls=AscendSiluAndMul,
name="SiluAndMul")
from vllm_ascend.ops.layernorm import AscendRMSNorm
CustomOp.register_oot(_decorated_op_cls=AscendRMSNorm, name="RMSNorm")
# NOTE: Keep this at last to ensure all custom actions are registered
_ASCEND_CUSTOMOP_IS_REIGISTERED = True