EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

[2/N][Refactor][Quantization] clean quantization patch (#2785)

Browse Source 
### What this PR does / why we need it?
quantization patch is unused code

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
tested by CI

- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55

Signed-off-by: 22dimensions <waitingwind@foxmail.com>
This commit is contained in:
22dimensions
2025-09-08 17:31:53 +08:00
committed by GitHub
parent cd88f89267
commit d51694a77b
4 changed files with 2 additions and 456 deletions
Download Patch File Download Diff File Expand all files Collapse all files

134
tests/ut/quantization/test_func_wrapper.py
View File

@@ -1,134 +0,0 @@
from unittest.mock import patch
import torch
from tests.ut.base import TestBase
from vllm_ascend.quantization.func_wrapper import (wrapper_rmsnorm_forward_oot,
wrapper_rmsnorm_init)
class MockRMSNorm:
def __init__(self, hidden_size: int, **extra_args):
self.hidden_size = hidden_size
self.weight = torch.ones(hidden_size)
self.input_scale = 1.0
self.input_offset = 0.0
self.variance_epsilon = 1e-6
self.bias = torch.nn.Parameter(torch.zeros(hidden_size),
requires_grad=False)
self.ignore_anti = extra_args.get('ignore_anti', True)
class TestFuncWrapper(TestBase):
def test_wrapper_rmsnorm_init(self):
@wrapper_rmsnorm_init
def init(self, hidden_size: int, **extra_args) -> None:
self.hidden_size = hidden_size
hidden_size = 128
extra_args = {'arg1': 'value1'}
rms_norm = MockRMSNorm(hidden_size, **extra_args)
init(rms_norm, hidden_size, **extra_args)
self.assertTrue(hasattr(rms_norm, 'ignore_anti'))
self.assertTrue(rms_norm.ignore_anti)
self.assertTrue(hasattr(rms_norm, 'bias'))
self.assertIsInstance(rms_norm.bias, torch.nn.Parameter)
self.assertEqual(rms_norm.bias.shape, torch.Size([hidden_size]))
self.assertFalse(rms_norm.bias.requires_grad)
@patch('torch_npu._npu_quant_rms_norm')
def test_wrapper_rmsnorm_forward_oot_with_residual(
self, mock_npu_quant_rms_norm):
hidden_size = 128
x = torch.randn(hidden_size)
residual = torch.randn(hidden_size)
expected_out = torch.randn(hidden_size)
mock_npu_quant_rms_norm.return_value = (expected_out, residual)
@wrapper_rmsnorm_forward_oot
def forward_oot(self, x: torch.Tensor, residual: torch.Tensor = None):
return x, residual
rms_norm = MockRMSNorm(hidden_size)
rms_norm.ignore_anti = False
output, res = forward_oot(rms_norm, x, residual)
mock_npu_quant_rms_norm.assert_called_once()
args, kwargs = mock_npu_quant_rms_norm.call_args
self.assertTrue(torch.equal(args[1], rms_norm.weight))
self.assertTrue(torch.equal(args[2], rms_norm.bias))
self.assertEqual(args[3], rms_norm.input_scale)
self.assertEqual(args[4], rms_norm.input_offset)
self.assertEqual(args[5], rms_norm.variance_epsilon)
self.assertTrue(torch.equal(res, residual))
@patch('torch_npu._npu_quant_rms_norm')
def test_wrapper_rmsnorm_forward_oot_without_residual(
self, mock_npu_quant_rms_norm):
hidden_size = 128
x = torch.randn(hidden_size)
expected_out = torch.randn(hidden_size)
mock_npu_quant_rms_norm.return_value = expected_out
@wrapper_rmsnorm_forward_oot
def forward_oot(self, x: torch.Tensor, residual: torch.Tensor = None):
return x
rms_norm = MockRMSNorm(hidden_size)
rms_norm.ignore_anti = False
output = forward_oot(rms_norm, x)
mock_npu_quant_rms_norm.assert_called_once()
args, kwargs = mock_npu_quant_rms_norm.call_args
self.assertTrue(torch.equal(args[0], x))
self.assertTrue(torch.equal(args[1], rms_norm.weight))
self.assertTrue(torch.equal(args[2], rms_norm.bias))
self.assertEqual(args[3], rms_norm.input_scale)
self.assertEqual(args[4], rms_norm.input_offset)
self.assertEqual(args[5], rms_norm.variance_epsilon)
self.assertTrue(torch.equal(output, expected_out))
def test_wrapper_rmsnorm_forward_oot_ignore_anti_with_residual(self):
hidden_size = 128
x = torch.randn(hidden_size)
residual = torch.randn(hidden_size)
@wrapper_rmsnorm_forward_oot
def forward_oot(self, x: torch.Tensor, residual: torch.Tensor = None):
return x, residual
rms_norm = MockRMSNorm(hidden_size)
rms_norm.ignore_anti = True
output, res = forward_oot(rms_norm, x, residual)
self.assertTrue(torch.equal(output, x.add_(rms_norm.bias)))
self.assertTrue(torch.equal(res, residual))
def test_wrapper_rmsnorm_forward_oot_ignore_anti_no_residual(self):
hidden_size = 128
x = torch.randn(hidden_size)
@wrapper_rmsnorm_forward_oot
def forward_oot(self, x: torch.Tensor, residual: torch.Tensor = None):
return x
rms_norm = MockRMSNorm(hidden_size)
rms_norm.ignore_anti = True
output = forward_oot(rms_norm, x)
self.assertTrue(torch.equal(output, x.add_(rms_norm.bias)))