0942d9aaab
### What this PR does / why we need it?
Some custom models in vllm-ascend define packed_modules_mapping, which
prevent keeping same model class with vllm community. So move these
custom packed_modules_mapping to quant utils.py. After this pr, some
custom models can be removed.
### Does this PR introduce _any_ user-facing change?
tested by CI
### How was this patch tested?
tested by CI
- vLLM version: v0.10.2
- vLLM main:
5089fd749c
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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import math
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import unittest
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import torch
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from vllm_ascend.torchair.models.qwen3_moe import CustomQwen3MoeAttention
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class DummyRMSNorm:
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def __init__(self, dim: int, eps: float = 1e-6):
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self.dim = dim
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self.eps = eps
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def __call__(self, x):
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mean_sq = x.pow(2).mean(dim=-1, keepdim=True)
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denom = (mean_sq + self.eps).sqrt()
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return x / denom
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class TestCustomQwen3MoeAttention(unittest.TestCase):
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def setUp(self):
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self.batch = 2
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self.seq_len = 3
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self.q_size = 8
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self.kv_size = 8
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self.head_dim = 4
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self.rms_eps = 1e-6
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total_dim = self.q_size + 2 * self.kv_size
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self.qkv = torch.arange(self.batch * self.seq_len * total_dim,
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dtype=torch.float32).reshape(
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self.batch, self.seq_len, total_dim)
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def test_constant_input_normalization(self):
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ones_qkv = torch.ones((1, 1, self.q_size + 2 * self.kv_size),
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dtype=torch.float32)
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q_norm = DummyRMSNorm(self.head_dim, self.rms_eps)
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k_norm = DummyRMSNorm(self.head_dim, self.rms_eps)
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q, k, v = CustomQwen3MoeAttention.normalize_qkv(
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ones_qkv, self.q_size, self.kv_size, self.head_dim, q_norm, k_norm)
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norm_val = 1.0 / math.sqrt(1.0 + self.rms_eps)
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expected_q = torch.full((1, 1, self.q_size), norm_val)
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expected_k = torch.full((1, 1, self.kv_size), norm_val)
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expected_v = torch.ones((1, 1, self.kv_size), dtype=torch.float32)
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self.assertTrue(torch.allclose(q, expected_q, atol=1e-6))
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self.assertTrue(torch.allclose(k, expected_k, atol=1e-6))
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self.assertTrue(torch.equal(v, expected_v))
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