7bec1a9b9c
### What this PR does / why we need it?
Added support for the TorchAir graph mode in qwen3_moe and qwen2.5
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
```bash
llm = LLM(
model=model,
tensor_parallel_size=GPUs_per_dp_rank,
enforce_eager=False,
enable_expert_parallel=True,
max_model_len=4096,
max_num_seqs=16,
trust_remote_code=trust_remote_code,
gpu_memory_utilization=0.4,
additional_config={
"torchair_graph_config": {
"enabled": True,
"use_cached_graph": False,
"graph_batch_sizes_init": False,
"graph_batch_sizes": [16]
},
"ascend_scheduler_config": {
"enabled": True,
"chunked_prefill_enabled":True,
},
"refresh": True,
},
)
```
- vLLM version: v0.10.0
- vLLM main:
b87cb97a53
Signed-off-by: taoyuxiang <oui.nicholas.tao@gmail.com>
99 lines
3.4 KiB
Python
99 lines
3.4 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 pytest
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import torch
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from vllm.model_executor.models.qwen3_moe import Qwen3MoeForCausalLM
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from vllm_ascend.models.qwen3_moe import CustomQwen3MoeForCausalLM
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from vllm_ascend.torchair.models.qwen3_moe import CustomQwen3MoeAttention
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class TestCustomQwen3MoeForCausalLM:
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def test_class_inheritance(self):
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assert issubclass(CustomQwen3MoeForCausalLM, Qwen3MoeForCausalLM)
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@pytest.mark.parametrize("key, expected", [
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("qkv_proj", ["q_proj", "k_proj", "v_proj"]),
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("gate_up_proj", ["gate_proj", "up_proj"]),
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("experts",
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["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]),
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])
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def test_packed_modules_mapping(self, key, expected):
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assert CustomQwen3MoeForCausalLM.packed_modules_mapping[
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key] == expected
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def test_packed_modules_mapping_structure(self):
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expected_mapping = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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"gate_up_proj": ["gate_proj", "up_proj"],
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"experts": [
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"experts.0.gate_proj", "experts.0.up_proj",
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"experts.0.down_proj"
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]
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}
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assert CustomQwen3MoeForCausalLM.packed_modules_mapping == expected_mapping
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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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