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[main] Optimize rope in Qwen Models (#2571)

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### What this PR does / why we need it?
Optimize rope by caching sin and cos at the first layer in Qwen Models.

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

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


- vLLM version: v0.10.1.1
- vLLM main:
562663a044

---------

Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: ZYang6263 <zy626375@gmail.com>
Signed-off-by: rjg-lyh <1318825571@qq.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
Co-authored-by: ZYang6263 <51255902183@stu.ecnu.edu.cn>
Co-authored-by: ZYang6263 <zy626375@gmail.com>
This commit is contained in:
rjg-lyh
2025-09-09 14:28:14 +08:00
committed by GitHub
parent 5bcb4c1528
commit 7a205dbaa8
4 changed files with 136 additions and 47 deletions
Download Patch File Download Diff File Expand all files Collapse all files

90
tests/ut/ops/test_rotary_embedding.py
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@@ -3,12 +3,18 @@ import unittest
from unittest.mock import MagicMock, PropertyMock, patch
import torch
from transformers.configuration_utils import PretrainedConfig
from vllm.config import ModelConfig, VllmConfig
from vllm.model_executor.layers.rotary_embedding import (
DeepseekScalingRotaryEmbedding, RotaryEmbedding)
from tests.ut.base import TestBase
from vllm_ascend.ascend_forward_context import set_ascend_forward_context
from vllm_ascend.ops.rotary_embedding import _custom_rotary_embedding_enabled
MODEL = "Qwen3-0.6B"
MAX_NUM_BATCHED_TOKEND = 10000
class TestCustomRotaryEmbeddingEnabled(unittest.TestCase):
@@ -93,6 +99,10 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
@patch('vllm_ascend.ops.rotary_embedding._custom_rotary_embedding_enabled',
return_value=True)
@patch('torch.ops._npu_rotary_embedding')
@patch('vllm.config.ModelConfig.__post_init__', MagicMock())
@patch('vllm.config.VllmConfig.__post_init__', MagicMock())
@patch('vllm.distributed.parallel_state._DP', MagicMock(world_size=1))
@patch('vllm.distributed.parallel_state._TP', MagicMock(world_size=1))
def test_rope_forward_oot_custom_kernel(self, mock_rotary_embedding,
mock_custom_enabled, mock_is_310p,
mock__c):
@@ -102,9 +112,15 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
# Setup mock for custom kernel path
mock__c.rotary_embedding.return_value = self.query, self.key
result_q, result_k = self.layer.forward(self.positions, self.query,
self.key)
vllm_config = VllmConfig()
model_config = ModelConfig(MODEL,
tokenizer=MODEL,
max_model_len=MAX_NUM_BATCHED_TOKEND)
model_config.hf_config = PretrainedConfig()
vllm_config.model_config = model_config
with set_ascend_forward_context(None, vllm_config):
result_q, result_k = self.layer.forward(self.positions, self.query,
self.key)
mock__c.rotary_embedding.assert_called_once()
self.assertEqual(result_q.shape, self.query.shape)
@@ -113,6 +129,10 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
@patch('vllm_ascend.ops.rotary_embedding._custom_rotary_embedding_enabled',
return_value=False)
@patch('torch_npu._npu_rotary_embedding')
@patch('vllm.config.ModelConfig.__post_init__', MagicMock())
@patch('vllm.config.VllmConfig.__post_init__', MagicMock())
@patch('vllm.distributed.parallel_state._DP', MagicMock(world_size=1))
@patch('vllm.distributed.parallel_state._TP', MagicMock(world_size=1))
def test_rope_forward_oot_contiguous(self, mock_npu_rotary,
mock_custom_enabled):
mock_config = MagicMock()
@@ -121,15 +141,25 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
# Test contiguous path when custom is disabled
non_contig_query = self.query.transpose(0, 1)
non_contig_key = self.key.transpose(0, 1)
result_q, result_k = self.layer.forward(self.positions,
non_contig_query,
non_contig_key)
vllm_config = VllmConfig()
model_config = ModelConfig(MODEL,
tokenizer=MODEL,
max_model_len=MAX_NUM_BATCHED_TOKEND)
model_config.hf_config = PretrainedConfig()
vllm_config.model_config = model_config
with set_ascend_forward_context(None, vllm_config):
result_q, result_k = self.layer.forward(self.positions,
non_contig_query,
non_contig_key)
mock_npu_rotary.assert_called_once()
self.assertEqual(result_q.shape, non_contig_query.shape)
self.assertEqual(result_k.shape, non_contig_key.shape)
@patch('vllm.config.ModelConfig.__post_init__', MagicMock())
@patch('vllm.config.VllmConfig.__post_init__', MagicMock())
@patch('vllm.distributed.parallel_state._DP', MagicMock(world_size=1))
@patch('vllm.distributed.parallel_state._TP', MagicMock(world_size=1))
def test_rope_forward_oot_with_offsets(self):
mock_config = MagicMock()
mock_config.torchair_graph_config.enabled = False
@@ -137,22 +167,41 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
# Test that NotImplementedError is raised when offsets is provided
offsets = torch.tensor([1, 2, 3])
with self.assertRaises(NotImplementedError):
self.layer.forward(self.positions, self.query, self.key, offsets)
vllm_config = VllmConfig()
model_config = ModelConfig(MODEL,
tokenizer=MODEL,
max_model_len=MAX_NUM_BATCHED_TOKEND)
model_config.hf_config = PretrainedConfig()
vllm_config.model_config = model_config
with set_ascend_forward_context(None, vllm_config):
self.layer.forward(self.positions, self.query, self.key,
offsets)
@patch('vllm_ascend.ops.rotary_embedding._custom_rotary_embedding_enabled',
return_value=False)
@patch('torch_npu._npu_rotary_embedding')
@patch('vllm.config.ModelConfig.__post_init__', MagicMock())
@patch('vllm.config.VllmConfig.__post_init__', MagicMock())
@patch('vllm.distributed.parallel_state._DP', MagicMock(world_size=1))
@patch('vllm.distributed.parallel_state._TP', MagicMock(world_size=1))
def test_rope_forward_oot_neox_style_override(self, mock_npu_rotary,
mock_custom_enabled):
mock_config = MagicMock()
mock_config.torchair_graph_config.enabled = False
# Test neox_style override
result_q, result_k = self.layer.forward(self.positions,
self.query,
self.key,
is_neox_style_override=False)
vllm_config = VllmConfig()
model_config = ModelConfig(MODEL,
tokenizer=MODEL,
max_model_len=MAX_NUM_BATCHED_TOKEND)
model_config.hf_config = PretrainedConfig()
vllm_config.model_config = model_config
with set_ascend_forward_context(None, vllm_config):
result_q, result_k = self.layer.forward(
self.positions,
self.query,
self.key,
is_neox_style_override=False)
# Check that neox_style=False was passed to the NPU function
args, kwargs = mock_npu_rotary.call_args
self.assertFalse(args[-1])
@@ -160,6 +209,10 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
@patch('vllm_ascend.ops.rotary_embedding._custom_rotary_embedding_enabled',
return_value=False)
@patch('torch_npu._npu_rotary_embedding')
@patch('vllm.config.ModelConfig.__post_init__', MagicMock())
@patch('vllm.config.VllmConfig.__post_init__', MagicMock())
@patch('vllm.distributed.parallel_state._DP', MagicMock(world_size=1))
@patch('vllm.distributed.parallel_state._TP', MagicMock(world_size=1))
def test_rope_forward_oot_rotary_dim_less_than_head_size(
self, mock_npu_rotary, mock_custom_enabled):
mock_config = MagicMock()
@@ -169,8 +222,15 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
org_rotary_dim = self.layer.rotary_dim
self.layer.rotary_dim = self.layer.head_size // 2
result_q, result_k = self.layer.forward(self.positions, self.query,
self.key)
vllm_config = VllmConfig()
model_config = ModelConfig(MODEL,
tokenizer=MODEL,
max_model_len=MAX_NUM_BATCHED_TOKEND)
model_config.hf_config = PretrainedConfig()
vllm_config.model_config = model_config
with set_ascend_forward_context(None, vllm_config):
result_q, result_k = self.layer.forward(self.positions, self.query,
self.key)
mock_npu_rotary.assert_called_once()
self.assertEqual(result_q.shape, self.query.shape)