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[Main2Main] Upgrade vllm commit to 0109 (#5752)

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### What this PR does / why we need it?
Upgrade vllm commit to 0109 (bde38c11df0ea066a740efe9b77fff5418be45df)

1. remove `init_cached_hf_modules ` due to
https://github.com/vllm-project/vllm/pull/31786
2. fix spec_decode e2e test due to
https://github.com/vllm-project/vllm/pull/29821 break
3. fix `vllm.v1.attention.backends.utils` duo to
https://github.com/vllm-project/vllm/pull/31891
4. fix `self.seq_lens - query_lens` on same device due to
https://github.com/vllm-project/vllm/pull/31773
5. skip model_runner_v2 e2e test due to `'_OpNamespace' '_C' object has
no attribute 'get_cuda_view_from_cpu_tensor'`

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

Signed-off-by: hfadzxy <starmoon_zhang@163.com>
This commit is contained in:
zhangxinyuehfad
2026-01-13 19:14:43 +08:00
committed by GitHub
parent eed9e366a7
commit f7b904641e
21 changed files with 203 additions and 38 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
.github/workflows/_e2e_test.yaml vendored
View File

@@ -119,7 +119,7 @@ jobs:
pytest -sv --durations=0 tests/e2e/singlecard/compile/test_norm_quant_fusion.py
# model_runner_v2
pytest -sv --durations=0 tests/e2e/singlecard/model_runner_v2/test_basic.py
# pytest -sv --durations=0 tests/e2e/singlecard/model_runner_v2/test_basic.py
# pooling
pytest -sv --durations=0 tests/e2e/singlecard/pooling/test_classification.py
@@ -309,7 +309,7 @@ jobs:
run: |
pytest -sv --durations=0 tests/e2e/multicard/4-cards/test_data_parallel_tp2.py
pytest -sv --durations=0 tests/e2e/multicard/4-cards/test_kimi_k2.py
pytest -sv --durations=0 tests/e2e/multicard/4-cards/test_qwen3_next.py
pytest -sv --durations=0 tests/e2e/multicard/4-cards/test_qwen3_next.py
# long_sequence
pytest -sv --durations=0 tests/e2e/multicard/4-cards/long_sequence/test_accuracy.py

2
.github/workflows/bot_pr_create.yaml vendored
View File

@@ -37,7 +37,7 @@ jobs:
steps:
- name: Get vLLM version
run: |
VLLM_COMMIT=2f4e6548efec402b913ffddc8726230d9311948d
VLLM_COMMIT=bde38c11df0ea066a740efe9b77fff5418be45df
echo "VLLM_COMMIT=https://github.com/vllm-project/vllm/commit/$VLLM_COMMIT" >> $GITHUB_ENV
- name: Checkout repository

2
.github/workflows/pr_test_full.yaml vendored
View File

@@ -75,7 +75,7 @@ jobs:
name: e2e-full
strategy:
matrix:
vllm_version: [2f4e6548efec402b913ffddc8726230d9311948d, v0.13.0]
vllm_version: [bde38c11df0ea066a740efe9b77fff5418be45df, v0.13.0]
needs: [changes]
if: ${{ needs.changes.outputs.e2e_tracker == 'true' }}
uses: ./.github/workflows/_e2e_test.yaml

6
.github/workflows/pr_test_light.yaml vendored
View File

@@ -41,7 +41,7 @@ jobs:
lint:
uses: ./.github/workflows/_pre_commit.yml
with:
vllm: 2f4e6548efec402b913ffddc8726230d9311948d
vllm: bde38c11df0ea066a740efe9b77fff5418be45df
changes:
runs-on: linux-aarch64-a2-0
outputs:
@@ -81,7 +81,7 @@ jobs:
if: ${{ needs.lint.result == 'success' && (needs.changes.outputs.e2e_tracker == 'true' || needs.changes.outputs.ut_tracker == 'true') }}
strategy:
matrix:
vllm_version: [2f4e6548efec402b913ffddc8726230d9311948d, v0.13.0]
vllm_version: [bde38c11df0ea066a740efe9b77fff5418be45df, v0.13.0]
uses: ./.github/workflows/_unit_test.yaml
with:
vllm: ${{ matrix.vllm_version }}
@@ -93,7 +93,7 @@ jobs:
name: e2e-light
strategy:
matrix:
vllm_version: [2f4e6548efec402b913ffddc8726230d9311948d, v0.13.0]
vllm_version: [bde38c11df0ea066a740efe9b77fff5418be45df, v0.13.0]
# Note (yikun): If CI resource are limited we can split job into two chain jobs
needs: [lint, changes]
# only trigger e2e test after lint passed and the change is e2e related with pull request.

2
.github/workflows/schedule_codecov_refresh.yaml vendored
View File

@@ -33,7 +33,7 @@ jobs:
name: refresh codecov
strategy:
matrix:
vllm_version: [2f4e6548efec402b913ffddc8726230d9311948d]
vllm_version: [bde38c11df0ea066a740efe9b77fff5418be45df]
uses: ./.github/workflows/_unit_test.yaml
with:
vllm: ${{ matrix.vllm_version }}

2
docs/source/community/versioning_policy.md
View File

@@ -51,7 +51,7 @@ If you're using v0.7.3, don't forget to install [mindie-turbo](https://pypi.org/
For main branch of vLLM Ascend, we usually make it compatible with the latest vLLM release and a newer commit hash of vLLM. Please note that this table is usually updated. Please check it regularly.
| vLLM Ascend | vLLM | Python | Stable CANN | PyTorch/torch_npu |
|-------------|--------------|------------------|-------------|--------------------|
| main | 2f4e6548efec402b913ffddc8726230d9311948d, v0.13.0 tag | >= 3.10, < 3.12 | 8.3.RC2 | 2.8.0 / 2.8.0 |
| main | bde38c11df0ea066a740efe9b77fff5418be45df, v0.13.0 tag | >= 3.10, < 3.12 | 8.3.RC2 | 2.8.0 / 2.8.0 |
## Release cadence

19
tests/e2e/singlecard/compile/test_norm_quant_fusion.py
View File

@@ -305,15 +305,16 @@ def test_rmsnorm_quant_fusion(
vllm_config = VllmConfig(model_config=ModelConfig(dtype=dtype))
update_environment_variables({
"RANK": "0",
"LOCAL_RANK": "0",
"WORLD_SIZE": "1",
"MASTER_ADDR": "localhost",
"MASTER_PORT": "12345",
})
init_distributed_environment()
ensure_model_parallel_initialized(1, 1)
with vllm.config.set_current_vllm_config(vllm_config):
update_environment_variables({
"RANK": "0",
"LOCAL_RANK": "0",
"WORLD_SIZE": "1",
"MASTER_ADDR": "localhost",
"MASTER_PORT": "12345",
})
init_distributed_environment()
ensure_model_parallel_initialized(1, 1)
with vllm.config.set_current_vllm_config(vllm_config):
with set_ascend_forward_context(None, vllm_config):

5
tests/ut/attention/test_attention_cp.py
View File

@@ -33,6 +33,11 @@ class TestAscendAttentionCPImpl(TestBase):
self.layer_no_quant.layer_name = "test_layer"
self.layer_no_quant._k_scale_float = 1.0
self.layer_no_quant._v_scale_float = 1.0
self.mock_vllm_config = MagicMock()
self.config_patcher = patch(
'vllm_ascend.attention.attention_v1.get_current_vllm_config',
return_value=self.mock_vllm_config)
self.config_patcher.start()
self.impl = AscendAttentionCPImpl(
num_heads=8,

35
tests/ut/attention/test_attention_v1.py
View File

@@ -13,6 +13,23 @@ from vllm_ascend.utils import AscendDeviceType
class TestAscendAttentionBackend(TestBase):
def setUp(self):
self.mock_config = MagicMock()
mock_parallel_config = MagicMock()
mock_parallel_config.prefill_context_parallel_size = 1
mock_parallel_config.decode_context_parallel_size = 1
self.mock_config.parallel_config = mock_parallel_config
self.utils_patcher = patch(
'vllm_ascend.attention.utils.get_current_vllm_config',
return_value=self.mock_config)
self.utils_patcher.start()
from vllm_ascend.attention.utils import enable_cp
enable_cp.cache_clear()
def test_get_name(self):
self.assertEqual(AscendAttentionBackend.get_name(), "CUSTOM")
@@ -102,6 +119,19 @@ class TestAscendAttentionMetadataBuilder(TestBase):
class TestAscendAttentionBackendImpl(TestBase):
def setUp(self):
self.mock_event = MagicMock()
self.mock_event.record.return_value = None
self.mock_event.wait.return_value = None
self.mock_stream = MagicMock()
self.event_patcher = patch('torch_npu.npu.Event',
return_value=self.mock_event)
self.stream_patcher = patch('torch_npu.npu.current_stream',
return_value=self.mock_stream)
self.event_patcher.start()
self.stream_patcher.start()
self.layer = MagicMock()
self.layer.layer_name = "test_layer"
self.layer._k_scale_float = 1.0
@@ -119,6 +149,11 @@ class TestAscendAttentionBackendImpl(TestBase):
self.layer_no_quant.layer_name = "test_layer"
self.layer_no_quant._k_scale_float = 1.0
self.layer_no_quant._v_scale_float = 1.0
self.mock_vllm_config = MagicMock()
self.config_patcher = patch(
'vllm_ascend.attention.attention_v1.get_current_vllm_config',
return_value=self.mock_vllm_config)
self.config_patcher.start()
self.impl = AscendAttentionBackendImpl(
num_heads=8,

17
tests/ut/attention/test_mla_v1.py
View File

@@ -22,6 +22,23 @@ from vllm_ascend.utils import vllm_version_is
class TestAscendMLABackend(TestBase):
def setUp(self):
self.mock_config = MagicMock()
mock_parallel_config = MagicMock()
mock_parallel_config.prefill_context_parallel_size = 1
mock_parallel_config.decode_context_parallel_size = 1
self.mock_config.parallel_config = mock_parallel_config
self.utils_patcher = patch(
'vllm_ascend.attention.utils.get_current_vllm_config',
return_value=self.mock_config)
self.utils_patcher.start()
from vllm_ascend.attention.utils import enable_cp
enable_cp.cache_clear()
def test_get_name(self):
self.assertEqual(AscendMLABackend.get_name(), "ASCEND_MLA")

22
tests/ut/attention/test_sfa_v1.py
View File

@@ -12,6 +12,7 @@ if 'torch_npu._inductor' not in sys.modules:
from vllm_ascend.attention.sfa_v1 import (AscendSFABackend, AscendSFAImpl,
AscendSFAMetadata,
AscendSFAMetadataBuilder)
from vllm_ascend.utils import enable_dsa_cp
class TestAscendSFABackend(TestBase):
@@ -83,6 +84,27 @@ class TestAscendSFAMetadata(TestBase):
class TestAscendSFAMetadataBuilder(TestBase):
def setUp(self):
self.mock_cfg = MagicMock()
self.mock_cfg.parallel_config = MagicMock()
self.mock_cfg.parallel_config.tensor_parallel_size = 1
self.mock_cfg.parallel_config.prefill_context_parallel_size = 1
self.mock_cfg.parallel_config.decode_context_parallel_size = 1
self.mock_cfg.compilation_config = MagicMock()
self.mock_cfg.compilation_config.pass_config = MagicMock()
self.mock_cfg.compilation_config.pass_config.enable_sp = False
self.mock_cfg.speculative_config.num_speculative_tokens = 0
self.patcher = patch("vllm.config.get_current_vllm_config",
return_value=self.mock_cfg)
self.patcher.start()
if hasattr(enable_dsa_cp, "cache_clear"):
enable_dsa_cp.cache_clear()
def test_ascend_sfa_metadata_builder_default(self):
kv_cache_spec = MagicMock()
layer_names = ["layer1", "layer2"]

19
tests/ut/ops/test_activation.py
View File

@@ -13,10 +13,11 @@
# This file is a part of the vllm-ascend project.
#
from unittest.mock import patch
from unittest.mock import MagicMock, patch
import pytest
import torch
from vllm.config import set_current_vllm_config
from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
from vllm_ascend.utils import AscendDeviceType
@@ -27,8 +28,20 @@ def dummy_tensor():
return torch.randn(4, 8, dtype=torch.float16)
@pytest.fixture
def default_vllm_config():
mock_config = MagicMock()
mock_config.compilation_config.dispatch_forward_backend = "eager"
mock_config.compilation_config.custom_ops = ["all"]
with set_current_vllm_config(mock_config):
yield mock_config
@patch("torch_npu.npu_fast_gelu", side_effect=lambda x: x + 1)
def test_QuickGELU_forward(mock_gelu, dummy_tensor):
def test_QuickGELU_forward(mock_gelu, dummy_tensor, default_vllm_config):
layer = QuickGELU()
out = layer.forward(dummy_tensor)
@@ -45,7 +58,7 @@ def test_QuickGELU_forward(mock_gelu, dummy_tensor):
side_effect=lambda x: None)
def test_SiluAndMul_forward(mock_maybe_prefetch_mlp_down_proj,
mock_maybe_wait_prefetch_done, mock_swiglu,
is_310p, dummy_tensor):
is_310p, dummy_tensor, default_vllm_config):
with patch("vllm_ascend.utils.get_ascend_device_type",
return_value=AscendDeviceType._310P

14
tests/ut/ops/test_layernorm.py
View File

@@ -1,7 +1,8 @@
from unittest.mock import patch
from unittest.mock import MagicMock, patch
import pytest
import torch
from vllm.config import set_current_vllm_config
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm_ascend.utils import AscendDeviceType
@@ -20,13 +21,22 @@ def mock_add_rms_norm(x, residual, weight, eps):
return 2 * x, None, 2 * residual
@pytest.fixture(autouse=True)
def default_vllm_config():
mock_config = MagicMock()
mock_config.compilation_config.custom_ops = ["all"]
with set_current_vllm_config(mock_config):
yield mock_config
@pytest.mark.parametrize("is_310p", [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, residual,
dummy_tensor):
dummy_tensor, default_vllm_config):
with patch("vllm_ascend.utils.get_ascend_device_type",
return_value=AscendDeviceType._310P

18
tests/ut/ops/test_rotary_embedding.py
View File

@@ -78,6 +78,12 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
def setUp(self):
# Common setup for tests
self.config_patcher = patch('vllm.config.vllm.get_current_vllm_config')
self.mock_get_config = self.config_patcher.start()
mock_config = MagicMock()
mock_config.compilation_config.custom_ops = ["all"]
self.mock_get_config.return_value = mock_config
self.positions = torch.tensor([1, 2, 3])
self.query = torch.randn(3, 1, 32, dtype=torch.float16)
self.key = torch.randn(3, 1, 32, dtype=torch.float16)
@@ -242,6 +248,12 @@ class TestAscendDeepseekScalingRotaryEmbedding(TestBase):
def setUp(self):
# Common setup for tests
self.config_patcher = patch('vllm.config.vllm.get_current_vllm_config')
self.mock_get_config = self.config_patcher.start()
mock_config = MagicMock()
mock_config.compilation_config.custom_ops = ["all"]
self.mock_get_config.return_value = mock_config
self.positions = torch.tensor([1, 2, 3])
self.query = torch.randn(3, 1, 32, dtype=torch.float16)
self.key = torch.randn(3, 1, 32, dtype=torch.float16)
@@ -368,7 +380,11 @@ class TestAscendDeepseekScalingRotaryEmbedding(TestBase):
class TestAscendMRotaryEmbedding(unittest.TestCase):
def setUp(self):
# Common setup for tests
self.config_patcher = patch('vllm.config.vllm.get_current_vllm_config')
self.mock_get_config = self.config_patcher.start()
mock_config = MagicMock()
mock_config.compilation_config.custom_ops = ["all"]
self.mock_get_config.return_value = mock_config
self.number_tokens = 3
self.num_head = 8
self.num_kvhead = 8

17
tests/ut/ops/test_token_dispatcher.py
View File

@@ -29,6 +29,23 @@ from vllm_ascend.ops.fused_moe.token_dispatcher import ( # isort: skip
class TestTokenDispatcherWithMC2(TestBase):
def setUp(self):
self.config_patcher = patch(
'vllm_ascend.ops.fused_moe.token_dispatcher.get_current_vllm_config'
)
self.mock_get_config = self.config_patcher.start()
mock_config = MagicMock()
mock_config.scheduler_config.max_num_seqs = 256
mock_config.scheduler_config.decode_max_num_seqs = 256
mock_config.compilation_config.custom_ops = ["all"]
mock_config.speculative_config = None
mock_config.parallel_config.tensor_parallel_size = 1
self.mock_get_config.return_value = mock_config
self.mc2_group = MagicMock()
self.mc2_group.device_group.return_value._get_backend.return_value.get_hccl_comm_name.return_value = "hccl_123"
self.mc2_group.rank_in_group = 0

9
tests/ut/ops/test_vocab_parallel_embedding.py
View File

@@ -208,6 +208,15 @@ class TestCustomVocabParallelEmbedding(unittest.TestCase):
class TestAscendLogitsProcessor(unittest.TestCase):
def setUp(self):
self.mock_vllm_config = MagicMock()
self.mock_vllm_config.compilation_config.custom_ops = ["all"]
from vllm.config.vllm import set_current_vllm_config
set_current_vllm_config(self.mock_vllm_config)
self.config_patch = patch("vllm.config.vllm.get_current_vllm_config",
return_value=self.mock_vllm_config)
self.config_patch.start()
self.vocab_size = 50
self.num_embeddings = 50
self.embedding_dim = 10

19
tests/ut/worker/test_worker_v1.py
View File

@@ -5,6 +5,7 @@ import torch
from vllm.config import CacheConfig, ModelConfig, ParallelConfig, VllmConfig
from tests.ut.base import TestBase
from vllm_ascend.utils import vllm_version_is
init_cached_hf_modules_path = "vllm.utils.import_utils.init_cached_hf_modules"
@@ -52,7 +53,7 @@ class TestNPUWorker(TestBase):
@patch("vllm_ascend.worker.worker.get_ascend_config")
@patch("vllm_ascend.worker.worker.init_ascend_config")
@patch("vllm_ascend.worker.worker.check_ascend_device_type")
@patch(init_cached_hf_modules_path)
@patch(init_cached_hf_modules_path, create=True)
@patch("vllm_ascend.worker.worker.NPUWorker._init_profiler")
def test_init_npu_worker_normal_case(
self,
@@ -106,7 +107,7 @@ class TestNPUWorker(TestBase):
@patch("vllm_ascend.worker.worker.get_ascend_config")
@patch("vllm_ascend.worker.worker.init_ascend_config")
@patch("vllm_ascend.worker.worker.check_ascend_device_type")
@patch(init_cached_hf_modules_path)
@patch(init_cached_hf_modules_path, create=True)
@patch("vllm_ascend.worker.worker.NPUWorker._init_profiler")
def test_init_npu_worker_with_trust_remote_code(
self,
@@ -140,7 +141,10 @@ class TestNPUWorker(TestBase):
)
# Verify init_cached_hf_modules is called (trust_remote_code=True)
mock_init_cached_hf_modules.assert_called_once()
if vllm_version_is('0.13.0'):
mock_init_cached_hf_modules.assert_called_once()
else:
mock_init_cached_hf_modules.assert_not_called()
@patch("vllm_ascend.utils.adapt_patch")
@patch("vllm_ascend.ops")
@@ -149,7 +153,7 @@ class TestNPUWorker(TestBase):
@patch("vllm_ascend.worker.worker.get_ascend_config")
@patch("vllm_ascend.worker.worker.init_ascend_config")
@patch("vllm_ascend.worker.worker.check_ascend_device_type")
@patch(init_cached_hf_modules_path)
@patch(init_cached_hf_modules_path, create=True)
@patch("vllm_ascend.worker.worker.NPUWorker._init_profiler")
def test_init_npu_worker_with_custom_cache_dtype(
self,
@@ -813,10 +817,11 @@ class TestNPUWorker(TestBase):
mock_scheduler_output, None)
self.assertEqual(result, mock_model_output)
@patch("vllm_ascend.worker.worker.enable_sp", return_value=False)
@patch("vllm_ascend.worker.worker.get_pp_group")
@patch("vllm_ascend.worker.worker.get_tp_group")
def test_execute_model_middle_rank(self, mock_get_tp_group,
mock_get_pp_group):
mock_get_pp_group, mock_enable_sp):
"""Test execute_model method - middle rank case"""
from vllm.sequence import IntermediateTensors
@@ -1113,12 +1118,14 @@ class TestNPUWorker(TestBase):
worker.model_runner.initialize_kv_cache.assert_called_once_with(
mock_kv_cache_config)
@patch("vllm_ascend.worker.worker.enable_sp", return_value=False)
@patch("vllm_ascend.worker.worker.get_pp_group")
@patch("vllm_ascend.worker.worker.get_tp_group")
@patch("vllm_ascend.worker.worker.EMPTY_MODEL_RUNNER_OUTPUT")
def test_execute_model_kv_connector_not_finished(self, mock_empty_output,
mock_get_tp_group,
mock_get_pp_group):
mock_get_pp_group,
mock_enable_sp):
"""Test execute_model method - kv_connector_output not finished sending/recving case"""
from vllm.sequence import IntermediateTensors

8
vllm_ascend/attention/mla_v1.py
View File

@@ -6,7 +6,6 @@ import torch
import torch_npu
import vllm.envs as envs_vllm
from vllm.attention.backends.abstract import AttentionBackend, MLAAttentionImpl
from vllm.attention.backends.utils import PAD_SLOT_ID
from vllm.config import VllmConfig, get_current_vllm_config
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.logger import logger
@@ -39,12 +38,17 @@ from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla
from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, maybe_trans_nz,
weak_ref_tensors)
vllm_version_is, weak_ref_tensors)
from vllm_ascend.worker.npu_input_batch import NPUInputBatch
if TYPE_CHECKING:
from vllm.v1.core.sched.output import SchedulerOutput
if vllm_version_is('0.13.0'):
from vllm.attention.backends.utils import PAD_SLOT_ID # type: ignore
else:
from vllm.v1.attention.backends.utils import PAD_SLOT_ID # type: ignore
MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
BUILD_METADATA_STEP_PREFILL = 0
BUILD_METADATA_STEP_DECODE = 1

8
vllm_ascend/ops/triton/mamba/causal_conv1d.py
View File

@@ -13,7 +13,13 @@ import torch
import torch.nn.functional as F
import triton
import triton.language as tl
from vllm.attention.backends.utils import PAD_SLOT_ID
from vllm_ascend.utils import vllm_version_is
if vllm_version_is('0.13.0'):
from vllm.attention.backends.utils import PAD_SLOT_ID # type: ignore
else:
from vllm.v1.attention.backends.utils import PAD_SLOT_ID # type: ignore
def causal_conv1d_ref(

4
vllm_ascend/worker/model_runner_v1.py
View File

@@ -1670,6 +1670,8 @@ class NPUModelRunner(GPUModelRunner):
attn_metadata,
aux_hidden_states,
)
if not vllm_version_is('0.13.0'):
self._copy_draft_token_ids_to_cpu(scheduler_output)
(
logprobs_lists,
@@ -1983,7 +1985,7 @@ class NPUModelRunner(GPUModelRunner):
query_start_loc_cpu=self.query_start_loc.cpu[:num_reqs +
1],
_seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
seq_lens=self.seq_lens.cpu[:num_reqs],
seq_lens=self.seq_lens.gpu[:num_reqs],
num_reqs=num_reqs,
num_actual_tokens=num_tokens,
block_table_tensor=block_table_tensor[:num_reqs],

9
vllm_ascend/worker/worker.py
View File

@@ -121,11 +121,12 @@ class NPUWorker(WorkerBase):
self.cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
self.cache_config.cache_dtype]
if self.model_config.trust_remote_code:
# note: lazy import to avoid importing torch before initializing
from vllm.utils.import_utils import init_cached_hf_modules
if vllm_version_is('0.13.0'):
if self.model_config.trust_remote_code:
# note: lazy import to avoid importing torch before initializing
from vllm.utils.import_utils import init_cached_hf_modules
init_cached_hf_modules()
init_cached_hf_modules()
self.profiler = self._init_profiler()
if vllm_config.model_config and vllm_config.model_config.enable_sleep_mode: