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xc-llm-ascend/tests/ut/test_platform.py
Angazenn 5e34c70ffc [Misc] Removes unnecessary graph size re-initialization (#6280) 
### What this PR does / why we need it?

This PR removes `update_default_aclgraph_sizes`. In earlier versions, we
add this function to change default `cudagraph_capture_sizes` because
`_npu_paged_attention` degrades significantly on certain shapes (which
is included in default `cudagraph_capture_sizes` of VLLM). Now since we
use FIA as default attention op (which does not contain such performance
degradation), there is no need to add this default change. Otherwise, it
could cause some conflicts if we set a small `cudagraph_capture_sizes`
that < 20 now.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.14.1
- vLLM main:
d68209402d

---------

Signed-off-by: Angazenn <supperccell@163.com>
2026-01-27 14:38:07 +08:00

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import importlib
from unittest.mock import MagicMock, patch
import pytest
import torch
from vllm.config.compilation import CompilationMode, CUDAGraphMode
from vllm.platforms import PlatformEnum
from vllm.v1.attention.selector import AttentionSelectorConfig # type: ignore
from tests.ut.base import TestBase
from vllm_ascend.platform import NPUPlatform
from vllm_ascend.utils import ASCEND_QUANTIZATION_METHOD, COMPRESSED_TENSORS_METHOD, AscendDeviceType
class TestNPUPlatform(TestBase):
@staticmethod
def mock_vllm_config():
mock_vllm_config = MagicMock()
mock_vllm_config.compilation_config = MagicMock()
mock_vllm_config.model_config = MagicMock()
mock_vllm_config.parallel_config = MagicMock()
mock_vllm_config.cache_config = MagicMock()
mock_vllm_config.scheduler_config = MagicMock()
mock_vllm_config.speculative_config = None
mock_vllm_config.compilation_config.pass_config.enable_sp = False
mock_vllm_config.compilation_config.cudagraph_mode = None
return mock_vllm_config
@staticmethod
def mock_vllm_ascend_config():
mock_ascend_config = MagicMock()
mock_ascend_config.xlite_graph_config.enabled = False
mock_ascend_config.enable_shared_expert_dp = False
return mock_ascend_config
def setUp(self):
self.platform = NPUPlatform()
self.platform.supported_quantization[:] = ["ascend", "compressed-tensors"]
def test_class_variables(self):
self.assertEqual(NPUPlatform._enum, PlatformEnum.OOT)
self.assertEqual(NPUPlatform.device_name, "npu")
self.assertEqual(NPUPlatform.device_type, "npu")
self.assertEqual(NPUPlatform.simple_compile_backend, "eager")
self.assertEqual(NPUPlatform.ray_device_key, "NPU")
self.assertEqual(NPUPlatform.device_control_env_var, "ASCEND_RT_VISIBLE_DEVICES")
self.assertEqual(NPUPlatform.dispatch_key, "PrivateUse1")
self.assertEqual(NPUPlatform.supported_quantization, [ASCEND_QUANTIZATION_METHOD, COMPRESSED_TENSORS_METHOD])
def test_is_sleep_mode_available(self):
self.assertTrue(self.platform.is_sleep_mode_available())
@patch("vllm_ascend.utils.adapt_patch")
@patch("vllm_ascend.quantization.modelslim_config.AscendModelSlimConfig")
def test_pre_register_and_update_with_parser(self, mock_quant_config,
mock_adapt_patch):
mock_parser = MagicMock()
mock_action = MagicMock()
mock_action.choices = ["awq", "gptq"]
mock_parser._option_string_actions = {"--quantization": mock_action}
self.platform.pre_register_and_update(mock_parser)
mock_adapt_patch.assert_called_once_with(is_global_patch=True)
self.assertTrue(ASCEND_QUANTIZATION_METHOD in mock_action.choices)
self.assertEqual(len(mock_action.choices), 3) # original 2 + ascend
@patch("vllm_ascend.utils.adapt_patch")
@patch("vllm_ascend.quantization.modelslim_config.AscendModelSlimConfig")
def test_pre_register_and_update_without_parser(self, mock_quant_config,
mock_adapt_patch):
self.platform.pre_register_and_update(None)
mock_adapt_patch.assert_called_once_with(is_global_patch=True)
@patch("vllm_ascend.utils.adapt_patch")
@patch("vllm_ascend.quantization.modelslim_config.AscendModelSlimConfig")
def test_pre_register_and_update_with_parser_no_quant_action(
self, mock_quant_config, mock_adapt_patch):
mock_parser = MagicMock()
mock_parser._option_string_actions = {}
self.platform.pre_register_and_update(mock_parser)
mock_adapt_patch.assert_called_once_with(is_global_patch=True)
@patch("vllm_ascend.utils.adapt_patch")
@patch("vllm_ascend.quantization.modelslim_config.AscendModelSlimConfig")
def test_pre_register_and_update_with_existing_ascend_quant(
self, mock_quant_config, mock_adapt_patch):
mock_parser = MagicMock()
mock_action = MagicMock()
mock_action.choices = ["awq", ASCEND_QUANTIZATION_METHOD]
mock_parser._option_string_actions = {"--quantization": mock_action}
self.platform.pre_register_and_update(mock_parser)
mock_adapt_patch.assert_called_once_with(is_global_patch=True)
self.assertEqual(len(mock_action.choices), 2)
def test_get_device_capability(self):
self.assertIsNone(self.platform.get_device_capability(device_id=0))
@patch("torch.npu.get_device_name")
def test_get_device_name(self, mock_get_device_name):
device_id = 0
device_name = "Ascend910B2"
mock_get_device_name.return_value = device_name
self.assertEqual(self.platform.get_device_name(device_id), device_name)
mock_get_device_name.assert_called_once_with(0)
@patch("torch.inference_mode")
def test_inference_mode(self, mock_inference_mode):
mock_inference_mode.return_value = None
self.assertIsNone(self.platform.inference_mode())
mock_inference_mode.assert_called_once()
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.utils.update_aclgraph_sizes")
@patch("vllm_ascend.utils.get_ascend_device_type", return_value=AscendDeviceType.A3)
@patch("os.environ", {})
@patch("vllm_ascend.core.recompute_scheduler.RecomputeSchedulerConfig.initialize_from_config")
def test_check_and_update_config_basic_config_update(
self, mock_init_recompute, mock_soc_version, mock_update_acl, mock_init_ascend
):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.parallel_config.enable_expert_parallel = False
vllm_config.parallel_config.decode_context_parallel_size = 1
vllm_config.parallel_config.prefill_context_parallel_size = 1
vllm_config.parallel_config.decode_context_parallel_size = 1
vllm_config.parallel_config.prefill_context_parallel_size = 1
vllm_config.parallel_config.tensor_parallel_size = 1
mock_init_recompute.return_value = MagicMock()
vllm_config.scheduler_config = MagicMock()
# Use importlib.reload to reload the platform module, ensuring the mocked init_ascend_config method is used.
# Without this reload, when calling self.platform.check_and_update_config,
# it would execute the original unmocked init_ascend_config method, causing the unit test to fail.
from vllm_ascend import platform
importlib.reload(platform)
self.platform.check_and_update_config(vllm_config)
mock_init_ascend.assert_called_once_with(vllm_config)
@patch("vllm_ascend.utils.get_ascend_device_type", return_value=AscendDeviceType.A3)
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.core.recompute_scheduler.RecomputeSchedulerConfig.initialize_from_config")
def test_check_and_update_config_no_model_config_warning(
self, mock_init_recompute, mock_init_ascend, mock_soc_version
):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.model_config = None
vllm_config.parallel_config.decode_context_parallel_size = 1
vllm_config.parallel_config.prefill_context_parallel_size = 1
vllm_config.parallel_config.tensor_parallel_size = 1
mock_init_recompute.return_value = MagicMock()
vllm_config.scheduler_config = MagicMock()
with self.assertLogs(logger="vllm", level="WARNING") as cm:
from vllm_ascend import platform
importlib.reload(platform)
self.platform = platform.NPUPlatform()
with patch.object(platform.NPUPlatform, "_fix_incompatible_config"):
self.platform.check_and_update_config(vllm_config)
self.assertTrue("Model config is missing" in cm.output[0])
@patch("vllm_ascend.utils.get_ascend_device_type", return_value=AscendDeviceType.A3)
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.core.recompute_scheduler.RecomputeSchedulerConfig.initialize_from_config")
def test_check_and_update_config_enforce_eager_mode(self, mock_init_recompute, mock_init_ascend, mock_soc_version):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.model_config.enforce_eager = True
vllm_config.parallel_config.decode_context_parallel_size = 1
vllm_config.parallel_config.prefill_context_parallel_size = 1
vllm_config.parallel_config.tensor_parallel_size = 1
mock_init_recompute.return_value = MagicMock()
vllm_config.scheduler_config = MagicMock()
with self.assertLogs(logger="vllm", level="INFO") as cm:
from vllm_ascend import platform
importlib.reload(platform)
self.platform = platform.NPUPlatform()
with patch.object(platform.NPUPlatform, "_fix_incompatible_config"):
self.platform.check_and_update_config(vllm_config)
self.assertTrue("Compilation disabled, using eager mode by default" in cm.output[0])
self.assertEqual(
vllm_config.compilation_config.mode,
CompilationMode.NONE,
)
self.assertEqual(
vllm_config.compilation_config.cudagraph_mode,
CUDAGraphMode.NONE,
)
@patch("vllm_ascend.utils.get_ascend_device_type", return_value=AscendDeviceType.A3)
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.core.recompute_scheduler.RecomputeSchedulerConfig.initialize_from_config")
def test_check_and_update_config_unsupported_compilation_level(
self, mock_init_recompute, mock_init_ascend, mock_soc_version
):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.model_config.enforce_eager = False
vllm_config.parallel_config.decode_context_parallel_size = 1
vllm_config.parallel_config.prefill_context_parallel_size = 1
vllm_config.parallel_config.tensor_parallel_size = 1
mock_init_recompute.return_value = MagicMock()
vllm_config.scheduler_config = MagicMock()
vllm_config.compilation_config.mode = CompilationMode.DYNAMO_TRACE_ONCE
with self.assertLogs(logger="vllm", level="WARNING") as cm:
from vllm_ascend import platform
importlib.reload(platform)
self.platform = platform.NPUPlatform()
with patch.object(platform.NPUPlatform, "_fix_incompatible_config"):
self.platform.check_and_update_config(vllm_config)
self.assertTrue("NPU does not support" in cm.output[0])
self.assertEqual(
vllm_config.compilation_config.mode,
CompilationMode.NONE,
)
self.assertEqual(
vllm_config.compilation_config.cudagraph_mode,
CUDAGraphMode.NONE,
)
@pytest.mark.skip("Revert me when vllm support setting cudagraph_mode on oot platform")
@patch("vllm_ascend.utils.get_ascend_device_type", return_value=AscendDeviceType.A3)
@patch("vllm_ascend.ascend_config.init_ascend_config")
def test_check_and_update_config_unsupported_cudagraph_mode(self, mock_init_ascend, mock_soc_version):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.model_config.enforce_eager = False
vllm_config.compilation_config.cudagraph_mode = CUDAGraphMode.FULL
with self.assertLogs(logger="vllm", level="INFO") as cm:
from vllm_ascend import platform
importlib.reload(platform)
self.platform.check_and_update_config(vllm_config)
self.assertTrue("cudagraph_mode is not support on NPU. falling back to NONE" in cm.output[0])
self.assertEqual(
vllm_config.compilation_config.mode,
CompilationMode.NONE,
)
self.assertEqual(
vllm_config.compilation_config.cudagraph_mode,
CUDAGraphMode.NONE,
)
@patch("vllm_ascend.utils.get_ascend_device_type", return_value=AscendDeviceType.A3)
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.core.recompute_scheduler.RecomputeSchedulerConfig.initialize_from_config")
def test_check_and_update_config_cache_config_block_size(
self, mock_init_recompute, mock_init_ascend, mock_soc_version
):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.cache_config.block_size = None
vllm_config.cache_config.enable_prefix_caching = True
vllm_config.parallel_config.decode_context_parallel_size = 1
vllm_config.parallel_config.prefill_context_parallel_size = 1
vllm_config.parallel_config.tensor_parallel_size = 1
mock_init_recompute.return_value = MagicMock()
vllm_config.scheduler_config = MagicMock()
from vllm_ascend import platform
importlib.reload(platform)
self.platform.check_and_update_config(vllm_config)
self.assertEqual(vllm_config.cache_config.block_size, 128)
@patch("vllm_ascend.utils.get_ascend_device_type", return_value=AscendDeviceType.A3)
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.core.recompute_scheduler.RecomputeSchedulerConfig.initialize_from_config")
def test_check_and_update_config_v1_worker_class_selection(
self, mock_init_recompute, mock_init_ascend, mock_soc_version
):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.parallel_config.worker_cls = "auto"
vllm_config.parallel_config.decode_context_parallel_size = 1
vllm_config.parallel_config.prefill_context_parallel_size = 1
vllm_config.parallel_config.tensor_parallel_size = 1
mock_init_recompute.return_value = MagicMock()
vllm_config.scheduler_config = MagicMock()
from vllm_ascend import platform
importlib.reload(platform)
self.platform.check_and_update_config(vllm_config)
self.assertEqual(
vllm_config.parallel_config.worker_cls,
"vllm_ascend.worker.worker.NPUWorker",
)
test_ascend_config = TestNPUPlatform.mock_vllm_ascend_config()
test_ascend_config.xlite_graph_config.enabled = True
mock_init_ascend.return_value = test_ascend_config
vllm_config.parallel_config.worker_cls = "auto"
self.platform.check_and_update_config(vllm_config)
self.assertEqual(
vllm_config.parallel_config.worker_cls,
"vllm_ascend.xlite.xlite_worker.XliteWorker",
)
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.utils.get_ascend_device_type", return_value=AscendDeviceType._310P)
@patch("vllm_ascend.core.recompute_scheduler.RecomputeSchedulerConfig.initialize_from_config")
def test_check_and_update_config_310p_no_custom_ops(self, mock_init_recompute, mock_soc_version, mock_init_ascend):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.compilation_config.custom_ops = []
vllm_config.parallel_config.decode_context_parallel_size = 1
vllm_config.parallel_config.prefill_context_parallel_size = 1
vllm_config.parallel_config.tensor_parallel_size = 1
mock_init_recompute.return_value = MagicMock()
vllm_config.scheduler_config = MagicMock()
from vllm_ascend import platform
importlib.reload(platform)
self.platform.check_and_update_config(vllm_config)
self.assertEqual(vllm_config.compilation_config.custom_ops, [])
def test_get_attn_backend_cls_use_v1_and_mla(self):
attn_selector_config = AttentionSelectorConfig(
dtype=torch.float16,
head_size=0,
kv_cache_dtype=None,
block_size=128,
use_mla=True,
use_sparse=False,
)
result = self.platform.get_attn_backend_cls("ascend", attn_selector_config)
self.assertEqual(result, "vllm_ascend.attention.mla_v1.AscendMLABackend")
def test_get_attn_backend_cls_use_v1_only(self):
attn_selector_config = AttentionSelectorConfig(
dtype=torch.float16,
head_size=0,
kv_cache_dtype=None,
block_size=128,
use_mla=False,
use_sparse=False,
)
result = self.platform.get_attn_backend_cls("ascend", attn_selector_config)
self.assertEqual(result, "vllm_ascend.attention.attention_v1.AscendAttentionBackend")
def test_get_punica_wrapper(self):
result = self.platform.get_punica_wrapper()
self.assertEqual(result, "vllm_ascend.lora.punica_npu.PunicaWrapperNPU")
@patch("torch.npu.reset_peak_memory_stats")
@patch("torch.npu.max_memory_allocated")
def test_get_current_memory_usage_with_specific_device(self, mock_max_memory, mock_reset_stats):
max_memory_allocated_result = 1024.0
mock_max_memory.return_value = max_memory_allocated_result
test_device = torch.device("npu:0")
result = self.platform.get_current_memory_usage(device=test_device)
mock_reset_stats.assert_called_once_with(test_device)
mock_max_memory.assert_called_once_with(test_device)
self.assertEqual(result, max_memory_allocated_result)
@patch("torch.npu.reset_peak_memory_stats")
@patch("torch.npu.max_memory_allocated")
def test_get_current_memory_usage_with_default_device(self, mock_max_memory, mock_reset_stats):
max_memory_allocated_result = 1024.0
mock_max_memory.return_value = max_memory_allocated_result
result = self.platform.get_current_memory_usage()
mock_reset_stats.assert_called_once_with(None)
mock_max_memory.assert_called_once_with(None)
self.assertEqual(result, max_memory_allocated_result)
@patch("torch.npu.reset_peak_memory_stats", side_effect=RuntimeError("Device error"))
@patch("torch.npu.max_memory_allocated")
def test_get_current_memory_usage_when_reset_stats_fails(self, mock_max_memory, mock_reset_stats):
with self.assertRaises(RuntimeError):
self.platform.get_current_memory_usage()
mock_reset_stats.assert_called_once()
mock_max_memory.assert_not_called()
@patch("torch.npu.reset_peak_memory_stats")
@patch(
"torch.npu.max_memory_allocated",
side_effect=RuntimeError("Memory query failed"),
)
def test_get_current_memory_usage_when_query_fails(self, mock_max_memory, mock_reset_stats):
with self.assertRaises(RuntimeError):
self.platform.get_current_memory_usage()
mock_reset_stats.assert_called_once()
mock_max_memory.assert_called_once()
def test_get_device_communicator_cls_returns_correct_value(self):
self.assertEqual(
self.platform.get_device_communicator_cls(),
"vllm_ascend.distributed.device_communicators.npu_communicator.NPUCommunicator",
)
def test_is_pin_memory_available_returns_true(self):
self.assertTrue(self.platform.is_pin_memory_available())
def test_get_static_graph_wrapper_cls_returns_correct_value(self):
self.assertEqual(
self.platform.get_static_graph_wrapper_cls(),
"vllm_ascend.compilation.acl_graph.ACLGraphWrapper",
)
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