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xc-llm-ascend/tests/ut/test_platform.py
linfeng-yuan 05a561129e [Graph][Bugfix] Set default cudagraph max capture size via platform defaults (#7572) 
### What this PR does / why we need it?

This PR lets NPU platform provide its own default
`max_cudagraph_capture_size` via
`NPUPlatform.apply_config_platform_defaults()`.

Previously, when cudagraph sizing was left unset, Ascend inherited
vLLM's upstream default heuristic in `_set_cudagraph_sizes()`, which
uses `max_num_seqs * decode_query_len * 2`. This PR changes Ascend's
default to `min(max_num_seqs * decode_query_len, 512)` while keeping the
rest of vLLM's cudagraph sizing logic unchanged.

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

Yes, but only for Ascend when users do not explicitly configure
cudagraph sizing.

If `max_cudagraph_capture_size` and `cudagraph_capture_sizes` are both
unset, we now uses `max_num_seqs * decode_query_len` (capped at `512`)
instead of the upstream `* 2` default. Explicit user settings are
unchanged.

### How was this patch tested?

Add unit tests to cover:

- default max injection via `apply_config_platform_defaults()`
- explicit `max_cudagraph_capture_size` is preserved
- explicit `cudagraph_capture_sizes` are preserved
- Ascend default max no longer uses the upstream `* 2`
- late `_set_cudagraph_sizes()` recomputation reuses the current max
input

- vLLM version: v0.18.0
- vLLM main:
ed359c497a

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>
2026-03-25 17:57:19 +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.scheduler_config.max_num_seqs = None
mock_vllm_config.speculative_config = None
mock_vllm_config.additional_config = {}
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.xlite_graph_config.full_mode = False
mock_ascend_config.ascend_compilation_config.enable_npugraph_ex = False
mock_ascend_config.ascend_fusion_config = None
mock_ascend_config.recompute_scheduler_enable = False
mock_ascend_config.SLO_limits_for_dynamic_batch = -1
mock_ascend_config.enable_shared_expert_dp = False
mock_ascend_config.update_compile_ranges_split_points = MagicMock()
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_apply_config_platform_defaults_sets_ascend_default_max(self):
test_cases = [
(40, 3, 160),
(200, 3, 512),
]
for max_num_seqs, num_speculative_tokens, expected_max in test_cases:
with self.subTest(
max_num_seqs=max_num_seqs,
num_speculative_tokens=num_speculative_tokens,
expected_max=expected_max,
):
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.scheduler_config.max_num_seqs = max_num_seqs
vllm_config.speculative_config = MagicMock(
num_speculative_tokens=num_speculative_tokens
)
vllm_config.compilation_config.max_cudagraph_capture_size = None
vllm_config.compilation_config.cudagraph_capture_sizes = None
self.platform.apply_config_platform_defaults(vllm_config)
self.assertEqual(
vllm_config.compilation_config.max_cudagraph_capture_size,
expected_max,
)
def test_apply_config_platform_defaults_respects_explicit_max(self):
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.compilation_config.max_cudagraph_capture_size = 456
vllm_config.compilation_config.cudagraph_capture_sizes = None
self.platform.apply_config_platform_defaults(vllm_config)
self.assertEqual(vllm_config.compilation_config.max_cudagraph_capture_size, 456)
def test_apply_config_platform_defaults_respects_explicit_sizes(self):
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.compilation_config.max_cudagraph_capture_size = None
vllm_config.compilation_config.cudagraph_capture_sizes = [1, 2, 4]
self.platform.apply_config_platform_defaults(vllm_config)
self.assertIsNone(vllm_config.compilation_config.max_cudagraph_capture_size)
self.assertEqual(vllm_config.compilation_config.cudagraph_capture_sizes, [1, 2, 4])
def test_apply_config_platform_defaults_skips_when_scheduler_max_num_seqs_is_missing(self):
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.compilation_config.max_cudagraph_capture_size = None
vllm_config.compilation_config.cudagraph_capture_sizes = None
self.platform.apply_config_platform_defaults(vllm_config)
self.assertIsNone(vllm_config.compilation_config.max_cudagraph_capture_size)
@patch("vllm_ascend.platform.refresh_block_size")
@patch("vllm_ascend.platform.get_ascend_device_type", return_value=AscendDeviceType.A3)
@patch("vllm_ascend.platform.enable_sp", return_value=False)
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.quantization.utils.maybe_auto_detect_quantization")
def test_check_and_update_config_preserves_platform_default_max_input(
self,
mock_auto_detect,
mock_init_ascend,
_mock_enable_sp,
_mock_device_type,
_mock_refresh_block_size,
):
mock_init_ascend.return_value = TestNPUPlatform.mock_vllm_ascend_config()
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.scheduler_config.max_num_seqs = 77
vllm_config.compilation_config.max_cudagraph_capture_size = None
vllm_config.compilation_config.cudagraph_capture_sizes = None
vllm_config.compilation_config.mode = CompilationMode.DYNAMO_TRACE_ONCE
vllm_config.compilation_config.cudagraph_mode = CUDAGraphMode.FULL_DECODE_ONLY
vllm_config.compilation_config.custom_ops = []
vllm_config.model_config.enforce_eager = False
vllm_config.model_config.enable_sleep_mode = True
vllm_config.model_config.is_encoder_decoder = 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
vllm_config.parallel_config.worker_cls = "manual"
vllm_config.parallel_config.cp_kv_cache_interleave_size = 1
vllm_config.cache_config.block_size = 1
self.platform.apply_config_platform_defaults(vllm_config)
observed_inputs: list[int | None] = []
vllm_config._set_cudagraph_sizes = MagicMock(
side_effect=lambda: observed_inputs.append(
vllm_config.compilation_config.max_cudagraph_capture_size
)
)
self.platform.check_and_update_config(vllm_config)
self.assertEqual(observed_inputs, [77])
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.npu.get_device_properties")
def test_get_device_uuid(self, mock_get_device_properties):
device_id = 0
device_properties = MagicMock()
device_properties.uuid = "01020304-0000-0000-0000-01020304"
mock_get_device_properties.return_value = device_properties
self.assertEqual(self.platform.get_device_uuid(device_id), device_properties.uuid)
mock_get_device_properties.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.quantization.utils.maybe_auto_detect_quantization")
@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_auto_detect
):
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.quantization.utils.maybe_auto_detect_quantization")
@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_auto_detect
):
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.quantization.utils.maybe_auto_detect_quantization")
@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_auto_detect):
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.quantization.utils.maybe_auto_detect_quantization")
@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_auto_detect
):
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.quantization.utils.maybe_auto_detect_quantization")
@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_auto_detect):
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.quantization.utils.maybe_auto_detect_quantization")
@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_auto_detect
):
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)
def test_update_block_size_for_backend_preserves_hybrid_block_size(self):
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.model_config.is_hybrid = True
vllm_config.cache_config.block_size = 1024
vllm_config.cache_config.user_specified_block_size = False
self.platform.update_block_size_for_backend(vllm_config)
self.assertEqual(vllm_config.cache_config.block_size, 1024)
def test_update_block_size_for_backend_preserves_user_block_size(self):
vllm_config = TestNPUPlatform.mock_vllm_config()
vllm_config.model_config.is_hybrid = False
vllm_config.cache_config.block_size = 512
vllm_config.cache_config.user_specified_block_size = True
self.platform.update_block_size_for_backend(vllm_config)
self.assertEqual(vllm_config.cache_config.block_size, 512)
@patch("vllm_ascend.quantization.utils.maybe_auto_detect_quantization")
@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_auto_detect
):
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.quantization.utils.maybe_auto_detect_quantization")
@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_auto_detect):
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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