b4aafd4293
### What this PR does / why we need it?
This PR removes the custom `ProfileExecuteDuration` utility and its
usages across the codebase. This utility was used for profiling
execution duration of different stages in the inference process. It is
replaced by the standard `vllm.v1.utils.record_function_or_nullcontext`,
which integrates with PyTorch's profiler.
This change simplifies the code by removing a custom implementation in
favor of an upstream utility, improving maintainability. Associated
documentation and tests for `ProfileExecuteDuration` are also removed.
### Does this PR introduce _any_ user-facing change?
`VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE` env is removed now.
### How was this patch tested?
CI passed. The changes are a cleanup and replacement with a standard
utility. Existing tests cover the functionality. The removed feature had
its own tests which are also removed.
Related RFC: #5304
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
249 lines
10 KiB
Python
249 lines
10 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 os
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from threading import Lock
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from unittest import mock
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import torch
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from vllm.config import (CompilationConfig, ModelConfig, ParallelConfig,
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VllmConfig)
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from tests.ut.base import TestBase
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from vllm_ascend import utils
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from vllm_ascend.utils import REGISTERED_ASCEND_OPS
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class TestUtils(TestBase):
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def setUp(self):
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import importlib
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from vllm_ascend import platform
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importlib.reload(platform)
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def test_nd_to_nz_2d(self):
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# can be divided by 16
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input_tensor = torch.randn(32, 64)
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output = utils.nd_to_nz_2d(input_tensor)
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self.assertEqual(output.shape[0], 1)
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self.assertEqual(output.shape[1], 64 // 16)
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self.assertEqual(output.shape[2], 32)
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self.assertEqual(output.shape[3], 16)
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# cannot be divided by 16
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input_tensor = torch.randn(30, 62)
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output = utils.nd_to_nz_2d(input_tensor)
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self.assertEqual(output.shape[0], 1)
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self.assertEqual(output.shape[1], math.ceil(62 / 16))
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self.assertEqual(output.shape[2], 32)
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self.assertEqual(output.shape[3], 16)
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# pad to 16
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input_tensor = torch.randn(8, 12)
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output = utils.nd_to_nz_2d(input_tensor)
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self.assertEqual(output.shape[0], 1)
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self.assertEqual(output.shape[1], 1) # 12->16, 16//16=1
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self.assertEqual(output.shape[2], 16) # 8->16
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self.assertEqual(output.shape[3], 16)
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# check if the output is contiguous
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input_tensor = torch.randn(32, 64)
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output = utils.nd_to_nz_2d(input_tensor)
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self.assertTrue(output.is_contiguous())
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# check if the output values are preserved
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input_tensor = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
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output = utils.nd_to_nz_2d(input_tensor)
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expected = torch.tensor(
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[[[[1, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[5, 6, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]])
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self.assertTrue(torch.allclose(output, expected))
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def test_aligned_16(self):
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# align to 16
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input_tensor = torch.randn(15, 64)
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output_tensor = utils.aligned_16(input_tensor)
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self.assertEqual(output_tensor.shape[0], 16)
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# align to 16
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input_tensor = torch.randn(16, 64)
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output_tensor = utils.aligned_16(input_tensor)
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self.assertEqual(output_tensor.shape[0], 16)
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self.assertTrue(torch.equal(input_tensor, output_tensor))
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# align to 32
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input_tensor = torch.randn(17, 64)
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output_tensor = utils.aligned_16(input_tensor)
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self.assertEqual(output_tensor.shape[0], 32)
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def test_enable_custom_op(self):
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result = utils.enable_custom_op()
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self.assertTrue(result)
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utils._CUSTOM_OP_ENABLED = None
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with mock.patch('builtins.__import__') as mock_import_module:
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mock_import_module.side_effect = ImportError("import error")
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self.assertFalse(utils.enable_custom_op())
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def test_find_hccl_library(self):
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with mock.patch.dict(os.environ,
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{"HCCL_SO_PATH": "/path/to/hccl/libhccl.so"}):
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self.assertEqual(utils.find_hccl_library(),
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"/path/to/hccl/libhccl.so")
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with mock.patch("torch.version.cann", None):
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self.assertRaises(ValueError, utils.find_hccl_library)
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with mock.patch("torch.version.cann", "Ascend910"):
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self.assertEqual(utils.find_hccl_library(), "libhccl.so")
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def test_current_stream(self):
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with mock.patch("torch.npu.current_stream") as mock_current_stream:
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self.assertEqual(utils.current_stream(), mock_current_stream())
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def test_vllm_version_is(self):
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with mock.patch.dict(os.environ, {"VLLM_VERSION": "1.0.0"}):
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with mock.patch("vllm.__version__", "1.0.0"):
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self.assertTrue(utils.vllm_version_is.__wrapped__("1.0.0"))
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self.assertFalse(utils.vllm_version_is.__wrapped__("2.0.0"))
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with mock.patch("vllm.__version__", "2.0.0"):
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self.assertTrue(utils.vllm_version_is.__wrapped__("1.0.0"))
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self.assertFalse(utils.vllm_version_is.__wrapped__("2.0.0"))
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with mock.patch("vllm.__version__", "1.0.0"):
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self.assertTrue(utils.vllm_version_is.__wrapped__("1.0.0"))
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self.assertFalse(utils.vllm_version_is.__wrapped__("2.0.0"))
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with mock.patch("vllm.__version__", "2.0.0"):
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self.assertTrue(utils.vllm_version_is.__wrapped__("2.0.0"))
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self.assertFalse(utils.vllm_version_is.__wrapped__("1.0.0"))
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# Test caching takes effect
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utils.vllm_version_is.cache_clear()
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utils.vllm_version_is("1.0.0")
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misses = utils.vllm_version_is.cache_info().misses
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hits = utils.vllm_version_is.cache_info().hits
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self.assertEqual(misses, 1)
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self.assertEqual(hits, 0)
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utils.vllm_version_is("1.0.0")
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hits = utils.vllm_version_is.cache_info().hits
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self.assertEqual(hits, 1)
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def test_get_max_hidden_layers(self):
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from transformers import PretrainedConfig
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class SimpleConfig(PretrainedConfig):
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def __init__(self, num_hidden_layers=12):
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self.num_hidden_layers = num_hidden_layers
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def to_dict(self):
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return {"num_hidden_layers": self.num_hidden_layers}
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self.assertEqual(utils.get_max_hidden_layers(SimpleConfig()), 12)
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self.assertEqual(utils.get_max_hidden_layers(SimpleConfig(24)), 24)
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class NestedConfig(PretrainedConfig):
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def to_dict(self):
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return {
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"model": {
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"encoder": {
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"num_hidden_layers": 8
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},
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"decoder": {
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"num_hidden_layers": 12
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}
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},
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"other_setting": True
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}
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self.assertEqual(utils.get_max_hidden_layers(NestedConfig()), 12)
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class MultiValueConfig(PretrainedConfig):
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def to_dict(self):
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return {
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"num_hidden_layers": 6,
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"submodule": {
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"num_hidden_layers": 18,
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"subsub": {
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"num_hidden_layers": 9
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}
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}
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}
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self.assertEqual(utils.get_max_hidden_layers(MultiValueConfig()), 18)
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class NoLayerConfig(PretrainedConfig):
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def to_dict(self):
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return {"attention_heads": 8}
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with self.assertRaises(ValueError) as context:
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utils.get_max_hidden_layers(NoLayerConfig())
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self.assertIn("num_hidden_layers", str(context.exception))
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def test_update_aclgraph_sizes(self):
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test_compilation_config = CompilationConfig(
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cudagraph_capture_sizes=[i for i in range(150)])
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model_path = os.path.join(os.path.dirname(__file__), "fake_weight")
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test_model_config = ModelConfig(model=model_path, enforce_eager=True)
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test_parallel_config = ParallelConfig()
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test_vllm_config = VllmConfig(
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model_config=test_model_config,
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compilation_config=test_compilation_config,
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parallel_config=test_parallel_config)
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utils.update_aclgraph_sizes(test_vllm_config)
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os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
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utils.update_aclgraph_sizes(test_vllm_config)
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del os.environ['HCCL_OP_EXPANSION_MODE']
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self.assertEqual(
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0,
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len(test_vllm_config.compilation_config.cudagraph_capture_sizes))
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@mock.patch("vllm.model_executor.custom_op.CustomOp")
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@mock.patch("vllm_ascend.ops.activation.AscendQuickGELU")
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@mock.patch("vllm_ascend.ops.activation.AscendSiluAndMul")
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@mock.patch("vllm_ascend.ops.layernorm.AscendRMSNorm")
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def test_register_ascend_customop(self, mock_ascend_rmsnorm,
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mock_ascend_silu_and_mul,
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mock_ascend_quick_gelu, mock_customop):
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utils._ASCEND_CUSTOMOP_IS_REIGISTERED = False
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# ascend custom op is not registered
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utils.register_ascend_customop()
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self.assertEqual(mock_customop.register_oot.call_count,
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len(REGISTERED_ASCEND_OPS))
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self.assertTrue(utils._ASCEND_CUSTOMOP_IS_REIGISTERED)
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# ascend custom op is already registered
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utils.register_ascend_customop()
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self.assertEqual(mock_customop.register_oot.call_count,
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len(REGISTERED_ASCEND_OPS))
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