69b817ed65
This PR added the unit test framework to enable ut for vLLM Ascend. Unit test runs on CPU machines. It'll be ran once lint check is passed the same as e2e test. For unit test, this PR created a new folder called `ut` under `tests` module. All the test file in `ut` should keep the same with the code in `vllm-ascend`. The file name should be start with `test_` prefix. For example, in this PR. the `test_ascend_config.py` is added for `ascend_config.py` test. A new fille `worker/test_worker_v1.py` is also added as the placeholder. This file should be the unit test for `vllm-ascend/worker/worker_v1.py`. Additional, a new `fake_weight` folder is added, it contains the config.json from `facebook/opt-125m`, so that the test will not always visit huggingface. TODO: We should add all the unit test file one by one in the future. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
191 lines
7.5 KiB
Python
191 lines
7.5 KiB
Python
# Copyright (c) China Merchants Bank Co., Ltd. 2025. All rights reserved.
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#
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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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#/
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# to run this test, you need to cd to the upper package which is 'tests',
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# and run with command 'pytest -s ops/test_multi_step.py'
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import torch
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import torch_npu # noqa: F401
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DTYPES = [torch.int32, torch.int64]
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DEVICES = [f"npu:{0}"]
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# Set tolerance to 0 for equals
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DEFAULT_ATOL = 0
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DEFAULT_RTOL = 0
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# test custom ops of https://github.com/vllm-project/vllm-ascend/tree/main/csrc/kernels/advance_step.cpp
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@torch.inference_mode()
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def test_single_generation_multi_step() -> None:
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input_tokens_data = [2926]
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input_tokens_ascendc = torch.tensor(input_tokens_data, device='npu:0')
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input_tokens_python = torch.tensor(input_tokens_data, device='npu:0')
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sampled_token_ids_data = [[13]]
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sampled_token_ids = torch.tensor(sampled_token_ids_data, device='npu:0')
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input_positions_data = [5]
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input_positions_ascendc = torch.tensor(input_positions_data,
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device='npu:0')
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input_positions_python = torch.tensor(input_positions_data, device='npu:0')
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seq_lens_data = [6]
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seq_lens_ascendc = torch.tensor(seq_lens_data,
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device='npu:0',
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dtype=torch.int32)
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seq_lens_python = torch.tensor(seq_lens_data,
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device='npu:0',
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dtype=torch.int32)
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slot_mapping_data = [5]
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slot_mapping_ascendc = torch.tensor(slot_mapping_data,
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device='npu:0',
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dtype=torch.int32)
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slot_mapping_python = torch.tensor(slot_mapping_data,
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device='npu:0',
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dtype=torch.int32)
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block_tables_data = [[0]]
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block_tables = torch.tensor(block_tables_data,
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device='npu:0',
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dtype=torch.int32)
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torch.ops._C.advance_step_flashattn_ascendc(
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1, 1, 128, input_tokens_ascendc, sampled_token_ids,
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input_positions_ascendc, seq_lens_ascendc, slot_mapping_ascendc,
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block_tables)
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normal(1, 1, 128, input_tokens_python, sampled_token_ids,
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input_positions_python, seq_lens_python, slot_mapping_python,
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block_tables)
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# Compare the results.
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torch.testing.assert_close(input_tokens_ascendc,
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input_tokens_python,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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torch.testing.assert_close(input_positions_ascendc,
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input_positions_python,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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torch.testing.assert_close(seq_lens_ascendc,
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seq_lens_python,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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torch.testing.assert_close(slot_mapping_ascendc,
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slot_mapping_python,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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@torch.inference_mode()
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def test_multi_result_generation_multi_step() -> None:
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input_tokens_data = [2926, 279, 12095, 1588]
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input_tokens_ascendc = torch.tensor(input_tokens_data, device='npu:0')
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input_tokens_python = torch.tensor(input_tokens_data, device='npu:0')
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sampled_token_ids_data = [[13], [1968], [13], [13]]
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sampled_token_ids = torch.tensor(sampled_token_ids_data, device='npu:0')
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input_positions_data = [5, 7, 5, 5]
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input_positions_ascendc = torch.tensor(input_positions_data,
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device='npu:0')
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input_positions_python = torch.tensor(input_positions_data, device='npu:0')
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seq_lens_data = [6, 8, 6, 6]
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seq_lens_ascendc = torch.tensor(seq_lens_data,
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device='npu:0',
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dtype=torch.int32)
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seq_lens_python = torch.tensor(seq_lens_data,
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device='npu:0',
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dtype=torch.int32)
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slot_mapping_data = [5, 135, 261, 389]
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slot_mapping_ascendc = torch.tensor(slot_mapping_data,
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device='npu:0',
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dtype=torch.int32)
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slot_mapping_python = torch.tensor(slot_mapping_data,
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device='npu:0',
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dtype=torch.int32)
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block_tables_data = [[0], [1], [2], [3]]
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block_tables = torch.tensor(block_tables_data,
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device='npu:0',
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dtype=torch.int32)
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torch.ops._C.advance_step_flashattn_ascendc(
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4, 4, 128, input_tokens_ascendc, sampled_token_ids,
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input_positions_ascendc, seq_lens_ascendc, slot_mapping_ascendc,
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block_tables)
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normal(4, 4, 128, input_tokens_python, sampled_token_ids,
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input_positions_python, seq_lens_python, slot_mapping_python,
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block_tables)
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# Compare the results.
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torch.testing.assert_close(input_tokens_ascendc,
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input_tokens_python,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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torch.testing.assert_close(input_positions_ascendc,
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input_positions_python,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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torch.testing.assert_close(seq_lens_ascendc,
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seq_lens_python,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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torch.testing.assert_close(slot_mapping_ascendc,
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slot_mapping_python,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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def normal(num_seqs: int, num_queries: int, block_size: int,
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input_tokens: torch.Tensor, sampled_token_ids: torch.Tensor,
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input_positions: torch.Tensor, seq_lens_tensor: torch.Tensor,
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slot_mapping: torch.Tensor, block_tables: torch.Tensor) -> None:
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sampled_token_ids_list = sampled_token_ids[:num_queries].squeeze(-1)
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input_tokens[:num_queries] = sampled_token_ids_list
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# get seq_lens and input_positions
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seq_lens = seq_lens_tensor[:num_queries]
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next_seq_lens = seq_lens + 1
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next_input_pos = next_seq_lens - 1
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# update seq_lens and input_positions
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seq_lens_tensor[:num_queries] = next_seq_lens
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input_positions[:num_queries] = next_input_pos # type: ignore
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# get block index and offset
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block_idx = next_input_pos // block_size
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block_offset = next_input_pos % block_size
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current_block_table = block_tables.gather(
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1, block_idx.unsqueeze(-1)).squeeze(-1)
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slot_num = current_block_table * block_size + block_offset
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# update slot_mapping
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slot_mapping[:num_queries] = slot_num
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