e2a0c19cea
1. remove some useless test func and file 2. fix format.sh problem 3. enable full test for singlecard and multicard 4. move long term test to long_term folder. For this kind of test, it only runs by labeled and daily test. Include: spec decode、accuracy test ## After refactor: There are 4 test modules - `singlecard`: contains the test running on one NPU. It'll be run for each PR and daily test. - `multicard`: contains the test running on multi NPUs. It'll be run for each PR and daily test. - `long_term`: contains the test that cost much time(Now include `spec decode` and `accuracy` test). It'll be run for the PR with `long-term-test` labeled and daily test. - `e2e`: contains the test for doc and pd feature. It'll be run for the PR with `pd-test` labeled and daily test. ## Todo: 1. some test are skipped, they should be fixed and reenabled in the future. 2. pyhccl test for multicard doesn't work at all. It should be enabled as well. 3. ensure long-term-test pass by daily test. ### Know issue Now, `ready` labels is required to start pd test or long term test. And when `long-term-test` or `pd-test` is labeled after another one, the old labeled test will be re-run again. So the labeled test should be ran in the following step: 1. decide which test need run, then label it. `long-term-test` or `pd-test` or both. 2. add `ready-for-test` label, then the test will be ran. 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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