d1f0df7b4b
We'll release 0.13.0 soon. The main branch is freeze. Let's revert the
newest change and redo it once 0.13.0 is released
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
474 lines
18 KiB
Python
474 lines
18 KiB
Python
# 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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from unittest.mock import MagicMock
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import numpy as np
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import pytest
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import torch
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from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
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@pytest.mark.parametrize(
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"pcp_size, dcp_size, num_reqs, query_lens, num_decodes, use_mla, total_tokens, expect_not_none",
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[
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(1, 1, 5, [10, 20, 30, 40, 50], 2, False, 100, False),
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(1, 2, 3, [20, 30, 40], 1, False, 50, True),
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(2, 1, 4, [5, 10, 40, 60], 2, False, 100, True),
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(2, 1, 4, [5, 10, 40, 60], 2, True, 100, True),
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(2, 1, 3, [5, 10, 15], 3, False, 50, True),
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(2, 1, 3, [40, 50, 60], 0, False, 150, True),
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])
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def test_generate_pcp_metadata_basic(pcp_size, dcp_size, num_reqs, query_lens,
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num_decodes, use_mla, total_tokens,
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expect_not_none):
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mock_runner = MagicMock(spec=NPUModelRunner)
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mock_runner.pcp_size = pcp_size
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mock_runner.dcp_size = dcp_size
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mock_runner.decode_threshold = 4
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mock_runner.pcp_rank = 0
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mock_runner.device = torch.device('cpu')
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mock_runner.dtype = torch.float32
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mock_runner.parallel_config = MagicMock()
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mock_runner.parallel_config.cp_kv_cache_interleave_size = 64
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mock_runner.vllm_config = MagicMock()
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mock_runner.vllm_config.model_config = MagicMock()
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mock_runner.vllm_config.model_config.use_mla = use_mla
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mock_runner.input_batch = MagicMock()
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mock_runner.input_batch.num_reqs = num_reqs
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mock_runner.speculative_config = None
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num_computed_tokens = []
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num_prompt_tokens = []
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num_tokens = []
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for i in range(num_reqs):
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if i < num_decodes:
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num_computed_tokens.append(query_lens[i])
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num_prompt_tokens.append(query_lens[i] // 2)
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num_tokens.append(query_lens[i])
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else:
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num_computed_tokens.append(0)
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num_prompt_tokens.append(query_lens[i])
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num_tokens.append(query_lens[i])
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mock_runner.input_batch.num_computed_tokens_cpu = torch.tensor(
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num_computed_tokens)
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mock_runner.input_batch.num_prompt_tokens = torch.tensor(num_prompt_tokens)
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mock_runner.input_batch.num_tokens = torch.tensor(num_tokens)
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mock_runner.query_lens = torch.tensor(query_lens)
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mock_runner._get_cp_local_seq_lens = NPUModelRunner._get_cp_local_seq_lens.__get__(
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mock_runner, NPUModelRunner)
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mock_runner.pcp_allgather_restore_idx = torch.arange(total_tokens * 2)
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mock_runner.cp_kv_recover_idx_for_chunk = torch.arange(total_tokens)
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mock_runner.long_seq_metadata = None
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mock_runner.num_actual_tokens_pcp_padded = 0
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mock_runner.kv_idx_names = {}
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mock_runner.extra_long_seq_kwargs = {}
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mock_runner.attn_mask = None
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mock_runner.q_head_idx_tensor = None
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mock_runner.q_tail_idx_tensor = None
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mock_runner.q_full_idx = None
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method = NPUModelRunner._generate_pcp_metadata.__get__(
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mock_runner, NPUModelRunner)
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result = method(total_tokens)
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if not expect_not_none:
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assert result is None, f"Expected to return None, but got {type(result)}"
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else:
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assert result is not None, "Expected to return a metadata object, but got None."
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assert hasattr(result, 'num_actual_tokens_pcp_padded')
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assert hasattr(result, 'num_computed_tokens_of_pcp_dcp')
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if pcp_size > 1:
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assert hasattr(result, 'pcp_allgather_restore_idx')
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has_prefill_requests = (num_reqs - num_decodes) > 0
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if has_prefill_requests:
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assert hasattr(result, 'q_head_idx_tensor')
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assert hasattr(result, 'q_tail_idx_tensor')
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assert hasattr(result, 'q_full_idx')
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assert hasattr(result, 'kv_with_q_head_nomask_idx_tensor')
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assert hasattr(result, 'kv_with_q_head_mask_idx_tensor')
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assert hasattr(result, 'kv_with_q_tail_nomask_idx_tensor')
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assert hasattr(result, 'kv_with_q_tail_mask_idx_tensor')
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assert hasattr(result, 'attn_mask_seqlens')
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assert hasattr(result, 'head_attn_nomask_seqlens')
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assert hasattr(result, 'tail_attn_nomask_seqlens')
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if hasattr(result, 'pcp_prefill_mask'
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) and result.pcp_prefill_mask is not None:
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if use_mla:
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assert result.pcp_prefill_mask.shape == (512, 512)
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else:
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assert result.pcp_prefill_mask.shape == (2048, 2048)
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else:
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if hasattr(result, 'pcp_prefill_mask'):
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if result.pcp_prefill_mask is not None:
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if use_mla:
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assert result.pcp_prefill_mask.shape == (512, 512)
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else:
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assert result.pcp_prefill_mask.shape == (2048,
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2048)
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def test_generate_pcp_metadata_edge_cases():
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mock_runner = MagicMock()
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mock_runner.pcp_size = 2
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mock_runner.dcp_size = 1
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mock_runner.input_batch = MagicMock()
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mock_runner.input_batch.num_reqs = 0
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mock_runner.query_lens = torch.tensor([10, 20, 30])
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assert (mock_runner.input_batch.num_reqs
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or mock_runner.query_lens.size(0)) == 3
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mock_runner.input_batch.num_reqs = 100
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mock_runner.query_lens = torch.ones(100) * 1000
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for rank in [0, 1]:
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mock_runner.pcp_rank = rank
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q_head_chunk_id = rank
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q_tail_chunk_id = 2 * 2 - 1 - rank
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assert q_head_chunk_id == rank
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assert q_tail_chunk_id == 3 - rank
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def test_pcp_allgather_restore_idx_slicing():
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mock_runner = MagicMock()
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mock_runner.pcp_size = 2
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mock_runner.pcp_allgather_restore_idx = torch.arange(1000)
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total_num_scheduled_tokens = 200
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num_actual_tokens_pcp_padded = total_num_scheduled_tokens * 2
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expected_slice = mock_runner.pcp_allgather_restore_idx[:
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num_actual_tokens_pcp_padded]
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assert len(expected_slice) == 400
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assert expected_slice[0] == 0
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assert expected_slice[-1] == 399
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@pytest.mark.parametrize(
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"tokens, num_reqs, num_computed_tokens, num_prompt_tokens," \
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"pcp_size, pcp_rank, decode_threshold, expected_pcp_tokens",
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[
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# Case 1: prefill only
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([8, 12, 16], 3, [0, 0, 0], [8, 12, 16], 4, 0, 1, [2, 4, 4]),
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# Case 2: mix prefill and decode (with spec decode)
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([8, 4, 12], 3, [8, 4, 0], [8, 4, 12], 4, 0, 8, [8, 4, 4]),
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# Case 3: request which need to be padded
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([3, 7, 9], 3, [0, 0, 0], [3, 7, 9], 4, 0, 1, [2, 2, 4]),
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# Case 4: single request
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([10], 1, [0], [10], 4, 0, 1, [4]),
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])
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def test_update_tokens_for_pcp_basic(tokens, num_reqs, num_computed_tokens,
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num_prompt_tokens, pcp_size, pcp_rank,
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decode_threshold, expected_pcp_tokens):
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mock_runner = MagicMock(spec=NPUModelRunner)
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mock_runner.pcp_size = pcp_size
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mock_runner.pcp_rank = pcp_rank
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mock_runner.input_batch = MagicMock()
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mock_runner.input_batch.num_reqs = num_reqs
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mock_runner.input_batch.num_computed_tokens_cpu = np.array(
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num_computed_tokens, dtype=np.int32)
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mock_runner.input_batch.num_prompt_tokens = np.array(num_prompt_tokens,
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dtype=np.int32)
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mock_runner.pcp_allgather_restore_idx = torch.zeros(1000, dtype=torch.long)
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mock_runner.num_pcp_pads = [0] * num_reqs
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mock_runner.arange_np = np.arange(10000)
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mock_runner.decode_threshold = decode_threshold
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mock_runner._update_tokens_for_pcp = NPUModelRunner._update_tokens_for_pcp.__get__(
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mock_runner, NPUModelRunner)
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mock_runner._get_cumsum_and_arange = NPUModelRunner._get_cumsum_and_arange.__get__(
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mock_runner, NPUModelRunner)
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pcp_tokens_result, positions_result, unpad_mask_result = mock_runner._update_tokens_for_pcp(
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tokens)
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assert np.array_equal(pcp_tokens_result, expected_pcp_tokens), \
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f"Expected pcp_tokens: {expected_pcp_tokens}, got: {pcp_tokens_result}"
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total_pcp_tokens: int = np.sum(pcp_tokens_result)
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assert positions_result.shape == (total_pcp_tokens,), \
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f"Positions shape mismatch. Expected length {total_pcp_tokens}, got {positions_result.shape}"
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padded_tokens = [
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(t + 2 * pcp_size - 1) // (2 * pcp_size) *
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(2 * pcp_size) if num_computed_tokens[i] == 0 else t * pcp_size
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for i, t in enumerate(tokens)
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]
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total_padded_tokens: int = np.sum(padded_tokens)
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assert unpad_mask_result.shape[0] == total_padded_tokens, \
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f"unpad_mask size mismatch: expected {total_padded_tokens}, got {unpad_mask_result.shape[0]}"
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def test_update_tokens_for_pcp_with_padding():
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mock_runner = MagicMock(spec=NPUModelRunner)
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mock_runner.pcp_size = 4
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mock_runner.pcp_rank = 0
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mock_runner.arange_np = np.arange(10000)
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mock_runner.input_batch = MagicMock()
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mock_runner.input_batch.num_reqs = 3
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mock_runner.input_batch.num_computed_tokens_cpu = np.array([0, 0, 0],
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dtype=np.int32)
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mock_runner.input_batch.num_prompt_tokens = np.array([5, 9, 13],
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dtype=np.int32)
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mock_runner.num_pcp_pads = [0, 0, 0]
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mock_runner.pcp_allgather_restore_idx = torch.zeros(1000, dtype=torch.long)
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mock_runner.decode_threshold = 1
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mock_runner._update_tokens_for_pcp = NPUModelRunner._update_tokens_for_pcp.__get__(
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mock_runner, NPUModelRunner)
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mock_runner._get_cumsum_and_arange = NPUModelRunner._get_cumsum_and_arange.__get__(
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mock_runner, NPUModelRunner)
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tokens = [5, 9, 13]
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pcp_tokens, positions, unpad_mask = mock_runner._update_tokens_for_pcp(
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tokens)
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expected_pcp_tokens = [2, 4, 4]
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assert np.array_equal(pcp_tokens, expected_pcp_tokens), \
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f"Expected {expected_pcp_tokens}, got {pcp_tokens}"
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expected_pads = [3, 7, 3]
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assert np.array_equal(mock_runner.num_pcp_pads, expected_pads), \
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f"Expected padding {expected_pads}, got {mock_runner.num_pcp_pads}"
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def test_update_tokens_for_pcp_unpad_mask():
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mock_runner = MagicMock(spec=NPUModelRunner)
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mock_runner.pcp_size = 4
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mock_runner.pcp_rank = 0
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mock_runner.arange_np = np.arange(10000)
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mock_runner.input_batch = MagicMock()
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mock_runner.input_batch.num_reqs = 2
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mock_runner.input_batch.num_computed_tokens_cpu = np.array([0, 0],
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dtype=np.int32)
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mock_runner.input_batch.num_prompt_tokens = np.array([5, 7],
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dtype=np.int32)
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mock_runner.num_pcp_pads = [0, 0]
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mock_runner.pcp_allgather_restore_idx = torch.zeros(1000, dtype=torch.long)
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mock_runner.decode_threshold = 1
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mock_runner._update_tokens_for_pcp = NPUModelRunner._update_tokens_for_pcp.__get__(
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mock_runner, NPUModelRunner)
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mock_runner._get_cumsum_and_arange = NPUModelRunner._get_cumsum_and_arange.__get__(
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mock_runner, NPUModelRunner)
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tokens = [5, 7]
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pcp_tokens, positions, unpad_mask = mock_runner._update_tokens_for_pcp(
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tokens)
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assert unpad_mask.dtype == torch.bool, \
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f"unpad_mask should be bool, got {unpad_mask.dtype}"
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padded_tokens = [8, 8]
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expected_length = sum(padded_tokens)
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assert unpad_mask.shape[0] == expected_length, \
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f"unpad_mask length mismatch: expected {expected_length}, got {unpad_mask.shape[0]}"
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expected_mask = [True] * 5 + [False] * 3 + [True] * 7 + [False] * 1
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actual_mask = unpad_mask.numpy().tolist()
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assert actual_mask == expected_mask, \
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f"unpad_mask incorrect. Expected {expected_mask}, got {actual_mask}"
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# yapf: disable
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@pytest.mark.parametrize(
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"seq_lens, pcp_world_size, dcp_world_size, cp_kv_cache_interleave_size, target",
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[
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# without pcp and dcp
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(torch.tensor([1, 2, 128, 129]), 1, 1, 1,
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torch.tensor([[[1]], [[2]], [[128]], [[129]]])),
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# pcp
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(torch.tensor([1, 2, 128, 129]), 2, 1, 1,
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torch.tensor([[[1], [0]], [[1], [1]], [[64], [64]], [[65], [64]]])),
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# dcp
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(torch.tensor([1, 2, 128, 129]), 1, 2, 1,
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torch.tensor([[[1, 0]], [[1, 1]], [[64, 64]], [[65, 64]]])),
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# pcp + dcp
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(torch.tensor([1, 2, 128, 129]), 2, 2, 1,
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torch.tensor([[[1, 0], [0, 0]], [[1, 1], [0, 0]],
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[[32, 32], [32, 32]], [[33, 32], [32, 32]]])),
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# specify interleave_size
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(torch.tensor([1, 2, 128, 129]), 2, 1, 2,
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torch.tensor([[[1], [0]], [[2], [0]], [[64], [64]], [[65], [64]]])),
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(torch.tensor([1, 2, 128, 129]), 2, 1, 128,
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torch.tensor([[[1], [0]], [[2], [0]], [[128], [0]], [[128], [1]]])),
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(torch.tensor([1, 2, 128, 129, 256, 257]), 2, 2, 128,
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torch.tensor([[[1, 0], [0, 0]], [[2, 0], [0, 0]],
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[[128, 0], [0, 0]], [[128, 1], [0, 0]],
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[[128, 128], [0, 0]], [[128, 128], [1, 0]]])),
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]
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)
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# yapf: enable
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def test_get_cp_local_seq_lens(
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seq_lens,
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pcp_world_size,
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dcp_world_size,
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cp_kv_cache_interleave_size,
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target,
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):
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mock_runner = MagicMock(spec=NPUModelRunner)
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ret = NPUModelRunner._get_cp_local_seq_lens(mock_runner, seq_lens,
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pcp_world_size, dcp_world_size,
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cp_kv_cache_interleave_size)
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assert torch.equal(ret, target)
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@pytest.fixture
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def pcp_mtp_mock_runner():
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# set up pcp & mtp related buffers
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max_num_reqs = 4
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max_model_len = 4096
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max_num_tokens = 4096
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mock_runner = MagicMock(spec=NPUModelRunner)
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mock_runner.device = 'cpu'
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mock_runner.pin_memory = False
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# Init model_runner pcp_mtp related buffers
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mock_runner.query_start_loc_pcp_full = NPUModelRunner._make_buffer(
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mock_runner, max_num_reqs + 1, dtype=torch.int32)
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positions_buff = torch.zeros(max_num_tokens,
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dtype=torch.int64,
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device="cpu")
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mock_runner.positions_pcp_full = positions_buff
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mock_runner.positions_pcp_full_np = positions_buff.numpy()
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mock_runner.input_ids_pcp_full = NPUModelRunner._make_buffer(
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mock_runner, max_num_tokens, dtype=torch.int32)
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mock_runner.query_lens_pcp_full = NPUModelRunner._make_buffer(
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mock_runner, max_num_reqs, dtype=torch.int32)
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mock_runner.decode_threshold = 1
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mock_runner.arange_np = np.arange(max_model_len)
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mock_runner.input_batch = MagicMock()
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mock_runner.input_batch.num_computed_tokens_cpu = \
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np.zeros(max_num_reqs, dtype=np.int32)
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token_ids_cpu_tensor = torch.zeros(
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(max_num_reqs, max_model_len),
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device="cpu",
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dtype=torch.int32,
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)
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mock_runner.input_batch.token_ids_cpu_tensor = token_ids_cpu_tensor
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mock_runner.input_batch.token_ids_cpu = token_ids_cpu_tensor.numpy()
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return mock_runner
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# yapf: disable
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@pytest.mark.parametrize(
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"req_ids, num_computed_tokens," \
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"token_ids_tensor_list," \
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"num_reqs, total_num_scheduled_tokens, num_scheduled_tokens," \
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"target_input_ids_pcp_full, target_query_start_loc_pcp_full",
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[
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# prefill
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(
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['0'], np.array([0]),
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[torch.tensor([0, 671, 6102, 294, 8760, 344])],
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1, 6, {'0': 6},
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torch.tensor([0, 671, 6102, 294, 8760, 344]),
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torch.tensor([0, 6])
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),
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# decode
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(
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['0'], np.array([6]),
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[torch.tensor([0, 671, 6102, 294, 8760, 344, 88907, 0])],
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1, 2, {'0': 2},
|
|
torch.tensor([88907, 0]),
|
|
torch.tensor([0, 2])
|
|
),
|
|
# decode + prefill
|
|
(
|
|
['0', '1'], np.array([6, 0]),
|
|
[
|
|
torch.tensor([0, 671, 6102, 294, 8760, 344, 88907, 0]),
|
|
torch.tensor([0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 1030]),
|
|
],
|
|
2, 12, {'0': 2, '1': 10},
|
|
torch.tensor([88907, 0, 0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 1030]),
|
|
torch.tensor([0, 2, 12])
|
|
),
|
|
# decodes + prefills
|
|
(
|
|
['0', '1', '2', '3'], np.array([6, 8, 0, 0]),
|
|
[
|
|
torch.tensor([0, 671, 6102, 294, 8760, 344, 88907, 0]),
|
|
torch.tensor([0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 0]),
|
|
torch.tensor([0, 671, 8749, 294, 3702, 4106, 344, 88907]),
|
|
torch.tensor([0, 671, 5335, 1469, 7539, 305, 6397]),
|
|
],
|
|
4, 19, {'0': 2, '1': 2, '2': 8, '3': 7},
|
|
torch.tensor([88907, 0, 342, 0, 0, 671, 8749, 294, 3702, 4106, 344, 88907,
|
|
0, 671, 5335, 1469, 7539, 305, 6397]),
|
|
torch.tensor([0, 2, 4, 12, 19])
|
|
),
|
|
])
|
|
# yapf: enable
|
|
def test_generate_pcp_mtp_input(
|
|
pcp_mtp_mock_runner,
|
|
req_ids,
|
|
num_computed_tokens,
|
|
token_ids_tensor_list,
|
|
num_reqs,
|
|
total_num_scheduled_tokens,
|
|
num_scheduled_tokens,
|
|
target_input_ids_pcp_full,
|
|
target_query_start_loc_pcp_full,
|
|
):
|
|
mock_runner = pcp_mtp_mock_runner
|
|
token_ids_cpu_tensor = mock_runner.input_batch.token_ids_cpu_tensor
|
|
|
|
# Set input_batch
|
|
mock_runner.input_batch.req_ids = req_ids
|
|
mock_runner.input_batch.num_computed_tokens_cpu[:num_computed_tokens.
|
|
size] = num_computed_tokens
|
|
for i, token_ids_tensor in enumerate(token_ids_tensor_list):
|
|
token_ids_cpu_tensor[i][:token_ids_tensor.size(0)] = token_ids_tensor
|
|
|
|
NPUModelRunner._generate_pcp_mtp_input(mock_runner, num_reqs,
|
|
total_num_scheduled_tokens,
|
|
num_scheduled_tokens)
|
|
assert torch.equal(
|
|
mock_runner.input_ids_pcp_full.cpu[:total_num_scheduled_tokens],
|
|
target_input_ids_pcp_full)
|
|
assert torch.equal(mock_runner.query_start_loc_pcp_full.cpu[:num_reqs + 1],
|
|
target_query_start_loc_pcp_full)
|