13c4a9c78b
### What this PR does / why we need it? This PR fixes an accuracy issue that occurs when using Prefill/Decode Context Parallelism (PCP/DCP) in conjunction with speculative decoding (MTP). The issue is caused by an irregular attention mask shape when both features are enabled. The fix involves flattening the `block_table` for speculative decoding requests under PCP/DCP to ensure a regular attention mask. This PR also introduces a `use_cp` property for cleaner code and updates dummy runs to handle this scenario correctly. ### Does this PR introduce _any_ user-facing change? No. This is a bug fix that improves accuracy and should not have user-facing API changes. ### How was this patch tested? - vLLM version: v0.15.0 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0 --------- Signed-off-by: Wang Kunpeng <1289706727@qq.com>
519 lines
20 KiB
Python
519 lines
20 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.pcp_utils import PCPManager
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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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vllm_config = MagicMock()
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vllm_config.model_config = MagicMock()
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vllm_config.model_config.use_mla = use_mla
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vllm_config.parallel_config.cp_kv_cache_interleave_size = 64
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vllm_config.speculative_config.num_speculative_tokens = 0
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pcp_manager = PCPManager(pcp_world_size=pcp_size,
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pcp_rank=0,
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dcp_world_size=dcp_size,
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dcp_rank=0,
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max_buffer_num_tokens=10000,
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max_num_reqs=1000,
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device="cpu",
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vllm_config=vllm_config,
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use_async_scheduling=False,
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pin_memory=False)
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input_batch = MagicMock()
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input_batch.num_reqs = num_reqs
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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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input_batch.num_computed_tokens_cpu = np.array(num_computed_tokens)
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input_batch.num_prompt_tokens = torch.tensor(num_prompt_tokens)
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input_batch.num_tokens = torch.tensor(num_tokens)
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num_scheduled_tokens = np.array(
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query_lens) - input_batch.num_computed_tokens_cpu
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query_lens = torch.tensor(query_lens)
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result, _ = pcp_manager.generate_pcp_metadata(total_tokens, query_lens,
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input_batch,
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num_scheduled_tokens,
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torch.tensor([]),
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num_reqs_padded=num_reqs,
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num_reqs=num_reqs)
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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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@pytest.mark.parametrize(
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"tokens, num_reqs, num_computed_tokens, num_prompt_tokens, pcp_size, pcp_rank, 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, [2, 4, 4]),
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# # Case 2: mix prefill and decode
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([8, 4, 12], 3, [8, 4, 0], [8, 0, 12], 4, 0, [2, 2, 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, [2, 2, 4]),
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# Case 4: single request
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([10], 1, [0], [10], 4, 0, [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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expected_pcp_tokens):
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vllm_config = MagicMock()
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vllm_config.model_config = MagicMock()
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vllm_config.speculative_config.num_speculative_tokens = 0
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vllm_config.scheduler_config.max_num_seqs = 1000
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pcp_manager = PCPManager(pcp_world_size=pcp_size,
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pcp_rank=0,
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dcp_world_size=1,
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dcp_rank=0,
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max_buffer_num_tokens=10000,
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max_num_reqs=1000,
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device="cpu",
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vllm_config=vllm_config,
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use_async_scheduling=False,
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pin_memory=False)
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input_batch = MagicMock()
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input_batch.num_reqs = num_reqs
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input_batch.num_computed_tokens_cpu = np.array(num_computed_tokens,
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dtype=np.int32)
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input_batch.num_prompt_tokens = np.array(num_prompt_tokens, dtype=np.int32)
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arange_np = np.arange(10000)
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num_scheduled_tokens = np.array(tokens)
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pcp_manager.init_batch_info(num_scheduled_tokens, num_reqs)
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pcp_tokens_result, positions_result = pcp_manager.update_tokens_for_pcp(
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num_scheduled_tokens, arange_np)
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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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# 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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vllm_config = MagicMock()
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vllm_config.model_config = MagicMock()
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vllm_config.speculative_config.num_speculative_tokens = 0
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pcp_manager = PCPManager(pcp_world_size=pcp_world_size,
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pcp_rank=0,
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dcp_world_size=dcp_world_size,
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dcp_rank=0,
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max_buffer_num_tokens=10000,
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max_num_reqs=1000,
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device="cpu",
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vllm_config=vllm_config,
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use_async_scheduling=False,
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pin_memory=False)
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ret = pcp_manager._get_cp_local_seq_lens(seq_lens, pcp_world_size,
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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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# 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},
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torch.tensor([88907, 0]),
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torch.tensor([0, 2])
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),
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# decode + prefill
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(
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['0', '1'], np.array([6, 0]),
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[
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torch.tensor([0, 671, 6102, 294, 8760, 344, 88907, 0]),
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torch.tensor([0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 1030]),
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],
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2, 12, {'0': 2, '1': 10},
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torch.tensor([88907, 0, 0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 1030]),
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torch.tensor([0, 2, 12])
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),
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# decodes + prefills
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(
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['0', '1', '2', '3'], np.array([6, 8, 0, 0]),
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[
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torch.tensor([0, 671, 6102, 294, 8760, 344, 88907, 0]),
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torch.tensor([0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 0]),
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torch.tensor([0, 671, 8749, 294, 3702, 4106, 344, 88907]),
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torch.tensor([0, 671, 5335, 1469, 7539, 305, 6397]),
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],
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4, 19, {'0': 2, '1': 2, '2': 8, '3': 7},
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torch.tensor([88907, 0, 342, 0, 0, 671, 8749, 294, 3702, 4106, 344, 88907,
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0, 671, 5335, 1469, 7539, 305, 6397]),
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torch.tensor([0, 2, 4, 12, 19])
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),
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])
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# yapf: enable
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def test_generate_pcp_mtp_input(
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req_ids,
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num_computed_tokens,
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token_ids_tensor_list,
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num_reqs,
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total_num_scheduled_tokens,
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num_scheduled_tokens,
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target_input_ids_pcp_full,
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target_query_start_loc_pcp_full,
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):
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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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vllm_config = MagicMock()
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vllm_config.model_config = MagicMock()
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vllm_config.speculative_config.num_speculative_tokens = 1
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vllm_config.scheduler_config.max_num_seqs = max_num_reqs
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vllm_config.scheduler_config.max_num_batched_tokens = max_model_len
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pcp_manager = PCPManager(pcp_world_size=2,
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pcp_rank=0,
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dcp_world_size=1,
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dcp_rank=0,
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max_buffer_num_tokens=max_num_tokens,
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max_num_reqs=max_num_reqs,
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device="cpu",
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vllm_config=vllm_config,
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use_async_scheduling=False,
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pin_memory=False)
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arange_np = np.arange(max_model_len)
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input_batch = MagicMock()
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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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input_batch.token_ids_cpu_tensor = token_ids_cpu_tensor
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input_batch.token_ids_cpu = token_ids_cpu_tensor.numpy()
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token_ids_cpu_tensor = input_batch.token_ids_cpu_tensor
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# Set input_batch
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input_batch.req_ids = req_ids
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input_batch.num_computed_tokens_cpu[:num_computed_tokens.
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size] = num_computed_tokens
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for i, token_ids_tensor in enumerate(token_ids_tensor_list):
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token_ids_cpu_tensor[i][:token_ids_tensor.size(0)] = token_ids_tensor
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pcp_manager.init_batch_info(np.array(list(num_scheduled_tokens.values())), num_reqs)
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pcp_manager.generate_pcp_mtp_input(total_num_scheduled_tokens, num_scheduled_tokens, False,
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input_batch, arange_np)
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assert torch.equal(
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pcp_manager.input_ids_pcp_full.cpu[:total_num_scheduled_tokens],
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target_input_ids_pcp_full)
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assert torch.equal(pcp_manager.query_start_loc_pcp_full.cpu[:num_reqs + 1],
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target_query_start_loc_pcp_full)
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@pytest.mark.parametrize(
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"pcp_world_rank, split_with_q_head_nomask_idx_reqs, split_kv_with_q_tail_nomask_idx_reqs,"
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"head_attn_nomask_seqlens, chunk_seqlens,"
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"target_split_q_head, target_split_q_tail, target_head_seqlens, target_tail_seqlens",
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[
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# case1: pcp_world_rank=0
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(0, [[10, 20, 30]], [[40, 50, 60]],
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torch.tensor([[64], [0]], dtype=torch.int32), [64], [
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torch.tensor([1, 2, 3], dtype=torch.int32)
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], [torch.tensor([40, 50, 60], dtype=torch.int32)], [
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torch.tensor([[64], [0]], dtype=torch.int32)
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], [torch.tensor([[64], [3]], dtype=torch.int32)]),
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# case2: pcp_world_rank=1
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(1, [[1, 2], [3, 4, 5]], [[6, 7], [8, 9, 10]],
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torch.tensor([[128, 128], [128, 128]], dtype=torch.int32), [128, 128],
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[torch.tensor([1, 2, 3, 4, 5], dtype=torch.int32)], [
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torch.tensor([6, 7, 8, 9, 10], dtype=torch.int32)
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], [torch.tensor([[128, 128], [2, 3]], dtype=torch.int32)
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], [torch.tensor([[128, 128], [2, 3]], dtype=torch.int32)]),
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# case3: pcp_world_rank=2
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(2, [[11, 12, 13, 14], [15, 16]], [[17, 18, 19], [20, 21, 22, 23]],
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torch.tensor([[256, 256], [512, 512]], dtype=torch.int32), [256, 256],
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[torch.tensor([11, 12, 13, 14, 15, 16], dtype=torch.int32)], [
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torch.tensor([17, 18, 19, 20, 21, 22, 23], dtype=torch.int32)
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], [torch.tensor([[256, 256], [4, 2]], dtype=torch.int32)
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], [torch.tensor([[256, 256], [3, 4]], dtype=torch.int32)]),
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# case4: empty input
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(
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0,
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[],
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[],
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torch.tensor([], dtype=torch.int32).reshape(2, 0),
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[],
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[],
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[],
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[],
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[],
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),
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# case5: single element input
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(
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0,
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[[10]],
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[[40]],
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torch.tensor([[64], [0]], dtype=torch.int32),
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[64],
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[torch.tensor([1, 2, 3], dtype=torch.int32)],
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[torch.tensor([40], dtype=torch.int32)],
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[torch.tensor([[64], [0]], dtype=torch.int32)],
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[torch.tensor([[64], [1]], dtype=torch.int32)],
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),
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# case6: pcp_world_rank=3
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(
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3,
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[[1, 2], [3, 4, 5]],
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[[6, 7], [8, 9, 10]],
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torch.tensor([[128, 128], [128, 128]], dtype=torch.int32),
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[128, 128],
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[torch.tensor([1, 2, 3, 4, 5], dtype=torch.int32)],
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[torch.tensor([6, 7, 8, 9, 10], dtype=torch.int32)],
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[torch.tensor([[128, 128], [2, 3]], dtype=torch.int32)],
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[torch.tensor([[128, 128], [2, 3]], dtype=torch.int32)],
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),
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])
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def test_split_nomask_idx_tensor_list(
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pcp_world_rank, split_with_q_head_nomask_idx_reqs,
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split_kv_with_q_tail_nomask_idx_reqs, head_attn_nomask_seqlens,
|
|
chunk_seqlens, target_split_q_head, target_split_q_tail,
|
|
target_head_seqlens, target_tail_seqlens):
|
|
# Mock input data
|
|
mock_runner = MagicMock(spec=PCPManager)
|
|
mock_runner.device = "cpu"
|
|
mock_runner.pcp_world_rank = 0
|
|
mock_runner.kv_idx_names = {
|
|
"kv_with_q_head_nomask_idx_tensor":
|
|
torch.tensor([1, 2, 3], dtype=torch.int32)
|
|
}
|
|
|
|
mock_runner.pcp_world_rank = pcp_world_rank
|
|
|
|
# Mock output
|
|
mock_runner._split_multi_batch_kv_idx.side_effect = PCPManager._split_multi_batch_kv_idx.__get__(
|
|
mock_runner, PCPManager)
|
|
mock_runner._list_to_tensor.side_effect = PCPManager._list_to_tensor.__get__(
|
|
mock_runner, PCPManager)
|
|
|
|
# Call the method under test
|
|
result = PCPManager._split_nomask_idx_tensor_list(
|
|
mock_runner,
|
|
split_with_q_head_nomask_idx_reqs=split_with_q_head_nomask_idx_reqs,
|
|
split_kv_with_q_tail_nomask_idx_reqs=
|
|
split_kv_with_q_tail_nomask_idx_reqs,
|
|
head_attn_nomask_seqlens=head_attn_nomask_seqlens,
|
|
chunk_seqlens=chunk_seqlens)
|
|
split_q_head, split_q_tail, head_seqlens, tail_seqlens = result
|
|
|
|
# Assert the method call
|
|
assert len(split_q_head) == len(target_split_q_head)
|
|
for res, target in zip(split_q_head, target_split_q_head):
|
|
assert torch.equal(res, target)
|
|
|
|
assert len(split_q_tail) == len(target_split_q_tail)
|
|
for res, target in zip(split_q_tail, target_split_q_tail):
|
|
assert torch.equal(res, target)
|
|
|
|
assert len(head_seqlens) == len(target_head_seqlens)
|
|
for res, target in zip(head_seqlens, target_head_seqlens):
|
|
if isinstance(target, torch.Tensor):
|
|
assert torch.equal(res, target)
|
|
else:
|
|
assert res == target
|
|
|
|
assert len(tail_seqlens) == len(target_tail_seqlens)
|
|
for res, target in zip(tail_seqlens, target_tail_seqlens):
|
|
if isinstance(target, torch.Tensor):
|
|
assert torch.equal(res, target)
|
|
else:
|
|
assert res == target
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"kv_nomask_idx_multi_batch, split_size, expected_merged_idx, expected_merged_len",
|
|
[
|
|
# case1: multiple batches + split size greater than batch length
|
|
(
|
|
[[0, 1, 2, 3, 4], [5, 6, 7]],
|
|
2,
|
|
# expected merged_split_kv_idx_3d
|
|
[[0, 1, 5, 6], [2, 3, 7], [4]],
|
|
# expected merged_split_kv_len_2d
|
|
[[2, 2], [2, 1], [1, 0]],
|
|
),
|
|
# case2: single batch + split size greater than batch length
|
|
(
|
|
[[0, 1, 2]],
|
|
5,
|
|
[[0, 1, 2]],
|
|
[[3]],
|
|
),
|
|
# case3: split size equals maximum batch length
|
|
(
|
|
[[0, 1, 2, 3], [5, 6]],
|
|
4,
|
|
[[0, 1, 2, 3, 5, 6]],
|
|
[[4, 2]],
|
|
),
|
|
# case4: Split size is 1 (minimum granularity split)
|
|
(
|
|
[[0, 1], [2]],
|
|
1,
|
|
[[0, 2], [1]],
|
|
[[1, 1], [1, 0]],
|
|
),
|
|
# case6: the batch contains an empty list
|
|
(
|
|
[[], [0, 1], [2]],
|
|
1,
|
|
[[0, 2], [1]],
|
|
[[0, 1, 1], [0, 1, 0]],
|
|
),
|
|
# case7: empty input
|
|
(
|
|
[],
|
|
2,
|
|
[],
|
|
[],
|
|
),
|
|
])
|
|
def test_split_multi_batch_kv_idx(
|
|
kv_nomask_idx_multi_batch,
|
|
split_size,
|
|
expected_merged_idx,
|
|
expected_merged_len,
|
|
):
|
|
# Mock input data
|
|
model_runner = MagicMock(spec=PCPManager)
|
|
|
|
# Call the method under test
|
|
result = PCPManager._split_multi_batch_kv_idx(
|
|
self=model_runner,
|
|
kv_nomask_idx_multi_batch=kv_nomask_idx_multi_batch,
|
|
split_size=split_size)
|
|
|
|
merged_split_kv_idx_3d, merged_split_kv_len_2d = result
|
|
|
|
# Assert the method call
|
|
assert len(merged_split_kv_idx_3d) == len(expected_merged_idx)
|
|
|
|
for t, (actual_seg, expected_seg) in enumerate(
|
|
zip(merged_split_kv_idx_3d, expected_merged_idx)):
|
|
assert actual_seg == expected_seg
|
|
|
|
assert len(merged_split_kv_len_2d) == len(expected_merged_len)
|
|
|
|
for t, (actual_len, expected_len) in enumerate(
|
|
zip(merged_split_kv_len_2d, expected_merged_len)):
|
|
assert actual_len == expected_len
|