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* mm prefix cache * add _revert_match_blocks * update code * update code * update code * fix bugs * add test case * fix bug * update code * update reserved_dec_block_ids
107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. 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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from dataclasses import asdict
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from types import SimpleNamespace
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from fastdeploy.cache_manager.cache_data import BlockNode
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from fastdeploy.cache_manager.prefix_cache_manager import PrefixCacheManager
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from fastdeploy.config import CacheConfig, FDConfig, ParallelConfig
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from fastdeploy.engine.args_utils import EngineArgs
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from fastdeploy.engine.request import ImagePosition, Request
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from fastdeploy.scheduler import SchedulerConfig
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def make_prefix_cache_manager(max_num_seqs, enable_mm=False, num_gpu_blocks_override=100, max_num_batched_tokens=3200):
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engine_args = EngineArgs(
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max_num_seqs=max_num_seqs,
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num_gpu_blocks_override=num_gpu_blocks_override,
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max_num_batched_tokens=max_num_batched_tokens,
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)
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args = asdict(engine_args)
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cache_cfg = CacheConfig(args)
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model_cfg = SimpleNamespace(enable_mm=enable_mm, max_model_len=8192)
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speculative_cfg = SimpleNamespace(method=None)
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model_cfg.print = print
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cache_cfg.bytes_per_layer_per_block = 1
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parallel_cfg = ParallelConfig(args)
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scheduler_cfg = SchedulerConfig()
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graph_opt_cfg = engine_args.create_graph_optimization_config()
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fd_config = FDConfig(
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model_config=model_cfg,
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cache_config=cache_cfg,
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parallel_config=parallel_cfg,
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graph_opt_config=graph_opt_cfg,
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speculative_config=speculative_cfg,
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scheduler_config=scheduler_cfg,
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)
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return PrefixCacheManager(config=fd_config, tensor_parallel_size=8, splitwise_role="mixed")
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def test_revert_match_blocks():
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block_size = 64
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cache_manager = make_prefix_cache_manager(max_num_seqs=3, enable_mm=True, num_gpu_blocks_override=100)
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multimodal_inputs = {
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"mm_positions": [ImagePosition(offset=120, length=1200)],
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"mm_hashes": ["image1"],
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}
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req1_dict = {
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"request_id": "req1",
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"prompt_token_ids": [1] * 120 + [-1] * 1200 + [2] * 120,
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"prompt_token_ids_len": 1440,
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"multimodal_inputs": multimodal_inputs,
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}
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request_1 = Request.from_dict(req1_dict)
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matched_token_num = 20 * 64
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match_node_ids = []
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matche_nodes = []
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match_gpu_block_ids = []
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match_cpu_block_ids = []
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for idx in range(20):
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node_id = idx + 10
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block = BlockNode(node_id, [], 0, 0, idx, 0, None, None, None)
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match_node_ids.append(node_id)
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matche_nodes.append(block)
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match_gpu_block_ids.append(idx)
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match_cpu_block_ids.append(match_gpu_block_ids.pop())
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match_cpu_block_ids.append(match_gpu_block_ids.pop())
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gpu_match_token_num = len(match_gpu_block_ids) * block_size
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cpu_match_token_num = len(match_cpu_block_ids) * block_size
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(
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gpu_match_token_num,
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cpu_match_token_num,
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current_match_node,
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) = cache_manager._revert_match_blocks(
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request=request_1,
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matched_token_num=matched_token_num,
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block_size=block_size,
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chunk_idx=0,
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match_node_ids=match_node_ids,
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matche_nodes=matche_nodes,
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match_gpu_block_ids=match_gpu_block_ids,
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match_cpu_block_ids=match_cpu_block_ids,
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gpu_match_token_num=gpu_match_token_num,
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cpu_match_token_num=cpu_match_token_num,
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swap_node_ids=[],
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)
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assert match_gpu_block_ids == [0, 1]
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assert match_cpu_block_ids == []
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assert gpu_match_token_num == 120
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assert cpu_match_token_num == 0
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assert match_node_ids == [10, 11]
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