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[Feature] support mm disable_chunked (#4803)
* support mm disable_chunked * update code * update code * update code
This commit is contained in:
kevin
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# 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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import unittest
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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(args)
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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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class TestIsChunkedMMInput(unittest.TestCase):
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def setUp(self):
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self.cache_manager = make_prefix_cache_manager(max_num_seqs=3, enable_mm=True, num_gpu_blocks_override=100)
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def test_is_chunked_mm_input_none_input(self):
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result, idx = self.cache_manager.is_chunked_mm_input(None, 10)
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self.assertFalse(result)
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self.assertEqual(idx, 0)
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def test_is_chunked_mm_input_no_mm_positions(self):
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mm_inputs = {"other_field": "value"}
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result, idx = self.cache_manager.is_chunked_mm_input(mm_inputs, 10)
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self.assertFalse(result)
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self.assertEqual(idx, 0)
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def test_is_chunked_mm_input_empty_positions(self):
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mm_inputs = {"mm_positions": []}
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result, idx = self.cache_manager.is_chunked_mm_input(mm_inputs, 10)
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self.assertFalse(result)
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self.assertEqual(idx, 0)
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def test_is_chunked_mm_input_matched_in_chunk(self):
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mm_inputs = {
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"mm_positions": [
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ImagePosition(offset=5, length=10),
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ImagePosition(offset=20, length=10),
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]
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}
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result, idx = self.cache_manager.is_chunked_mm_input(mm_inputs, 8)
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self.assertTrue(result)
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self.assertEqual(idx, 0)
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def test_is_chunked_mm_input_matched_in_second_chunk(self):
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mm_inputs = {
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"mm_positions": [
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ImagePosition(offset=5, length=10),
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ImagePosition(offset=20, length=10),
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]
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}
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result, idx = self.cache_manager.is_chunked_mm_input(mm_inputs, 25)
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self.assertTrue(result)
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self.assertEqual(idx, 1)
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def test_is_chunked_mm_input_before_first_chunk(self):
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mm_inputs = {
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"mm_positions": [
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ImagePosition(offset=5, length=10),
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ImagePosition(offset=20, length=10),
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]
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}
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result, idx = self.cache_manager.is_chunked_mm_input(mm_inputs, 3)
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self.assertFalse(result)
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self.assertEqual(idx, 0)
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def test_is_chunked_mm_input_after_last_chunk(self):
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mm_inputs = {
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"mm_positions": [
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ImagePosition(offset=5, length=10),
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ImagePosition(offset=20, length=10),
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]
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}
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result, idx = self.cache_manager.is_chunked_mm_input(mm_inputs, 35)
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self.assertFalse(result)
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self.assertEqual(idx, 0)
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class TestRevertMatchBlocks(unittest.TestCase):
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def setUp(self):
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self.block_size = 64
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self.cache_manager = make_prefix_cache_manager(max_num_seqs=3, enable_mm=True, num_gpu_blocks_override=100)
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def make_match_blocks(self, gpu_block_num, cpu_block_num):
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block_num = gpu_block_num + cpu_block_num
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matched_token_num = block_num * self.block_size
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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(block_num):
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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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for _ in range(cpu_block_num):
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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) * self.block_size
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cpu_match_token_num = len(match_cpu_block_ids) * self.block_size
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return (
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matched_token_num,
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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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gpu_match_token_num,
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cpu_match_token_num,
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)
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def test_revert_full_blocks(self):
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# Setup test data
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multimodal_inputs = {
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"mm_positions": [ImagePosition(offset=0, length=1200)],
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"mm_hashes": ["image1"],
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}
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req_dict = {
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"request_id": "req1",
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"prompt_token_ids": [-1] * 1200 + [2] * 120,
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"prompt_token_ids_len": 1320,
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"multimodal_inputs": multimodal_inputs,
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}
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(
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matched_token_num,
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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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gpu_match_token_num,
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cpu_match_token_num,
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) = self.make_match_blocks(gpu_block_num=2, cpu_block_num=0)
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# Call method
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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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) = self.cache_manager._revert_match_blocks(
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request=Request.from_dict(req_dict),
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matched_token_num=matched_token_num,
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block_size=self.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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# Assertions
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self.assertEqual(gpu_match_token_num, 0)
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self.assertEqual(cpu_match_token_num, 0)
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self.assertEqual(len(match_node_ids), 0)
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self.assertEqual(len(match_gpu_block_ids), 0)
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def test_revert_partial_block(self):
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# Setup test data
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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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req_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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(
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matched_token_num,
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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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gpu_match_token_num,
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cpu_match_token_num,
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) = self.make_match_blocks(gpu_block_num=20, cpu_block_num=0)
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# Call method
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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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) = self.cache_manager._revert_match_blocks(
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request=Request.from_dict(req_dict),
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matched_token_num=matched_token_num,
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block_size=self.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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# Assertions
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self.assertEqual(gpu_match_token_num, 120)
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self.assertEqual(cpu_match_token_num, 0)
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self.assertEqual(len(match_node_ids), 2)
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self.assertEqual(len(match_gpu_block_ids), 2)
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def test_revert_with_cpu_blocks(self):
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# Setup test data
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multimodal_inputs = {
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"mm_positions": [ImagePosition(offset=120, length=1200), ImagePosition(offset=1440, length=420)],
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"mm_hashes": ["image1", "image2"],
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}
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req_dict = {
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"request_id": "req1",
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"prompt_token_ids": [1] * 120 + [-1] * 1200 + [2] * 120 + [-1] * 420,
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"prompt_token_ids_len": 1860,
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"multimodal_inputs": multimodal_inputs,
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}
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(
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matched_token_num,
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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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gpu_match_token_num,
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cpu_match_token_num,
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) = self.make_match_blocks(gpu_block_num=22, cpu_block_num=6)
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# Call method
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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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) = self.cache_manager._revert_match_blocks(
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request=Request.from_dict(req_dict),
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matched_token_num=matched_token_num,
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block_size=self.block_size,
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chunk_idx=1,
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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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# Assertions
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self.assertEqual(gpu_match_token_num, 22 * self.block_size)
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self.assertEqual(cpu_match_token_num, 32)
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self.assertEqual(len(match_node_ids), 23)
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self.assertEqual(len(match_gpu_block_ids), 22)
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self.assertEqual(len(match_cpu_block_ids), 1)
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if __name__ == "__main__":
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unittest.main()
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