| Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame^] | 1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. |
| 2 | # |
| 3 | # SPDX-License-Identifier: Apache-2.0 |
| 4 | # |
| 5 | # Licensed under the Apache License, Version 2.0 (the License); you may |
| 6 | # not use this file except in compliance with the License. |
| 7 | # You may obtain a copy of the License at |
| 8 | # |
| 9 | # www.apache.org/licenses/LICENSE-2.0 |
| 10 | # |
| 11 | # Unless required by applicable law or agreed to in writing, software |
| 12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| 13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | # See the License for the specific language governing permissions and |
| 15 | # limitations under the License. |
| 16 | |
| 17 | |
| 18 | # Description: |
| 19 | # Allocate tensor addresses using a greedy algorithm. |
| 20 | |
| 21 | from . import numeric_util |
| 22 | |
| 23 | |
| 24 | class GreedyAllocator: |
| 25 | def __init__(self, nng, arch, live_ranges, mem_area): |
| 26 | self.nng = nng |
| 27 | self.arch = arch |
| 28 | self.mem_area = mem_area |
| 29 | |
| 30 | self.live_ranges = live_ranges |
| 31 | self.memory_required = 0 |
| 32 | |
| 33 | self.current_allocs = [] |
| 34 | |
| 35 | def alloc(self, new_lr): |
| 36 | size = new_lr.size |
| 37 | current_top = 0 |
| 38 | if self.current_allocs: |
| 39 | current_top = max(start_addr + lr.size for start_addr, lr in self.current_allocs) |
| 40 | best_offset = numeric_util.round_up(current_top, new_lr.get_alignment()) |
| 41 | best_offset_fit = (1 << 64) - 1 |
| 42 | |
| 43 | current_offset = 0 |
| 44 | for start_addr, lr in self.current_allocs: |
| 45 | aligned_current_offset = numeric_util.round_up(current_offset, new_lr.get_alignment()) |
| 46 | if aligned_current_offset + size <= start_addr and start_addr - current_offset < best_offset_fit: |
| 47 | best_offset = current_offset |
| 48 | best_offset_fit = start_addr - current_offset |
| 49 | |
| 50 | current_offset = start_addr + lr.size |
| 51 | |
| 52 | self.memory_required = max(self.memory_required, best_offset + size) |
| 53 | new_lr.set_address(best_offset) |
| 54 | self.current_allocs.append((best_offset, new_lr)) |
| 55 | self.current_allocs = list(sorted(self.current_allocs)) |
| 56 | |
| 57 | def dealloc(self, lr_to_dealloc): |
| 58 | self.current_allocs = [(start_addr, lr) for start_addr, lr in self.current_allocs if lr != lr_to_dealloc] |
| 59 | |
| 60 | def allocate_live_ranges(self, verbose_allocation): |
| 61 | lrs = set() |
| 62 | for lr in self.live_ranges.ranges.values(): |
| 63 | lrs.add((lr.start_time, lr.end_time, lr)) |
| 64 | |
| 65 | lrs = sorted(lrs) |
| 66 | |
| 67 | for curr_time, _, new_lr in lrs: |
| 68 | for _, lr in list(self.current_allocs): |
| 69 | if lr.end_time < curr_time: |
| 70 | self.dealloc(lr) |
| 71 | |
| 72 | self.alloc(new_lr) |
| 73 | |
| 74 | assert self.verify_allocation() |
| 75 | return self.memory_required |
| 76 | |
| 77 | def verify_allocation(self): |
| 78 | lrs = list(self.live_ranges.ranges.values()) |
| 79 | for n in lrs: |
| 80 | for m in lrs: |
| 81 | if n != m and n.overlaps_ranges(m): |
| 82 | overlap, tens_n, tens_m = n.overlaps_address(m) |
| 83 | if overlap: |
| 84 | print("Solution failed, overlapping buffer!") |
| 85 | print(tens_n.address, tens_n.address + n.size, n.name) |
| 86 | print(tens_m.address, tens_m.address + m.size, m.name) |
| 87 | print() |
| 88 | return False |
| 89 | |
| 90 | return True |
| 91 | |
| 92 | |
| 93 | def allocate_live_ranges(nng, arch, live_ranges, mem_area, verbose_allocation=False): |
| 94 | g = GreedyAllocator(nng, arch, live_ranges, mem_area) |
| 95 | return g.allocate_live_ranges(verbose_allocation) |