1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
|
# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the License); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Description:
# Wrapping function to do tensor address allocation. That is, assigning addresses to tensors based on what has been
# worked out from the allowable overlaps that are calculated by the live range analysis.
import math
import numpy as np
from . import live_range
from . import numeric_util
from .tensor import MemArea
from .nn_graph import TensorAllocator
from .greedy_allocation import allocate_live_ranges as greedy_allocate_live_ranges
def linear_allocate_live_ranges(live_ranges, alloc_granularity=256):
total_sz = 0
allocated_tensors = []
# just assign increasing addresses
for tens, lr in live_ranges.ranges.items():
if tens in allocated_tensors:
continue
lr.set_address(total_sz)
allocated_tensors += lr.tensors
total_sz += numeric_util.round_up(int(math.ceil(lr.size)), alloc_granularity)
return total_sz
def mark_sram_used_for_cascaded_passes(sg, lrs):
end_pos = max(ps.time for ps in sg.cascaded_passes) + 2
mem_usage = np.zeros(end_pos, dtype=np.int64)
for tens, rng in lrs.ranges.items():
storage_size = tens.storage_size()
mem_usage[rng.start_time : rng.end_time] += storage_size
for cps in sg.cascaded_passes:
sram_used = max(mem_usage[cps.time], mem_usage[cps.time + 1])
cps.sram_used = sram_used
for ps in cps.passes:
ps.sram_used = sram_used
def print_allocation(lrs, mem_area, sg, verbose_allocation, show_minimum_possible_allocation):
if verbose_allocation:
if mem_area == MemArea.Sram:
print("allocation for", mem_area, "- non-constant tensors in Cpu and Npu subgraphs")
else:
print("allocation for", mem_area, "- constant tensors in", sg.placement.name, "subgraph(s)")
for start_time, start, end, name, end_time in sorted(
(
lr.start_time,
tens.address,
tens.address + int(math.ceil(tens.storage_size())),
tens.name + " " + str(tens.purpose),
lr.end_time,
)
for tens, lr in lrs.ranges.items()
):
name = name.replace("\x00", "")
print("%9d: %#12x - %#12x: %3d - %3d %s" % ((end - start), start, end, start_time, end_time, name))
print()
if show_minimum_possible_allocation and mem_area == MemArea.Sram:
min_possible_allocation = max(cps.sram_used for cps in sg.cascaded_passes)
print(
"Min possible allocation %d bytes / %.1f KB / %.1f MB"
% (min_possible_allocation, min_possible_allocation / 1024, min_possible_allocation / 1024 / 1024)
)
def allocate_tensors(
nng,
sg,
arch,
mem_area,
use_ifm_ofm_overlap=True,
tensor_allocator=TensorAllocator.Greedy,
verbose_allocation=False,
show_minimum_possible_allocation=False,
lr_graph=None,
):
ignore_subgraph_input_output_tensors = False
lrs = live_range.extract_live_ranges_from_cascaded_passes(
sg,
mem_area,
mark_output_tensors_overlapping_with_input_tensors=False,
use_ifm_ofm_overlap=use_ifm_ofm_overlap,
ignore_subgraph_input_output_tensors=ignore_subgraph_input_output_tensors,
lr_graph=lr_graph,
)
if lrs.ranges:
tens_alloc = tensor_allocator
if tens_alloc == TensorAllocator.Greedy:
total_sz = greedy_allocate_live_ranges(sg, arch, lrs, mem_area, verbose_allocation)
elif tens_alloc == TensorAllocator.LinearAlloc:
total_sz = linear_allocate_live_ranges(lrs)
else:
assert 0
sg.memory_used[mem_area] = total_sz
nng.total_size[mem_area] = nng.total_size.get(mem_area, 0) + sum(tens.storage_size() for tens in lrs.ranges)
nng.total_elements[mem_area] = nng.total_elements.get(mem_area, 0) + sum(tens.elements() for tens in lrs.ranges)
print_allocation(lrs, mem_area, sg, verbose_allocation, show_minimum_possible_allocation)
if mem_area == MemArea.Sram:
# Mark Sram usage for all subgraphs
for sg_ in nng.subgraphs:
mark_sram_used_for_cascaded_passes(sg_, lrs)
if sg == nng.get_root_subgraph():
nng.memory_used = sg.memory_used
for mem_area in nng.total_elements.keys():
try:
nng.bits_per_element[mem_area] = nng.total_size[mem_area] * 8 / nng.total_elements[mem_area]
except ZeroDivisionError:
nng.bits_per_element[mem_area] = 0.0
|