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# 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 .greedy_allocation import allocate_live_ranges as greedy_allocate_live_ranges
from .nn_graph import TensorAllocator
from .tensor import MemArea
from .tensor import MemType
from .tensor import TensorPurpose
def linear_allocate_live_ranges(live_ranges, alloc_granularity=16):
# Allocates using increasing addresses. Duplicate constant tensors will be allocated to the same address
total_sz = 0
allocated_tensors = []
# just assign increasing addresses, except for duplicates
for tens, lr in live_ranges.ranges.items():
if tens in allocated_tensors:
continue
address = total_sz
if tens.weight_compression_config is not None:
for allocated_tens in allocated_tensors:
if allocated_tens.weight_compression_config == tens.weight_compression_config:
address = allocated_tens.address
break
if tens.purpose == TensorPurpose.LUT:
for allocated_tens in allocated_tensors:
if allocated_tens.equivalent(tens):
address = allocated_tens.address
break
lr.set_address(address)
allocated_tensors += lr.tensors
if address == total_sz:
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, mem_type_set, sg, verbose_allocation, show_minimum_possible_allocation):
if verbose_allocation:
if mem_type_set == set((MemType.Permanent_NPU,)) or mem_type_set == set((MemType.Permanent_CPU,)):
print("allocation for", mem_area, "- constant tensors in", sg.placement.name, "subgraph(s)")
else:
print("allocation for", mem_area, "- non-constant tensors in Cpu and Npu subgraphs")
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,
mem_type_set,
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,
mem_type_set,
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, 16)
else:
assert 0
if sg.memory_used.get(mem_area, 0) == 0:
sg.memory_used[mem_area] = total_sz
else:
sg.memory_used[mem_area] += total_sz
# Keep track of how much should be used for scratch or permanent storage for NPU
for mem_type in mem_type_set:
if sg.memory_used_per_type.get(mem_type, 0) == 0:
sg.memory_used_per_type[mem_type] = total_sz
else:
sg.memory_used_per_type[mem_type] += 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, mem_type_set, 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
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