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# Copyright (C) 2021 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:
# Groups Operators in a schedule together to form Cascades.
from collections import namedtuple
from .high_level_command_to_npu_op import ifm_ifm2_correct_order
from .numeric_util import round_up
from .operation import NpuBlockType
from .operation import Op
from .operation import Padding
from .shape4d import Shape4D
non_cascadable_blocks = (
NpuBlockType.Default,
NpuBlockType.VectorProduct,
NpuBlockType.ReduceSum,
)
class CascadeInfo:
"""Contains metadata about a cascade"""
def __init__(self, start, end, buffers, mem_usage: int):
self.start = start
self.end = end
self.buffers = buffers
self.mem_usage = mem_usage
class BufferMap:
"""Caches the buffers seen"""
def __init__(self):
self.buffer_map = {}
def get_buffer(self, producer, consumer, cost):
assert producer or consumer
key = (producer, consumer)
if key not in self.buffer_map:
# No cached buffer between these two SchedulerOperations
if consumer is None:
# There are either no consumers or multiple consumers - FeatureMap needs to be stored in full
buffer_shape = producer.ofm.shape
buffer_size = producer.ofm_size_in_bytes()
elif producer is None:
# First Op in subgraph or cascade - FeatureMap needs to be stored in full
buffer_shape = consumer.ifm.shape
buffer_size = consumer.ifm_size_in_bytes()
elif producer.requires_full_ofm or consumer.requires_full_ifm:
# FeatureMap needs to be stored in full
buffer_shape = max(producer.ofm.shape, consumer.ifm.shape)
buffer_size = max(producer.ofm_size_in_bytes(), consumer.ifm_size_in_bytes())
else:
# Use a rolling buffer
buffer_shape = rolling_buffer_shape(cost[producer].stripe, cost[consumer].stripe_input)
buffer_size = buffer_shape.elements() * producer.ofm.dtype.size_in_bytes()
self.buffer_map[key] = (buffer_shape, buffer_size)
return self.buffer_map[key]
def rolling_buffer_shape(producer_stripe: Shape4D, consumer_stripe_input: Shape4D) -> Shape4D:
"""Calculates the storage shape of the rolling buffer between two SchedulerOperations in a Cascade"""
buffer_height = round_up(producer_stripe.height + consumer_stripe_input.height, consumer_stripe_input.height)
# Rolling buffers have to conform to NHCWB16 format
return consumer_stripe_input.with_height(buffer_height).with_depth(round_up(producer_stripe.depth, 16))
class CascadeBuilder:
"""Class for grouping SchedulerOperations into cascades"""
def __init__(self, sched_ops, spilling, non_local_mem_usage=None):
self.sched_ops = sched_ops
self.no_cascade = 0
self.non_local_mem_usage = non_local_mem_usage if non_local_mem_usage else {}
self.spilling = spilling
def _is_cascadable(self, sched_op, cost) -> bool:
"""Checks if 'sched_op' can be cascaded"""
return (
sched_op.op_type.npu_block_type not in non_cascadable_blocks
and cost.stripe.height < sched_op.ofm.shape.height
and sched_op.parent_op.read_offsets[0] is None
and sched_op.parent_op.read_offsets[1] is None
and self.element_wise_cascading_conformity(sched_op)
and not sched_op.parent_op.type.is_resize_op()
and not sched_op.parent_op.type == Op.Conv2DBackpropInputSwitchedBias
and sched_op.parent_op.attrs.get("padding", None) != Padding.TILE
)
def _is_mergeable(self, sched_op) -> bool:
# Code based on merge_elementwise_op_ranges from live_range.py
if not sched_op.op_type.is_elementwise_op():
return False
elem_op = sched_op.parent_op
# Check if overwriting the inputs can be allowed
OpShapeTens = namedtuple("OpShapeTens", ["op_shape", "tens"])
outp = OpShapeTens(elem_op.ofm_shapes[0], elem_op.ofm)
# check output tensor only has one producer
if len(outp.tens.ops) != 1:
return False
inps = []
if elem_op.ifm is not None:
inps.append(OpShapeTens(elem_op.ifm_shapes[0], elem_op.ifm))
if elem_op.ifm2 is not None:
inps.append(OpShapeTens(elem_op.ifm_shapes[1], elem_op.ifm2))
# find an input tensor that can be overwritten by the output
for inp in inps:
if (
# check op input and output shapes allow overlapping
inp.op_shape == outp.op_shape
# check input tensor is valid
and inp.tens is not None
and inp.tens.shape != []
# check input and output tensors are compatible
and inp.tens.format == outp.tens.format
and inp.tens.dtype == outp.tens.dtype
# check input tensor only has one consumer
and len(inp.tens.consumer_list) == 1
):
return True
return False
def _estimate_sram_usage(self, sched_op, cost) -> int:
"""Estimate the SRAM required for the Op if all FeatureMaps are in SRAM"""
ifm2_size = sched_op.ifm2_size_in_bytes()
if sched_op.requires_full_ifm:
ifm_size = sched_op.ifm_size_in_bytes()
else:
ifm_size = (
cost.stripe_input.with_depth(round_up(cost.stripe_input.depth, 16)).elements()
* sched_op.ifm.dtype.size_in_bytes()
)
if sched_op.requires_full_ofm:
ofm_size = sched_op.ofm_size_in_bytes()
else:
ofm_size = (
cost.stripe.with_depth(round_up(cost.stripe.depth, 16)).elements() * sched_op.ofm.dtype.size_in_bytes()
)
if self._is_mergeable(sched_op):
# ofm will use the ifm buffer to reduce SRAM usage, hence ofm_size = 0
ofm_size = 0
return ifm_size + ifm2_size + ofm_size + self.non_local_mem_usage.get(sched_op, 0)
@staticmethod
def element_wise_cascading_conformity(sched_op):
"""Check the inputs of the op to see if it's a candidate for cascading."""
ifm = sched_op.parent_op.ifm
ifm2 = sched_op.parent_op.ifm2
# Cascading elementwise operations with reverse operand order is not handled
if sched_op.parent_op.type.is_binary_elementwise_op() and ifm and ifm2:
# We cannot rule out cascadability if at least one IFM is constant
ifm2_const = ifm2.ops != [] and ifm2.ops[0].type == Op.Const
return ifm_ifm2_correct_order(ifm.shape, ifm2.shape) and ifm2_const
else:
# Either one IFM is not variable or it is not a binary elementwise op - we cannot rule out cascadability
return True
def build_cascades(self, ref_schedule, fallback_schedule, guiding_mem_limit):
ref_cost = ref_schedule.cost_map
fallback_cost = fallback_schedule.cost_map
cost = {}
cascade_map = {}
buffers = BufferMap()
# Peak memory usage so far - updated continously, unless dedicated SRAM where this is a hard limit
peak_sram_usage = guiding_mem_limit
idx = 0
while idx < len(self.sched_ops):
op = self.sched_ops[idx]
if op in cost:
# Already processed this Op
idx += 1
continue
if not self._is_cascadable(op, ref_cost[op]):
# Op is not a candidate for cascading - assign fallback cost
cost[op] = fallback_cost[op]
if not self.spilling:
peak_sram_usage = max(self._estimate_sram_usage(op, fallback_cost[op]), peak_sram_usage)
idx += 1
continue
# Propose a cascade starting with this Op
cascade_start = op.index
# Keep track of which Ops are in the proposed cascade as well as the best cascade so far
ops_in_cascade = [op]
ops_in_best_cascade = [op]
# Get the size of the weight buffer(s)
weight_buffer = sum(tens.storage_size() for tens in ref_cost[op].buffered_weight_tensors)
# The first IFM needs to be stored in full
cascade_ifm_size = op.ifm_size_in_bytes() if not self.spilling else 0
# Add non-local memory usage
cascade_ifm_size += self.non_local_mem_usage.get(op, 0)
# Sum of all intermediate cascade buffers (including weight buffers)
cascade_buffers = weight_buffer
# Best cascade size - Initially it's the fallback cost of the first Op in the cascade
best_cascade_size = self._estimate_sram_usage(op, fallback_cost[op])
# Op is the producer of the OFM consumed by the next Op to consider
producer = op
while True:
dependants = producer.get_dependants()
if len(dependants) != 1:
# producer is either the last Op in the schedule or the start of a branch
break
current_op = dependants[0]
if (
current_op in cost
or current_op not in ref_cost
or not self._is_cascadable(current_op, ref_cost[current_op])
or producer.ofm.shape != current_op.ifm.shape
or current_op.requires_full_ifm
or producer.requires_full_ofm
):
# Current op has already been processed or cannot be cascaded
break
if producer.index + 1 != current_op.index:
# Cascading is possible, but requires reordering of operations in the schedule,
# this is currently not supported
break
# Get the size of the FeatureMap buffers between current and neighbouring Ops
op_full_ifm = current_op.ifm_size_in_bytes()
op_full_ofm = current_op.ofm_size_in_bytes()
_, op_ifm_buffer = buffers.get_buffer(producer, current_op, ref_cost)
# Get the size of the weight buffer(s)
op_weight_buffer = sum(tens.storage_size() for tens in ref_cost[current_op].buffered_weight_tensors)
# Calculate the uncascaded memory requirement for current Op
uncascaded_sram_usage = op_full_ifm + op_full_ofm + self.non_local_mem_usage.get(current_op, 0)
# Add current Op to cascade
ops_in_cascade.append(current_op)
# Increase the accumulated intermediate buffers in the cascade
cascade_buffers += op_ifm_buffer + op_weight_buffer
if self.spilling:
# For Dedicated SRAM only the intermediate buffers are in SRAM
if uncascaded_sram_usage < peak_sram_usage or cascade_buffers > peak_sram_usage:
# Cascade until an Op fits in its entirety or the accumulated buffers no longer fit
break
else:
# Any addition to the cascade that fits is the new best cascade for Dedicated SRAM
ops_in_best_cascade = [op for op in ops_in_cascade]
best_cascade_size = cascade_buffers
else:
# Calculate the total size of the current cascade
cascade_size = cascade_ifm_size + cascade_buffers + op_full_ofm
# Determine if cascading search should stop
if (
uncascaded_sram_usage < peak_sram_usage
and best_cascade_size < peak_sram_usage
or (cascade_ifm_size + cascade_buffers) > best_cascade_size
):
# Both the existing cascade and current Op fits
break
"""
One of two conditions will update the best cascade:
- cascade_size < best_cascade_size or
- cascade_size < uncascaded_sram_usage
The last condition is illustrated below, showing an example where it is
better to choose a larger cascade_size (with more OPs) because it will
use less total SRAM usage.
For simplicity, all featuremaps have same size.
Cascade OP1-OP2, OP3 is standalone
-> |OP1| -> roll buffer -> |OP2| -> FM -> |OP3| -> FM
/
|OP0| -> FM
\
-> ....
best_cascade_size : FM + roll buffer + FM
uncascaded_sram_usage: FM + FM + FM
compared with:
Cascade OP1-OP3
-> |OP1| -> roll buffer -> |OP2| -> roll buffer -> |OP3| -> FM
/
|OP0| -> FM
\
-> ....
cascade_size : FM + roll buffer + roll buffer + FM
So, for this use case the comparison will be
(FM + roll buffer + roll buffer + FM) < (FM + roll buffer + FM) or
(FM + roll buffer + roll buffer + FM) < (FM + FM + FM)
hence, better to choose Cascade OP1-OP3 in this case.
"""
if cascade_size < best_cascade_size or cascade_size < uncascaded_sram_usage:
best_cascade_size = cascade_ifm_size + cascade_buffers + op_full_ofm
ops_in_best_cascade = [op for op in ops_in_cascade]
producer = current_op
if len(ops_in_best_cascade) > 1:
# A cascade was created - assign cascade and ref_cost to all of the Ops
cascade_end = cascade_start + (len(ops_in_best_cascade) - 1)
buffers_in_cascade = {}
prev_op = None
for cascaded_op in ops_in_best_cascade:
assert cascade_start <= cascaded_op.index <= cascade_end
cost[cascaded_op] = ref_cost[cascaded_op]
cost[cascaded_op].cascade = cascade_end
if prev_op:
rolling_buffer_shape, _ = buffers.get_buffer(prev_op, cascaded_op, ref_cost)
buffers_in_cascade[cascaded_op] = rolling_buffer_shape
prev_op = cascaded_op
# Create a CascadeInfo for the cascade
cascade_map[cascade_end] = CascadeInfo(
cascade_start, cascade_end, buffers_in_cascade, best_cascade_size
)
if not self.spilling:
# Update peak memory usage
peak_sram_usage = max(best_cascade_size, peak_sram_usage)
else:
# Assign fallback cost to the initial Op
cost[op] = fallback_cost[op]
if not self.spilling:
peak_sram_usage = max(self._estimate_sram_usage(op, fallback_cost[op]), peak_sram_usage)
# Update costing and cascade information for the ref_schedule
ref_schedule.cost_map = cost
ref_schedule.cascades = cascade_map
return ref_schedule
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