'''
Cost model.
'''
#import numpy as np
from operator import mul
from operator import add
from functools import reduce
import copy
import math
from . import loop_enum as le
from . import buffer_enum as be
def get_comp_cost(layer):
'''
Compute the total # of MAC computation, it is independent of other optimizations
Also it is independent of input size and input/filter stride
Total # of computation = OX*OY*IC*OC*ON*FX*FY
'''
cost = layer.wofm * layer.hofm * layer.nifm * layer.nofm \
* layer.nimg * layer.wfil * layer.hfil
return cost
def get_ideal_performance(layer, resource):
'''
Compute the ideal runtime in cycles by assuming 100% PE array utilization
Ideal # of cycles = Total # of MAC computation / Total # of PEs
#LMEI Need to be modified if later when adding precision-scalable PE.
# of functional PE will change depending on different precision modes.
'''
total_comp = get_comp_cost(layer)
number_pe = reduce(mul, resource.para_count_list, 1)
runtime = math.ceil(total_comp *1.0 / number_pe)
return runtime
def get_layer_size(layer):
'''
Get size of ifmap, ofmap, filter of the layer
#LMEI ifmap_size should be able to calculate based on ofmap_size and input stride(IS) /filter stride(FS)
IX = IS*(OX-1) + FS*(FX-1) + 1
wifm = wistd*(wofm-1) + wfstd*(wfil-1) + 1
'''
ifmap_size = layer.wifm * layer.hifm * layer.nifm * layer.nimg
ofmap_size = layer.wofm * layer.hofm * layer.nofm * layer.nimg
flmap_size = layer.wfil * layer.hfil * layer.nifm * layer.nofm
return [ifmap_size, ofmap_size, flmap_size]
def get_hinted_para(level, hint):
'''
Get the actual total spatial unrolling size from loop schedule
'''
assert hint
hinted_para = 1
for loop in range(le.NUM):
if loop in hint:
hinted_loop_para = hint[loop][level][2]
hinted_para *= hinted_loop_para
return hinted_para
def valid_dataflow(resource, hint):
'''
Check if the actual spatial unrolling size from loop schedule meets the HW utilization requirement
by comparing it with real HW parallelism size * utilization threshold.
'''
num_levels = resource.buffer_levels()
for level in range(num_levels):
if resource.paras[level].count != 1 and \
get_hinted_para(level, hint) < (resource.paras[level].count * resource.utilization_threshold):
return False
return True
def get_if_access(level, point, layer, mac_capacity = 1):
'''
Get per element # of access of Input at current level
Not accurate because [FX, FY] is not totally irrelevant terms for ifmap..
#LMEI Need to be modified by using the concept of the dataset.
'''
if level == 0 and mac_capacity == 0:
return layer.wfil * layer.hfil * layer.nofm / (layer.wstd * layer.hstd)
ex_order_index = min(point.loop_orders[le.OX][level],
point.loop_orders[le.OY][level],
point.loop_orders[le.IC][level],
point.loop_orders[le.ON][level])
fx_exclusive = point.loop_orders[le.FX][level] < ex_order_index
fy_exclusive = point.loop_orders[le.FY][level] < ex_order_index
oc_exclusive = point.loop_orders[le.OC][level] < ex_order_index
fx_acc = reduce(mul, point.loop_blockings[le.FX][level+fx_exclusive:], 1)
fy_acc = reduce(mul, point.loop_blockings[le.FY][level+fy_exclusive:], 1)
oc_acc = reduce(mul, point.loop_blockings[le.OC][level+oc_exclusive:], 1)
# No loop orders among unrolled loops, they have the same order
fx_par = reduce(mul, point.loop_partitionings[le.FX][level:], 1)
fy_par = reduce(mul, point.loop_partitionings[le.FY][level:], 1)
oc_par = reduce(mul, point.loop_partitionings[le.OC][level:], 1)
return fx_acc * fy_acc * oc_acc * fx_par * fy_par * oc_par / (layer.wstd * layer.hstd)
def get_of_access(level, point, layer, mac_capacity = 1):
'''
Get per element # of access of Output at current level
For output:
Relevant terms [OX, OY, OC, ON]
irrelevant terms [FX, FY, IC]
Calculating rule:
At lowest mem level (directly talk to MAC), calculate per element access
by timing all irrelevant terms [FX, FY, IC] together
For the rest higher mem levels,
firstly, check if there is stationary possibility
(irrelevant loops for filter [FX, FY, IC] are at the innermost position of this level)
if there is, exclude the irrelevant loop(s) from the current level's # of per element access computing
because they have been taken into account in lower level's # of per element access computing
secondly, calculate the current level's # of per element access
by multiplying all the irrelevant terms from current level to the highest level
including both temporal unrolling part and spatial unrolling part (parallelism).
'''
if level == 0 and mac_capacity == 0 :
return layer.wfil * layer.hfil * layer.nifm
ex_order_index = min(point.loop_orders[le.OX][level],
point.loop_orders[le.OY][level],
point.loop_orders[le.OC][level],
point.loop_orders[le.ON][level])
fx_exclusive = point.loop_orders[le.FX][level] < ex_order_index
fy_exclusive = point.loop_orders[le.FY][level] < ex_order_index
ic_exclusive = point.loop_orders[le.IC][level] < ex_order_index
fx_acc = reduce(mul, point.loop_blockings[le.FX][level+fx_exclusive:], 1)
fy_acc = reduce(mul, point.loop_blockings[le.FY][level+fy_exclusive:], 1)
ic_acc = reduce(mul, point.loop_blockings[le.IC][level+ic_exclusive:], 1)
fx_par = reduce(mul, point.loop_partitionings[le.FX][level:], 1)
fy_par = reduce(mul, point.loop_partitionings[le.FY][level:], 1)
ic_par = reduce(mul, point.loop_partitionings[le.IC][level:], 1)
return fx_acc * fy_acc * ic_acc * fx_par * fy_par * ic_par
def get_fl_access(level, point, layer, mac_capacity = 1):
'''
Get per element # of access of Weight at current level
For filter:
Relevant terms [FX, FY, IC, OC]
irrelevant terms [OX, OY, ON]
Calculating rule:
At lowest mem level (directly talk to MAC), calculate per element access
by timing all irrelevant terms [OX, OY, ON] together
For the rest higher mem levels,
firstly, check if there is stationary possibility
(irrelevant loops for filter [OX, OY, ON] are at the innermost position of this level)
if there is, exclude the irrelevant loop(s) from the current level's # of per element access computing
because they have been taken into account in lower level's # of per element access computing
secondly, calculate the current level's # of per element access
by multiplying all the irrelevant terms from current level to the highest level
including both temporal unrolling part and spatial unrolling part (parallelism).
'''
if level == 0 and mac_capacity == 0:
return layer.wofm * layer.hofm * layer.nimg
ex_order_index = min(point.loop_orders[le.FX][level],
point.loop_orders[le.FY][level],
point.loop_orders[le.IC][level],
point.loop_orders[le.OC][level])
ox_exclusive = point.loop_orders[le.OX][level] < ex_order_index
oy_exclusive = point.loop_orders[le.OY][level] < ex_order_index
on_exclusive = point.loop_orders[le.ON][level] < ex_order_index
ox_acc = reduce(mul, point.loop_blockings[le.OX][level+ox_exclusive:], 1)
oy_acc = reduce(mul, point.loop_blockings[le.OY][level+oy_exclusive:], 1)
on_acc = reduce(mul, point.loop_blockings[le.ON][level+on_exclusive:], 1)
ox_par = reduce(mul, point.loop_partitionings[le.OX][level:], 1)
oy_par = reduce(mul, point.loop_partitionings[le.OY][level:], 1)
on_par = reduce(mul, point.loop_partitionings[le.ON][level:], 1)
return ox_acc * oy_acc * on_acc * ox_par * oy_par * on_par
def opt_get_if_access(level, point, ba_arr, pa_arr):
'''
Get # access of if block at current level
The repeated access to ifmap is determined by the blocking factors and
parallelism counts of those loops other than ifmap-related loops outside of
this level.
At the same buffer level, if the other loops are outside of the innermost
loop of ifmap-related loops, their blocking factors and parallelism counts
at this level should also contribute to the number of accesses.
'''
ex_order_index = min(point.loop_orders[le.OX][level],
point.loop_orders[le.OY][level],
point.loop_orders[le.IC][level],
point.loop_orders[le.ON][level])
fx_exclusive = point.loop_orders[le.FX][level] < ex_order_index
fy_exclusive = point.loop_orders[le.FY][level] < ex_order_index
oc_exclusive = point.loop_orders[le.OC][level] < ex_order_index
fx_acc = ba_arr[le.FX][level+fx_exclusive] #reduce(mul, point.loop_blockings[le.FX][level+fx_exclusive:], 1)
fy_acc = ba_arr[le.FY][level+fy_exclusive] #reduce(mul, point.loop_blockings[le.FY][level+fy_exclusive:], 1)
oc_acc = ba_arr[le.OC][level+oc_exclusive] #reduce(mul, point.loop_blockings[le.OC][level+oc_exclusive:], 1)
fx_par = pa_arr[le.FX][level] #reduce(mul, point.loop_partitionings[le.FX][level+fx_exclusive:], 1)
fy_par = pa_arr[le.FY][level] #reduce(mul, point.loop_partitionings[le.FY][level+fy_exclusive:], 1)
oc_par = pa_arr[le.OC][level] #reduce(mul, point.loop_partitionings[le.OC][level+oc_exclusive:], 1)
return fx_acc * fy_acc * oc_acc * fx_par * fy_par * oc_par
def opt_get_of_access(level, point, ba_arr, pa_arr):
'''
Get # access of of block at current level
See comments in routine for ifmap.
'''
ex_order_index = min(point.loop_orders[le.OX][level],
point.loop_orders[le.OY][level],
point.loop_orders[le.OC][level],
point.loop_orders[le.ON][level])
fx_exclusive = point.loop_orders[le.FX][level] < ex_order_index
fy_exclusive = point.loop_orders[le.FY][level] < ex_order_index
ic_exclusive = point.loop_orders[le.IC][level] < ex_order_index
#TODO
fx_acc = ba_arr[le.FX][level+fx_exclusive] #reduce(mul, point.loop_blockings[le.FX][level+fx_exclusive:], 1)
fy_acc = ba_arr[le.FY][level+fy_exclusive] #reduce(mul, point.loop_blockings[le.FY][level+fy_exclusive:], 1)
ic_acc = ba_arr[le.IC][level+ic_exclusive] #reduce(mul, point.loop_blockings[le.OC][level+oc_exclusive:], 1)
fx_par = pa_arr[le.FX][level] #reduce(mul, point.loop_partitionings[le.FX][level+fx_exclusive:], 1)
fy_par = pa_arr[le.FY][level] #reduce(mul, point.loop_partitionings[le.FY][level+fy_exclusive:], 1)
ic_par = pa_arr[le.IC][level] #reduce(mul, point.loop_partitionings[le.OC][level+oc_exclusive:], 1)
return fx_acc * fy_acc * ic_acc * fx_par * fy_par * ic_par
def opt_get_fl_access(level, point, ba_arr, pa_arr):
'''
Get # access of fl block at current level
See comments in routine for ifmap.
'''
ex_order_index = min(point.loop_orders[le.FX][level],
point.loop_orders[le.FY][level],
point.loop_orders[le.IC][level],
point.loop_orders[le.OC][level])
ox_exclusive = point.loop_orders[le.OX][level] < ex_order_index
oy_exclusive = point.loop_orders[le.OY][level] < ex_order_index
on_exclusive = point.loop_orders[le.ON][level] < ex_order_index
ox_acc = ba_arr[le.OX][level+ox_exclusive] #reduce(mul, point.loop_blockings[le.OX][level+ox_exclusive:], 1)
oy_acc = ba_arr[le.OY][level+oy_exclusive] #reduce(mul, point.loop_blockings[le.OY][level+oy_exclusive:], 1)
on_acc = ba_arr[le.ON][level+on_exclusive] #reduce(mul, point.loop_blockings[le.ON][level+on_exclusive:], 1)
ox_par = pa_arr[le.OX][level] #reduce(mul, point.loop_partitionings[le.OX][level+ox_exclusive:], 1)
oy_par = pa_arr[le.OY][level] #reduce(mul, point.loop_partitionings[le.OY][level+oy_exclusive:], 1)
on_par = pa_arr[le.ON][level] #reduce(mul, point.loop_partitionings[le.ON][level+on_exclusive:], 1)
return ox_acc * oy_acc * on_acc * ox_par * oy_par * on_par
def get_if_size(blocking_accum_list, partitioning_accum_list, partitioning_list, layer):
'''
Get size of if block at current level including both temporal and spatial loop part
blocking -> temporal loop part
partitioning -> spatial loop part
#LMEI to support filter stride(FS) later
right now, FS/wfstd = 1 in
IX = IS*(OX-1) + FS*(FX-1) + 1 or
wifm = wistd*(wofm-1) + wfstd*(wfil-1) + 1
#LMEI (new HW template) no need for Input Duplication when OC partitions
by letting one reg broadcast Input to a row of OC partitioned PE
and remove inner PE ifamp register
'''
fx_acc = blocking_accum_list[le.FX] * partitioning_accum_list[le.FX]
fy_acc = blocking_accum_list[le.FY] * partitioning_accum_list[le.FY]
ox_acc = blocking_accum_list[le.OX] * partitioning_accum_list[le.OX]
oy_acc = blocking_accum_list[le.OY] * partitioning_accum_list[le.OY]
width = fx_acc + (ox_acc - 1) * layer.wstd
height = fy_acc + (oy_acc - 1) * layer.hstd
return width * height * \
blocking_accum_list[le.IC] * partitioning_accum_list[le.IC] * \
blocking_accum_list[le.ON] * partitioning_accum_list[le.ON] * \
partitioning_list[le.OC] # Duplication when OC partitions
def get_of_size(blocking_accum_list, partitioning_accum_list, partitioning_list):
'''
Get size of of block at current level including both temporal and spatial loop part
#LMEI (new HW template) no need for Output Duplication when IC, FX or FY partitions
by letting output data from a row of IC, FX or FY partitioned PE add together
and remove inner PE ofamp register
'''
return blocking_accum_list[le.OX] * partitioning_accum_list[le.OX] * \
blocking_accum_list[le.OY] * partitioning_accum_list[le.OY] * \
blocking_accum_list[le.OC] * partitioning_accum_list[le.OC] * \
blocking_accum_list[le.ON] * partitioning_accum_list[le.ON] * \
partitioning_list[le.IC] * partitioning_list[le.FX] * \
partitioning_list[le.FY] # Duplication when IC, FX or FY partitions
def get_fl_size(blocking_accum_list, partitioning_accum_list, partitioning_list):
'''
Get size of fl block at current level
#LMEI (new HW template) no need for Weight Duplication when OX, OY or ON partitions
by letting one reg broadcast Weight to a row of OX, OY or ON partitioned PE
and remove inner PE weight register
'''
return blocking_accum_list[le.FX] * partitioning_accum_list[le.FX] * \
blocking_accum_list[le.FY] * partitioning_accum_list[le.FY] * \
blocking_accum_list[le.IC] * partitioning_accum_list[le.IC] * \
blocking_accum_list[le.OC] * partitioning_accum_list[le.OC] * \
partitioning_list[le.OX] * partitioning_list[le.OY] *\
partitioning_list[le.ON] # Duplication when OX, OY or ON partitions
def get_if_bank_size(blocking_accum_list, layer):
'''
Get size of if block at current level
blocking -> temporal loop part
#LMEI to support filter stride(FS) later
right now, FS/wfstd = 1 in
IX = IS*(OX-1) + FS*(FX-1) + 1 or
wifm = wistd*(wofm-1) + wfstd*(wfil-1) + 1
'''
fx_acc = blocking_accum_list[le.FX]
fy_acc = blocking_accum_list[le.FY]
ox_acc = blocking_accum_list[le.OX]
oy_acc = blocking_accum_list[le.OY]
width = fx_acc + (ox_acc - 1) * layer.wstd
height = fy_acc + (oy_acc - 1) * layer.hstd
return width * height * \
blocking_accum_list[le.IC] * blocking_accum_list[le.ON]
def get_of_bank_size(blocking_accum_list):
'''
Get size of of block at current level
blocking -> temporal loop part
'''
return blocking_accum_list[le.OX] * blocking_accum_list[le.OY] * \
blocking_accum_list[le.OC] * blocking_accum_list[le.ON]
def get_fl_bank_size(blocking_accum_list):
'''
Get size of fl block at current level
blocking -> temporal loop part
'''
return blocking_accum_list[le.FX] * blocking_accum_list[le.FY] * \
blocking_accum_list[le.IC] * blocking_accum_list[le.OC]
def get_array_access_and_cost(level, para, access_list, point):
'''
Get the access at array level from the access at the
lower level of memory hierarchy
'''
para_mode = para.access_mode
assert para_mode == 1 or para_mode == 2 # Don't get it
array_dim = para.array_dim
para_count = para.array_width
para_cost = para.array_access_cost * 1.0
nearest_pe_cost = para_cost
[if_block_access, of_block_access, fl_block_access] = access_list
partitions = list(zip(*point.loop_partitionings))[level]
para_dim = point.para_loop_dim[level]
partitions_nearest = [1,]*le.NUM
partitions_far = []
across_block_cost = [0]*array_dim
if para_mode == 1:
for i in range(len(para_dim)):
para_index = para_dim[i]
partitions_far.append([1,]*le.NUM)
if len(para_index) == 1:
partitions_nearest[para_index[0]] = partitions[para_index[0]]
else:
inner_loop, outer_loop = para_index
partitions_nearest[inner_loop] = partitions[inner_loop]
partitions_far[i][outer_loop] = partitions[outer_loop]
across_block_cost[i] = para_cost * partitions[inner_loop]
array_if_block_access_nearest = if_block_access * partitions_nearest[le.FX] * \
partitions_nearest[le.FY] * partitions_nearest[le.OC]
array_of_block_access_nearest = of_block_access * partitions_nearest[le.FX] * \
partitions_nearest[le.FY] * partitions_nearest[le.IC]
array_fl_block_access_nearest = fl_block_access * partitions_nearest[le.OX] * \
partitions_nearest[le.OY] * partitions_nearest[le.ON]
array_access = [[array_if_block_access_nearest, array_of_block_access_nearest, array_fl_block_access_nearest]]
for i in range(array_dim): # Don't get it
if_partitions_far = partitions_far[i][le.FX] * partitions_far[i][le.FY] * partitions_far[i][le.OC]
if_partitions_far = if_partitions_far if if_partitions_far != 1 else 0
of_partitions_far = partitions_far[i][le.FX] * partitions_far[i][le.FY] * partitions_far[i][le.IC]
of_partitions_far = of_partitions_far if of_partitions_far != 1 else 0
fl_partitions_far = partitions_far[i][le.OX] * partitions_far[i][le.OY] * partitions_far[i][le.ON]
fl_partitions_far = fl_partitions_far if fl_partitions_far != 1 else 0
if_array_block_access = if_block_access * if_partitions_far
of_array_block_access = of_block_access * of_partitions_far
fl_array_block_access = fl_block_access * fl_partitions_far
array_access.append([if_array_block_access, of_array_block_access, fl_array_block_access])
return [array_access, [nearest_pe_cost] + across_block_cost]
elif para_mode == 2:
for i in range(len(para_dim)):
para_index = para_dim[i]
for j in para_index:
partitions_nearest[j] = partitions[j]
array_if_block_access_nearest = if_block_access * partitions_nearest[le.FX] * \
partitions_nearest[le.FY] * partitions_nearest[le.OC]
array_of_block_access_nearest = of_block_access * partitions_nearest[le.FX] * \
partitions_nearest[le.FY] * partitions_nearest[le.IC]
array_fl_block_access_nearest = fl_block_access * partitions_nearest[le.OX] * \
partitions_nearest[le.OY] * partitions_nearest[le.ON]
array_access = [[array_if_block_access_nearest, array_of_block_access_nearest, array_fl_block_access_nearest]]
return [array_access, [nearest_pe_cost]]
def get_access(point, layer, resource):
'''
Get the total access of each block at each level,
return the list as
[[if_block_access, of_block_access, fl_block_access], ...].
Assume all the buffers are inclusive, so buffers in lower level
appear in higher level as well.
For the parallelism case assume read from next memory level,
Support more access modes in parallelism case
'''
#TODO support more customized memory
#TODO more access at overlapped boundary
num_levels = resource.buffer_levels()
mac_capacity = resource.mac_capacity
access_list = []
for level in range(num_levels):
if_block_access = get_if_access(level, point, layer, mac_capacity)
of_block_access = 2 * get_of_access(level, point, layer, mac_capacity) - 1
fl_block_access = get_fl_access(level, point, layer, mac_capacity)
access_list.append([if_block_access, of_block_access, fl_block_access])
#para_mode = [e.access_mode for i, e in enumerate(resource.paras) if e.access_mode != 0]
para_mode_level = [i for i, e in enumerate(resource.paras) if e.access_mode != 0]
partitions = list(zip(*point.loop_partitionings))
array_costs = []
if para_mode_level:
# access at array level
#para_mode_level = [i for i, e in enumerate(resource.paras) if e.access_mode != 0]
delta = 0
for level in para_mode_level:
if level + delta + 1 >= num_levels :
next_level_access = [1, 1, 1]
else:
next_level_access = copy.copy(access_list[level + delta + 1])
next_level_access[1] = (next_level_access[1] + 1)/2
array_access, array_cost = get_array_access_and_cost(level, resource.paras[level], next_level_access, point)
array_costs.append(array_cost)
access_list.insert(level + delta + 1, array_access)
delta += 1
return [access_list, array_costs]
def opt_get_access(num_levels, point, mac_capacity):
'''
See the above function's comments. This function is just an
optimized version of the above function
'''
''' blocking_accum_arr is reversed cumprod numpy array '''
#TODO support mac_capacity
#blocking_arr = np.ones((le.NUM, num_levels+1))
#partitioning_arr = np.ones((le.NUM, num_levels+1))
#blocking_arr[:,:-1] = np.array(point.loop_blockings)
#partitioning_arr[:,:-1] = np.array(point.loop_partitionings)
#blocking_accum_arr = np.ones((le.NUM, num_levels+1))
#partitioning_accum_arr = np.ones((le.NUM, num_levels+1))
#for i in range(le.NUM):
# blocking_accum_arr[i][:-1] = np.cumprod(blocking_arr[i][::-1])[::-1]
# partitioning_accum_arr[i][:-1] = np.cumprod(partitioning_arr[i][::-1])[::-1]
#blocking_accum_arr = blocking_arr[...,::-1].cumprod(axis=-1)[...,::-1]
#partitioning_accum_arr = partitioning_arr[...,::-1].cumprod(axis=-1)[...,::-1]
#blocking_accum_arr = np.hstack((blocking_accum_arr, np.ones((le.NUM, 1))))
#partitioning_accum_arr = np.hstack((partitioning_accum_arr, np.ones((le.NUM, 1))))
blocking_accum_arr = []
partitioning_accum_arr = []
for i in range(le.NUM):
ba_current_level = [1]
pa_current_level = [1]
ba_tmp = 1
pa_tmp = 1
for level in range(num_levels-1, -1, -1):
ba_tmp = ba_tmp * point.loop_blockings[i][level]
pa_tmp = pa_tmp * point.loop_partitionings[i][level]
ba_current_level.append(ba_tmp)
pa_current_level.append(pa_tmp)
blocking_accum_arr.append(ba_current_level[::-1])
partitioning_accum_arr.append(pa_current_level[::-1])
access_arr = np.zeros((num_levels, 3))
for level in range(num_levels):
access_arr[level][0] = opt_get_if_access(level, point, blocking_accum_arr, partitioning_accum_arr)
access_arr[level][1] = 2 * opt_get_of_access(level, point, blocking_accum_arr, partitioning_accum_arr) - 1
access_arr[level][2] = opt_get_fl_access(level, point, blocking_accum_arr, partitioning_accum_arr)
return access_arr
def get_bank_size(point, layer, level):
blocking_accum_list = []
for i in range(le.NUM):
blocking_accum_list.append(reduce(mul, point.loop_blocking(i)[:level+1], 1))
if_bank_size = get_if_bank_size(blocking_accum_list, layer)
of_bank_size = get_of_bank_size(blocking_accum_list)
fl_bank_size = get_fl_bank_size(blocking_accum_list)
return (if_bank_size, of_bank_size, fl_bank_size)
def get_block_size(point, layer, level):
blocking_accum_list = []
partitioning_accum_list = []
partitioning_reshape = list(zip(*point.loop_partitionings))
partitioning_list = partitioning_reshape[level]
for i in range(le.NUM):
blocking_accum_list.append(reduce(mul, point.loop_blocking(i)[:level+1], 1))
partitioning_accum_list.append(reduce(mul, point.loop_partitioning(i)[:level+1], 1)) #FIXME inclusive mode also duplicates data
if_block_size = get_if_size(blocking_accum_list, partitioning_accum_list, partitioning_list, layer)
of_block_size = get_of_size(blocking_accum_list, partitioning_accum_list, partitioning_list)
fl_block_size = get_fl_size(blocking_accum_list, partitioning_accum_list, partitioning_list)
return (if_block_size, of_block_size, fl_block_size)
def get_block_sizes(num_levels, point, layer):
'''
Get size of ifmap, ofmap, filter
'''
bank_list = []
block_list = []
for level in range(num_levels):
block_list.append(get_block_size(point, layer, level))
bank_list.append(get_bank_size(point, layer, level))
return [bank_list, block_list]
def fit_in_level(cap, blocks, invalid_underutilized, level,memory_partitions):
'''
Check if the current level mem size >= current level loop blocking size
invalid_underutilized is used to exclude mapping points with too low memory utilization (< 50%)
#LMEI can later put the memory utilization threshold as a user defined parameter
'''
if type(cap) is list:
#I/O/W example: [0,0,1] I is stored in memory 0, O is stored in memory 0, W is stored in memory 1
#leave last empty
#memory_partitions = [[0,1, 2],[0,0,1],[0,0,None]] #if 3 level do not contain weights [0, 0, None]
#capacity = [[2,2], [30000,30000], [1000000,1000000]]
for i in range(len(cap)):
indices = [index for index,partition in enumerate(memory_partitions[level]) if partition == i]
size = sum([blocks[j] for j in indices])
if size == 0:
continue
if (size > cap[i]) == True:
return False #it does not fit
check_if_underutilized = 0
#print level, i, invalid_underutilized, memory_partitions[level+1][i], size, cap[i]
if invalid_underutilized:
last_layer = []
for mem in indices:
last_layer.append(memory_partitions[level+1][mem])
if None not in last_layer:
if ((size <= cap[i]) and (2*size <= cap[i])) == True: #if double the size fit then there will be a better to block partition that will utilized all memory,
#print "NO level: ", level,"blocks: ", blocks, "size: ", size, "cap: ", cap, "indices: ", indices, "last_layer", last_layer
check_if_underutilized += 1
else:
test = 1
else:
#print "OK level: ", level,"blocks: ", blocks, "size: ", size, "cap: ", cap, "indices: ", indices, "last_layer", last_layer
test =2
if check_if_underutilized == len(cap):
return False
return True
else:
total_size = sum(blocks)
# for size,contain in zip(blocks, contains):
# if contain:
# total_size += size
# total_capacity = 0
# for size,contain in zip(cap, contains):
# if contain:
# total_capacity += size
# total_size = sum(blocks)
if invalid_underutilized:
return (total_size <= cap) and (2*total_size >= cap)
else:
return (total_size <= cap)
def valid_partition_number(resource, partitioning, level):
max_parallelism = resource.parallelism(level).count
actual_parallelism = reduce(mul, partitioning[level], 1)
return actual_parallelism <= max_parallelism
def valid_partitioning_current_level(resource, point, layer, level, verbose=False):
valid_size = fit_in_level(resource.buffer(level).capacity, \
get_bank_size(point, layer, level), resource.invalid_underutilized, level,resource.memory_partitions)
return valid_size
def valid_mapping_point_current_level(resource, point, layer, level, verbose=False):
if resource.paras[level].count > 1:
valid_size = fit_in_level(resource.buffer(level).capacity,
get_bank_size(point, layer, level), resource.invalid_underutilized,level,resource.memory_partitions)
else :
valid_size = fit_in_level(resource.buffer(level).capacity,
get_block_size(point, layer, level), resource.invalid_underutilized,level,resource.memory_partitions)
partitioning = list(zip(*(point.loop_partitionings)))
valid_para = valid_partition_number(resource, partitioning, level)
if verbose == 3:
print("Level ", level, ": Partitioned block size fit in bank: ", valid_size)
print("Level ", level, ": Partition number is valid: ", valid_para)
return valid_size and valid_para
def valid_partitioning(resource, point, layer, verbose=False):
para_level = resource.para_index
for level in para_level:
if not valid_partitioning_current_level(resource, point, layer, level, verbose):
return False
return True
def valid_blocking_size_current_level(resource, point, layer, level, verbose=False):
if level == resource.buffer_levels()-1:
return True
if type(resource.buffer(level).capacity) is list:
capacity = copy.deepcopy(resource.buffer(level).capacity)
for i in range(len(capacity)):
capacity[i] =capacity[ i]* resource.paras[level].count
return fit_in_level(capacity,get_block_size(point, layer, level), (resource.invalid_underutilized and (level not in resource.para_index)),level,resource.memory_partitions)
else:
return fit_in_level(resource.buffer(level).capacity * resource.paras[level].count,
get_block_size(point, layer, level), (resource.invalid_underutilized and (level not in resource.para_index)),level,resource.memory_partitions)
#get_block_size(point, layer, level), (level > min(resource.para_index)))
def valid_blocking_size(resource, point, layer, verbose=False):
for level in range(resource.buffer_levels()):
if not valid_blocking_size_current_level(resource, point, layer, level, verbose):
return False
return True
def valid_mapping_point(resource, point, layer, verbose=False):
for i in range(resource.buffer_levels()):
if not valid_mapping_point_current_level(resource, point, layer, i, verbose):
return False
return True
def get_total_access_cost(resource, array_cost):
total_access_cost = copy.deepcopy(resource.access_cost)
if not resource.array_access_cost:
return total_access_cost
para_index = [i for i, e in enumerate(resource.paras) if e.access_mode != 0]
addition_levels = len(para_index)
delta = 1
for i in range(addition_levels):
index = para_index[i]
total_access_cost.insert(index+delta, array_cost[i])
delta += 1
return total_access_cost
def get_array_level_cost(resource, point, layer_size, level, next_level_access, verbose=False):
'''
Given next_level_access (above-level memory access)
calculate the current level (paralleled level) inter-PE data access
thus calculate the current level (paralleled level) inter-PE communication energy
i.e. the energy spent on interconnection
Specific to Systolic Array template.
level_access: [[close access for I/O/W],[far access on one dimension for I/O/W],[far access on another dimension]]
close access means data are passing from one PE to its neighbour PE
Far access means data need to jump from one PE to PEs far away from it.
Far jump happens because of dataflow spatial replication (e.g. 2D array -> kinds of 3D array)
'''
# TODO add support for other access_mode # don't get it
# LMEI to distinguish O (partial sum) in buffer_access from A and W
assert resource.paras[level].count and resource.paras[level].access_mode
level_access, level_cost = get_array_access_and_cost(level, resource.paras[level], next_level_access, point)
total_cost = 0
for i in range(len(level_access)):
buffer_access = list(map(mul, level_access[i], layer_size))
total_cost += sum(buffer_access) *level_cost[i]
if verbose >= 3:
print("Level ", level, " array level access: ", level_access)
return total_cost
def get_array_and_curr_level_cost(resource, point, layer, level, verbose=False):
'''
Get the energy from current level of memory access + inter-PE access
'''
# LMEI to distinguish O (partial sum) in buffer_access from A and W
layer_size = get_layer_size(layer)
mac_capacity = resource.mac_capacity
level_access = [get_if_access(level, point, layer, mac_capacity), \
get_of_access(level, point, layer, mac_capacity), \
get_fl_access(level, point, layer, mac_capacity)]
[if_access, of_access, fl_access] = level_access
buffer_level_access = [if_access, 2*of_access-1, fl_access]
total_buffer_access = list(map(mul, buffer_level_access, layer_size))
# level_cost = sum(total_buffer_access) * resource.access_cost[level]
level_cost = 0
for i in range(len(total_buffer_access)):
index = resource.memory_partitions[level][i]
if index is not None:
level_cost += total_buffer_access[i] * resource.access_cost[level][index]
# operand_costs = [access_cost * num_accesses for access_cost,num_accesses in zip(total_buffer_access,resource.access_cost[level]) ]
# level_cost = sum(operand_costs)
if verbose >= 3:
print("Level ", level, " access: ", buffer_level_access)
level_cost += get_array_level_cost(resource, point, layer_size, level-1, level_access, verbose)
return level_cost
def get_level_cost(resource, point, layer, level, verbose=False):
'''
Get the energy from current level of memory access
#LMEI to distinguish O (partial sum) in buffer_access from A and W
'''
layer_size = get_layer_size(layer)
mac_capacity = resource.mac_capacity
level_access = [get_if_access(level, point, layer, mac_capacity), \
2 * get_of_access(level, point, layer, mac_capacity) - 1, \
get_fl_access(level, point, layer, mac_capacity)]
buffer_access = list(map(mul, level_access, layer_size))
# Inputs, weights, and outputs may have different costs
# level_cost = sum(buffer_access) * resource.access_cost[level]
level_cost = 0
for i in range(len(buffer_access)):
index = resource.memory_partitions[level][i]
if index is not None:
level_cost += buffer_access[i] * resource.access_cost[level][index]
# resouce.memory_partitions
# operand_costs = [access_cost * num_accesses for access_cost,num_accesses in zip(buffer_access,resource.access_cost[level]) ]
# level_cost = sum(operand_costs)
if verbose >= 3:
print("Level", level, " access: ", level_access)
return level_cost
def get_total_access(resource, point, layer, verbose=False):
layer_size = get_layer_size(layer)
access_list, array_cost = get_access(point, layer, resource)
if verbose >= 3:
print("access breakdown: ", access_list)
total_level_access = []
for i in range(len(access_list)):
''' List of total access of each buffer at level i'''
if not isinstance(access_list[i][0], list):
buffer_access = list(map(mul, access_list[i], layer_size))
total_level_access.append(sum(buffer_access))
else :
for j in range(len(access_list[i])):
buffer_access = list(map(mul, access_list[i][j], layer_size))
total_level_access.append(sum(buffer_access))
return total_level_access
def get_level_costs(resource, point, layer, verbose=False):
num_levels = resource.buffer_levels()
level_energy = []
for level in range(num_levels):
level_energy.append(get_level_cost(resource, point, layer, level))
para_index = [i for i, e in enumerate(resource.paras) if e.access_mode != 0]
delta = 1
for index in para_index:
array_energy = get_array_and_curr_level_cost(resource, point, layer, index+1) - level_energy[index+delta]
level_energy.insert(index+delta, array_energy)
delta += 1
return level_energy
#FIXME
def get_block_cost(resource, point, layer, verbose=False):
'''
Get the cost of the given mapping point on given resource.
If the point is not feasible on the resource, return inf.
'''
#TODO include static energy
num_levels = resource.buffer_levels()
access_list, array_cost = get_access(point, layer, resource)
layer_size = get_layer_size(layer)
total_access_cost = get_total_access_cost(resource, array_cost)
assert len(total_access_cost) == len(access_list)
block_costs = [0.0, 0.0, 0.0]
for i in range(len(total_access_cost)):
buffer_access = [a*b for a,b in list(zip(access_list[i], layer_size))]
block_cost = [x * total_access_cost[i] for x in buffer_access]
block_costs = list(map(add, block_cost, block_costs))
if verbose:
print('access_list: ', access_list)
bank_size_list, block_size_list = get_block_sizes(num_levels, point, layer)
print('bank_size_list: ', bank_size_list)
print('block_size_list: ', block_size_list)
print('layer_size: ', layer_size)
print('block costs: ', block_costs)
return block_costs
def get_cost(resource, point, layer, verbose=False):
'''
Get the cost of the given mapping point on given resource.
If the point is not feasible on the resource, return inf.
'''
#TODO include static energy
#TODO support other access_mode
num_levels = resource.buffer_levels()
assert len(point.loop_blockings[0]) == num_levels, \
"number of blockings does not match with number of memory " \
"levels: %d" % num_levels
access_list, array_cost = get_access(point, layer, resource)
layer_size = get_layer_size(layer)
total_access_cost = get_total_access_cost(resource, array_cost)
assert len(total_access_cost) == len(access_list)
total_cost = 0.0
for i in range(len(total_access_cost)):
''' List of total access of each buffer at level i'''
if not isinstance(access_list[i][0], list):
buffer_access = list(map(mul, access_list[i], layer_size))
total_cost += (sum(buffer_access) * total_access_cost[i][0])
else :
for j in range(len(access_list[i])):
buffer_access = list(map(mul, access_list[i][j], layer_size))
total_cost += sum(buffer_access) * total_access_cost[i][j]
if verbose:
#print("total_access_cost", total_access_cost)
#print("access_list", access_list)
#print("layer_size",layer_size)
idx_adjust = 0
if len(total_access_cost) > 4:
idx_adjust = 1
layer_access_cost = total_access_cost[:1 + idx_adjust] + total_access_cost[2 + idx_adjust:]
print('16b_Access_Energy_[RegisterFile(s),Buffer,DRAM]_(pJ): \n\tifmap: {}\n\tofmap: {}\n\tfilter: {}'\
.format([item[0] for item in layer_access_cost], [item[1] for item in layer_access_cost], [item[2] for item in layer_access_cost]))
print('PE_Access_Cost_(pJ): \n\tifmap: {}\n\tofmap: {}\n\tfilter: {}'\
.format(total_access_cost[1 + idx_adjust][0], total_access_cost[1 + idx_adjust][1], total_access_cost[1 + idx_adjust][2]))
layer_num_access = access_list[:1 + idx_adjust] + access_list[2 + idx_adjust:]
print('Tiles_Accessed_from_[RegisterFile(s),Buffer,DRAM]_in_Layer: \n\tifmap: {}\n\tofmap: {}\n\tfilter: {}'\
.format([item[0] for item in layer_num_access], [item[1] for item in layer_num_access], [item[2] for item in layer_num_access]))
print("Tiles_Accessed_from_[RegisterFile(s),Buffer,DRAM]_PEs_in_Layer: \n\tifmap: {}\n\tofmap: {}\n\tfilter: {}"\
.format(access_list[1 + idx_adjust][0], access_list[1 + idx_adjust][1], access_list[1 + idx_adjust][2]))
bank_size_list, block_size_list = get_block_sizes(num_levels, point, layer)
#print("bank_size_list", bank_size_list)
#print("block_size_list", block_size_list)
print('Memory_Bank_Size_List_When_Parallelized/Unrolled_[RegisterFile(s),Buffer,DRAM]_(bytes): \n\tifmap: {}\n\tofmap: {}\n\tfilter: {}'\
.format([item[0] for item in bank_size_list], [item[1] for item in bank_size_list], [item[2] for item in bank_size_list]))
print('Memory_Block_Size_List_When_NOT_Parallelized/Unrolled_[RegisterFile(s),Buffer,DRAM]_(bytes): \n\tifmap: {}\n\tofmap: {}\n\tfilter: {}'\
.format([item[0] for item in block_size_list], [item[1] for item in block_size_list], [item[2] for item in block_size_list]))
print('Layer_Size_(number_of_pixels): \n\tifmap: {}\n\tofmap: {}\n\tfilter: {}'.format(layer_size[0], layer_size[1], layer_size[2]))
#print('total cost: ', total_cost)
#return total_cost
return total_cost, total_access_cost, access_list,layer_size