From 3fa233a0157e0a439c99428f03db33c8f3487fda Mon Sep 17 00:00:00 2001
From: bmccolm <briannamccolm@gmail.com>
Date: Tue, 9 Jan 2024 20:11:53 -0800
Subject: [PATCH] test of move to python3, hw1 ex appear to work
---
cnn_mapping/__init__.py | 10 +-
cnn_mapping/__init__.pyc | Bin 0 -> 696 bytes
.../__pycache__/__init__.cpython-36.pyc | Bin 0 -> 563 bytes
.../__pycache__/buffer_enum.cpython-36.pyc | Bin 0 -> 294 bytes
cnn_mapping/__pycache__/cache.cpython-36.pyc | Bin 0 -> 1505 bytes
.../__pycache__/cost_model.cpython-36.pyc | Bin 0 -> 26101 bytes
.../__pycache__/extract_input.cpython-36.pyc | Bin 0 -> 3320 bytes
cnn_mapping/__pycache__/layer.cpython-36.pyc | Bin 0 -> 1936 bytes
.../__pycache__/loop_enum.cpython-36.pyc | Bin 0 -> 639 bytes
.../__pycache__/mapping_point.cpython-36.pyc | Bin 0 -> 3979 bytes
.../mapping_point_generator.cpython-36.pyc | Bin 0 -> 20273 bytes
.../__pycache__/optimizer.cpython-36.pyc | Bin 0 -> 1442 bytes
.../__pycache__/resource.cpython-36.pyc | Bin 0 -> 6276 bytes
.../__pycache__/schedule.cpython-36.pyc | Bin 0 -> 1529 bytes
cnn_mapping/__pycache__/utils.cpython-36.pyc | Bin 0 -> 759 bytes
cnn_mapping/buffer_enum.pyc | Bin 0 -> 329 bytes
cnn_mapping/cache.pyc | Bin 0 -> 1675 bytes
cnn_mapping/cost_model.py | 111 +++++++--------
cnn_mapping/cost_model.pyc | Bin 0 -> 30426 bytes
cnn_mapping/extract_input.py | 4 +-
cnn_mapping/extract_input.pyc | Bin 0 -> 3774 bytes
cnn_mapping/layer.pyc | Bin 0 -> 2367 bytes
cnn_mapping/loop_enum.pyc | Bin 0 -> 696 bytes
cnn_mapping/mapping_point.pyc | Bin 0 -> 4308 bytes
cnn_mapping/mapping_point_generator.py | 126 +++++++++---------
cnn_mapping/mapping_point_generator.pyc | Bin 0 -> 25156 bytes
cnn_mapping/optimizer.py | 22 +--
cnn_mapping/optimizer.pyc | Bin 0 -> 1752 bytes
cnn_mapping/resource.py | 9 +-
cnn_mapping/resource.pyc | Bin 0 -> 6605 bytes
cnn_mapping/schedule.py | 6 +-
cnn_mapping/schedule.pyc | Bin 0 -> 1818 bytes
cnn_mapping/utils.py | 16 +--
cnn_mapping/utils.pyc | Bin 0 -> 919 bytes
tools/run_optimizer.py | 75 +++++++++--
35 files changed, 220 insertions(+), 159 deletions(-)
create mode 100644 cnn_mapping/__init__.pyc
create mode 100644 cnn_mapping/__pycache__/__init__.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/buffer_enum.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/cache.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/cost_model.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/extract_input.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/layer.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/loop_enum.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/mapping_point.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/mapping_point_generator.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/optimizer.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/resource.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/schedule.cpython-36.pyc
create mode 100644 cnn_mapping/__pycache__/utils.cpython-36.pyc
create mode 100644 cnn_mapping/buffer_enum.pyc
create mode 100644 cnn_mapping/cache.pyc
create mode 100644 cnn_mapping/cost_model.pyc
create mode 100644 cnn_mapping/extract_input.pyc
create mode 100644 cnn_mapping/layer.pyc
create mode 100644 cnn_mapping/loop_enum.pyc
create mode 100644 cnn_mapping/mapping_point.pyc
create mode 100644 cnn_mapping/mapping_point_generator.pyc
create mode 100644 cnn_mapping/optimizer.pyc
create mode 100644 cnn_mapping/resource.pyc
create mode 100644 cnn_mapping/schedule.pyc
create mode 100644 cnn_mapping/utils.pyc
diff --git a/cnn_mapping/__init__.py b/cnn_mapping/__init__.py
index 2d9e011..db5bff0 100644
--- a/cnn_mapping/__init__.py
+++ b/cnn_mapping/__init__.py
@@ -2,13 +2,15 @@
cnn_mapping package
'''
-from .mapping_point import MappingPoint
-from .resource import Resource
-from .layer import Layer
-from .schedule import Schedule
from . import loop_enum as le
from . import utils
from . import extract_input
from . import cost_model
from . import mapping_point_generator
from . import optimizer
+
+from .mapping_point import MappingPoint
+from .resource import Resource
+from .layer import Layer
+from .schedule import Schedule
+
diff --git a/cnn_mapping/__init__.pyc b/cnn_mapping/__init__.pyc
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diff --git a/cnn_mapping/cost_model.py b/cnn_mapping/cost_model.py
index f60fa89..a230bc2 100644
--- a/cnn_mapping/cost_model.py
+++ b/cnn_mapping/cost_model.py
@@ -4,11 +4,12 @@ Cost model.
#import numpy as np
from operator import mul
from operator import add
+from functools import reduce
import copy
import math
-import loop_enum as le
-import buffer_enum as be
+from . import loop_enum as le
+from . import buffer_enum as be
def get_comp_cost(layer):
@@ -61,7 +62,7 @@ def get_hinted_para(level, hint):
assert hint
hinted_para = 1
- for loop in xrange(le.NUM):
+ for loop in range(le.NUM):
if loop in hint:
hinted_loop_para = hint[loop][level][2]
hinted_para *= hinted_loop_para
@@ -76,7 +77,7 @@ def valid_dataflow(resource, hint):
'''
num_levels = resource.buffer_levels()
- for level in xrange(num_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
@@ -418,7 +419,7 @@ def get_array_access_and_cost(level, para, access_list, point):
nearest_pe_cost = para_cost
[if_block_access, of_block_access, fl_block_access] = access_list
- partitions = zip(*point.loop_partitionings)[level]
+ partitions = list(zip(*point.loop_partitionings))[level]
para_dim = point.para_loop_dim[level]
partitions_nearest = [1,]*le.NUM
@@ -426,7 +427,7 @@ def get_array_access_and_cost(level, para, access_list, point):
across_block_cost = [0]*array_dim
if para_mode == 1:
- for i in xrange(len(para_dim)):
+ for i in range(len(para_dim)):
para_index = para_dim[i]
partitions_far.append([1,]*le.NUM)
if len(para_index) == 1:
@@ -446,7 +447,7 @@ def get_array_access_and_cost(level, para, access_list, point):
array_access = [[array_if_block_access_nearest, array_of_block_access_nearest, array_fl_block_access_nearest]]
- for i in xrange(array_dim): # Don't get it
+ 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]
@@ -463,7 +464,7 @@ def get_array_access_and_cost(level, para, access_list, point):
return [array_access, [nearest_pe_cost] + across_block_cost]
elif para_mode == 2:
- for i in xrange(len(para_dim)):
+ for i in range(len(para_dim)):
para_index = para_dim[i]
for j in para_index:
partitions_nearest[j] = partitions[j]
@@ -502,7 +503,7 @@ def get_access(point, layer, resource):
mac_capacity = resource.mac_capacity
access_list = []
- for level in xrange(num_levels):
+ 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)
@@ -510,7 +511,7 @@ def get_access(point, layer, resource):
#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 = zip(*point.loop_partitionings)
+ partitions = list(zip(*point.loop_partitionings))
array_costs = []
if para_mode_level:
# access at array level
@@ -546,7 +547,7 @@ def opt_get_access(num_levels, point, mac_capacity):
#blocking_accum_arr = np.ones((le.NUM, num_levels+1))
#partitioning_accum_arr = np.ones((le.NUM, num_levels+1))
- #for i in xrange(le.NUM):
+ #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]
@@ -559,12 +560,12 @@ def opt_get_access(num_levels, point, mac_capacity):
blocking_accum_arr = []
partitioning_accum_arr = []
- for i in xrange(le.NUM):
+ for i in range(le.NUM):
ba_current_level = [1]
pa_current_level = [1]
ba_tmp = 1
pa_tmp = 1
- for level in xrange(num_levels-1, -1, -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)
@@ -574,7 +575,7 @@ def opt_get_access(num_levels, point, mac_capacity):
partitioning_accum_arr.append(pa_current_level[::-1])
access_arr = np.zeros((num_levels, 3))
- for level in xrange(num_levels):
+ 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)
@@ -584,7 +585,7 @@ def opt_get_access(num_levels, point, mac_capacity):
def get_bank_size(point, layer, level):
blocking_accum_list = []
- for i in xrange(le.NUM):
+ 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)
@@ -597,9 +598,9 @@ def get_block_size(point, layer, level):
blocking_accum_list = []
partitioning_accum_list = []
- partitioning_reshape = zip(*point.loop_partitionings)
+ partitioning_reshape = list(zip(*point.loop_partitionings))
partitioning_list = partitioning_reshape[level]
- for i in xrange(le.NUM):
+ 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
@@ -617,7 +618,7 @@ def get_block_sizes(num_levels, point, layer):
'''
bank_list = []
block_list = []
- for level in xrange(num_levels):
+ for level in range(num_levels):
block_list.append(get_block_size(point, layer, level))
bank_list.append(get_bank_size(point, layer, level))
@@ -709,12 +710,12 @@ def valid_mapping_point_current_level(resource, point, layer, level, verbose=Fal
valid_size = fit_in_level(resource.buffer(level).capacity,
get_block_size(point, layer, level), resource.invalid_underutilized,level,resource.memory_partitions)
- partitioning = zip(*(point.loop_partitionings))
+ 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
+ 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
@@ -744,14 +745,14 @@ def valid_blocking_size_current_level(resource, point, layer, level, verbose=Fal
def valid_blocking_size(resource, point, layer, verbose=False):
- for level in xrange(resource.buffer_levels()):
+ 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 xrange(resource.buffer_levels()):
+ for i in range(resource.buffer_levels()):
if not valid_mapping_point_current_level(resource, point, layer, i, verbose):
return False
return True
@@ -766,7 +767,7 @@ def get_total_access_cost(resource, array_cost):
addition_levels = len(para_index)
delta = 1
- for i in xrange(addition_levels):
+ for i in range(addition_levels):
index = para_index[i]
total_access_cost.insert(index+delta, array_cost[i])
delta += 1
@@ -795,12 +796,12 @@ def get_array_level_cost(resource, point, layer_size, level, next_level_access,
level_access, level_cost = get_array_access_and_cost(level, resource.paras[level], next_level_access, point)
total_cost = 0
- for i in xrange(len(level_access)):
- buffer_access = map(mul, level_access[i], layer_size)
+ 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
+ print("Level ", level, " array level access: ", level_access)
return total_cost
@@ -822,7 +823,7 @@ def get_array_and_curr_level_cost(resource, point, layer, level, verbose=False):
[if_access, of_access, fl_access] = level_access
buffer_level_access = [if_access, 2*of_access-1, fl_access]
- total_buffer_access = map(mul, buffer_level_access, layer_size)
+ 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)):
@@ -833,7 +834,7 @@ def get_array_and_curr_level_cost(resource, point, layer, level, verbose=False):
# level_cost = sum(operand_costs)
if verbose >= 3:
- print "Level ", level, " access: ", buffer_level_access
+ print("Level ", level, " access: ", buffer_level_access)
level_cost += get_array_level_cost(resource, point, layer_size, level-1, level_access, verbose)
@@ -854,7 +855,7 @@ def get_level_cost(resource, point, layer, level, verbose=False):
2 * get_of_access(level, point, layer, mac_capacity) - 1, \
get_fl_access(level, point, layer, mac_capacity)]
- buffer_access = map(mul, level_access, layer_size)
+ 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
@@ -869,7 +870,7 @@ def get_level_cost(resource, point, layer, level, verbose=False):
if verbose >= 3:
- print "Level", level, " access: ", level_access
+ print("Level", level, " access: ", level_access)
return level_cost
@@ -879,17 +880,17 @@ def get_total_access(resource, point, layer, verbose=False):
access_list, array_cost = get_access(point, layer, resource)
if verbose >= 3:
- print "access breakdown: ", access_list
+ print("access breakdown: ", access_list)
total_level_access = []
- for i in xrange(len(access_list)):
+ 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 = map(mul, access_list[i], layer_size)
+ buffer_access = list(map(mul, access_list[i], layer_size))
total_level_access.append(sum(buffer_access))
else :
- for j in xrange(len(access_list[i])):
- buffer_access = map(mul, access_list[i][j], layer_size)
+ 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
@@ -900,7 +901,7 @@ def get_level_costs(resource, point, layer, verbose=False):
num_levels = resource.buffer_levels()
level_energy = []
- for level in xrange(num_levels):
+ 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]
@@ -931,18 +932,18 @@ def get_block_cost(resource, point, layer, verbose=False):
block_costs = [0.0, 0.0, 0.0]
- for i in xrange(len(total_access_cost)):
- buffer_access = [a*b for a,b in zip(access_list[i], layer_size)]
+ 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 = map(add, block_cost, block_costs)
+ block_costs = list(map(add, block_cost, block_costs))
if verbose:
- print 'access_list: ', access_list
+ 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
+ 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
@@ -966,24 +967,24 @@ def get_cost(resource, point, layer, verbose=False):
assert len(total_access_cost) == len(access_list)
total_cost = 0.0
- for i in xrange(len(total_access_cost)):
+ 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 = map(mul, access_list[i], layer_size)
+ buffer_access = list(map(mul, access_list[i], layer_size))
total_cost += sum(buffer_access) * total_access_cost[i]
else :
- for j in xrange(len(access_list[i])):
- buffer_access = map(mul, access_list[i][j], layer_size)
+ 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 'access_cost: ', total_access_cost
- print 'access_list: ', access_list
+ print('access_cost: ', total_access_cost)
+ 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 'total cost: ', total_cost
+ print('bank_size_list: ', bank_size_list)
+ print('block_size_list: ', block_size_list)
+ print('layer_size: ', layer_size)
+ print('total cost: ', total_cost)
#return total_cost
return total_cost, total_access_cost, access_list,layer_size
diff --git a/cnn_mapping/cost_model.pyc b/cnn_mapping/cost_model.pyc
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diff --git a/cnn_mapping/extract_input.py b/cnn_mapping/extract_input.py
index 88b88fd..e0a9620 100644
--- a/cnn_mapping/extract_input.py
+++ b/cnn_mapping/extract_input.py
@@ -1,6 +1,6 @@
import json
import os
-import loop_enum as le
+from . import loop_enum as le
def extract_arch_info(arch_file):
@@ -30,7 +30,7 @@ def extract_arch_info(arch_file):
data["mac_capacity"] = 0
if "parallel_mode" not in data:
data["parallel_mode"] = [0, ] * data["mem_levels"]
- for level in xrange(data["mem_levels"]):
+ for level in range(data["mem_levels"]):
if data["parallel_count"][level] != 1:
data["parallel_mode"][level] = 1
else:
diff --git a/cnn_mapping/extract_input.pyc b/cnn_mapping/extract_input.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..484996abc43531675d33c2bc2f63c5fb1bbe34d4
GIT binary patch
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diff --git a/cnn_mapping/mapping_point_generator.py b/cnn_mapping/mapping_point_generator.py
index e563f95..aa39396 100644
--- a/cnn_mapping/mapping_point_generator.py
+++ b/cnn_mapping/mapping_point_generator.py
@@ -11,21 +11,22 @@ import copy
from operator import mul
import math
import pickle
+from functools import reduce
-from mapping_point import MappingPoint
-from cache import Cache
-import cost_model
+from .mapping_point import MappingPoint
+from .cache import Cache
+from . import cost_model
-import loop_enum as le
-import buffer_enum as be
-import utils
+from . import loop_enum as le
+from . import buffer_enum as be
+from . import utils
def get_hinted_para(layer, level, hint):
assert hint
hinted_para = 1
- for loop in xrange(le.NUM):
+ for loop in range(le.NUM):
if loop in hint:
hinted_loop_para = hint[loop][level][2]
hinted_para *= hinted_loop_para
@@ -36,7 +37,7 @@ def get_hinted_para(layer, level, hint):
def get_hinted_partitioning(level, hint):
hinted_partitioning = []
hinted_para_dim = []
- for loop in xrange(le.NUM):
+ for loop in range(le.NUM):
if loop in hint:
hinted_partitioning.append(hint[loop][level][2])
hinted_para_dim.append([loop])
@@ -57,10 +58,10 @@ def get_fixed_partitioning(num_levels, hint):
return [(1,) * num_levels] * le.NUM
partitioning_list = []
- for loop in xrange(le.NUM):
+ for loop in range(le.NUM):
partitioning = [1] * num_levels
if loop in hint:
- for i in xrange(num_levels):
+ for i in range(num_levels):
if hint[loop][i]:
partitioning[i] = hint[loop][i][2]
partitioning_list.append(tuple(partitioning))
@@ -72,11 +73,11 @@ def get_non_empty_loops(point, num_levels):
non_empty_loops is a list that contains #levels tuples,
each tuple contains the loop whose size is not 1 at this level
'''
- blocking = zip(*(point.loop_blockings))
- partitioning = zip(*(point.loop_partitionings))
+ blocking = list(zip(*(point.loop_blockings)))
+ partitioning = list(zip(*(point.loop_partitionings)))
non_empty_loops = []
- for i in xrange(num_levels):
+ for i in range(num_levels):
t0 = blocking[i]
t1 = partitioning[i]
non_empty_blocking = [i for i, e in enumerate(t0) if e != 1]
@@ -88,7 +89,7 @@ def get_non_empty_loops(point, num_levels):
def get_loop_order(partial_order, non_empty_loops, level):
order_curr_level = [le.NUM - 1] * le.NUM
- for i in xrange(len(non_empty_loops[level])):
+ for i in range(len(non_empty_loops[level])):
order_curr_level[non_empty_loops[level][i]] = partial_order[i]
return order_curr_level
@@ -107,14 +108,14 @@ def opt_order_generator_function(point, num_loops, num_levels):
# print "non_empty_loops: ", non_empty_loops
all_order_permutations = []
- for level in xrange(num_levels):
+ for level in range(num_levels):
one_level_permutations = []
for order in itertools.permutations(range(len(non_empty_loops[level]))):
one_level_permutations.append(get_loop_order(order, non_empty_loops, level))
all_order_permutations.append(one_level_permutations)
for loop_order in itertools.product(*all_order_permutations):
- yield zip(*loop_order)
+ yield list(zip(*loop_order))
def level_order_generator_function(point, num_loops, non_empty_loops, level):
@@ -135,14 +136,14 @@ def order_generator_function(num_loops, num_levels):
one_level_permutations.append(order)
all_order_permutations = []
- for level in xrange(num_levels):
+ for level in range(num_levels):
all_order_permutations.append(one_level_permutations)
'''Consider system with all buffer levels, generator all
possible loop orders, then transform the data
organization to match with loop_order_list'''
for order in itertools.product(*all_order_permutations):
- yield zip(*order)
+ yield list(zip(*order))
def factors(n):
@@ -152,7 +153,7 @@ def factors(n):
def bounded_factor(n, end):
l = []
- for i in xrange(1, int(n ** 0.5) + 1):
+ for i in range(1, int(n ** 0.5) + 1):
if n % i == 0 and n // i <= end:
l.__iadd__([i, n // i])
elif n % i == 0:
@@ -184,7 +185,7 @@ def loop_tile_with_para_hint(tile_permutations, loop_extent, num_level, loop_hin
def loop_tile_with_hint(tile_permutations, loop_extent, num_level, loop_hint):
# TODO support more than 1 level of para hint
- for level in xrange(num_level):
+ for level in range(num_level):
if loop_hint[level] != None:
loop_hint_level = level
break
@@ -233,7 +234,7 @@ def loop_tile(loop_extent, num_level, loop_hint=None):
def opt_valid_blocking(blocking_cache, resource, layer, blocking):
num_levels = resource.buffer_levels()
- blocking_tuple = zip(*blocking)
+ blocking_tuple = list(zip(*blocking))
dummy_partitioning = [(1,) * num_levels] * le.NUM
dummy_mapping_point = MappingPoint(None, list(blocking), dummy_partitioning)
@@ -250,7 +251,7 @@ def opt_valid_blocking(blocking_cache, resource, layer, blocking):
if not valid:
return False
- for level in xrange(1, num_levels):
+ for level in range(1, num_levels):
if not cost_model.valid_blocking_size_current_level(resource, dummy_mapping_point, layer, level):
return False
return True
@@ -267,7 +268,7 @@ def blocking_generator_function(resource, layer, schedule=None, verbose=False):
num_levels = resource.buffer_levels()
all_tile_permutations = []
- for i in xrange(le.NUM):
+ for i in range(le.NUM):
loop_hint = hint[i] if hint and i in hint else None
all_tile_permutations.append(loop_tile(layer.sizes[i], num_levels, loop_hint))
@@ -320,7 +321,7 @@ def current_level_partition_blocking_1d_no_replication(loop_tiles, slb, para_cou
para_permutation = []
para_dim_permutation = []
- for l0 in xrange(le.NUM):
+ for l0 in range(le.NUM):
for f0 in loop_tiles[l0]:
slp = [1, ] * le.NUM
slp[l0] = f0
@@ -336,7 +337,7 @@ def current_level_partition_blocking_1d_replication(loop_tiles, slb, para_count,
para_permutation = []
para_dim_permutation = []
- for l0 in xrange(le.NUM):
+ for l0 in range(le.NUM):
for f0 in loop_tiles[l0]:
slp = [1, ] * le.NUM
slp[l0] = f0
@@ -345,7 +346,7 @@ def current_level_partition_blocking_1d_replication(loop_tiles, slb, para_count,
para_permutation.append(slp)
para_dim_permutation.append([para_index])
else:
- for l1 in xrange(le.NUM):
+ for l1 in range(le.NUM):
if l1 == l0:
continue
for f1 in loop_tiles[l1]:
@@ -370,6 +371,7 @@ def current_level_partition_blocking_1d(loop_tiles, slb, para_count, layer, u_th
def current_level_partition_blocking_1d_with_hint(loop_tiles, slb, para_count, layer, level, cur_loop, schedule,
u_threshold):
+
hint = schedule.schedule_hint
partition_loops = schedule.partition_loops
para_permutation = []
@@ -440,7 +442,7 @@ def current_level_partition_blocking_2d_with_hint(loop_tiles, slb, para_count, l
slp0, slp1 = slps
para_index0, para_index1 = para_index_generator.next()
if set(para_index0[0]).isdisjoint(set(para_index1[0])):
- combined_slp = [a * b for a, b in zip(slp0, slp1)]
+ combined_slp = [a * b for a, b in list(zip(slp0, slp1))]
para_permutation.append(combined_slp)
combined_dim = [para_index0[0], para_index1[0]]
para_dim_permutation.append(combined_dim)
@@ -460,7 +462,7 @@ def current_level_partition_blocking_2d(loop_tiles, slb, para_count, layer, u_th
slp0, slp1 = slps
para_index0, para_index1 = para_index_generator.next()
if set(para_index0[0]).isdisjoint(set(para_index1[0])):
- combined_slp = [a * b for a, b in zip(slp0, slp1)]
+ combined_slp = [a * b for a, b in list(zip(slp0, slp1))]
para_permutation.append(combined_slp)
combined_dim = [para_index0[0], para_index1[0]]
para_dim_permutation.append(combined_dim)
@@ -471,7 +473,7 @@ def current_level_partition_blocking_2d(loop_tiles, slb, para_count, layer, u_th
def current_level_partition_blocking(slb, para, layer, u_threshold, replication):
para_count = para.array_width
loop_tiles = []
- for l in xrange(le.NUM):
+ for l in range(le.NUM):
loop_tiles.append(bounded_factor(slb[l], para_count))
# print "loop tile ", loop_tiles
@@ -484,7 +486,7 @@ def current_level_partition_blocking(slb, para, layer, u_threshold, replication)
def current_level_partition_blocking_with_hint(slb, para, layer, level, schedule, u_threshold):
para_count = para.array_width
loop_tiles = []
- for l in xrange(le.NUM):
+ for l in range(le.NUM):
loop_tiles.append(bounded_factor(slb[l], para_count))
# print "loop tile ", loop_tiles
@@ -510,7 +512,7 @@ def parallel_blocking_generator_function(lp, resource, layer, schedule=None):
para_permutations = []
para_dim_permutations = []
- for level in xrange(num_level):
+ for level in range(num_level):
if resource.paras[level].count == 1:
para_permutations.append([[1] * le.NUM])
para_dim_permutations.append([None])
@@ -545,7 +547,7 @@ def parallel_blocking_generator_function(lp, resource, layer, schedule=None):
para_dim_generator = para_dim_generator_function(para_dim_permutations)
for partition in itertools.product(*para_permutations):
- para_dim = para_dim_generator.next()
+ para_dim = next(para_dim_generator)
# print partition, para_dim
yield [partition, para_dim]
@@ -570,39 +572,39 @@ def blocking_partitioning_generator_function(resource, layer, schedule, verbose=
for loop_blocking in blocking_generator:
if verbose == 3:
- print "loop_tilling: ", loop_blocking
+ print("loop_tilling: ", loop_blocking)
- loop_blocking_reshape = zip(*loop_blocking)
+ loop_blocking_reshape = list(zip(*loop_blocking))
pb_generator = parallel_blocking_generator_function(loop_blocking_reshape, resource, layer, schedule)
for pi in pb_generator:
partition, para_dim = pi
partitioned_loop_blocking_reshape = []
- for level in xrange(num_level):
+ for level in range(num_level):
partitioned_loop_blocking_reshape.append(
- [(x + y - 1) // y for x, y in zip(loop_blocking_reshape[level], partition[level])]) # TODO check if using two maps with floordiv is faster
- blocking_list = zip(*partitioned_loop_blocking_reshape)
- partitioning_list = zip(*partition)
+ [(x + y - 1) // y for x, y in list(zip(loop_blocking_reshape[level], partition[level]))]) # TODO check if using two maps with floordiv is faster
+ blocking_list = list(zip(*partitioned_loop_blocking_reshape))
+ partitioning_list = list(zip(*partition))
if verbose == 3:
- print "loop_blocking: ", blocking_list
- print "loop_partition: ", partitioning_list
- print "para_dimension: ", para_dim
+ print("loop_blocking: ", blocking_list)
+ print("loop_partition: ", partitioning_list)
+ print("para_dimension: ", para_dim)
dummy_mapping_point = MappingPoint(None, blocking_list, partitioning_list, para_dim)
if cost_model.valid_partitioning(resource, dummy_mapping_point, layer, verbose):
# if cost_model.valid_mapping_point(resource, dummy_mapping_point, layer, verbose):
if verbose == 3:
- print "Valid"
- print ""
+ print("Valid")
+ print("")
yield [blocking_list, partitioning_list, para_dim]
# else:
# print "invalid"
# print ""
else:
if verbose == 3:
- print "invalid"
- print ""
+ print("invalid")
+ print("")
def opt_get_best_loop_order(resource, layer, point, verbose=False):
@@ -637,7 +639,7 @@ def opt_get_best_loop_order(resource, layer, point, verbose=False):
best_cost = 0
para_level = 0
- for level in xrange(num_levels):
+ for level in range(num_levels):
smallest_cost = float("inf")
# LMEI later, might can speed up the exhaustive order search by identifying symmetrical terms,
# e.g. ox and oy, fx and fx, to remove some similar orders
@@ -645,7 +647,7 @@ def opt_get_best_loop_order(resource, layer, point, verbose=False):
for curr_level_order in level_order_generator_function(point, le.NUM, non_empty_loops, level):
dummy_loop_order = [[0] * le.NUM] * num_levels
dummy_loop_order[level] = curr_level_order
- mapping_point = MappingPoint(zip(*dummy_loop_order), blocking, partitioning, para_dim)
+ mapping_point = MappingPoint(list(zip(*dummy_loop_order)), blocking, partitioning, para_dim)
if level <= 0 or resource.paras[level - 1].count <= 1 \
or resource.paras[level - 1].access_mode < 1: # don't get it
curr_cost = cost_model.get_level_cost(resource, mapping_point, layer, level, verbose)
@@ -656,9 +658,9 @@ def opt_get_best_loop_order(resource, layer, point, verbose=False):
smallest_cost = curr_cost
if verbose >= 3:
- print "Level", level, "Current order:", curr_level_order, " Best order:", best_curr_level_order
- print "Level", level, "Current energy:", '%20d' % curr_cost, " Best energy:", '%20d' % smallest_cost
- print ""
+ print("Level", level, "Current order:", curr_level_order, " Best order:", best_curr_level_order)
+ print("Level", level, "Current energy:", '%20d' % curr_cost, " Best energy:", '%20d' % smallest_cost)
+ print("")
# LMEI later, instead of using mac_capacity, we could use 4-level memory model, treat mac_capacity
# as the innermost memory level for output.
@@ -668,7 +670,7 @@ def opt_get_best_loop_order(resource, layer, point, verbose=False):
best_loop_order.append(best_curr_level_order)
best_cost += smallest_cost
- return best_cost, zip(*best_loop_order)
+ return best_cost, list(zip(*best_loop_order))
def opt_mapping_point_generator_function(resource, layer, schedule=None, verbose=False):
@@ -696,7 +698,7 @@ def opt_mapping_point_generator_function(resource, layer, schedule=None, verbose
an invalid blocking_partitioning
'''
if verbose >= 2:
- print "Find best order for schedule: ", blocking_partitioning
+ print("Find best order for schedule: ", blocking_partitioning)
[blocking, partitioning, para_dim] = blocking_partitioning
dummy_mapping_point = MappingPoint(None, blocking, partitioning, para_dim)
# print "blocking_partitioning: ", blocking_partitioning
@@ -709,9 +711,9 @@ def opt_mapping_point_generator_function(resource, layer, schedule=None, verbose
perf = ideal_perf / utilization
if verbose >= 2:
- print "best loop order: ", best_mapping_point.loop_orders
- print "Update smallest cost: ", smallest_cost
- print "Update best schedule: ", utils.print_loop_nest(best_mapping_point)
+ print("best loop order: ", best_mapping_point.loop_orders)
+ print("Update smallest cost: ", smallest_cost)
+ print("Update best schedule: ", utils.print_loop_nest(best_mapping_point))
assert best_mapping_point, "No valid mapping point found."
return smallest_cost, perf, best_mapping_point
@@ -755,7 +757,7 @@ def partitioned_loop_string(partitioning, parallel_levels, para_dim):
res = ""
utilized = 1
- partitioning_reshape = zip(*partitioning)
+ partitioning_reshape = list(zip(*partitioning))
for level in parallel_levels:
for para_idx in para_dim[level]:
res += "("
@@ -769,7 +771,7 @@ def partitioned_loop_string(partitioning, parallel_levels, para_dim):
def get_utilization(utilized, resource):
# utilized = 1
- # for i in xrange(len(partitioning)):
+ # for i in range(len(partitioning)):
# utilized *= reduce(mul, partitioning[i], 1)
total = resource.total_parallelism()
@@ -805,7 +807,7 @@ def dataflow_exploration(resource, layer, file_name, verbose=False):
an invalid blocking_partitioning
'''
if verbose >= 2:
- print "Find best order for schedule: ", blocking_partitioning
+ print("Find best order for schedule: ", blocking_partitioning)
[blocking, partitioning, para_dim] = blocking_partitioning
dummy_mapping_point = MappingPoint(None, blocking, partitioning, para_dim)
# print "partitioning: ", partitioning
@@ -818,11 +820,11 @@ def dataflow_exploration(resource, layer, file_name, verbose=False):
best_mapping_point = MappingPoint(loop_order, blocking, partitioning, para_dim)
dataflow_tb[unrolled_loops] = (cost, utilization, best_mapping_point) # TODO utilization
if verbose:
- print "unrolled loops: ", unrolled_loops, " with utilization ", utilization
+ print("unrolled loops: ", unrolled_loops, " with utilization ", utilization)
# print "best loop order: ", best_mapping_point.loop_orders
- print "blocking: ", blocking
- print "partitioning: ", partitioning
- print "Update smallest cost: ", dataflow_tb[unrolled_loops][0]
+ print("blocking: ", blocking)
+ print("partitioning: ", partitioning)
+ print("Update smallest cost: ", dataflow_tb[unrolled_loops][0])
# print "Update best shedule: ", utils.print_loop_nest(best_mapping_point)
# assert best_mapping_point, "No valid mapping point found."
pickle_file_name = file_name + ".pickle"
diff --git a/cnn_mapping/mapping_point_generator.pyc b/cnn_mapping/mapping_point_generator.pyc
new file mode 100644
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HcmV?d00001
diff --git a/cnn_mapping/optimizer.py b/cnn_mapping/optimizer.py
index c2ddff0..3886d7a 100644
--- a/cnn_mapping/optimizer.py
+++ b/cnn_mapping/optimizer.py
@@ -1,11 +1,11 @@
'''
Top level function of optimization framework
'''
-import mapping_point_generator
-import cost_model
+from . import mapping_point_generator
+from . import cost_model
-import loop_enum as le
-import buffer_enum as be
+from . import loop_enum as le
+from . import buffer_enum as be
def opt_optimizer(resource, layer, hint=None, verbose=False):
'''
@@ -20,9 +20,9 @@ def opt_optimizer(resource, layer, hint=None, verbose=False):
# assert total_cost == smallest_cost
if verbose:
- print "Optimal energy (pJ): ", smallest_cost
- print "Runtime (cycles):", perf
- print "Best mapping_point: ", best_mapping_point.loop_blockings, best_mapping_point.loop_partitionings, best_mapping_point.loop_orders
+ print("Optimal energy (pJ): ", smallest_cost)
+ print("Runtime (cycles):", perf)
+ print("Best mapping_point: ", best_mapping_point.loop_blockings, best_mapping_point.loop_partitionings, best_mapping_point.loop_orders)
return [smallest_cost, best_mapping_point, perf]
def optimizer(resource, layer, hint=None, verbose=False):
@@ -43,13 +43,13 @@ def optimizer(resource, layer, hint=None, verbose=False):
smallest_cost = cost
best_mapping_point = mapping_point
if verbose:
- print "Current smallest cost: ", smallest_cost
- print "Current best mapping_point: ", mapping_point.loop_blockings, mapping_point.loop_orders
+ print("Current smallest cost: ", smallest_cost)
+ print("Current best mapping_point: ", mapping_point.loop_blockings, mapping_point.loop_orders)
#print counter
if verbose:
- print smallest_cost
- print "Best mapping_point: ", best_mapping_point.loop_blockings, mapping_point.loop_partitionings, best_mapping_point.loop_orders
+ print(smallest_cost)
+ print("Best mapping_point: ", best_mapping_point.loop_blockings, mapping_point.loop_partitionings, best_mapping_point.loop_orders)
return [smallest_cost, best_mapping_point]
diff --git a/cnn_mapping/optimizer.pyc b/cnn_mapping/optimizer.pyc
new file mode 100644
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GIT binary patch
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diff --git a/cnn_mapping/resource.py b/cnn_mapping/resource.py
index adbbf6f..8200893 100644
--- a/cnn_mapping/resource.py
+++ b/cnn_mapping/resource.py
@@ -5,6 +5,7 @@ Hardware resource types.
from collections import namedtuple
from operator import mul
import math
+from functools import reduce
class Buffer(namedtuple('Buffer',
['capacity', 'access_cost', 'unit_static_cost'])):
@@ -91,11 +92,11 @@ class Resource(object):
partition_mode = [0] * len(para_count_list)
else :
array_level = 0
- for i in xrange(self.num_levels):
+ for i in range(self.num_levels):
# when using non-default partition mode, the parallelism
# count needs to be large than 1
- assert partition_mode[i] == 0 or para_count_list <= 1 \
- or (partition_mode[i] > 0 and para_count_list > 1)
+ assert partition_mode[i] == 0 or para_count_list[i] <= 1 \
+ or (partition_mode[i] > 0 and para_count_list[i] > 1)
if partition_mode[i] == 1 or partition_mode[i] == 2:
array_access_costs[i] = array_access_cost[array_level]
array_level += 1
@@ -109,7 +110,7 @@ class Resource(object):
array_dim = [2 if e != 1 else 1 for e in para_count_list]
# LMEI always assume square-shape array, could change later
- array_width = [para_count_list[i] if array_dim[i] == 1 else int(math.sqrt(para_count_list[i])) for i in xrange(self.num_levels)]
+ array_width = [para_count_list[i] if array_dim[i] == 1 else int(math.sqrt(para_count_list[i])) for i in range(self.num_levels)]
self.paras = [Parallelism(*t) for t in zip(para_count_list, \
partition_mode, array_access_costs, array_dim, array_width)]
diff --git a/cnn_mapping/resource.pyc b/cnn_mapping/resource.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..463d3443d75ee556d8374634ce93afbf96dee89d
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diff --git a/cnn_mapping/schedule.py b/cnn_mapping/schedule.py
index cdfdb21..9ef6445 100644
--- a/cnn_mapping/schedule.py
+++ b/cnn_mapping/schedule.py
@@ -2,7 +2,7 @@
Schedule hint
'''
-import loop_enum as le
+from . import loop_enum as le
class Schedule(object):
'''
@@ -26,10 +26,10 @@ class Schedule(object):
else:
self.partition_loops = partition_loops
- num_levels = len(schedule_hint.values()[0])
+ num_levels = len(list(schedule_hint.values())[0][0])
hint_para_index = {}
for loop in schedule_hint:
- for level in xrange(num_levels):
+ for level in range(num_levels):
if schedule_hint[loop][level] != None and schedule_hint[loop][level][2] != None:
if level not in hint_para_index:
hint_para_index[level] = [loop]
diff --git a/cnn_mapping/schedule.pyc b/cnn_mapping/schedule.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..39c0af312b808f473051ef4b2f9bd6ca26c020df
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diff --git a/cnn_mapping/utils.py b/cnn_mapping/utils.py
index 520d37c..b1d79b8 100644
--- a/cnn_mapping/utils.py
+++ b/cnn_mapping/utils.py
@@ -1,16 +1,16 @@
-import loop_enum as le
-import buffer_enum as be
+from . import loop_enum as le
+from . import buffer_enum as be
def print_loop_nest(point):
- loop_orders = zip(*point.loop_orders)
- loop_blockings = zip(*point.loop_blockings)
- loop_partitionings = zip(*point.loop_partitionings)
+ loop_orders = list(zip(*point.loop_orders))
+ loop_blockings = list(zip(*point.loop_blockings))
+ loop_partitionings = list(zip(*point.loop_partitionings))
para_dims = point.para_loop_dim
num_level = len(loop_orders)
order_lists = []
- for level in xrange(num_level):
+ for level in range(num_level):
order_list = [None] * le.NUM
- for order in xrange(le.NUM):
+ for order in range(le.NUM):
if loop_blockings[level][order] != 1 or loop_partitionings[level][order] != 1 :
order_list[loop_orders[level][order]] = (le.table[order],
loop_blockings[level][order],
@@ -18,5 +18,5 @@ def print_loop_nest(point):
order_lists.append(order_list)
- print order_lists, para_dims
+ print(order_lists, para_dims)
diff --git a/cnn_mapping/utils.pyc b/cnn_mapping/utils.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c1e5298fb725ac5ed6c4d698976cfb8efb71495e
GIT binary patch
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diff --git a/tools/run_optimizer.py b/tools/run_optimizer.py
index e785a16..e4469e0 100644
--- a/tools/run_optimizer.py
+++ b/tools/run_optimizer.py
@@ -16,9 +16,10 @@ def basic_optimizer(arch_info, network_info, schedule_info=None, basic=False, ve
level_costs = cm.cost_model.get_level_costs(resource, best_mapping_point, layer, verbose)
if verbose or basic:
- print "best energy: ", smallest_cost
- print "cost for each level: ", level_costs #TODO
- print "best schedule: ", cm.utils.print_loop_nest(best_mapping_point)
+ print("best energy: ", smallest_cost)
+ print("cost for each level: ", level_costs) #TODO
+ print("best schedule: ")
+ print(cm.utils.print_loop_nest(best_mapping_point))
return smallest_cost, perf
@@ -46,7 +47,7 @@ def mem_explore_optimizer(arch_info, network_info, schedule_info, verbose=False)
# continue
arch_info["capacity"][1] = capacity1 * (arch_info["capacity_scale"][1]**y)
arch_info["access_cost"][1] = cost1 * (arch_info["access_cost_scale"][1]**y)
- print arch_info
+ print(arch_info)
energy, perf = basic_optimizer(arch_info, network_info, schedule_info, False, verbose)
energy_list[x][y] = energy
cur_point = network_info["layer_info"] + arch_info["capacity"][:-1] + [energy]
@@ -54,8 +55,8 @@ def mem_explore_optimizer(arch_info, network_info, schedule_info, verbose=False)
# np.savetxt(output_filename, summary_array, delimiter=",")
i += 1
- print list(energy_list)
- print "optiaml energy for all memory systems: ", np.min(np.array(energy_list))
+ print(list(energy_list))
+ print("optimal energy for all memory systems: ", np.min(np.array(energy_list)))
def mac_explore_optimizer(arch_info, network_info, schedule_info, verbose=False):
@@ -68,7 +69,7 @@ def mac_explore_optimizer(arch_info, network_info, schedule_info, verbose=False)
dataflow_res.append[energy]
if verbose:
- print "optimal energy for all dataflows: ", dataflow_res
+ print("optimal energy for all dataflows: ", dataflow_res)
return dataflow_res
@@ -83,12 +84,64 @@ def dataflow_explore_optimizer(arch_info, network_info, file_name, verbose=False
dataflow_tb = cm.mapping_point_generator.dataflow_exploration(resource, layer, file_name, verbose)
if verbose:
- print "dataflow table done "
+ print("dataflow table done ")
+
+def mem_explore_optimizer_4_level(arch_info, network_info, schedule_info, verbose=False):
+ assert "explore_points" in arch_info, "missing explore_points in arch file"
+ assert "capacity_scale" in arch_info, "missing capacity_scale in arch file"
+ assert "access_cost_scale" in arch_info, "missing access_cost_scale in arch file"
+ cwd = os.getcwd()
+# output_filename = os.path.join(cwd, "dataset", network_info['layer_name'] + '_128.csv')
+ explore_points = arch_info["explore_points"]
+ energy_list = np.zeros(tuple(explore_points))
+ summary_array = np.zeros([np.product(explore_points), 13])
+ capacity0 = arch_info["capacity"][0]
+ capacity1 = arch_info["capacity"][1]
+ capacity2 = arch_info["capacity"][2]
+ cost0 = arch_info["access_cost"][0]
+ cost1 = arch_info["access_cost"][1]
+ cost2 = arch_info["access_cost"][2]
+ i = 0
+ for x in xrange(explore_points[0]):
+ arch_info["capacity"][0] = capacity0 * (arch_info["capacity_scale"][0]**x)
+ arch_info["access_cost"][0] = cost0 * (arch_info["access_cost_scale"][0]**x)
+ for y in xrange(explore_points[1]):
+ arch_info["capacity"][1] = capacity1 * (arch_info["capacity_scale"][1]**y)
+ arch_info["access_cost"][1] = cost1 * (arch_info["access_cost_scale"][1]**y)
+ for z in xrange(explore_points[2]):
+ arch_info["capacity"][2] = capacity2 * (arch_info["capacity_scale"][2]**z)
+ arch_info["access_cost"][2] = cost2 * (arch_info["access_cost_scale"][2]**z)
+ print(arch_info)
+
+ # TODO: Calculate the area of the design
+ # total_area = sum(area(MAC)) + sum(2 * area(register file)) + 2 * area(buffer)
+ area = (2 * (arch_info["capacity"][0] + arch_info["capacity"][1] + arch_info["capacity"][2]) \
+ + 2 * arch_info["capacity"][3]) / 1e9
+
+ print("Area: ", area)
+ if area > 2:
+ energy_list[x][y][z] = np.nan
+ print("INFO: Architecture does not fulfill area constraint.")
+ continue
+ try:
+ energy, perf = basic_optimizer(arch_info, network_info, schedule_info, False, verbose)
+ energy_list[x][y][z] = energy
+ cur_point = network_info["layer_info"] + arch_info["capacity"][:-1] + [energy]
+ summary_array[i] = cur_point
+ # np.savetxt(output_filename, summary_array, delimiter=",")
+ i += 1
+ except Exception as e:
+ energy_list[x][y][z] = np.nan
+ print("WARNING: No valid mapping point found.")
+ print("=" * 80)
+
+ print(list(energy_list))
+ print("optimal energy for all memory systems: ", np.nanmin(np.array(energy_list)))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
- parser.add_argument("type", choices=["basic", "mem_explore", "dataflow_explore"], help="optimizer type")
+ parser.add_argument("type", choices=["basic", "mem_explore", "dataflow_explore", "mem_explore_4"], help="optimizer type")
parser.add_argument("arch", help="architecture specification")
parser.add_argument("network", help="network specification")
parser.add_argument("-s", "--schedule", help="restriction of the schedule space")
@@ -117,6 +170,8 @@ if __name__ == "__main__":
mem_explore_optimizer(arch_info, network_info, schedule_info, args.verbose)
elif args.type == "dataflow_explore":
dataflow_explore_optimizer(arch_info, network_info, args.name, args.verbose)
+ elif args.type == "mem_explore_4":
+ mem_explore_optimizer_4_level(arch_info, network_info, schedule_info, args.verbose)
end = time.time()
- print "elapsed time: ", (end-start)
+ print("elapsed time: ", (end-start))
--
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