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tiling.py
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# -*- coding: future_fstrings -*- # should work even without -*-
#
# tiling.py
# Alessio Burrello <alessio.burrello@unibo.it>
# Francesco Conti <f.conti@unibo.it>
# Thorir Mar Ingolfsson <thoriri@iis.ee.ethz.ch>
#
# Copyright (C) 2018-2020 University of Bologna
#
# 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
#
# http://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.
import math
import numpy as np
import torch
import torch.nn as nn
# constraint solver for optimization
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import solver_parameters_pb2
# template for output
from template import print_template_layer
from template import print_template_layer_1D
from template import print_template_layer_L3
from template import print_pool_template_layer_L3
import logging
import os
import sys
class Tiling():
# Class to generate the Tiling of the layer.
def __init__(self, module, out_ch, filter_size, stride, padding, groups, x_shape, L1_buffer, L2_buffer, platform, chip, test_location, BitIn, BitW, BitOut, BitActivation, optional_type, sdk, dma_parallelization):
self.module = module
self.out_ch = out_ch
self.filter_size = filter_size
self.stride = stride
self.padding = padding
self.groups = groups
self.x_shape = x_shape
self.buffer_size = L1_buffer
self.L2_buffer_size = L2_buffer
self.platform = platform
self.chip = chip
self.test_location = test_location
self.BitIn = BitIn
self.BitW = BitW
self.BitOut = BitOut
self.BitActivation = BitActivation
self.optional_type = optional_type
self.sdk = sdk
self.dma_parallelization = dma_parallelization
def get_tiling(self, **kwargs):
# This function is used to create the tiling of either a convolutional layer or a fully connected or a pooling layer.
# The relu is included automatically in conv/FC.
try:
if 'Conv1D' in self.module:
return self.get_tiling_conv1d(**kwargs)
elif 'Conv' in self.module:
return self.get_tiling_conv2d(**kwargs)
elif 'Pool' in self.module:
return self.get_tiling_pool2d(**kwargs)
elif self.module is 'Add':
return self.get_tiling_Add(**kwargs)
else:
print("Not supported Layer.")
return None
except:
pass
def get_tiling_conv_normal(self, fs,
p,
stride,
dilation,
in_channels,
out_channels,
x_shape,
y_shape,
BN,
buffer_size=44000
):
#### LIMITATION: PADDING ONLY IN FIRST TILE
s = stride
n_in = in_channels
n_out = out_channels
h_in = x_shape
# p = 0
h_out = y_shape
max_tile_n_out = n_out
max_tile_n_in = n_in
min_tile_h_in = fs
min_tile_h_out = 1
# this is to renormalize all costs
max_obj_value = sys.maxsize
# constraints
input_dim = 8 * n_in * h_in
output_dim = 8 * n_out * h_out
weight_dim = 8 * n_in * n_out * fs
im2col_dim = 8 * 2 * 8 * fs * n_in
bn_dim = self.BitActivation * n_out * 2
buffer_total = input_dim + output_dim + weight_dim + im2col_dim + bn_dim
if BN == 0:
buffer_total -= bn_dim
if buffer_total <= buffer_size * 8:
### MALE MALE MALE
obj_expr = 58+(h_out/2)*(119+(n_out/4)*((14*n_in*fs/4)+159))
print(f'tile in: {n_in} x {h_in}, tile out: {n_out} x {h_out}' )
return (n_in, n_out, h_in, h_out, obj_expr, buffer_total/8)
else:
db = 2
parameters = pywrapcp.Solver.DefaultSolverParameters()
solver = pywrapcp.Solver("simple_CP", parameters)
# ottimizzato channel first per kernel HWC
tile_n_in = solver.IntVar(1, max_tile_n_in, 'tile_n_out')
tile_n_out = solver.IntVar(1, max_tile_n_out, 'tile_n_out')
tile_h_in = solver.IntVar(min_tile_h_in, h_in, 'tile_h_in')
tile_h_out = solver.IntVar(min_tile_h_out, h_out, 'tile_h_out')
zero_variable = solver.IntVar(0, 0, 'zero variable')
h_out_intvar = solver.IntVar(min_tile_h_out,h_out,'h_out_intvar')
solver.Add(h_out_intvar == h_out)
if ((fs - 1)*dilation+1)*2 <= h_in:
solver.Add(0 == (tile_h_in - ((fs - 1)*dilation+1)) % s)
solver.Add(tile_h_out * s == (tile_h_in - (fs - 1)*dilation + (s - 1)))
# padding added
solver.Add(solver.Max((h_in - tile_h_in - (tile_h_in - (fs - 1)*dilation - p)), 0) % (tile_h_in - (fs - 1)*dilation + 1) + abs(solver.Min(solver.Max((h_in - tile_h_in - (tile_h_in - (fs - 1)*dilation - p)), 0) % (tile_h_in - (fs - 1)*dilation), 1) - 1) * ((fs - 1)*dilation+1) >= ((fs - 1)*dilation+1))
#TO MAKE SURE TILING doesn't fail for dilation
solver.Add(h_in >= s*(tile_h_out*(h_out_intvar//tile_h_out)-1)-p+dilation*(fs-1)+1)
else:
solver.Add(h_in == tile_h_in )
solver.Add(h_out == tile_h_out )
solver.Add(tile_n_in == n_in)
constr_in = db * 8 * tile_n_in * tile_h_in #* tile_w_in
constr_out = db * 8 * tile_n_out * tile_h_out #* tile_w_out
constr_weight = db * 8 * tile_n_in * tile_n_out * fs
constr_im2col = 2 * 8 * 8 * fs * tile_n_in
constr_bn = self.BitActivation * n_out * 2
constraint_all = constr_in + constr_out + constr_weight + constr_bn + constr_im2col + 20*32 # 20 are the + 4 added between buffers
if BN == 0:
constraint_all -= constr_bn
solver.Add(constraint_all <= buffer_size * 8)
# objective
obj_expr = solver.IntVar(0, max_obj_value, "obj_expr")
n_tiles_h = solver.IntVar(1, h_in, "n_tiles_h")
leftover_tile_h_out = solver.IntVar(0, h_out, "leftover_tile_h_out")
n_tiles_nout = solver.IntVar(1, n_out, "n_tiles_nout")
leftover_tile_nout = solver.IntVar(0, n_out, "leftover_tile_nout")
solver.Add(n_tiles_h == (h_out + tile_h_out - 1) // tile_h_out)
solver.Add(leftover_tile_h_out == (h_out + zero_variable) % tile_h_out)
solver.Add(n_tiles_nout == (n_out + tile_n_out - 1) // tile_n_out)
solver.Add(leftover_tile_nout == (n_out + zero_variable) % tile_n_out)
solver.Add(obj_expr == (64 * 10000 * tile_n_out
+ constraint_all
+ 64 * 1000000 * ((tile_h_out - 1) % 16)
+ 64 * 1000000 * ((tile_n_out - 1) % 4)
+ 64 * 10000 * (leftover_tile_nout)
+ 64 * 10000 * (leftover_tile_nout % 4)
+ 64 * 10000 * (leftover_tile_h_out % 16)))
objective = solver.Maximize(obj_expr, 1)
decision_builder = solver.Phase([tile_n_in, tile_n_out, tile_h_in, tile_h_out, n_tiles_h, leftover_tile_h_out, n_tiles_nout, leftover_tile_nout],
solver.CHOOSE_FIRST_UNBOUND,
solver.ASSIGN_MIN_VALUE)
# Create a solution collector.
collector = solver.LastSolutionCollector()
# Add the decision variables.
collector.Add(tile_n_in)
collector.Add(tile_n_out)
collector.Add(tile_h_in)
collector.Add(tile_h_out)
collector.Add(n_tiles_h)
collector.Add(leftover_tile_h_out)
collector.Add(n_tiles_nout)
collector.Add(leftover_tile_nout)
collector.Add(obj_expr)
collector.Add(constraint_all)
# Add the objective.
collector.AddObjective(obj_expr)
solver.Solve(decision_builder, [objective, collector])
if collector.SolutionCount() > 0:
best_solution = collector.SolutionCount() - 1
tile_n_in = collector.Value(best_solution, tile_n_in)
tile_n_out = collector.Value(best_solution, tile_n_out)
tile_h_in = collector.Value(best_solution, tile_h_in)
tile_h_out = collector.Value(best_solution, tile_h_out)
n_tiles_h = collector.Value(best_solution, n_tiles_h)
leftover_tile_h_out = collector.Value(best_solution, leftover_tile_h_out)
n_tiles_nout = collector.Value(best_solution, n_tiles_nout)
leftover_tile_nout = collector.Value(best_solution, leftover_tile_nout)
obj_expr = collector.Value(best_solution, obj_expr)
memory = collector.Value(best_solution, constraint_all)
if tile_h_in >= h_in:
tile_h_in = h_in
tile_h_out = int((tile_h_in -(fs - 1)*dilation + (2*p) + (s - 1))/s)
MAC = tile_n_out*tile_n_in*tile_h_out*fs
cycles_im2col = (61 + 32 * fs) * ((32 * fs) > ((fs * tile_n_in * 2) // 8) ) + (29 + (fs * tile_n_in * 2) // 8) * ((32 * fs) < ((fs * tile_n_in * 2) // 8) )
cycles_im2col_leftover = (38 + 16 * fs) * ((16 * fs) > ((fs * tile_n_in) // 8) ) + (29 + (fs * tile_n_in) // 8) * ((16 * fs) < ((fs * tile_n_in) // 8) )
n_4_2 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) // 2)
n_4_1 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (tile_n_out // 4) * (159 + 14 * ((tile_n_in*fs) // 4) + 18 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_2 = (tile_n_out % 4) * (52 + ((tile_n_in*fs) // 4) * 5 + 8 * ((tile_n_in*fs) % 4))
cycles_chout_4_1 = (tile_n_out // 4) * (82 + ((tile_n_in*fs) //4) * 9 + 12 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_1 = (tile_n_out % 4) * (28 + 3 * ((tile_n_in*fs) // 4) + 5 * ((tile_n_in*fs) % 4))
instr = (100 + n_4_2 * (27 + cycles_im2col + (127 + cycles_chout_4_2 + cycles_leftover_chout_4_2)) + n_4_1 * (20 + cycles_im2col_leftover + (94 + cycles_chout_4_1 + cycles_leftover_chout_4_1)))
constr1 = (MAC * 1000) // instr
## left over h
MAC = leftover_tile_h_out*tile_n_in*tile_n_out*fs
cycles_im2col = (61 + 32 * fs) * ((32 * fs) > ((fs * tile_n_in * 2) // 8) ) + (29 + (fs * tile_n_in * 2) // 8) * ((32 * fs) < ((fs * tile_n_in * 2) // 8) )
cycles_im2col_leftover = (38 + 16 * fs) * ((16 * fs) > ((fs * tile_n_in) // 8) ) + (29 + (fs * tile_n_in) // 8) * ((16 * fs) < ((fs * tile_n_in) // 8) )
n_4_2 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) // 2)
n_4_1 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (tile_n_out // 4) * (159 + 14 * ((tile_n_in*fs) // 4) + 18 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_2 = (tile_n_out % 4) * (52 + ((tile_n_in*fs) // 4) * 5 + 8 * ((tile_n_in*fs) % 4))
cycles_chout_4_1 = (tile_n_out // 4) * (82 + ((tile_n_in*fs) //4) * 9 + 12 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_1 = (tile_n_out % 4) * (28 + 3 * ((tile_n_in*fs) // 4) + 5 * ((tile_n_in*fs) % 4))
instr = (100 + n_4_2 * (27 + cycles_im2col + (127 + cycles_chout_4_2 + cycles_leftover_chout_4_2)) + n_4_1 * (20 + cycles_im2col_leftover + (94 + cycles_chout_4_1 + cycles_leftover_chout_4_1)))
constr2 = (MAC * 1000) // instr
## left over n
MAC = leftover_tile_nout*tile_n_in*tile_h_out*fs
cycles_im2col = (61 + 32 * fs) * ((32 * fs) > ((fs * tile_n_in * 2) // 8) ) + (29 + (fs * tile_n_in * 2) // 8) * ((32 * fs) < ((fs * tile_n_in * 2) // 8) )
cycles_im2col_leftover = (38 + 16 * fs) * ((16 * fs) > ((fs * tile_n_in) // 8) ) + (29 + (fs * tile_n_in) // 8) * ((16 * fs) < ((fs * tile_n_in) // 8) )
n_4_2 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) // 2)
n_4_1 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (leftover_tile_nout // 4) * (159 + 14 * ((tile_n_in*fs) // 4) + 18 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_2 = (leftover_tile_nout % 4) * (52 + ((tile_n_in*fs) // 4) * 5 + 8 * ((tile_n_in*fs) % 4))
cycles_chout_4_1 = (leftover_tile_nout // 4) * (82 + ((tile_n_in*fs) //4) * 9 + 12 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_1 = (leftover_tile_nout % 4) * (28 + 3 * ((tile_n_in*fs) // 4) + 5 * ((tile_n_in*fs) % 4))
instr = (100 + n_4_2 * (27 + cycles_im2col + (127 + cycles_chout_4_2 + cycles_leftover_chout_4_2)) + n_4_1 * (20 + cycles_im2col_leftover + (94 + cycles_chout_4_1 + cycles_leftover_chout_4_1)))
constr3 = (MAC * 1000) // instr
## left over all
MAC = leftover_tile_nout*tile_n_in*leftover_tile_h_out*fs
cycles_im2col = (61 + 32 * fs) * ((32 * fs) > ((fs * tile_n_in * 2) // 8) ) + (29 + (fs * tile_n_in * 2) // 8) * ((32 * fs) < ((fs * tile_n_in * 2) // 8) )
cycles_im2col_leftover = (38 + 16 * fs) * ((16 * fs) > ((fs * tile_n_in) // 8) ) + (29 + (fs * tile_n_in) // 8) * ((16 * fs) < ((fs * tile_n_in) // 8) )
n_4_2 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) // 2)
n_4_1 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (leftover_tile_nout // 4) * (159 + 14 * ((tile_n_in*fs) // 4) + 18 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_2 = (leftover_tile_nout % 4) * (52 + ((tile_n_in*fs) // 4) * 5 + 8 * ((tile_n_in*fs) % 4))
cycles_chout_4_1 = (leftover_tile_nout // 4) * (82 + ((tile_n_in*fs) //4) * 9 + 12 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_1 = (leftover_tile_nout % 4) * (28 + 3 * ((tile_n_in*fs) // 4) + 5 * ((tile_n_in*fs) % 4))
instr = (100 + n_4_2 * (27 + cycles_im2col + (127 + cycles_chout_4_2 + cycles_leftover_chout_4_2)) + n_4_1 * (20 + cycles_im2col_leftover + (94 + cycles_chout_4_1 + cycles_leftover_chout_4_1)))
constr4 = (MAC * 1000) // instr
constr = (constr1*(n_tiles_h-(leftover_tile_h_out>0))*(n_tiles_nout-(leftover_tile_nout>0)) + constr2*(n_tiles_nout-(leftover_tile_nout>0))*(leftover_tile_h_out>0) + constr3*(n_tiles_h-(leftover_tile_h_out>0))*(leftover_tile_nout>0) + constr4)//((n_tiles_h-(leftover_tile_h_out>0))*(n_tiles_nout-(leftover_tile_nout>0)) + (n_tiles_nout-(leftover_tile_nout>0))*(leftover_tile_h_out>0) + (n_tiles_h-(leftover_tile_h_out>0))*(leftover_tile_nout>0) + 1*(leftover_tile_nout>0)*(leftover_tile_h_out>0))
print(f'Conv normal MAC/cycle simulated: {(constr/1000)}')
return (tile_n_in, tile_n_out, tile_h_in, tile_h_out, (constr/1000), memory/8)
return None
def get_tiling_conv_nodilation(self,
fs,
p,
stride,
dilation,
in_channels,
out_channels,
x_shape,
y_shape,
BN,
buffer_size=44000
):
s = stride
n_in = in_channels
n_out = out_channels
h_in = x_shape
# p = 0
h_out = y_shape
max_tile_n_out = n_out
max_tile_n_in = n_in
min_tile_h_in = fs
min_tile_h_out = 1
# this is to renormalize all costs
max_obj_value = 10000000000000
# constraints
input_dim = 8 * n_in * h_in
output_dim = 8 * n_out * h_out
weight_dim = 8 * n_in * n_out * fs
bn_dim = self.BitActivation * n_out * 2
buffer_total = input_dim + output_dim + weight_dim + bn_dim
if BN == 0:
buffer_total -= bn_dim
if buffer_total <= buffer_size * 8:
obj_expr = 58+(h_out/2)*(119+(n_out/4)*((14*n_in*fs/4)+159))
print(f'tile in: {n_in} x {h_in}, tile out: {n_out} x {h_out}' )
return (n_in, n_out, h_in, h_out, obj_expr, buffer_total/8)
else:
db = 2
parameters = pywrapcp.Solver.DefaultSolverParameters()
solver = pywrapcp.Solver("simple_CP", parameters)
# ottimizzato channel first per kernel HWC
tile_n_in = solver.IntVar(1, max_tile_n_in, 'tile_n_out')
tile_n_out = solver.IntVar(1, max_tile_n_out, 'tile_n_out')
tile_h_in = solver.IntVar(min_tile_h_in, h_in, 'tile_h_in')
tile_h_out = solver.IntVar(min_tile_h_out, h_out, 'tile_h_out')
zero_variable = solver.IntVar(0, 0, 'zero variable')
solver.Add(0 == (tile_h_in - ((fs - 1)*dilation+1)) % s)
solver.Add(tile_h_out * s == (tile_h_in - (fs - 1)*dilation + (s - 1)))
solver.Add(tile_n_in == n_in)
# padding added
solver.Add(solver.Max((h_in - tile_h_in - (tile_h_in - fs + 1 - p)), 0) % (tile_h_in - fs + 1) + abs(solver.Min(
solver.Max((h_in - tile_h_in - (tile_h_in - fs + 1 - p)), 0) % (tile_h_in - fs + 1), 1) - 1) * fs >= fs)
constr_in = db * 8 * tile_n_in * tile_h_in #* tile_w_in
constr_out = db * 8 * tile_n_out * tile_h_out #* tile_w_out
constr_weight = db * 8 * tile_n_in * tile_n_out * fs
constr_bn = self.BitActivation * n_out * 2
constraint_all = constr_in + constr_out + constr_weight + constr_bn + 20*32 # 20 are the + 4 added between buffers
if BN == 0:
constraint_all -= constr_bn
solver.Add(constraint_all <= buffer_size * 8)
# objective
obj_expr = solver.IntVar(0, max_obj_value, "obj_expr")
n_tiles_h = solver.IntVar(1, h_in, "n_tiles_h")
leftover_tile_h_out = solver.IntVar(0, h_out, "leftover_tile_h_out")
n_tiles_nout = solver.IntVar(1, n_out, "n_tiles_nout")
leftover_tile_nout = solver.IntVar(0, n_out, "leftover_tile_nout")
solver.Add(n_tiles_h == (h_out + tile_h_out - 1) // tile_h_out)
solver.Add(leftover_tile_h_out == (h_out + zero_variable) % tile_h_out)
solver.Add(n_tiles_nout == (n_out + tile_n_out - 1) // tile_n_out)
solver.Add(leftover_tile_nout == (n_out + zero_variable) % tile_n_out)
## principal kernel
solver.Add(obj_expr == (64 * 10000 * tile_n_out
+ constraint_all
+ 64 * 1000000 * ((tile_h_out - 1) % 16)
+ 64 * 1000000 * ((tile_n_out - 1) % 4)
+ 64 * 10000 * (leftover_tile_nout)
+ 64 * 10000 * (leftover_tile_nout % 4)
+ 64 * 10000 * (leftover_tile_h_out % 16)))
objective = solver.Maximize(obj_expr, 1)
decision_builder = solver.Phase([tile_n_in, tile_n_out, tile_h_in, tile_h_out, n_tiles_h, leftover_tile_h_out, n_tiles_nout, leftover_tile_nout],
solver.CHOOSE_FIRST_UNBOUND,
solver.ASSIGN_MIN_VALUE)
# Create a solution collector.
collector = solver.LastSolutionCollector()
# Add the decision variables.
collector.Add(tile_n_in)
collector.Add(tile_n_out)
collector.Add(tile_h_in)
collector.Add(tile_h_out)
collector.Add(n_tiles_h)
collector.Add(leftover_tile_h_out)
collector.Add(n_tiles_nout)
collector.Add(leftover_tile_nout)
collector.Add(obj_expr)
collector.Add(constraint_all)
# Add the objective.
collector.AddObjective(obj_expr)
solver.Solve(decision_builder, [objective, collector])
if collector.SolutionCount() > 0:
best_solution = collector.SolutionCount() - 1
tile_n_in = collector.Value(best_solution, tile_n_in)
tile_n_out = collector.Value(best_solution, tile_n_out)
tile_h_in = collector.Value(best_solution, tile_h_in)
tile_h_out = collector.Value(best_solution, tile_h_out)
n_tiles_h = collector.Value(best_solution, n_tiles_h)
leftover_tile_h_out = collector.Value(best_solution, leftover_tile_h_out)
n_tiles_nout = collector.Value(best_solution, n_tiles_nout)
leftover_tile_nout = collector.Value(best_solution, leftover_tile_nout)
obj_expr = collector.Value(best_solution, obj_expr)
memory = collector.Value(best_solution, constraint_all)
if tile_h_in >= h_in:
tile_h_in = h_in
tile_h_out = int((tile_h_in -(fs - 1)*dilation + (2*p) + (s - 1))/s)
MAC = tile_n_out*tile_n_in*tile_h_out*fs
n_4_2 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) // 2)
n_4_1 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (tile_n_out // 4) * (159 + 14 * ((tile_n_in*fs) // 4) + 18 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_2 = (tile_n_out % 4) * (52 + ((tile_n_in*fs) // 4) * 5 + 8 * ((tile_n_in*fs) % 4))
cycles_chout_4_1 = (tile_n_out // 4) * (82 + ((tile_n_in*fs) //4) * 9 + 12 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_1 = (tile_n_out % 4) * (28 + 3 * ((tile_n_in*fs) // 4) + 5 * ((tile_n_in*fs) % 4))
instr = 62 + n_4_2 * (27 + (135 + cycles_chout_4_2 + cycles_leftover_chout_4_2)) + n_4_1 * (21 + (94 + cycles_chout_4_1 + cycles_leftover_chout_4_1))
constr1 = ( MAC * 1000 ) // instr
## left over h
MAC = tile_n_out*tile_n_in*leftover_tile_h_out*fs
n_4_2 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) // 2)
n_4_1 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (tile_n_out // 4) * (159 + 14 * ((tile_n_in*fs) // 4) + 18 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_2 = (tile_n_out % 4) * (52 + ((tile_n_in*fs) // 4) * 5 + 8 * ((tile_n_in*fs) % 4))
cycles_chout_4_1 = (tile_n_out // 4) * (82 + ((tile_n_in*fs) //4) * 9 + 12 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_1 = (tile_n_out % 4) * (28 + 3 * ((tile_n_in*fs) // 4) + 5 * ((tile_n_in*fs) % 4))
instr = 62 + n_4_2 * (27 + (135 + cycles_chout_4_2 + cycles_leftover_chout_4_2)) + n_4_1 * (21 + (94 + cycles_chout_4_1 + cycles_leftover_chout_4_1))
constr2 = ( MAC * 1000 ) // instr
## left over n
MAC = leftover_tile_nout*tile_n_in*tile_h_out*fs
n_4_2 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) // 2)
n_4_1 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (leftover_tile_nout // 4) * (159 + 14 * ((tile_n_in*fs) // 4) + 18 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_2 = (leftover_tile_nout % 4) * (52 + ((tile_n_in*fs) // 4) * 5 + 8 * ((tile_n_in*fs) % 4))
cycles_chout_4_1 = (leftover_tile_nout // 4) * (82 + ((tile_n_in*fs) //4) * 9 + 12 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_1 = (leftover_tile_nout % 4) * (28 + 3 * ((tile_n_in*fs) // 4) + 5 * ((tile_n_in*fs) % 4))
instr = 62 + n_4_2 * (27 + (135 + cycles_chout_4_2 + cycles_leftover_chout_4_2)) + n_4_1 * (21 + (94 + cycles_chout_4_1 + cycles_leftover_chout_4_1))
constr3 = ( MAC * 1000 ) // instr
## left over all
MAC = leftover_tile_nout*tile_n_in*leftover_tile_h_out*fs
n_4_2 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) // 2)
n_4_1 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (leftover_tile_nout // 4) * (159 + 14 * ((tile_n_in*fs) // 4) + 18 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_2 = (leftover_tile_nout % 4) * (52 + ((tile_n_in*fs) // 4) * 5 + 8 * ((tile_n_in*fs) % 4))
cycles_chout_4_1 = (leftover_tile_nout // 4) * (82 + ((tile_n_in*fs) //4) * 9 + 12 * ((tile_n_in*fs) % 4))
cycles_leftover_chout_4_1 = (leftover_tile_nout % 4) * (28 + 3 * ((tile_n_in*fs) // 4) + 5 * ((tile_n_in*fs) % 4))
instr = 62 + n_4_2 * (27 + (135 + cycles_chout_4_2 + cycles_leftover_chout_4_2)) + n_4_1 * (21 + (94 + cycles_chout_4_1 + cycles_leftover_chout_4_1))
constr4 = ( MAC * 1000 ) // instr
constr = (constr1*(n_tiles_h-(leftover_tile_h_out>0))*(n_tiles_nout-(leftover_tile_nout>0)) + constr2*(n_tiles_nout-(leftover_tile_nout>0))*(leftover_tile_h_out>0) + constr3*(n_tiles_h-(leftover_tile_h_out>0))*(leftover_tile_nout>0) + constr4)//((n_tiles_h-(leftover_tile_h_out>0))*(n_tiles_nout-(leftover_tile_nout>0)) + (n_tiles_nout-(leftover_tile_nout>0))*(leftover_tile_h_out>0) + (n_tiles_h-(leftover_tile_h_out>0))*(leftover_tile_nout>0) + 1*(leftover_tile_nout>0)*(leftover_tile_h_out>0))
print(f'Conv no dilation MAC/cycle simulated: {(constr/1000)}')
return (tile_n_in, tile_n_out, tile_h_in, tile_h_out, (constr/1000), memory/8)
return None
def get_tiling_conv_indirect(self, fs,
p,
stride,
dilation,
in_channels,
out_channels,
x_shape,
y_shape,
BN,
buffer_size=44000
):
s = stride
n_in = in_channels
n_out = out_channels
h_in = x_shape
# p = 0
h_out = y_shape
max_tile_n_out = n_out
max_tile_n_in = n_in
min_tile_h_in = fs
min_tile_h_out = 1
# this is to renormalize all costs
max_obj_value = sys.maxsize
# constraints
input_dim = 8 * n_in * h_in
output_dim = 8 * n_out * h_out
weight_dim = 8 * n_in * n_out * fs
bn_dim = self.BitActivation * n_out * 2
buffer_total = input_dim + output_dim + weight_dim + bn_dim
if BN == 0:
buffer_total -= bn_dim
if buffer_total <= buffer_size * 8:
obj_expr = 58+(h_out/2)*(119+(n_out/4)*((14*n_in*fs/4)+159))
print(f'tile in: {n_in} x {h_in}, tile out: {n_out} x {h_out}' )
return (n_in, n_out, h_in, h_out, obj_expr, buffer_total/8)
else:
db = 2
parameters = pywrapcp.Solver.DefaultSolverParameters()
solver = pywrapcp.Solver("simple_CP", parameters)
# ottimizzato channel first per kernel HWC
tile_n_in = solver.IntVar(1, max_tile_n_in, 'tile_n_out')
tile_n_out = solver.IntVar(1, max_tile_n_out, 'tile_n_out')
tile_h_in = solver.IntVar(min_tile_h_in, h_in, 'tile_h_in') #Temporal
tile_h_out = solver.IntVar(min_tile_h_out, h_out, 'tile_h_out') #Temporal
zero_variable = solver.IntVar(0, 0, 'zero variable')
h_out_intvar = solver.IntVar(min_tile_h_out,h_out,'h_out_intvar')
solver.Add(h_out_intvar == h_out)
if ((fs - 1)*dilation+1) <= h_in: #Receptive field size > temporal lenght -> H_in = tile_h_in
#Adding constraints for geometrical concerns.
solver.Add(0 == (tile_h_in - ((fs - 1)*dilation+1)) % s)
solver.Add(tile_h_out * s == (tile_h_in - (fs - 1)*dilation + (s - 1)))
# padding added
solver.Add(solver.Max((h_in - tile_h_in - (tile_h_in - (fs - 1)*dilation - p)), 0) % (tile_h_in - (fs - 1)*dilation + 1) + abs(solver.Min(
solver.Max((h_in - tile_h_in - (tile_h_in - (fs - 1)*dilation - p)), 0) % (tile_h_in - (fs - 1)*dilation), 1) - 1) * ((fs - 1)*dilation+1) >= ((fs - 1)*dilation+1))
solver.Add(h_in >= s*(tile_h_out*(h_out_intvar//tile_h_out)-1)-p+dilation*(fs-1)+1)
else:
solver.Add(h_in == tile_h_in )
solver.Add(h_out == tile_h_out )
solver.Add(tile_n_in == n_in)
constr_in = db * 8 * tile_n_in * tile_h_in #* tile_w_in
constr_out = db * 8 * tile_n_out * tile_h_out #* tile_w_out
constr_weight = db * 8 * tile_n_in * tile_n_out * fs
constr_bn = self.BitActivation * n_out * 2
constraint_all = constr_in + constr_out + constr_weight + constr_bn + 20*32 # 20 are the + 4 added between buffers
if BN == 0:
constraint_all -= constr_bn
solver.Add(constraint_all <= buffer_size * 8)
# objective
obj_expr = solver.IntVar(0, max_obj_value, "obj_expr")
n_tiles_h = solver.IntVar(1, h_in, "n_tiles_h")
leftover_tile_h_out = solver.IntVar(0, h_out, "leftover_tile_h_out")
n_tiles_nout = solver.IntVar(1, n_out, "n_tiles_nout")
leftover_tile_nout = solver.IntVar(0, n_out, "leftover_tile_nout")
solver.Add(n_tiles_h == (h_out + tile_h_out - 1) // tile_h_out)
solver.Add(leftover_tile_h_out == (h_out + zero_variable) % tile_h_out)
solver.Add(n_tiles_nout == (n_out + tile_n_out - 1) // tile_n_out)
solver.Add(leftover_tile_nout == (n_out + zero_variable) % tile_n_out)
## principal kernel
solver.Add(obj_expr == (64 * 10000 * tile_n_out
+ constraint_all
+ 64 * 1000000 * ((tile_h_out - 1) % 16) #Because of 8 cores -> 2 Pixels
+ 64 * 1000000 * ((tile_n_out - 1) % 4) #Because of 4x2 computation.
+ 64 * 10000 * (leftover_tile_nout)
+ 64 * 10000 * (leftover_tile_nout % 4)
+ 64 * 10000 * (leftover_tile_h_out % 16)))
objective = solver.Maximize(obj_expr, 1)
decision_builder = solver.Phase([tile_n_in, tile_n_out, tile_h_in, tile_h_out, n_tiles_h, leftover_tile_h_out, n_tiles_nout, leftover_tile_nout],
solver.CHOOSE_FIRST_UNBOUND,
solver.ASSIGN_MIN_VALUE)
# Create a solution collector.
collector = solver.LastSolutionCollector()
# Add the decision variables.
collector.Add(tile_n_in)
collector.Add(tile_n_out)
collector.Add(tile_h_in)
collector.Add(tile_h_out)
collector.Add(n_tiles_h)
collector.Add(leftover_tile_h_out)
collector.Add(n_tiles_nout)
collector.Add(leftover_tile_nout)
collector.Add(obj_expr)
collector.Add(constraint_all)
# Add the objective.
collector.AddObjective(obj_expr)
solver.Solve(decision_builder, [objective, collector])
if collector.SolutionCount() > 0: #Calculating the theoretical cycles to compute this layer.
best_solution = collector.SolutionCount() - 1
tile_n_in = collector.Value(best_solution, tile_n_in)
tile_n_out = collector.Value(best_solution, tile_n_out)
tile_h_in = collector.Value(best_solution, tile_h_in)
tile_h_out = collector.Value(best_solution, tile_h_out)
n_tiles_h = collector.Value(best_solution, n_tiles_h)
leftover_tile_h_out = collector.Value(best_solution, leftover_tile_h_out)
n_tiles_nout = collector.Value(best_solution, n_tiles_nout)
leftover_tile_nout = collector.Value(best_solution, leftover_tile_nout)
obj_expr = collector.Value(best_solution, obj_expr)
memory = collector.Value(best_solution, constraint_all)
if tile_h_in >= h_in:
tile_h_in = h_in
tile_h_out = int((tile_h_in -(fs - 1)*dilation + (2*p) + (s - 1))/s)
MAC = tile_n_out*tile_n_in*tile_h_out*fs
n_4_2 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) // 2)
n_4_1 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (tile_n_out // 4) * (164 + fs * (29+14*(tile_n_in // 4) + 22 * (tile_n_in % 4)))
cycles_leftover_chout_4_2 = (tile_n_out%4)*(49+fs*(15+5*(tile_n_in//4)+8*(tile_n_in%4)))
cycles_chout_4_1 = (tile_n_out//4)*(90+fs*(15+9*(tile_n_in//4)+17*(tile_n_in%4)))
cycles_leftover_chout_4_1 = (tile_n_out%4)*(43+fs*(8+2*(tile_n_in//4)+7*(tile_n_in%4)))
instr = (82+(n_4_2*(62+8*fs+(110+cycles_chout_4_2+cycles_leftover_chout_4_2)) + n_4_1*(36+4*fs+(87+cycles_chout_4_1+cycles_leftover_chout_4_1))))
constr1 = (MAC * 1000) // instr
## left over h
MAC = leftover_tile_h_out*tile_n_in*tile_n_out*fs
n_4_2 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) // 2)
n_4_1 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (tile_n_out // 4) * (164 + fs * (29+14*(tile_n_in // 4) + 22 * (tile_n_in % 4)))
cycles_leftover_chout_4_2 = (tile_n_out%4)*(49+fs*(15+5*(tile_n_in//4)+8*(tile_n_in%4)))
cycles_chout_4_1 = (tile_n_out//4)*(90+fs*(15+9*(tile_n_in//4)+17*(tile_n_in%4)))
cycles_leftover_chout_4_1 = (tile_n_out%4)*(43+fs*(8+2*(tile_n_in//4)+7*(tile_n_in%4)))
instr = (82+(n_4_2*(62+8*fs+(110+cycles_chout_4_2+cycles_leftover_chout_4_2)) + n_4_1*(36+4*fs+(87+cycles_chout_4_1+cycles_leftover_chout_4_1))))
constr2 = (MAC * 1000) // instr
## left over n
MAC = leftover_tile_nout*tile_n_in*tile_h_out*fs
n_4_2 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) // 2)
n_4_1 = ((tile_h_out // 8 + ((tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (leftover_tile_nout // 4) * (164 + fs * (29+14*(tile_n_in // 4) + 22 * (tile_n_in % 4)))
cycles_leftover_chout_4_2 = (leftover_tile_nout%4)*(49+fs*(15+5*(tile_n_in//4)+8*(tile_n_in%4)))
cycles_chout_4_1 = (leftover_tile_nout//4)*(90+fs*(15+9*(tile_n_in//4)+17*(tile_n_in%4)))
cycles_leftover_chout_4_1 = (leftover_tile_nout%4)*(43+fs*(8+2*(tile_n_in//4)+7*(tile_n_in%4)))
instr = (82+(n_4_2*(62+8*fs+(110+cycles_chout_4_2+cycles_leftover_chout_4_2)) + n_4_1*(36+4*fs+(87+cycles_chout_4_1+cycles_leftover_chout_4_1))))
constr3 = (MAC * 1000) // instr
## left over all
MAC = leftover_tile_nout*tile_n_in*leftover_tile_h_out*fs
n_4_2 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) // 2)
n_4_1 = ((leftover_tile_h_out // 8 + ((leftover_tile_h_out % 8) > 0)) % 2)
cycles_chout_4_2 = (leftover_tile_nout // 4) * (164 + fs * (29+14*(tile_n_in // 4) + 22 * (tile_n_in % 4)))
cycles_leftover_chout_4_2 = (leftover_tile_nout%4)*(49+fs*(15+5*(tile_n_in//4)+8*(tile_n_in%4)))
cycles_chout_4_1 = (leftover_tile_nout//4)*(90+fs*(15+9*(tile_n_in//4)+17*(tile_n_in%4)))
cycles_leftover_chout_4_1 = (leftover_tile_nout%4)*(43+fs*(8+2*(tile_n_in//4)+7*(tile_n_in%4)))
instr = (82+(n_4_2*(62+8*fs+(110+cycles_chout_4_2+cycles_leftover_chout_4_2)) + n_4_1*(36+4*fs+(87+cycles_chout_4_1+cycles_leftover_chout_4_1))))
constr4 = (MAC * 1000) // instr
constr = (constr1*(n_tiles_h-(leftover_tile_h_out>0))*(n_tiles_nout-(leftover_tile_nout>0)) + constr2*(n_tiles_nout-(leftover_tile_nout>0))*(leftover_tile_h_out>0) + constr3*(n_tiles_h-(leftover_tile_h_out>0))*(leftover_tile_nout>0) + constr4)//((n_tiles_h-(leftover_tile_h_out>0))*(n_tiles_nout-(leftover_tile_nout>0)) + (n_tiles_nout-(leftover_tile_nout>0))*(leftover_tile_h_out>0) + (n_tiles_h-(leftover_tile_h_out>0))*(leftover_tile_nout>0) + 1*(leftover_tile_nout>0)*(leftover_tile_h_out>0))
print(f'Conv indirect MAC/cycle simulated: {(constr/1000)}')
return (tile_n_in, tile_n_out, tile_h_in, tile_h_out, (constr/1000), memory/8)
return None
def get_tiling_conv1d(self, X, Y, W,
relu,
BN,
dilation,
has_bias,
out_mul, out_shift,
type_data='char',
full_computation=False,
multiple_buffering_factor=2,
name='conv',
forcing ='None'
):
# This function generate the layer function to be included in the project for the conv2d operations (Convolutions and Fully Connected layers).
ds_x = self.BitIn
ds_y = self.BitOut
ds_W = self.BitW
fs1 = self.filter_size[1]
p_left = self.padding[1]
p_right = self.padding[3]
n_in = self.x_shape[0]
n_out = self.out_ch
name_include = []
# L3 tiling
h_in = self.x_shape[-2]
w_in = self.x_shape[-1]
h_out = 1
if dilation > 1:
w_out = int(np.floor((w_in - ((fs1 - 1)*dilation) + p_left + p_right + (self.stride - 1)) / self.stride))
else:
w_out = int(np.floor((w_in - (fs1 - 1) + p_left + p_right + (self.stride - 1)) / self.stride))
if p_left==0 and p_right==0 and dilation ==1:
tiling_MAC_cycle_nodilation = self.get_tiling_conv_nodilation(
fs1,
0,
self.stride,
dilation,
n_in,
n_out,
w_in,
w_out,
BN,
buffer_size=self.buffer_size
)
tiling_MAC_cycle_normal = self.get_tiling_conv_normal(fs1,
self.padding[1],
self.stride,
dilation,
n_in,
n_out,
w_in,
w_out,
BN,
buffer_size=self.buffer_size
)
tiling_MAC_cycle_indirect = self.get_tiling_conv_indirect(fs1,
self.padding[1],
self.stride,
dilation,
n_in,
n_out,
w_in,
w_out,
BN,
buffer_size=self.buffer_size
)
if p_left==0 and p_right==0 and dilation ==1:
_, _, _, _, MAC_cycle_nodilation, _ = tiling_MAC_cycle_nodilation
else:
MAC_cycle_nodilation = 0
_, _, _, _, MAC_cycle_normal, _ = tiling_MAC_cycle_normal
_, _, _, _, MAC_cycle_indirect, _ = tiling_MAC_cycle_indirect
max_MAC = MAC_cycle_nodilation
layer_type = 'nodilation'
if p_left==0 and p_right==0 and dilation ==1:
tiling = tiling_MAC_cycle_nodilation
if MAC_cycle_normal > max_MAC:
max_MAC = MAC_cycle_normal
layer_type = 'normal'
tiling = tiling_MAC_cycle_normal
if MAC_cycle_indirect > max_MAC:
max_MAC = MAC_cycle_indirect
layer_type = 'indirect'
tiling = tiling_MAC_cycle_indirect
### FOR TEST
if forcing == 'normal':
max_MAC = MAC_cycle_normal
layer_type = 'normal'
tiling = tiling_MAC_cycle_normal
elif forcing == 'indirect':
max_MAC = MAC_cycle_indirect
layer_type = 'indirect'
tiling = tiling_MAC_cycle_indirect
elif forcing == 'nodilation':
max_MAC = MAC_cycle_nodilation
layer_type = 'nodilation'
tiling = tiling_MAC_cycle_nodilation
if tiling is not None:
tile_n_in, tile_n_out, tile_w_in, tile_w_out, MAC_cycle, memory = tiling
x_tot_str = '[%dx%d]' % (n_in, w_in)
y_tot_str = '[%dx%d]' % (n_out, w_out)
W_tot_str = '[%dx%dx%d]' % (n_out, n_in, fs1)
x_tot_size_str = "%.2f KiB" % (1. / 1024. / 8. * (ds_x * n_in * w_in )) if ds_x * \
n_in * h_in > 1024 else '%d B' % (ds_x * n_in * w_in * 1 / 8.)
y_tot_size_str = '%.2f KiB' % (1. / 1024. / 8. * (ds_y * n_out * w_out )) if ds_y * \
n_out * h_out * w_out > 1024 else '%d B' % (ds_y * n_out * w_out * 1 / 8.)
W_tot_size_str = '%.2f KiB' % (1. / 1024. / 8. * (ds_W * n_out * n_in * fs1)) if ds_W * \
n_out * n_in * fs1 > 1024 else '%d B' % (ds_W * n_out * n_in * fs1 * 1 / 8.)
x_tile_str = '[%dx%d]' % (tile_n_in, tile_w_in)
y_tile_str = '[%dx%d]' % (tile_n_out, tile_w_out)
W_tile_str = '[%dx%dx%d]' % (tile_n_out, tile_n_in, fs1)
x_size_str = "%.2f KiB" % (1. / 1024. / 8. * (ds_x * tile_n_in * tile_w_in )) if ds_x * tile_n_in * tile_w_in > 1024 else '%d B' % (ds_x * tile_n_in * tile_w_in * 1 / 8.)
y_size_str = '%.2f KiB' % (1. / 1024. / 8. * (ds_y * tile_n_out * tile_w_out)) if ds_y * tile_n_out * tile_w_out > 1024 else '%d B' % (ds_y * tile_n_out * tile_w_out * 1 / 8.)
y_no_str = '%d' % (max(math.ceil((n_out) / (tile_n_out)), 1) * max(math.ceil((w_out) / (tile_w_out)), 1))
W_size_str = '%.2f KiB' % (1. / 1024. / 8. * (ds_W * tile_n_out * tile_n_in * fs1)) if (ds_W * tile_n_out * tile_n_in * fs1) > 1024 else '%d B' % (ds_W * tile_n_out * tile_n_in * fs1 * 1 / 8.)
W_no_str = '%d' % (max(math.ceil((n_out - tile_n_out) / (tile_n_out) + 1), 1) * 1)
x_no_str = '%d' % (int(int(y_no_str)/int(W_no_str)) * pow(max(math.ceil((n_in - tile_n_in) / (tile_n_in) + 1), 1),2))
L1_tiles_size = ds_x * tile_n_in * tile_w_in / 8. * (1 + int(int(x_no_str) > 1)) + ds_y * tile_n_out * tile_w_out / 8. * (1 + int(int(y_no_str) > 1)) + n_out * 8 * 2
L1_tiles_size += (ds_W * tile_n_out * tile_n_in * fs1 / 8.) * (1 + int(int(W_no_str) > 1))
logging.debug(" L2 size:".ljust(18) + "x: " + x_tot_str.ljust(15) +"y: " + y_tot_str.ljust(15) + "W: " + W_tot_str.ljust(15))
logging.debug(" L2 buff:".ljust(18) + "x: " + x_tot_size_str.ljust(15) +"y: " + y_tot_size_str.ljust(15) + "W: " + W_tot_size_str.ljust(15))
logging.debug(" tiles L2-L1:".ljust(18) + "x: " + x_tile_str.ljust(15) +"y: " + y_tile_str.ljust(15) + "W: " + W_tile_str.ljust(15))
logging.debug(" L1 buff:".ljust(18) + "x: " + x_size_str.ljust(15) +"y: " + y_size_str.ljust(15) + "W: " + W_size_str.ljust(15))
logging.debug(" no. tiles:".ljust(18) + "x: " + x_no_str.ljust(15) +"y: " + y_no_str.ljust(15) + "W: " + W_no_str.ljust(15))
logging.debug(" Total L1 occupation:".ljust(18) + str(memory * 1.).ljust(15))
print_template_layer_1D(X, Y, W,
n_in, w_in,
n_out, w_out,
tile_n_in, tile_w_in, tile_w_out,
tile_n_out,
ds_x, ds_y, ds_W, self.BitActivation, type_data,
fs1, p_left, p_right, self.stride,
dilation,
relu, BN,
out_mul, out_shift,
name_layer=name,
test=False,
test_location=self.test_location,
has_bias=has_bias,
conv_order='PULP-NN',
optional='conv',
l1_buffer=self.buffer_size,
platform=self.platform,
chip=self.chip,
optional_type=self.optional_type,
layer_type = layer_type)
### L2 memory calculation
n_out_temp = self.out_ch
w_in_temp = self.x_shape[-1]
#h_out_temp = int(np.floor((h_in_temp - (fs1 - 1) + p_left + p_right + (self.stride - 1)) / self.stride))
w_out_temp = int(np.floor((w_in_temp - ((fs1 - 1)*dilation) + p_left + p_right + (self.stride - 1)) / self.stride))
out_dim1 = n_out_temp * w_out_temp
n_in_temp = self.x_shape[0]
w_in_temp = self.x_shape[-1]
in_dim1 = n_in_temp * w_in_temp
n_in_temp = self.x_shape[0]
n_out_temp = self.out_ch
weights_dim = n_in_temp * n_out_temp * fs1
if BN == 1:
weights_dim +=n_out_temp * int(self.BitActivation / 4)
return in_dim1, out_dim1, weights_dim, L1_tiles_size
return None
def get_tiling_pool2d_L3(self,
BN,
input_L3,
input_dim_constraint,
output_weights_dim_constraint
):
# tiling for L3-L2 management
# parameters instantiation
s = self.stride
p_top = self.padding[0]
p_left = self.padding[1]
p_bottom = self.padding[2]
p_right = self.padding[3]
fs1 = self.filter_size[0]
fs2 = self.filter_size[1]
conv_overlap_h = 2 * (fs1 // 2) + fs1 % 2 - 1 - (s - 1)
n_in = self.x_shape[0]
n_out = self.out_ch
h_in = self.x_shape[-2] + p_top + p_bottom
w_in = self.x_shape[-1] + p_left + p_right
h_out = int(np.floor((h_in - (fs1 - 1) + (s - 1)) / s))
w_out = int(np.floor((w_in - (fs2 - 1) + (s - 1)) / s))
h_in = self.x_shape[-2]
w_in = self.x_shape[-1]
max_tile_n_out = n_out
max_tile_n_in = n_in
min_tile_h_in = fs1
min_tile_h_out = 1
# this is to renormalize all costs
max_obj_value = self.L2_buffer_size * 8 * 32 * 100000
# constraints
input_dim = self.BitIn * n_in * h_in * w_in
output_dim = self.BitOut * n_out * h_out * w_out
bn_dim = self.BitActivation * n_out * 2
buffer_total = input_dim + output_dim + bn_dim
if BN == 0:
buffer_total -= bn_dim
## execute in L2 if constraints are respected
if (buffer_total <= self.L2_buffer_size * 8) and input_L3==0:
return (n_in, n_out, h_in, h_out, w_in, w_out)
else:
db_O = 1
# 4 iterations, adding each time a different part to be tiled, either weights, outputs, or both. Input is forced
for iteration in range(0, 2):
parameters = pywrapcp.Solver.DefaultSolverParameters()
solver = pywrapcp.Solver("simple_CP", parameters)
tile_n_out = solver.IntVar(max_tile_n_out, max_tile_n_out, 'tile_n_out')
tile_h_out = solver.IntVar(min_tile_h_out, h_out, 'tile_h_out')
if input_L3 == 0:
tile_h_in = solver.IntVar(h_in, h_in, 'tile_h_in')
db_x = 1
else:
tile_h_in = solver.IntVar(min_tile_h_in, h_in, 'tile_h_in')
solver.Add(0 == (tile_h_in - fs1) % s)
db_x = 2
if iteration == 0:
if db_x == 1:
db_O = 2
else:
solver.Add(tile_h_out == h_out)
db_O = 1
elif iteration == 1:
if db_x == 2:
db_O = 2
# L2 constraints on input and output dimension
if input_dim_constraint > 0:
solver.Add(db_x * n_in * tile_h_in * w_in <= input_dim_constraint)
if output_weights_dim_constraint > 0:
constr_out = db_O * n_out * tile_h_out * w_out
constraint_all = constr_out
solver.Add(constraint_all <= output_weights_dim_constraint)
# scaling is used to ensure datasize is integer
ds_x_scale = int(math.floor(32 * self.BitIn))
ds_y_scale = int(math.floor(32 * self.BitOut))
ds_W_scale = int(math.floor(32 * self.BitW))
ds_bn_scale = int(math.floor(32 * self.BitActivation))
# geometrical constraint
if db_x == 2 and db_O == 2:
solver.Add(tile_h_out * s == (tile_h_in - (fs1 - 1) + (s - 1)))
solver.Add(solver.Max((h_in - tile_h_in - (tile_h_in - fs1 + 1 - p_top)), 0) % (tile_h_in - fs1 + 1) + abs(solver.Min(solver.Max((h_in - tile_h_in - (tile_h_in - fs1 + 1 - p_top)), 0) % (tile_h_in - fs1 + 1), 1) - 1) * fs1 >= fs1)
constr_in = db_x * ds_x_scale * n_in * tile_h_in * w_in
constr_out = db_O * ds_y_scale * n_out * tile_h_out * w_out
constr_bn = ds_bn_scale * n_out * 2
constraint_all = constr_in + constr_out + constr_bn
# size constraint
if BN == 0:
constraint_all -= constr_bn
solver.Add(constraint_all <= 32 * self.L2_buffer_size * 8)
# objective
obj_expr = solver.IntVar(0, max_obj_value, "obj_expr")
# objective function:
# 1. constraints for pulp-nn perfromance optimization
# 2. constraints to have all tiles of same dimension
solver.Add(obj_expr == constraint_all
+ 32 * 2 * 100000 * ((tile_h_out - 1) % 8)
+ 32 * 2 * 1000000 * (((h_in - tile_h_in + p_top) % (tile_h_in - conv_overlap_h )) == 0)
+ 32 * 2 * 100000 * ((tile_h_in - 1) % 4))
# maximize the objective
objective = solver.Maximize(obj_expr, 1)
decision_builder = solver.Phase([tile_n_out, tile_h_in, tile_h_out],
solver.CHOOSE_FIRST_UNBOUND,
solver.ASSIGN_MIN_VALUE)
# Create a solution collector.
collector = solver.LastSolutionCollector()
# Add the decision variables.
collector.Add(tile_n_out)
collector.Add(tile_h_in)
collector.Add(tile_h_out)
# Add the objective.
collector.AddObjective(obj_expr)
solver.Solve(decision_builder, [objective, collector])
if collector.SolutionCount() > 0:
best_solution = collector.SolutionCount() - 1
tile_n_out = collector.Value(best_solution, tile_n_out)
tile_h_in = collector.Value(best_solution, tile_h_in)
tile_h_out = collector.Value(best_solution, tile_h_out)
return (n_in, tile_n_out, tile_h_in, tile_h_out, w_in, w_out)
print(" Pool2D ERROR: no L3-L2 tiling found. Exiting...")
os._exit(0)
return None
def get_tiling_conv2d_L3(self,
DW,
BN,
input_L3,
input_dim_constraint,
output_weights_dim_constraint,
weight_constraint, name
):
# tiling for L3-L2 management
# parameters instantiation
fs1 = self.filter_size[0]
fs2 = self.filter_size[1]
s = self.stride
p_top = self.padding[0]
p_left = self.padding[1]
p_bottom = self.padding[2]
p_right = self.padding[3]
conv_overlap_h = 2 * (fs1 // 2) + fs1 % 2 - 1 - (s - 1)
n_in = self.x_shape[0]
n_out = self.out_ch
if DW == 1:
g = self.groups
n_in = 1
else:
g = 1
h_in = self.x_shape[-2] + p_top + p_bottom
w_in = self.x_shape[-1] + p_left + p_right
h_out = int(np.floor((h_in - (fs1 - 1) + (s - 1)) / s))
w_out = int(np.floor((w_in - (fs2 - 1) + (s - 1)) / s))
h_in = self.x_shape[-2]
w_in = self.x_shape[-1]
max_tile_n_out = n_out
max_tile_n_in = n_in*g
min_tile_h_in = fs1
min_tile_h_out = 1
# this is to renormalize all costs
max_obj_value = self.L2_buffer_size * 8 * 32 * 100000
# constraints
input_dim = self.BitIn * n_in * g * h_in * w_in
output_dim = self.BitOut * n_out * h_out * w_out
weight_dim = self.BitW * n_in * n_out * fs1 * fs2
bn_dim = self.BitActivation * n_out * 2
buffer_total = input_dim + output_dim + weight_dim + bn_dim
if BN == 0:
buffer_total -= bn_dim
if weight_constraint > 0:
if (n_in * n_out * fs1 * fs2 <= weight_constraint):
flag_weight_ok = True
else:
flag_weight_ok = False
else:
flag_weight_ok = True
## execute in L2 if constraints are respected
if (buffer_total <= self.L2_buffer_size * 8) and input_L3==0 and flag_weight_ok==True:
return (n_in, n_out, h_in, h_out, w_in, w_out)
else:
db_W = 1
db_O = 1
# 4 iterations, adding each time a different part to be tiled, either weights, outputs, or both. Input is forced
for iteration in range(0, 4):
parameters = pywrapcp.Solver.DefaultSolverParameters()
solver = pywrapcp.Solver("simple_CP", parameters)
tile_n_out = solver.IntVar(1, max_tile_n_out, 'tile_n_out')
tile_h_out = solver.IntVar(min_tile_h_out, h_out, 'tile_h_out')
if input_L3 == 0:
tile_h_in = solver.IntVar(h_in, h_in, 'tile_h_in')
db_x = 1
else:
tile_h_in = solver.IntVar(min_tile_h_in, h_in, 'tile_h_in')
solver.Add(0 == (tile_h_in - fs1) % s)
db_x = 2
if iteration == 0:
if db_x == 1:
db_W = 2
db_O = 1
solver.Add(tile_h_out == h_out)
else:
solver.Add(tile_h_out == h_out)
solver.Add(tile_n_out == n_out)
db_W = 1
db_O = 1
elif iteration == 1:
if db_x == 1:
db_W = 1
db_O = 2
solver.Add(tile_n_out == n_out)
else:
solver.Add(tile_n_out == n_out)
db_W = 1
db_O = 2
elif iteration == 2:
if db_x == 1:
db_W = 2
db_O = 2
else:
solver.Add(tile_h_out == h_out)
db_W = 2
db_O = 1
else:
db_W = 2
db_O = 2
# L2 constraints on input and output dimension
if input_dim_constraint > 0:
solver.Add(db_x * n_in * g * tile_h_in * w_in <= input_dim_constraint)
if output_weights_dim_constraint > 0:
constr_out = db_O * n_out * tile_h_out * w_out
if DW == 0:
constr_weight = db_W * n_in * tile_n_out * fs1 * fs2
else:
constr_weight = db_W * n_in * g * fs1 * fs2
if self.BitActivation == 32:
constr_bn = tile_n_out * 2 * 4 * db_W
else:
constr_bn = tile_n_out * 2 * 8 * db_W
constraint_all = constr_out + constr_weight + constr_bn
solver.Add(constraint_all <= output_weights_dim_constraint)
if weight_constraint > 0:
if DW == 0: