-
Notifications
You must be signed in to change notification settings - Fork 10
/
Copy pathlayer_base_method.py
173 lines (137 loc) · 5.89 KB
/
layer_base_method.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
#!/usr/bin/python2.7
# public library
import math
import numpy as np
class LayerBaseMethod(object):
"""docstring for LayerBaseMethod"""
# info for systolic array
A = None # systolic array dimension
# memory bandwith number of bytes can be transferred.
B = None
# on-chip buffer size
buf_size = None
# info for weights
K_w = None # kernel width
K_h = None # kernel height
S = None # stride size
# input layer dimension
H = None # height of ofmap
W = None # width of ofmap
Ci = None # channels for weights
Co = None # channels for ofmap
# on-chip buffer size
bufi_size = None
bufo_size = None
bufw_size = None
# array to store the result from the four different results
res = []
"""docstring for LayerBaseMethod"""
def __init__(self, data, sys_info):
self.data = data
self.sys_info = sys_info
self.A = sys_info["sa_size"]
self.B = sys_info["memory_bandwidth"]/(sys_info["bit_width"]/8)
self.buf_size = sys_info["bufsize"]
self.res = []
def init_setup(self):
self.res = []
layer_info = self.data
# set up the new layer information
[self.W, self.H, self.Ci] = layer_info["ifmap"]
self.Co = layer_info["out_channel"]
[self.K_w, self.K_h] = layer_info["kernel"]
self.S = layer_info["stride"]
self.W /= self.S
self.H /= self.S
###############################################################
# general process #
###############################################################
# compute buffer utilization
def buffer_utilization(self, x):
# buffer = ofmap + weights + ifmap
return (x[0]*x[1]*x[2] + self.Ci*self.K_h*self.K_w*x[0]
+ self.Ci*(self.S*x[1]+self.K_h/2)*(self.S*x[2]+self.K_h/2))
def total_batch_number(self, h_0, w_0, c_0):
return math.ceil(float(self.H*self.W*self.Co) / (h_0*w_0*c_0))
# (ofmap + ifmap)*total_batch + (ofmap+weights)*Co/c_0
def row_major_data_transfer(self, h_0, w_0, c_0):
# calculate the total batch
total_batch = self.total_batch_number(h_0, w_0, c_0)
# ofmap, ifmap and kernel tile size
ofmap_tile_size = h_0*w_0*c_0
ifmap_tile_size = (self.S*h_0+self.K_h/2)*(self.S*w_0+self.K_w/2)*self.Ci
kernel_tile_size = self.K_h*self.K_w*self.Ci*c_0
# ofmap + ifmap transfer
total_transfer = (ofmap_tile_size + ifmap_tile_size) * total_batch
# add additional data transfer
total_transfer += (ofmap_tile_size + kernel_tile_size) * self.Co/c_0
return total_transfer
# (ofmap + weights)*total_batch + (ofmap+ifmap)*(H*W)/(h_0*w_0)
def channel_major_data_transfer(self, h_0, w_0, c_0):
# calculate the total batch
total_batch = self.total_batch_number(h_0, w_0, c_0)
# ofmap and ifmap tile size
ofmap_tile_size = h_0*w_0*c_0
ifmap_tile_size = (self.S*h_0+self.K_h/2)*(self.S*w_0+self.K_w/2)*self.Ci
kernel_tile_size = self.K_h*self.K_w*self.Ci*c_0
# ofmap + weight transfer
total_transfer = (ofmap_tile_size + kernel_tile_size) * total_batch
# add additional data transfer
total_transfer += (ofmap_tile_size + ifmap_tile_size) \
* self.H*self.W / (h_0*w_0)
return total_transfer
def systolic_array_utilization(self, xi, area):
area_size = area[0] * area[1]
A = self.A
total_usage = xi * area_size
round_up_val = math.ceil(float(xi/A))*A \
* math.ceil(float(area_size)/A)*A
return total_usage / round_up_val
def compute_bound_cycle(self, util_rate):
# total number of ops
total_computation = (self.H*self.W*self.Co) * \
(self.Ci*self.K_h*self.K_w)
# systolic array calculation capacity
comp_cap = (self.A*self.A) * util_rate
return total_computation / comp_cap
def process_parameter(self, x, row_major, comp_bound):
x = list(map(lambda i: math.floor(i), x))
bound = "C"
# make the tile size even for every batch
c_0 = min(self.Co/math.ceil(self.Co/round(x[0])), self.Co)
w_0 = min(self.W/math.ceil(self.W/round(x[1])), self.W)
h_0 =min(self.H/math.ceil(self.H/round(x[2])), self.H)
# compute the total number of elements needed to be updated
# if it is row-major.
if row_major:
# (ofmap + ifmap)*total_batch + (ofmap+weights)*Co/c_0
total_transfer = self.row_major_data_transfer(h_0, w_0, c_0)
# compute the total number of elements needed to be updated
# if it is channel-major.
else:
# (ofmap + weights)*total_batch + (ofmap+ifmap)*(H*W)/(h_0*w_0)
total_transfer = self.channel_major_data_transfer(h_0, w_0, c_0)
# compute the utilization of systolic array
util_sys_arr = self.systolic_array_utilization(c_0, [w_0, h_0])
# compute the utilization of systolic array
util_buf = self.buffer_utilization([c_0, w_0, h_0])/self.buf_size
if util_buf > 1.01:
return
# calculate the amount of cycles of computing all elements.
if comp_bound:
bound = "C"
total_cycle = self.compute_bound_cycle(util_sys_arr)
else:
bound = "M"
total_cycle = total_transfer/self.B
ret = {
"total_transfer": round(total_transfer),
"total_cycle": round(total_cycle),
"systolic_array_utilization": util_sys_arr,
"buffer_utilization": util_buf,
"c_0, w_0, h_0": [round(c_0), round(w_0), round(h_0)],
"Tile size" : [self.Co/c_0, self.W/w_0, self.H/h_0],
"Bound" : bound
}
self.res.append(ret)
return