cma_approach_square_size.py 15.6 KB
Newer Older
harry1080ti's avatar
harry1080ti committed
1
2
3
4
5
import cma
from multiprocessing import Pool
from os import cpu_count
import time
import path_constant as pc
harry1080ti's avatar
harry1080ti committed
6
import packing_penalty as pp
7
from os import makedirs
harry1080ti's avatar
harry1080ti committed
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

class cma_approach(object):
    def __init__(self,
        # data path
        path_to_datasrc = "alexnet_data.csv",
        path_to_topology = "alexnet.csv",
        target_col = "Cycles",

        # problem definition
        number_of_partition = 4, max_iteration = 100,
        sigma = 0.5, population_size = 10,

        # constraint
        max_res_unit = 960, initial_res = 0,
        res_step = 1,
        penalty_offest = 10000000000,
        seeding_type="optimised",
25
        hybrid = True,
harry1080ti's avatar
harry1080ti committed
26
27
        print_to_csv = True,
        max_pack_size = 129
harry1080ti's avatar
harry1080ti committed
28
29
30
31
32
33
34
35
36
37
38
        ):
        self.target_col = target_col
        self.start = time.time()
        self.k = number_of_partition
        self.max_iter = max_iteration
        self.sigma = sigma
        self.max_res_unit = max_res_unit
        self.res_step = res_step
        self.population_size = population_size
        self.penalty_offest = penalty_offest
        self.ending_iter = 0
39
        self.is_hybrid = hybrid
harry1080ti's avatar
harry1080ti committed
40
41
42
43
44
45
46
47
48
49
50
51
52
        self.data_src = {}

        self.topology_file = path_to_topology
        self.layers = self.parse_topology_file()
        self.parse_data_set_file(path_to_datasrc)

        self.best_layer = number_of_partition * [0]
        self.best_res = number_of_partition * [0]

        self.total_valid_solution = 0
        self.trial = 1
        self.seeding_type = seeding_type

53
        self.max_res_available = max_res_unit
harry1080ti's avatar
harry1080ti committed
54
        self.print_to_csv = print_to_csv
harry1080ti's avatar
harry1080ti committed
55

harry1080ti's avatar
harry1080ti committed
56
57
        self.max_pack_size = max_pack_size

harry1080ti's avatar
harry1080ti committed
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
    def parse_topology_file(self):
        layers = []
        with open(pc.TOPOLOGIES_PATH+self.topology_file, 'r') as f:
            next(f)
            for line in f:
                elems = line.strip().split(',')
                layers.append(elems[0])

        for layer in layers:
            self.data_src[layer] = {}
        return layers

    def parse_data_set_file(self, path_to_data_csv):
        first = True
        target_idx = 2
        with open(pc.DATA_SOURCE_PATH+path_to_data_csv, 'r') as f:
            for line in f:
                elems = line.strip().split(',')
                # print(elems)
                if first:
                    for idx, col in enumerate(elems):
                        if self.target_col in col:
                            target_idx = idx
                            break
                    first = False
                else:
                    self.data_src[elems[1]][int(elems[0])] = int(float(elems[target_idx]))

    def regroup_layers(self, sample):
        # #print("DEBUG", sample)
        detail_sample = []
        idx = 0
        for size in sample:
            part = []

            if size == 1:
                part.append(self.layers[idx])
                idx += 1
            else:
                for i in range(0, size):
                    part.append(self.layers[i + idx])
                idx += size

            detail_sample.append(part)

        return detail_sample

    def decode(self, val, max_val):
        return int(val * max_val)

    def encode(self, val, max_val):
        return float(val / max_val)

    def filter_layer(self, layer):
        for idx in range(self.k):
            if layer[idx] <= 0:
                return False

        if sum(layer) != len(self.layers):
            return False

        return True

    def filter_res(self, res):
        # #print(layer, res)
        for idx in range(self.k):
            if res[idx] <= 0:
                return False

        if sum(res) != self.max_res_unit:
            return False

        return True

    def penalty_layer(self, layer):
        penalty_score = self.penalty_offest

        if sum(layer) != len(self.layers):
            penalty_score += self.penalty_offest
        else:
            layer = [abs(val) for val in layer]

        for idx in range(self.k):
            if layer[idx] <= 0:
                penalty_score *= 1.05

        percent_diff = (abs(sum(layer) - len(self.layers)) / len(self.layers))
        penalty_score += percent_diff * self.penalty_offest

        return penalty_score

    def find_max_latency(self, layer_partition, res_partitions):
        latencies = [0] * len(layer_partition)
        max_latency_idx = 0
        # print(layer_partition)
        # print(res_partitions)
        for idx, part in enumerate(layer_partition):
            res = res_partitions[idx]
            for layer in part:
                latencies[idx] += self.data_src[layer][res]
            if latencies[idx] > latencies[max_latency_idx]:
                max_latency_idx = idx

        return latencies, max_latency_idx

justinborromeo's avatar
WIP    
justinborromeo committed
163
164
165
    """
    Decide partition sizes and evaluate.
    """
166
    def eva_hybrid_sq(self, layer):
justinborromeo's avatar
WIP    
justinborromeo committed
167
        # res is a list of length # of partitions.  Step size is 3
harry1080ti's avatar
harry1080ti committed
168
169
        res = [self.res_step] * self.k
        latencies = []
justinborromeo's avatar
WIP    
justinborromeo committed
170
171
172
173
174
        
        # TODO Change this to a binary search.

        # max_res_unit = 1920*9*1 from sq_approach_faster
        variable_max_res_unit = self.max_res_unit
harry1080ti's avatar
harry1080ti committed
175

justinborromeo's avatar
WIP    
justinborromeo committed
176
177
        # Generate initial solution
        while sum([r*r for r in res]) < variable_max_res_unit:
harry1080ti's avatar
harry1080ti committed
178
179
            latencies, max_idx = self.find_max_latency(layer, res)
            res[max_idx] += self.res_step
justinborromeo's avatar
WIP    
justinborromeo committed
180
181
182
183
184
185
186
187
188
189
        
        while pp.packingPenalty(res, self) != 0:
            while sum([r*r for r in res]) < variable_max_res_unit:
                latencies, max_idx = self.find_max_latency(layer, res)
                res[max_idx] += self.res_step
            variable_max_res_unit -= 100

        # TODO we want to penalize based on how much we had to decrease
        # variable_max_res_unit.
        max_res_unit_decrease = self.max_res_unit - variable_max_res_unit
190
191
192
193
        
        # If all layers couldn't be packed, packingPenalty returns 0.
        packing_penalty = pp.packingPenalty(res, self.max_pack_size)
        return latencies[max_idx] + packing_penalty, latencies, res, layer
harry1080ti's avatar
harry1080ti committed
194
195
196
197
198
199
200
201
202

    def evaluation_top_level(self, in_val):
        pid, sampling = in_val
        layer = [self.decode(val, len(self.layers)) for val in sampling]
        layer.append(len(self.layers) - sum(layer))
        penalty = 0

        if not self.filter_layer(layer):
            penalty = self.penalty_layer(layer)
203
            if self.is_hybrid:
harry1080ti's avatar
harry1080ti committed
204
205
206
207
                return pid, penalty
            else:
                return pid, penalty*4

justinborromeo's avatar
WIP    
justinborromeo committed
208
209
        # regroup_layers assigns layers to the partitions.  Returns a list of
        # partition lists which contain layers.
harry1080ti's avatar
harry1080ti committed
210
211
        layer = self.regroup_layers(layer)

212
        return pid, self.eva_hybrid_sq(layer)[0]
harry1080ti's avatar
harry1080ti committed
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233

    def run(self):
        self.trial += self.trial
        if (self.seeding_type=="allzeros"):
            self.seed = [0]*(self.k-1)
            self.seed_od = self.seed
        elif (self.seeding_type=="optimised"):
            self.seed = []
            for i in range(self.k - 1):
                self.seed.append(int(len(self.layers)/self.k))
            self.seed.append(len(self.layers) - sum(self.seed))
            self.seed_od = self.seed
            self.seed = [self.encode(val, len(self.layers)) for val in self.seed[:-1]]
        else:
            raise ValueError('Invalid Seeding Strategy')

        self.es = cma.CMAEvolutionStrategy(self.seed, self.sigma, \
            {'popsize' : self.population_size})

        best_overall = self.penalty_offest
        self.i = 0
harry1080ti's avatar
harry1080ti committed
234
        temp_out = []
harry1080ti's avatar
harry1080ti committed
235
236
237
238
239
240
241
242
        while not self.es.stop() and self.i < self.max_iter:
            samples = self.es.ask()
            id_list = [(idx, sample) for idx, sample in enumerate(samples)]
            scores = [0] * self.es.popsize
            invalid_sampling = 0

            res_combintaions = [0] * self.es.popsize

harry1080ti's avatar
harry1080ti committed
243
244
245
246
            # pool = Pool(processes = cpu_count() - 4)
            # for result in pool.imap_unordered(self.evaluation_top_level, id_list):
            #     scores[result[0]] = result[1]
            #     if result[1] >= self.penalty_offest:
harry1080ti's avatar
harry1080ti committed
247
            #         invalid_sampling += 1
harry1080ti's avatar
harry1080ti committed
248
            #     else:
249
            #         if not self.is_hybrid:
harry1080ti's avatar
harry1080ti committed
250
251
252
253
254
255
256
257
            #             res_combintaions[result[0]] = result[2]
            # pool.close()
            # pool.join()

            for tup in id_list:
                _, scores[tup[0]] = self.evaluation_top_level(tup)
                if scores[tup[0]] >= self.penalty_offest:
                    invalid_sampling += 1
harry1080ti's avatar
harry1080ti committed
258

259
            if not self.is_hybrid:
harry1080ti's avatar
harry1080ti committed
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
                best_in_iteration = min(scores)

                if best_in_iteration < best_overall and best_in_iteration < self.penalty_offest:
                    best_overall = best_in_iteration
                    self.best_res = res_combintaions[scores.index(min(scores))]

            ##print(str(self.i) + ":", \
            #    "Sigma:",round(self.es.sigma, 4), \
            #    "|| Valid sampling percentage:", \
            #        (self.population_size - invalid_sampling) /self.population_size *100)
            ##print("invalid sampling", invalid_sampling)
            self.valid_sampling_percentage = (self.population_size - invalid_sampling) /self.population_size *100
            self.total_valid_solution += self.population_size - invalid_sampling
            self.samples = samples
            self.scores = scores
            self.es.tell(samples, scores)

            self.end = time.time()
            self.best_layer = [self.decode(val, len(self.layers)) for val in self.es.result[0]]
            self.best_layer.append(len(self.layers) - sum(self.best_layer))
280
            temp_out.append(self.report(False)[1])
harry1080ti's avatar
harry1080ti committed
281
282
283
            self.i += 1

        self.ending_iter = self.i
LongChan's avatar
LongChan committed
284
        return temp_out
harry1080ti's avatar
harry1080ti committed
285

286
    def report(self, output_png):
harry1080ti's avatar
harry1080ti committed
287
288
289
290
291
292
        ##print(self.i, self.es.sigma)
        max_latency = 0
        layer = []
        res = []
        latencies = []

293
        if self.is_hybrid:
harry1080ti's avatar
harry1080ti committed
294
295
296
297
            if not self.filter_layer(self.best_layer):
                ##print("RESULT NOT VALID")
                ##print("Layer:", self.best_layer, "sum: ", sum(self.best_layer))
                #print(self.penalty_layer(self.best_layer))
harry1080ti's avatar
harry1080ti committed
298
299
300
301
302
                if self.print_to_csv:
                    with open(pc.RESULT_CSV_PATH+'cma_logmore_sq.csv', 'a') as csvFile:
                        writer = csv.writer(csvFile, delimiter=',', lineterminator="\n")
                        writer.writerow([self.target_col,self.i,self.k, self.topology_file, 0, 0, 0, 0, 0, 0, 0, layer, res, self.end-self.start, self.es.sigma, self.seed_od,self.valid_sampling_percentage, self.trial, self.population_size, self.max_res_unit, self.seeding_type])
                    csvFile.close
harry1080ti's avatar
harry1080ti committed
303

harry1080ti's avatar
harry1080ti committed
304
                result = [self.target_col,self.i,self.k, self.topology_file, 0, 0, 0, 0, 0, 0, 0, layer, res, self.end-self.start, self.es.sigma, self.seed_od,self.valid_sampling_percentage, self.trial, self.population_size, self.max_res_unit, self.seeding_type]
harry1080ti's avatar
harry1080ti committed
305

harry1080ti's avatar
harry1080ti committed
306
                return False, result
harry1080ti's avatar
harry1080ti committed
307
308

            layer = self.regroup_layers(self.best_layer)
309
            max_latency, latencies, res, layers = self.eva_hybrid_sq(layer)
harry1080ti's avatar
harry1080ti committed
310
311
312
313
314
315
316
317
318
319
320
321
        else:
            if not self.filter_res(self.best_res) and not self.filter_layer(self.best_layer):
                #print("RESULT NOT VALID")
                #print("Layer:", self.best_layer, "sum: ", sum(self.best_layer))
                #print("Res:", self.best_res, "sum: ", sum(self.best_res))
                return False

            layer = self.regroup_layers(self.best_layer)
            res = self.best_res
            latencies, max_idx = self.find_max_latency(layer, self.best_res)
            max_latency = latencies[max_idx]

harry1080ti's avatar
harry1080ti committed
322
        # generate data for mapping the full array (129 * 129)
harry1080ti's avatar
harry1080ti committed
323
324
325
        full_latency, full_max_idx = self.find_max_latency([self.layers], [129]*len(self.layers))

        # PLEASE UNCOMMENT OUT THIS PART IF YOU NOT USING THE BASH SCRIPT WE HAVE PROVIDED 
harry1080ti's avatar
harry1080ti committed
326
327
328
329
330
331
332
333
334
335
336
337
338
339
        # print("================================= RESULT =================================")
        # print("Solution: (out of", self.total_valid_solution, "solutions)")
        # print(layer)
        # print("Res mapping:")
        # print(res)
        # print("Latency for each partition: ")
        # print(latencies)
        # print("Final Latency:", max_latency*self.k, "|| Throught put:", 1/max_latency)
        # print("==========================================================================")
        # print("Map to full array (", self.max_res_unit, ")")
        # print("Final Latency:", full_latency[full_max_idx], "|| Throught put:", 1/full_latency[full_max_idx])
        # print("==========================================================================")
        # print("Throughtput Ratio:", (1/max_latency)/(1/full_latency[full_max_idx]))
        # print("Latency increase:", (max_latency*self.k)/full_latency[full_max_idx])
harry1080ti's avatar
harry1080ti committed
340

harry1080ti's avatar
harry1080ti committed
341
342
343
344
345
        if self.print_to_csv:
            with open(pc.RESULT_CSV_PATH+'cma_logmore_sq.csv', 'a') as csvFile:
                writer = csv.writer(csvFile, delimiter=',', lineterminator="\n")
                writer.writerow([self.target_col,self.i,self.k, self.topology_file, 1,(1/max_latency), max_latency*self.k, 1/full_latency[full_max_idx], full_latency[full_max_idx], (1/max_latency)/(1/full_latency[full_max_idx]), (max_latency*self.k)/full_latency[full_max_idx], layer, res, self.end-self.start, self.es.sigma, self.seed_od,self.valid_sampling_percentage, self.trial, self.population_size, self.max_res_unit, self.seeding_type])
            csvFile.close
harry1080ti's avatar
harry1080ti committed
346
347
        
        if self.valid_sampling_percentage > 0:
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
            directory_path = pc.RESULT_SCREENSHOT_PATH + \
                str(self.topology_file.replace(".csv", "")) + "/" + \
                "pack_size_" + str(self.max_pack_size) + "/" + \
                "linear_penalty_constant_" + str(pp.PENALTY_CONSTANT) + "/"
            makedirs(directory_path, exist_ok = True)
            pngFileName = "k=" + str(self.k) + "_max=" + str(self.max_res_unit) \
                + ".png"
            packing_penalty = pp.packingPenalty(res, self.max_pack_size)
            if packing_penalty == 0 and output_png:
                bin_area = self.max_pack_size ** 2
                packed_area = 0
                for rect in res:
                    square_area = rect ** 2
                    packed_area += square_area
                percentage_wasted = 100 * (bin_area - packed_area) / bin_area
                consumed_area = 0
                pp.printPNG(res, self.max_pack_size, directory_path + pngFileName)
harry1080ti's avatar
harry1080ti committed
365
        
harry1080ti's avatar
harry1080ti committed
366
367
        result = [self.target_col,self.i,self.k, self.topology_file, 1,(1/max_latency), max_latency*self.k, 1/full_latency[full_max_idx], full_latency[full_max_idx], (1/max_latency)/(1/full_latency[full_max_idx]), (max_latency*self.k)/full_latency[full_max_idx], layer, res, self.end-self.start, self.es.sigma, self.seed_od,self.valid_sampling_percentage, self.trial, self.population_size, self.max_res_unit, self.seeding_type]
        return True, result
harry1080ti's avatar
harry1080ti committed
368
369
370
371
372
373
374
375
376
377
378
379

if __name__ == "__main__":
    import csv
    import sys

    topology = sys.argv[1]
    k = int(sys.argv[2])
    population_size = int(sys.argv[3])
    max_res_unit = int(sys.argv[4])
    seeding_type = sys.argv[5]
    target_col = sys.argv[6]

380
    es_hybrid = cma_approach(
harry1080ti's avatar
harry1080ti committed
381
382
383
384
385
386
387
388
389
390
391
        path_to_datasrc = str(topology)+"_square_mem_bound.csv",
        path_to_topology = str(topology)+".csv",
        target_col = str(target_col),

        number_of_partition = k, max_iteration = 10000,
        sigma = 0.5, population_size = population_size,

        max_res_unit = max_res_unit, initial_res = 0,
        res_step = 3,
        penalty_offest = 100000000000,
        seeding_type = seeding_type,
392
        hybrid = True,
harry1080ti's avatar
harry1080ti committed
393
        print_to_csv = True
harry1080ti's avatar
harry1080ti committed
394
395
396
    )

    trials = 1
397
398
399
    es_hybrid.run()
    while not es_hybrid.report(True) and trials < 20:
        es_hybrid.run()
harry1080ti's avatar
harry1080ti committed
400
401
        trials += 1

harry1080ti's avatar
harry1080ti committed
402
    # k += 1
harry1080ti's avatar
harry1080ti committed
403
    #print("convergence takes", trials, "trials")