python_script/read_rocprof.csv at main · kangwangamd/python_script · GitHub

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
#!/usr/bin/env python
import csv
profile_path = 'blas_problem03.stats.csv'

def get_csv_name_calls(file_path):
    res = dict()
    with open(file_path, newline='') as csvfile:
        profreader = csv.reader(csvfile, delimiter=',')
        next(profreader, None)
        for row in profreader:
            key = row[0]
            calls = int(row[1])
            duration = int(row[2])
            res[key] = [calls, duration]
    return res


def update_data(file1, file2):
    stats= dict()
    with open(file1, newline='') as csvfile:
        profreader = csv.reader(csvfile, delimiter=',')
        next(profreader, None)
        for row in profreader:
            key = row[0]
            calls = int(row[1])
            TotalDuration = int(row[2])
            stats[key] = [calls, TotalDuration]
    with open(file2, newline='') as csvfile:
        profreader = csv.reader(csvfile, delimiter=',')
        next(profreader, None)
        for row in profreader:
            key = row[0]
            calls = int(row[1])
            TotalDuration = int(row[2])
            stats[key][0] += calls
            stats[key][1] += TotalDuration
    res = dict()
    for key, val in stats.items():
        res[key] = val[1] / val[0]
    return res

import collections
def classify(prof):
    res = {'RCCL': [0, 0],
            'GEMM': [0, 0],
            'Flash_attention': [0, 0],
            'element_wise': [0, 0],
            'reduce_kernel': [0, 0],
            'layer_norm': [0, 0],
            'barrier': [0, 0],
            'others': [0, 0]
            }
    for key, val in prof.items():
        call, duration = val
        if 'ccl' in key.lower():
            res['RCCL'][0] += call
            res['RCCL'][1] += duration
        elif 'cijk' in key.lower():
            res['GEMM'][0] += call
            res['GEMM'][1] += duration
        elif 'gemm_softmax_gemm' in key.lower():
            res['Flash_attention'][0] += call
            res['Flash_attention'][1] += duration
        elif 'elementwise' in key.lower():
            res['element_wise'][0] += call
            res['element_wise'][1] += duration
        elif 'reduce_kernel' in key.lower():
            res['reduce_kernel'][0] += call
            res['reduce_kernel'][1] += duration
        elif 'layer_norm' in key.lower():
            res['layer_norm'][0] += call
            res['layer_norm'][1] += duration
        elif 'barrier' in key.lower():
            res['barrier'][0] += call
            res['barrier'][1] += duration
        else:
            res['others'][0] += call
            res['others'][1] += duration
    return res

prof1_calls = get_csv_name_calls(profile_path)
print(prof1_calls)
order = ['RCCL', 'GEMM', 'Flash_attention', 'element_wise', 'reduce_kernel', 'layer_norm', 'barrier', 'others']

for key in order:
    val = classify(prof1_calls)[key]
    print(key, val[0], val[1])


'''
prof2_calls = get_csv_name_calls(path + profile_2)
for key in order:
    val = classify(prof2_calls)[key]
    print(key, val[0], val[1])
'''

'''
new_stats = update_data(path + profile_1, path + profile_2)

#print(len(new_stats))
# usd prof_with_2_batch remove the one in prof_with_1batch
for key in prof2_calls:
    if key not in prof1_calls:
        print('ERROR')
    else:
        val_2 = prof2_calls[key]
        val_1 = prof1_calls[key]
        if val_2 >= val_1:
            prof2_calls[key] = val_2 - val_1

one_batch_no_overlap = {
        'RCCL': 0.0,
        'GEMM': 0.0,
        'Flash_attention': 0.0,
        'element_wise': 0.0,
        'reduce_kernel': 0.0,
        'layer_norm': 0.0,
        'others': 0.0
        }

for key, calls in prof2_calls.items():
    #one_batch_no_overlap[key] = new_stats[key] * calls
    if 'ccl' in key.lower():
        one_batch_no_overlap['RCCL'] += new_stats[key] * calls
    elif 'cijk' in key.lower():
        one_batch_no_overlap['GEMM'] += new_stats[key] * calls
    elif 'gemm_softmax_gemm' in key.lower():
        one_batch_no_overlap['Flash_attention'] += new_stats[key] * calls
    elif 'elementwise' in key.lower():
        one_batch_no_overlap['element_wise'] += new_stats[key] * calls
    elif 'reduce_kernel' in key.lower():
        one_batch_no_overlap['reduce_kernel'] += new_stats[key] * calls
    elif 'layer_norm' in key.lower():
        one_batch_no_overlap['layer_norm'] += new_stats[key] * calls
    elif 'barrier' not in key.lower():
        one_batch_no_overlap['others'] += new_stats[key] * calls
    else:
        continue
        # print('only barrier can go HERE!!')
        # print('key=', key)
total_time = sum(one_batch_no_overlap.values())
print(total_time)
for key, val in one_batch_no_overlap.items():
    print(key, val, val/total_time)
'''