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
174
175
176
177
178
179
180
181
182
183
184
185
|
#################################################################################################
#
# Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#################################################################################################
"""
Profiler based on the cuda events
"""
import re
import subprocess
from cuda import cuda, cudart
import numpy as np
from cutlass import CUTLASS_PATH
from cutlass.backend.library import DataTypeSize
from cutlass.op.op import OperationBase
from cutlass.shape import GemmCoord
from cutlass.utils.datatypes import is_numpy_tensor
class GpuTimer:
def __init__(self) -> None:
self.events = [
cuda.cuEventCreate(cuda.CUevent_flags.CU_EVENT_DEFAULT)[1],
cuda.cuEventCreate(cuda.CUevent_flags.CU_EVENT_DEFAULT)[1],
]
def start(self, stream=cuda.CUstream(0)):
(err,) = cuda.cuEventRecord(self.events[0], stream)
if err != cuda.CUresult.CUDA_SUCCESS:
raise RuntimeError(f"CUDA Error {str(err)}")
def stop(self, stream=cuda.CUstream(0)):
(err,) = cuda.cuEventRecord(self.events[1], stream)
if err != cuda.CUresult.CUDA_SUCCESS:
raise RuntimeError(f"CUDA Error {str(err)}")
pass
def stop_and_wait(self, stream=cuda.CUstream(0)):
self.stop(stream)
if stream:
(err,) = cuda.cuStreamSynchronize(stream)
if err != cuda.CUresult.CUDA_SUCCESS:
raise RuntimeError(f"CUDA Error {str(err)}")
else:
(err,) = cudart.cudaDeviceSynchronize()
if err != cuda.CUresult.CUDA_SUCCESS:
raise RuntimeError(f"CUDA Error {str(err)}")
def duration(self, iterations=1):
err, duration = cuda.cuEventElapsedTime(self.events[0], self.events[1])
if err != cuda.CUresult.CUDA_SUCCESS:
raise RuntimeError(f"CUDA Error {str(err)}")
return duration / float(iterations)
class CUDAEventProfiler:
def __init__(self, op: OperationBase, warmup_iterations: int=500, iterations: int=500, *args, **kwargs) -> None:
self.arguments = op.run(*args, **kwargs)
self.operation = op.operation
self.warmup_iterations = warmup_iterations
self.iterations = iterations
self.timer = GpuTimer()
#
# Cutlass Python Interface Profiler
#
def __call__(self):
for _ in range(self.warmup_iterations):
self.operation.run(self.arguments)
self.timer.start()
for _ in range(self.iterations):
self.operation.run(self.arguments)
self.timer.stop_and_wait()
runtime = self.timer.duration(self.iterations)
return runtime
#
# CUTLASS Profiler
#
def run_cutlass_profiler(self):
alpha = 1.0
beta = 1.0
profiler_path = CUTLASS_PATH + "/build/tools/profiler/cutlass_profiler"
kernel_name = self.operation.procedural_name()
verification_providers = "device"
provider = "cutlass"
problem_size = self.arguments.problem_size
if "cutlass3x" in kernel_name:
# cutlass3x generator only have column-major output
layout_name = self.operation.layout_name_3x()
if layout_name[-1] == "t":
new_layout_name = "".join(["n" for l in layout_name if l == "t" or "t"])
problem_size = GemmCoord(problem_size.n, problem_size.m, problem_size.k)
kernel_name = kernel_name.replace(layout_name, new_layout_name)
batch_count = self.arguments.batch_count
cmd = f"{profiler_path} --kernels={kernel_name} --verification-providers={verification_providers} " \
f"--providers={provider} --m={problem_size.m()} --n={problem_size.n()} --k={problem_size.k()} " \
f"--batch_count={batch_count} --alpha={alpha} --beta={beta} "\
f"--warmup-iterations={self.warmup_iterations} --profiling-iterations={self.iterations}"
result = subprocess.getoutput(cmd)
m = re.search(r"Runtime:\s+(?P<runtime>\d+.\d+)", result)
runtime = float(m.group("runtime"))
m = re.search(r"Bytes:\s+(?P<bytes>\d+)", result)
bytes = int(m.group("bytes"))
m = re.search(r"FLOPs:\s+(?P<flops>\d+)", result)
flops = int(m.group("flops"))
# check if the problem size matches
assert bytes == self.bytes(problem_size, batch_count, beta)
assert flops == self.flops(problem_size, batch_count, beta)
return runtime
def bytes(self, problem_size, batch_count=1, beta=0.0):
m = problem_size.m()
n = problem_size.n()
k = problem_size.k()
bytes = (
(DataTypeSize[self.operation.A.element] * m // 8) * k
+ (DataTypeSize[self.operation.B.element] * n // 8) * k
+ (DataTypeSize[self.operation.C.element] * m // 8) * n
)
if beta != 0:
bytes += (DataTypeSize[self.operation.C.element] * m // 8) * n
bytes *= batch_count
return bytes
def flops(self, problem_size, batch_count=1, beta=0.0):
m = problem_size.m()
n = problem_size.n()
k = problem_size.k()
flops_ = (m * n * k) * 2 * batch_count
if beta != 0:
flops_ += m * n * batch_count * 2
return flops_
|
