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
|
from contextlib import contextmanager
from typing import Any, List, Tuple, cast
import random
import torch
import time
from torch.utils.benchmark import Timer
def extract_ir(filename: str) -> List[str]:
BEGIN = "<GRAPH_EXPORT>"
END = "</GRAPH_EXPORT>"
pfx = None
current = ""
graphs = []
with open(filename, "r") as f:
split_strs = f.read().split(BEGIN)
for i, split_str in enumerate(split_strs):
if i == 0:
continue
end_loc = split_str.find(END)
if end_loc == -1:
continue
s = split_str[:end_loc]
pfx = split_strs[i - 1].splitlines()[-1]
lines = [x[len(pfx):] for x in s.splitlines(keepends=True)]
graphs.append(''.join(lines))
return graphs
def make_tensor_from_type(inp_type: torch._C.TensorType):
size = inp_type.sizes()
stride = inp_type.strides()
device = inp_type.device()
dtype = inp_type.dtype()
assert size is not None
assert stride is not None
assert device is not None
assert dtype is not None
return torch.empty_strided(size=size, stride=stride, device=device, dtype=dtype)
def load_graph_and_inputs(ir: str) -> Tuple[Any, List[Any]]:
graph = torch._C.parse_ir(ir, parse_tensor_constants=True)
graph.makeMultiOutputIntoTuple()
inputs = []
for inp in graph.inputs():
if isinstance(inp.type(), torch._C.FloatType):
inputs.append(random.uniform(.1, 100))
elif isinstance(inp.type(), torch._C.IntType):
inputs.append(random.randint(1, 100))
elif isinstance(inp.type(), torch._C.TensorType):
tensorType = cast(torch._C.TensorType, inp.type())
inputs.append(make_tensor_from_type(tensorType))
elif isinstance(inp.type(), torch._C.BoolType):
inputs.append(random.randint(0, 1) == 1)
else:
raise NotImplementedError(f"A default value is not implemented for type {inp.type()}")
func = torch._C._create_function_from_graph("forward", graph)
torch._C._jit_pass_erase_shape_information(func.graph)
return (func, inputs)
def time_cuda(fn, inputs, test_runs):
t = Timer(stmt="fn(*inputs)", globals={"fn": fn, "inputs" : inputs})
times = t.blocked_autorange()
return times.median * 1000 # time in ms
def time_cpu(fn, inputs, test_runs):
s = time.perf_counter()
for _ in range(test_runs):
fn(*inputs)
e = time.perf_counter()
return (e - s) / test_runs * 1000 # time in ms
def run_test(ir, inputs, *, warmup_runs=10, test_runs=20) -> float:
graph, _ = load_graph_and_inputs(ir)
for _ in range(warmup_runs):
graph(*inputs)
is_cpu = None
for input in inputs:
if isinstance(input, torch.Tensor):
is_cpu = input.device.type == "cpu"
break
assert is_cpu is not None
out = time_cpu(graph, inputs, test_runs) if is_cpu else time_cuda(graph, inputs, test_runs)
return out
@contextmanager
def no_fuser(*args, **kwargs):
old_optimize = torch._C._get_graph_executor_optimize(False)
try:
yield
finally:
torch._C._get_graph_executor_optimize(old_optimize)
def run_baseline_no_fusion(ir, inputs) -> float:
with no_fuser():
return run_test(ir, inputs)
def run_nnc(ir, inputs, dynamic) -> float:
try:
strat = [("DYNAMIC", 10)] if dynamic else [("STATIC", 10)]
old_strat = torch.jit.set_fusion_strategy(strat)
with torch.jit.fuser("fuser1"):
return run_test(ir, inputs)
finally:
torch.jit.set_fusion_strategy(old_strat)
def run_nvfuser(ir, inputs) -> float:
with torch.jit.fuser("fuser2"):
return run_test(ir, inputs)
|
