File size: 5,193 Bytes
02c783d | 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 | # Modifications Copyright(C)[2025] Advanced Micro Devices, Inc. All rights reserved.
# https://github.com/thunlp/TritonBench - Apache License 2.0
import torch
import triton
import triton.language as tl
from typing import Callable
import json
import os
class do_bench_config():
def __init__(
self,
warm_up=25,
repetition=100,
grad_to_none=None,
quantiles=[0.5, 0.8, 0.2],
return_mode="median"
):
self.warm_up = warm_up
self.repetition = repetition
self.grad_to_none = grad_to_none
self.quantiles = quantiles
self.return_mode = return_mode
class Performance_Metrics:
def __init__(
self,
op_name,
dtype=None,
is_backward=False,
**kwargs
):
self.op_name = op_name
self.dtype = dtype
if is_backward:
self.op_name += 'backward'
self.kwargs = kwargs
self.input_tensors = []
self.do_bench_config = do_bench_config()
def get_input_tensors(self):
raise NotImplementedError("You must implement this method to get input tensors")
def to_cuda(self, input_tensor):
raise NotImplementedError("You must implement this method to get input tensors")
def call_op(self, input_tensor):
raise NotImplementedError("You must implement this method to call the op")
def get_do_bench_config(self, warmup=None, rep=None):
if warmup != None and rep != None:
self.do_bench_config = do_bench_config(
warm_up=warmup,
repetition=rep,
)
return
if self.input_tensors == []:
raise NotImplementedError("You must implement this method to get input_tensors")
previous_ms = None
epsilon = 1e-4
stable_count = 0
max_stable_count = 3
input_tensor = self.to_cuda(self.input_tensors[-1])
for t in range(1, 11):
warmup = 100 * t
rep = 1000 * t
ms, min_ms, max_ms = triton.testing.do_bench(
lambda: self.call_op(input_tensor),
warmup=warmup,
rep=rep,
quantiles=[0.5, 0.8, 0.2],
return_mode="median"
)
print("warmup time:", warmup, "rep time:", rep, "runtime:", ms)
if previous_ms is not None:
relative_change = abs(ms - previous_ms) / abs(previous_ms) if previous_ms != 0 else float('inf')
if relative_change < epsilon:
stable_count += 1
else:
stable_count = 0
if stable_count >= max_stable_count:
print(f"MS stabilized with warmup={warmup} and rep={rep}")
self.do_bench_config = do_bench_config(
warm_up=warmup,
repetition=rep,
)
return
previous_ms = ms
print("MS did not stabilize. Returning default config.")
raise NotImplementedError("You must implement this method to make the runtime stable")
def get_runtime(self, op: Callable):
ms, min_ms, max_ms = triton.testing.do_bench(
op,
warmup=self.do_bench_config.warm_up,
rep=self.do_bench_config.repetition,
quantiles=self.do_bench_config.quantiles,
return_mode=self.do_bench_config.return_mode
)
return ms
def get_gbps(self, input_tensor, runtime):
raise NotImplementedError("You must implement this method to get the method to calculate GBPS")
def get_tflops(self, input_tensor, runtime):
raise NotImplementedError("You must implement this method to get the method to calculate TFLOPS")
def run_benchmark(self):
results = []
for input_tensor_ in self.input_tensors:
try:
input_tensor = self.to_cuda(input_tensor_)
# print(input_tensor)
op = lambda : self.call_op(input_tensor)
ms = self.get_runtime(op)
gbps = self.get_gbps(input_tensor, ms)
tflops = self.get_tflops(input_tensor, ms)
result = {
"input_size": [item.shape if type(item)==torch.Tensor else item for item in input_tensor],
"ms": ms,
"GB/s": gbps,
"TFLOPS": tflops
}
print(result)
results.append(result)
except Exception as e:
print(f"Failed to run benchmark for input tensor. Error: {e}")
input_tensor = None
folder_path = "/home/vinajosh/code/TB-eval/expts/O1-par-scale-output/O1_dvue-aoai-001-o1_2024-12-01-preview_TritonBench_G_comp_alpac_v1_fixed_with_difficulty_passk_0/exec/gen_perf"
file_name = self.op_name + ".json"
file_path = os.path.join(folder_path, file_name)
with open(file_path, 'w', encoding='utf8') as f:
json.dump(results, f, indent=4)
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