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import torch
import triton
import triton.language as tl
from typing import Callable
import json
import os
import random
def get_random_choice(item_list):
return random.choice(item_list)
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.ref_op_name = op_name + '_ref'
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 call_op_ref(self, input_tensor):
raise NotImplementedError("You must implement this method to call the reference 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 last config.")
self.do_bench_config = do_bench_config(
warm_up=warmup,
repetition=rep,
)
return
# 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 check_close(self, a, b, rtol=1e-05, atol=1e-08):
if isinstance(a, (list, tuple)):
return all(self.check_close(x, y, rtol=rtol, atol=atol) for x, y in zip(a, b))
if isinstance(a, dict):
return all(key in b and self.check_close(a[key], b[key], rtol=rtol, atol=atol) for key in a)
if isinstance(a, torch.Tensor) and isinstance(b, torch.Tensor):
return torch.allclose(a, b, rtol=rtol, atol=atol)
return a == b
def get_num_elements(self, input_tensor):
if isinstance(input_tensor, (list, tuple)):
return sum(self.get_num_elements(x) for x in input_tensor)
if isinstance(input_tensor, dict):
return sum(self.get_num_elements(v) for v in input_tensor.values())
if isinstance(input_tensor, torch.Tensor):
return input_tensor.numel()
return 1
def run_benchmark(self):
results = []
perf = []
perf_ref = []
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)
op_ref = lambda : self.call_op_ref(input_tensor)
## Keep dummy initial calls to converge to optimal triton autotune configs regardless it exists or not!
output = self.call_op(input_tensor)
output_ref = self.call_op_ref(input_tensor)
## The following calls should be using the optimal triton autotune configs for given inputs!
output = self.call_op( input_tensor.clone() )
output_ref = self.call_op_ref( input_tensor.clone() )
if not self.check_close(output, output_ref, rtol=1e-3, atol=1e-3):
print(f"Failed to run benchmark for input tensor. Error: {e}")
return False, f"Output mismatch between the operation and its reference implementation for input tensor shape"
# Randomly choose which operation to run first
# to avoid any bias in the performance measurement
if get_random_choice([0, 1]) == 0:
ms = self.get_runtime(op)
ms_ref = self.get_runtime(op_ref)
else:
ms_ref = self.get_runtime(op_ref)
ms = self.get_runtime(op)
gbps = self.get_gbps(input_tensor, ms)
tflops = self.get_tflops(input_tensor, ms)
result = {
"input_size": self.get_num_elements(input_tensor_),
"ms": ms,
"ms_ref": ms_ref,
"GB/s": gbps,
"TFLOPS": tflops
}
# print(result)
results.append(result)
perf.append(ms)
perf_ref.append(ms_ref)
except Exception as e:
print(f"Failed to run benchmark for input tensor. Error: {e}")
return False, f"Failed to run benchmark for an input tensor shape due to {e}"
input_tensor = None
## calculate average performance
if perf and perf_ref:
avg_perf = sum(perf_ref) / sum(perf)
results.append({
"speedup": avg_perf
})
print(f"```json\n{json.dumps(results, indent=4)}\n```")
return True, f"```json\n{json.dumps(results, indent=4)}\n```"
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