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[CODEGEN] Fixed bug that caused conditional operator to not always

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properly mask load operations

Also includes minor improvement to benchmarking infrastructure
This commit is contained in:
Philippe Tillet
2021-03-07 14:53:48 -05:00
parent d1d09566b1
commit 5b9afaa688
9 changed files with 146 additions and 64 deletions
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75
python/triton/testing.py
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@@ -26,35 +26,34 @@ def allclose(x, y):
return err < tol
def do_bench(fn, warmup=10, rep=50, grad_to_none=None, clear_l2=False):
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
# warmup to put the clock in a stable regime
ret = fn()
for i in range(warmup):
fn()
torch.cuda.synchronize()
total_ms = 0
def do_bench(fn, warmup=10, rep=50, grad_to_none=None):
# We maintain a buffer of 256 MB that we clear
# before each kernel call to make sure that the L2
# doesn't contain any input data before the run
start_event = [torch.cuda.Event(enable_timing=True) for i in range(rep)]
end_event = [torch.cuda.Event(enable_timing=True) for i in range(rep)]
cache = torch.empty(int(256e6), dtype=torch.int8, device='cuda')
for i in range(rep):
for i in range(warmup + rep):
# we don't want `fn` to accumulate gradient values
# if it contains a backward pass. So we clear the
# provided gradients
if grad_to_none is not None:
grad_to_none.grad = None
# reset L2
# we clear the L2 cache before each run
cache.zero_()
# record time of `fn`
start_event.record()
if i >= warmup:
start_event[i - warmup].record()
fn()
end_event.record()
torch.cuda.synchronize()
total_ms += start_event.elapsed_time(end_event)
# return the average runtime of `fn`
return total_ms / rep
if i >= warmup:
end_event[i - warmup].record()
torch.cuda.synchronize()
times = torch.tensor([s.elapsed_time(e) for s, e in zip(start_event, end_event)])
q = torch.quantile(times, torch.tensor([0.1, 0.5, 0.9]))
min_ms = q[0].item()
mean_ms = q[1].item()
max_ms = q[2].item()
return mean_ms, min_ms, max_ms
class Benchmark:
@@ -79,22 +78,42 @@ class Mark:
import matplotlib.pyplot as plt
import pandas as pd
import os
df = pd.DataFrame(columns=[bench.x_names[0]] + bench.y_lines)
y_mean = bench.y_lines
y_min = [f'{x}-min' for x in bench.y_lines]
y_max = [f'{x}-max' for x in bench.y_lines]
df = pd.DataFrame(columns=[bench.x_names[0]] + y_mean + y_min + y_max)
for x in bench.x_vals:
x_args = {x_name: x for x_name in bench.x_names}
row = [self.fn(**x_args, **{bench.y_name: y}, **bench.args) for y in bench.y_vals]
df.loc[len(df)] = [x] + row
row_mean, row_min, row_max = [], [], []
for y in bench.y_vals:
ret = self.fn(**x_args, **{bench.y_name: y}, **bench.args)
try:
y_mean, y_min, y_max = ret
except TypeError:
y_mean, y_min, y_max = ret, None, None
row_mean += [y_mean]
row_min += [y_min]
row_max += [y_max]
df.loc[len(df)] = [x] + row_mean + row_min + row_max
if with_plot and bench.plot_name:
plt.figure()
ax = plt.subplot()
xlabel = " = ".join(bench.x_names)
plot = df.plot(x=bench.x_names[0], y=bench.y_lines)
plot.set_xlabel(xlabel)
plot.set_ylabel(bench.ylabel)
plot.set_title(bench.plot_name)
plot.set_xscale("log" if bench.loglog else "linear")
plot.set_yscale("log" if bench.loglog else "linear")
x = bench.x_names[0]
for y in bench.y_lines:
y_min, y_max = df[y + '-min'], df[y + '-max']
ax.plot(df[x], df[y], label=y)
if y_min is not None and y_max is not None:
ax.fill_between(df[x], y_min, y_max, alpha=0.5)
ax.legend()
ax.set_xlabel(xlabel)
ax.set_ylabel(bench.ylabel)
ax.set_title(bench.plot_name)
ax.set_xscale("log" if bench.loglog else "linear")
ax.set_yscale("log" if bench.loglog else "linear")
plt.savefig(os.path.join(result_path, f"{bench.plot_name}.png"))
df.to_csv(os.path.join(result_path, f"{bench.plot_name}.csv"))
df = df[[bench.x_names[0]] + bench.y_lines]
df.to_csv(os.path.join(result_path, f"{bench.plot_name}.csv"), float_format='%.1f', index=False)
def run(self, result_path, with_plot):
with open(os.path.join(result_path, "results.html"), "w") as html: