From 41a88da85e811c9d69fbd5f1620faa6860947f36 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ren=C3=A9=20He=C3=9F?= <rene.hess@iwr.uni-heidelberg.de>
Date: Thu, 4 Apr 2019 15:26:18 +0200
Subject: [PATCH] [skip ci] Pass transformation string to autotuning
This can be used to distinguish between kernels with the same name but
different transformations.
---
python/dune/codegen/sumfact/autotune.py | 27 +++++++++++--
.../dune/codegen/sumfact/transformations.py | 38 +++++++++++++------
2 files changed, 51 insertions(+), 14 deletions(-)
diff --git a/python/dune/codegen/sumfact/autotune.py b/python/dune/codegen/sumfact/autotune.py
index 64d9d43a..393b21ff 100644
--- a/python/dune/codegen/sumfact/autotune.py
+++ b/python/dune/codegen/sumfact/autotune.py
@@ -304,8 +304,14 @@ def generate_standalone_code(sf, filename):
set_option("opcounter", opcounting)
-def generate_standalone_kernel_code(kernel, signature, filename):
+def generate_standalone_kernel_code(kernel, signature, filename, transformations=None):
with open(filename, 'w') as f:
+ if transformations:
+ f.write('// Transformations:\n')
+ for trafo in transformations:
+ f.write('// {}\n'.format(trafo))
+ f.write('\n')
+
# Write headers
headers = ['#include "config.h"',
'#include <iostream>',
@@ -410,7 +416,17 @@ def generate_standalone_kernel_code(kernel, signature, filename):
f.write('\n'.join(main))
-def autotune_realization(sf=None, kernel=None, signature=None):
+def autotune_realization(sf=None, kernel=None, signature=None, transformations=None):
+ """Generate an microbenchmark, compile run and return time
+
+ Parameters
+ ----------
+ sf: SumfactKernel or VectorizedSumfactKernel
+ kernel: loopy.kernel.LoopKernel
+ signature: str
+ transformation: list of str
+ Will be used to distinguish between autotune targets
+ """
if sf is None:
assert kernel is not None
assert signature is not None
@@ -427,6 +443,9 @@ def autotune_realization(sf=None, kernel=None, signature=None):
basename = "autotune_sumfact_{}".format(kernel.name)
else:
basename = "autotune_sumfact_{}".format(sf.function_name)
+ if transformations:
+ for trafo in transformations:
+ basename = '{}_{}'.format(basename, trafo)
basename = hashlib.sha256(basename.encode()).hexdigest()
filename = os.path.join(dir, "{}.cc".format(basename))
@@ -434,6 +453,8 @@ def autotune_realization(sf=None, kernel=None, signature=None):
lock = os.path.join(dir, "{}.lock".format(basename))
executable = os.path.join(dir, basename)
+ print("palpo filename: {}".format(filename))
+
# Generate and compile a benchmark program
#
# Note: cache restoring is only necessary when generating from SumfactKernel
@@ -441,7 +462,7 @@ def autotune_realization(sf=None, kernel=None, signature=None):
with filelock.FileLock(lock):
if not os.path.isfile(logname):
if sf is None:
- generate_standalone_kernel_code(kernel, signature, filename)
+ generate_standalone_kernel_code(kernel, signature, filename, transformations)
elif get_option("autotune_google_benchmark"):
generate_standalone_code_google_benchmark(sf, filename)
else:
diff --git a/python/dune/codegen/sumfact/transformations.py b/python/dune/codegen/sumfact/transformations.py
index b0a97923..8342a343 100644
--- a/python/dune/codegen/sumfact/transformations.py
+++ b/python/dune/codegen/sumfact/transformations.py
@@ -94,7 +94,7 @@ def move_zero_assignment_up(kernel, move_up_inames):
return kernel
-def reorder_loops_in_tensor_contraction(kernel, iname_order):
+def _reorder_loops_in_tensor_contraction_direct(kernel, iname_order):
"""Reorder the loop nest of the tensor contractions
iname_order is a string that specifies the loop order. We use the following convention:
@@ -172,12 +172,15 @@ def reorder_loops_in_tensor_contraction(kernel, iname_order):
return kernel
-def reorder_loops_in_tensor_contraction_with_accumulation_variable(kernel, iname_order):
+def _reorder_loops_in_tensor_contraction_accum(kernel, iname_order):
dim = world_dimension()
assert dim == 3
+ if iname_order.endswith('j'):
+ return kernel
+
# kernel = remove_all_reductions(kernel)
- kernel = reorder_loops_in_tensor_contraction(kernel, iname_order)
+ kernel = _reorder_loops_in_tensor_contraction_direct(kernel, iname_order)
cond = lp.match.Tagged('set_zero')
for instr in lp.find_instructions(kernel, cond):
@@ -281,17 +284,30 @@ def reorder_loops_in_tensor_contraction_with_accumulation_variable(kernel, iname
return kernel
+def reorder_loops_in_tensor_contraction(kernel, iname_order, accum_variable=True):
+ if accum_variable:
+ return _reorder_loops_in_tensor_contraction_accum(kernel, iname_order)
+ else:
+ return _reorder_loops_in_tensor_contraction_direct(kernel, iname_order)
+
+
def tensor_contraction_loop_order_generator(kernel):
dim = world_dimension()
assert dim == 3
- yield kernel
indices = ['l', 'k', 'i', 'j']
import itertools
for loop_order in itertools.permutations(indices):
- loop_order = ''.join(loop_order)
- new_kernel = reorder_loops_in_tensor_contraction(kernel, loop_order)
- yield new_kernel
+ # palpo TODO: Heavy culling for 'quick' tests during development
+ if loop_order[0] != 'l' or loop_order[1] != 'k':
+ continue
+
+ order = ''.join(loop_order)
+ new_kernel = reorder_loops_in_tensor_contraction(kernel, order, True)
+ yield new_kernel, ['reorder_loops_in_tensor_contraction_{}_True'.format(order),]
+
+ new_kernel = reorder_loops_in_tensor_contraction(kernel, loop_order, False)
+ yield new_kernel, ['reorder_loops_in_tensor_contraction_{}_False'.format(order),]
def simple_autotuner(kernel_generator, signature):
@@ -299,11 +315,11 @@ def simple_autotuner(kernel_generator, signature):
from dune.codegen.options import set_option
set_option("autotune_google_benchmark", True)
- kernel = next(kernel_generator)
- best_cost = autotune_realization(kernel=kernel, signature=signature)
+ kernel, transformations = next(kernel_generator)
+ best_cost = autotune_realization(kernel=kernel, signature=signature, transformations=transformations)
best_kernel = kernel
- for kernel in kernel_generator:
- cost = autotune_realization(kernel=kernel, signature=signature)
+ for kernel, transformations in kernel_generator:
+ cost = autotune_realization(kernel=kernel, signature=signature, transformations=transformations)
if cost < best_cost:
best_cost = cost
best_kernel = kernel
--
GitLab