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refactor(autotuner): replace string format with f strings

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This commit is contained in:
Anthony Berg 2025-06-24 18:39:54 +02:00
parent 2726a4dfce
commit ae9d2b1595
1 changed files with 7 additions and 7 deletions
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14
GPUSimulators/Autotuner.py
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@ -46,7 +46,7 @@ def run_benchmark(simulator, arguments, timesteps=10, warmup_timesteps=2):
sim = simulator(**arguments)
except:
# An exception raised - not possible to continue
logger.debug("Failed creating %s with arguments %s", simulator.__name__, str(arguments))
logger.debug(f"Failed creating {simulator.__name__} with arguments {str(arguments)}")
# raise RuntimeError("Failed creating %s with arguments %s", simulator.__name__, str(arguments))
return np.nan
@ -183,7 +183,7 @@ class Autotuner:
block_widths=range(8, 32, 1),
block_heights=range(8, 32, 1)):
logger = logging.getLogger(__name__)
self.filename = "autotuning_data_" + gethostname() + ".npz"
self.filename = f"autotuning_data_{gethostname()}.npz"
self.nx = nx
self.ny = ny
self.block_widths = block_widths
@ -195,13 +195,13 @@ class Autotuner:
# Run through simulators and benchmark
key = str(simulator.__name__)
logger.info("Benchmarking %s to %s", key, self.filename)
logger.info(f"Benchmarking {key} to {self.filename}")
# If this simulator has been benchmarked already, skip it
if force == False and os.path.isfile(self.filename):
with np.load(self.filename) as data:
if key in data["simulators"]:
logger.info("%s already benchmarked - skipping", key)
logger.info(f"{key} already benchmarked - skipping")
return
# Set arguments to send to the simulators during construction
@ -258,12 +258,12 @@ class Autotuner:
else:
# Run simulation if required
if not os.path.isfile(self.filename):
logger.debug("Could not get autotuned peak performance for %s: benchmarking", key)
logger.debug(f"Could not get autotuned peak performance for {key}: benchmarking")
self.benchmark(simulator)
with np.load(self.filename) as data:
if key not in data['simulators']:
logger.debug("Could not get autotuned peak performance for %s: benchmarking", key)
logger.debug(f"Could not get autotuned peak performance for {key}: benchmarking")
data.close()
self.benchmark(simulator)
data = np.load(self.filename)
@ -280,7 +280,7 @@ class Autotuner:
self.performance[key] = {"block_width": block_widths[i],
"block_height": block_heights[j],
"megacells": megacells[j, i]}
logger.debug("Returning %s as peak performance parameters", self.performance[key])
logger.debug(f"Returning {self.performance[key]} as peak performance parameters")
return self.performance[key]
# This should never happen