def replay_dir(request):
def make_replay(tactic):
return TacticReplayData().add(
"layer0",
Algorithm.from_trt(fake_context("layer0"), fake_algo(0, tactic)))
with tempfile.TemporaryDirectory() as dir:
def make_path(prefix, *args):
path = os.path.join(dir, prefix)
if request.param:
path = os.path.join(path, request.param)
path = os.path.join(path, *args)
return path
# Good tactics
save_json(make_replay(0), make_path("good", "0.json"))
save_json(make_replay(1), make_path("good", "1.json"))
# Bad tactics
save_json(make_replay(1), make_path("bad", "0.json"))
save_json(make_replay(2), make_path("bad", "1.json"))
EXPECTED_OUTPUT = dedent("""
[I] Loaded 2 good tactic replays.
[I] Loaded 2 bad tactic replays.
[I] Found potentially bad tactics:
[I] Layer: layer0
Algorithms: ["(Implementation: 0, Tactic: 2) | Inputs: (('TensorFormat.LINEAR', 'DataType.FLOAT'),) | Outputs: (('TensorFormat.LINEAR', 'DataType.FLOAT'),)"]
""")
yield dir, EXPECTED_OUTPUT
def __init__(self, arr):
"""
Args:
arr (np.ndarray): The NumPy array.
"""
self.arr = None
self.tmpfile = None
if config.ARRAY_SWAP_THRESHOLD_MB >= 0 and arr.nbytes > (config.ARRAY_SWAP_THRESHOLD_MB << 20):
self.tmpfile = tempfile.NamedTemporaryFile(mode="w+", suffix=".json")
G_LOGGER.extra_verbose("Evicting large array ({:.3f} MiB) from memory and saving to {:}".format(
arr.nbytes / (1024.0 ** 2), self.tmpfile.name))
save_json(arr, self.tmpfile)
else:
self.arr = arr
def run(self, args):
import pickle
import polygraphy
from polygraphy.comparator.struct import RunResults
class LegacyRunResults(list):
pass
polygraphy.comparator.struct.RunResults = LegacyRunResults
with open(args.pickle_data, "rb") as f:
data = pickle.load(f)
if isinstance(data, LegacyRunResults):
data = RunResults(list(data))
save_json(data, args.output)
def set_input_metadata(self, input_metadata):
"""
Set the input metadata for the data loader.
Args:
input_metadata (TensorMetadata):
Input Metadata, including shape and type information. The cache may attempt to transform inputs to
match the specified input_metadata when data already in the cache does not exactly match.
"""
self.input_metadata = input_metadata
with contextlib.suppress(AttributeError):
self.data_loader.input_metadata = input_metadata
if not self.cache:
G_LOGGER.verbose("Loading inputs from data loader")
self.cache = list(self.data_loader)
if not self.cache:
G_LOGGER.warning("Data loader did not yield any input data.")
# Only save inputs the first time the cache is generated
if self.save_inputs_path is not None:
save_json(self.cache, self.save_inputs_path, "inference input data")