def replay(request):
"""
Returns:
Tuple[FakeAlgorithmContext, Algorithm, FakeAlgorithm,
Union[str, TacticReplayData], Union[str, TacticReplayData]]:
This fixture returns 5 things:
1. A fake TensorRT algorithm context
2. A Polygraphy Algorithm instance
3. A fake TensorRT algorithm (with the same information as (2))
4. An input tactic replay data, populated with the Polygraphy Algorithm (2), either
as a ``TacticReplayData`` instance, or a path.
5. An output tactic replay data, empty, either as a ``TacticReplayData`` instance, or
a path.
"""
jsonify = request.param
name = "node_of_y"
context = fake_context(name)
trt_algo = fake_algo()
poly_algo = Algorithm.from_trt(context, trt_algo)
in_replay_data = TacticReplayData().add(name, poly_algo)
out_replay_data = TacticReplayData()
if jsonify:
inpath = util.NamedTemporaryFile("w")
in_replay_data.save(inpath.name)
in_replay_data = inpath.name
outpath = util.NamedTemporaryFile("r")
out_replay_data = outpath.name
yield context, poly_algo, trt_algo, in_replay_data, out_replay_data
def test_cannot_save_load_to_different_types(self):
run_result = JSONABLE_CASES[0]
encoded = run_result.to_json()
with pytest.raises(PolygraphyException,
match="JSON cannot be decoded into"):
TacticReplayData.from_json(encoded)
def test_tactics(self, trt_config_args, opt, cls):
with util.NamedTemporaryFile("w+", suffix=".json") as f:
if opt == "--load-tactics":
TacticReplayData().save(f)
trt_config_args.parse_args([opt, f.name])
builder, network = create_network()
with builder, network, trt_config_args.create_config(builder, network=network) as config:
recorder = config.algorithm_selector
assert recorder.make_func == cls
assert recorder.path == f.name
def make_iter_result():
return IterationResult(
runtime=4.5,
runner_name="test",
outputs={
"out0": np.random.random_sample((1, 2, 1)),
"out1": np.ones((1, 2), dtype=np.float32),
},
)
JSONABLE_CASES = [
RunResults([("runner0", [make_iter_result()]),
("runner0", [make_iter_result()])]),
TacticReplayData().add("hi", algorithm=make_algo()),
]
class TestImplementations(object):
@pytest.mark.parametrize(
"obj",
[
Algorithm(
implementation=4,
tactic=5,
inputs=[(trt.TensorFormat.LINEAR, trt.float32)],
outputs=[(trt.TensorFormat.LINEAR, trt.float32)],
),
Algorithm(
implementation=4,
def make_replay(tactic):
return TacticReplayData().add(
"layer0",
Algorithm.from_trt(fake_context("layer0"), fake_algo(0, tactic)))