def monotonic_increasing_ints(draw: Any) -> NDArray:
# Draw increasing ints with repeats, e.g. [0, 0, 5, 7, 7, 7]
n = draw(st.integers(min_value=0, max_value=5))
repeats = draw(
st_arrays(dtype=int, shape=n, elements=st.integers(min_value=1, max_value=10))
)
values = draw(
st_arrays(dtype=int, shape=n, elements=st.integers(min_value=0, max_value=10))
)
values = np.cumsum(values)
return np.repeat(values, repeats)
def init_fields(self, data, backend):
self.nx = data.draw(hyp_st.integers(min_value=7, max_value=32),
label="nx")
self.ny = data.draw(hyp_st.integers(min_value=7, max_value=32),
label="ny")
self.nz = data.draw(hyp_st.integers(min_value=1, max_value=32),
label="nz")
shape = (self.nx, self.ny, self.nz)
self.in_phi = gt_storage.from_array(
data.draw(st_arrays(dtype=float, shape=shape)),
backend=backend,
default_origin=(0, 0, 0),
dtype=float,
)
self.in_u = gt_storage.from_array(
data.draw(st_arrays(dtype=float, shape=shape)),
backend=backend,
default_origin=(0, 0, 0),
dtype=float,
)
self.in_v = gt_storage.from_array(
data.draw(st_arrays(dtype=float, shape=shape)),
backend=backend,
default_origin=(0, 0, 0),
dtype=float,
)
self.tmp_phi = gt_storage.from_array(
data.draw(st_arrays(dtype=float, shape=shape)),
backend=backend,
default_origin=(1, 1, 0),
dtype=float,
)
self.out_phi = gt_storage.from_array(
data.draw(st_arrays(dtype=float, shape=shape)),
backend=backend,
default_origin=(3, 3, 0),
dtype=float,
)
self.alpha = 1 / 32
def test_small(client, model_data, hypothesis_data):
"""Test Triton-served model on many small Hypothesis-generated examples"""
all_model_inputs = defaultdict(list)
total_output_sizes = {}
all_triton_outputs = defaultdict(list)
default_arrays = {
name: np.random.rand(TOTAL_SAMPLES, *shape).astype('float32')
for name, shape in model_data.input_shapes.items()
}
for i in range(TOTAL_SAMPLES):
model_inputs = {
name: hypothesis_data.draw(
st.one_of(
st.just(default_arrays[name][i:i+1, :]),
st_arrays('float32', [1] + shape)
)
) for name, shape in model_data.input_shapes.items()
}
if model_data.name == 'sklearn' or model_data.name == 'xgboost_shap':
for array in model_inputs.values():
assume(not np.any(np.isnan(array)))
model_output_sizes = {
name: size
for name, size in model_data.output_sizes.items()
}
shared_mem = hypothesis_data.draw(st.one_of(
st.just(mode) for mode in valid_shm_modes()
))
result = client.predict(
model_data.name, model_inputs, model_data.output_sizes,
shared_mem=shared_mem
)
for name, input_ in model_inputs.items():
all_model_inputs[name].append(input_)
for name, size in model_output_sizes.items():
total_output_sizes[name] = total_output_sizes.get(name, 0) + size
for name, output in result.items():
all_triton_outputs[name].append(output)
all_model_inputs = {
name: np.concatenate(arrays)
for name, arrays in all_model_inputs.items()
}
all_triton_outputs = {
name: np.concatenate(arrays)
for name, arrays in all_triton_outputs.items()
}
try:
ground_truth = model_data.ground_truth_model.predict(all_model_inputs)
except Exception:
assume(False)
for output_name in sorted(ground_truth.keys()):
if model_data.ground_truth_model.predict_proba:
arrays_close(
all_triton_outputs[output_name],
ground_truth[output_name],
rtol=1e-3,
atol=1e-2,
assert_close=True
)
else:
arrays_close(
all_triton_outputs[output_name],
ground_truth[output_name],
atol=0.1,
total_atol=3,
assert_close=True
)
# Test entire batch of Hypothesis-generated inputs at once
shared_mem = hypothesis_data.draw(st.one_of(
st.just(mode) for mode in valid_shm_modes()
))
all_triton_outputs = client.predict(
model_data.name, all_model_inputs, total_output_sizes,
shared_mem=shared_mem
)
for output_name in sorted(ground_truth.keys()):
if model_data.ground_truth_model.predict_proba:
arrays_close(
all_triton_outputs[output_name],
ground_truth[output_name],
rtol=1e-3,
atol=1e-2,
assert_close=True
)
else:
arrays_close(
all_triton_outputs[output_name],
ground_truth[output_name],
atol=0.1,
total_atol=3,
assert_close=True
)