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[PyOV] move rest of start_async tests to test_async #21643

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merged 1 commit into from
Dec 14, 2023
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[PyOV] move rest of start_async tests to test_async #21643

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132 changes: 121 additions & 11 deletions src/bindings/python/tests/test_runtime/test_async_infer_request.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
from copy import deepcopy
import numpy as np
import pytest
import time

import openvino.runtime.opset13 as ops
from openvino import (
Expand All @@ -18,7 +19,7 @@
Tensor,
)
from tests import skip_need_mock_op
from tests.utils.helpers import generate_image, get_relu_model, generate_model_with_memory
from tests.utils.helpers import generate_image, get_relu_model


def concat_model_with_data(device, ov_type, numpy_dtype):
Expand All @@ -43,16 +44,6 @@ def concat_model_with_data(device, ov_type, numpy_dtype):
return request, tensor1, array1


def create_model_with_memory(input_shape, data_type):
input_data = ops.parameter(input_shape, name="input_data", dtype=data_type)
rv = ops.read_value(input_data, "var_id_667", data_type, input_shape)
add = ops.add(rv, input_data, name="MemoryAdd")
node = ops.assign(add, "var_id_667")
res = ops.result(add, "res")
model = Model(results=[res], sinks=[node], parameters=[input_data], name="name")
return model


def abs_model_with_data(device, ov_type, numpy_dtype):
input_shape = [1, 4]
param = ops.parameter(input_shape, ov_type)
Expand Down Expand Up @@ -325,3 +316,122 @@ def test_shared_memory_deprecation(device, shared_flag):
with pytest.warns(FutureWarning, match="`shared_memory` is deprecated and will be removed in 2024.0"):
queue.start_async(input_data, shared_memory=shared_flag)
queue.wait_all()


@pytest.mark.skip(reason="Sporadically failed. Need further investigation. Ticket - 95967")
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@ilya-lavrenov ilya-lavrenov Dec 13, 2023 •
edited
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what kind of errors does it report?

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It sporadically failed in CI some time ago.
Unfortunately, I couldn't find what exactly the error was.
BTW, we have a ticket for this skip -- 95967.
I may try to remove skip after CF.

def test_cancel(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
img = generate_image()
request = compiled_model.create_infer_request()

request.start_async({0: img})
request.cancel()
with pytest.raises(RuntimeError) as e:
request.wait()
assert "[ INFER_CANCELLED ]" in str(e.value)

request.start_async({"data": img})
request.cancel()
with pytest.raises(RuntimeError) as e:
request.wait_for(1)
assert "[ INFER_CANCELLED ]" in str(e.value)


@pytest.mark.parametrize("share_inputs", [True, False])
def test_start_async(device, share_inputs):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
img = generate_image()
jobs = 3
requests = []
for _ in range(jobs):
requests.append(compiled_model.create_infer_request())

def callback(callbacks_info):
time.sleep(0.01)
callbacks_info["finished"] += 1

callbacks_info = {}
callbacks_info["finished"] = 0
for request in requests:
request.set_callback(callback, callbacks_info)
request.start_async({0: img}, share_inputs=share_inputs)
for request in requests:
request.wait()
assert request.latency > 0
assert callbacks_info["finished"] == jobs


@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
(Type.f32, np.float32),
(Type.f64, np.float64),
(Type.f16, np.float16),
(Type.bf16, np.float16),
(Type.i8, np.int8),
(Type.u8, np.uint8),
(Type.i32, np.int32),
(Type.u32, np.uint32),
(Type.i16, np.int16),
(Type.u16, np.uint16),
(Type.i64, np.int64),
(Type.u64, np.uint64),
(Type.boolean, bool),
])
@pytest.mark.parametrize("share_inputs", [True, False])
def test_async_mixed_values(device, ov_type, numpy_dtype, share_inputs):
request, tensor1, array1 = concat_model_with_data(device, ov_type, numpy_dtype)

request.start_async([tensor1, array1], share_inputs=share_inputs)
request.wait()
assert np.array_equal(request.output_tensors[0].data, np.concatenate((tensor1.data, array1)))


@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
(Type.f32, np.float32),
(Type.f64, np.float64),
(Type.f16, np.float16),
(Type.i8, np.int8),
(Type.u8, np.uint8),
(Type.i32, np.int32),
(Type.i16, np.int16),
(Type.u16, np.uint16),
(Type.i64, np.int64),
])
@pytest.mark.parametrize("share_inputs", [True, False])
def test_async_single_input(device, ov_type, numpy_dtype, share_inputs):
_, request, tensor1, array1 = abs_model_with_data(device, ov_type, numpy_dtype)

request.start_async(array1, share_inputs=share_inputs)
request.wait()
assert np.array_equal(request.get_output_tensor().data, np.abs(array1))

request.start_async(tensor1, share_inputs=share_inputs)
request.wait()
assert np.array_equal(request.get_output_tensor().data, np.abs(tensor1.data))


@pytest.mark.parametrize("share_inputs", [True, False])
def test_array_like_input_async(device, share_inputs):
class ArrayLikeObject:
# Array-like object accepted by np.array to test inputs similar to torch tensor and tf.Tensor
def __init__(self, array) -> None:
self.data = array

def __array__(self):
return np.array(self.data)

_, request, _, input_data = abs_model_with_data(device, Type.f32, np.single)
model_input_object = ArrayLikeObject(input_data.tolist())
model_input_list = [ArrayLikeObject(input_data.tolist())]
# Test single array-like object in InferRequest().start_async()
request.start_async(model_input_object, share_inputs=share_inputs)
request.wait()
assert np.array_equal(request.get_output_tensor().data, np.abs(input_data))

# Test list of array-like objects in InferRequest().start_async()
request.start_async(model_input_list)
request.wait()
assert np.array_equal(request.get_output_tensor().data, np.abs(input_data))
120 changes: 0 additions & 120 deletions src/bindings/python/tests/test_runtime/test_sync_infer_request.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
import os
import pytest
import datetime
import time

import openvino.runtime.opset13 as ops
from openvino import (
Expand Down Expand Up @@ -286,53 +285,6 @@ def test_inputs_outputs_property_and_method(device):
assert list(output_tensor.get_shape()) == input_shape


@pytest.mark.skip(reason="Sporadically failed. Need further investigation. Ticket - 95967")
def test_cancel(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
img = generate_image()
request = compiled_model.create_infer_request()

request.start_async({0: img})
request.cancel()
with pytest.raises(RuntimeError) as e:
request.wait()
assert "[ INFER_CANCELLED ]" in str(e.value)

request.start_async({"data": img})
request.cancel()
with pytest.raises(RuntimeError) as e:
request.wait_for(1)
assert "[ INFER_CANCELLED ]" in str(e.value)


@pytest.mark.parametrize("share_inputs", [True, False])
def test_start_async(device, share_inputs):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
img = generate_image()
jobs = 3
requests = []
for _ in range(jobs):
requests.append(compiled_model.create_infer_request())

def callback(callbacks_info):
time.sleep(0.01)
callbacks_info["finished"] += 1

callbacks_info = {}
callbacks_info["finished"] = 0
for request in requests:
request.set_callback(callback, callbacks_info)
request.start_async({0: img}, share_inputs=share_inputs)
for request in requests:
request.wait()
assert request.latency > 0
assert callbacks_info["finished"] == jobs


@pytest.mark.parametrize("share_inputs", [True, False])
def test_infer_list_as_inputs(device, share_inputs):
num_inputs = 4
Expand Down Expand Up @@ -401,30 +353,6 @@ def test_infer_mixed_values(device, ov_type, numpy_dtype, share_inputs):
assert np.array_equal(request.output_tensors[0].data, np.concatenate((tensor1.data, array1)))


@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
(Type.f32, np.float32),
(Type.f64, np.float64),
(Type.f16, np.float16),
(Type.bf16, np.float16),
(Type.i8, np.int8),
(Type.u8, np.uint8),
(Type.i32, np.int32),
(Type.u32, np.uint32),
(Type.i16, np.int16),
(Type.u16, np.uint16),
(Type.i64, np.int64),
(Type.u64, np.uint64),
(Type.boolean, bool),
])
@pytest.mark.parametrize("share_inputs", [True, False])
def test_async_mixed_values(device, ov_type, numpy_dtype, share_inputs):
request, tensor1, array1 = concat_model_with_data(device, ov_type, numpy_dtype)

request.start_async([tensor1, array1], share_inputs=share_inputs)
request.wait()
assert np.array_equal(request.output_tensors[0].data, np.concatenate((tensor1.data, array1)))


@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
(Type.f32, np.float32),
(Type.f64, np.float64),
Expand All @@ -447,30 +375,6 @@ def test_infer_single_input(device, ov_type, numpy_dtype, share_inputs):
assert np.array_equal(request.get_output_tensor().data, np.abs(tensor1.data))


@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
(Type.f32, np.float32),
(Type.f64, np.float64),
(Type.f16, np.float16),
(Type.i8, np.int8),
(Type.u8, np.uint8),
(Type.i32, np.int32),
(Type.i16, np.int16),
(Type.u16, np.uint16),
(Type.i64, np.int64),
])
@pytest.mark.parametrize("share_inputs", [True, False])
def test_async_single_input(device, ov_type, numpy_dtype, share_inputs):
_, request, tensor1, array1 = abs_model_with_data(device, ov_type, numpy_dtype)

request.start_async(array1, share_inputs=share_inputs)
request.wait()
assert np.array_equal(request.get_output_tensor().data, np.abs(array1))

request.start_async(tensor1, share_inputs=share_inputs)
request.wait()
assert np.array_equal(request.get_output_tensor().data, np.abs(tensor1.data))


def test_get_compiled_model(device):
core = Core()
param = ops.parameter([10])
Expand Down Expand Up @@ -765,30 +669,6 @@ def __array__(self):
assert np.array_equal(res_dict[request.model_outputs[0]], np.abs(input_data))


@pytest.mark.parametrize("share_inputs", [True, False])
def test_array_like_input_async(device, share_inputs):
class ArrayLikeObject:
# Array-like object accepted by np.array to test inputs similar to torch tensor and tf.Tensor
def __init__(self, array) -> None:
self.data = array

def __array__(self):
return np.array(self.data)

_, request, _, input_data = abs_model_with_data(device, Type.f32, np.single)
model_input_object = ArrayLikeObject(input_data.tolist())
model_input_list = [ArrayLikeObject(input_data.tolist())]
# Test single array-like object in InferRequest().start_async()
request.start_async(model_input_object, share_inputs=share_inputs)
request.wait()
assert np.array_equal(request.get_output_tensor().data, np.abs(input_data))

# Test list of array-like objects in InferRequest().start_async()
request.start_async(model_input_list)
request.wait()
assert np.array_equal(request.get_output_tensor().data, np.abs(input_data))


def test_convert_infer_request(device):
request, arr_1, arr_2 = create_simple_request_and_inputs(device)
inputs = [arr_1, arr_2]
Expand Down
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