Merge remote-tracking branch 'upstream/master' into scatter_eu_reference

src/bindings/python/src/openvino/runtime/ie_api.py

@@ -58,7 +58,7 @@ def infer(self, inputs: Any = None, shared_memory: bool = False) -> OVDict:
  Tensors for every input in form of:
  * `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
  Data that is going to be copied:
- * `numpy.ndarray` which are not C contiguous
+ * `numpy.ndarray` which are not C contiguous and/or not writable (WRITEABLE flag is set to False)
  * inputs which data types are mismatched from Infer Request's inputs
  * inputs that should be in `BF16` data type
  * scalar inputs (i.e. `np.float_`/`int`/`float`)
@@ -118,7 +118,7 @@ def start_async(
  Tensors for every input in form of:
  * `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
  Data that is going to be copied:
- * `numpy.ndarray` which are not C contiguous
+ * `numpy.ndarray` which are not C contiguous and/or not writable (WRITEABLE flag is set to False)
  * inputs which data types are mismatched from Infer Request's inputs
  * inputs that should be in `BF16` data type
  * scalar inputs (i.e. `np.float_`/`int`/`float`)
@@ -246,7 +246,7 @@ def __call__(self,
  Tensors for every input in form of:
  * `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
  Data that is going to be copied:
- * `numpy.ndarray` which are not C contiguous
+ * `numpy.ndarray` which are not C contiguous and/or not writable (WRITEABLE flag is set to False)
  * inputs which data types are mismatched from Infer Request's inputs
  * inputs that should be in `BF16` data type
  * scalar inputs (i.e. `np.float_`/`int`/`float`)
@@ -340,7 +340,7 @@ def start_async(
  Tensors for every input in form of:
  * `numpy.ndarray` and all the types that are castable to it, e.g. `torch.Tensor`
  Data that is going to be copied:
- * `numpy.ndarray` which are not C contiguous
+ * `numpy.ndarray` which are not C contiguous and/or not writable (WRITEABLE flag is set to False)
  * inputs which data types are mismatched from Infer Request's inputs
  * inputs that should be in `BF16` data type
  * scalar inputs (i.e. `np.float_`/`int`/`float`)

src/bindings/python/src/openvino/runtime/utils/data_helpers/data_dispatcher.py

@@ -70,6 +70,11 @@ def _(
  tensor = Tensor(tensor_type, value.shape)
  tensor.data[:] = value.view(tensor_dtype)
  return tensor
+ # WA for "not writeable" edge-case, always copy.
+ if value.flags["WRITEABLE"] is False:
+ tensor = Tensor(tensor_type, value.shape)
+ tensor.data[:] = value.astype(tensor_dtype) if tensor_dtype != value.dtype else value
+ return tensor
  # If types are mismatched, convert and always copy.
  if tensor_dtype != value.dtype:
  return Tensor(value.astype(tensor_dtype), shared_memory=False)

src/bindings/python/tests/test_runtime/test_infer_request.py

@@ -1112,3 +1112,33 @@ def test_mixed_dynamic_infer(device, shared_flag, input_data):
  else:
  assert not np.shares_memory(input_data[0], input_tensor0.data)
  assert not np.shares_memory(input_data[1], input_tensor1.data)
+
+
+@pytest.mark.parametrize("shared_flag", [True, False])
+@pytest.mark.parametrize(("input_data", "change_flags"), [
+ ({0: np.frombuffer(b"\x01\x02\x03\x04", np.uint8)}, False),
+ ({0: np.array([1, 2, 3, 4], dtype=np.uint8)}, True),
+])
+def test_not_writable_inputs_infer(device, shared_flag, input_data, change_flags):
+ if change_flags is True:
+ input_data[0].setflags(write=0)
+ # identity model
+ input_shape = [4]
+ param_node = ops.parameter(input_shape, np.uint8, name="data0")
+ core = Core()
+ model = Model(param_node, [param_node])
+ compiled = core.compile_model(model, "CPU")
+
+ results = compiled(input_data, shared_memory=shared_flag)
+
+ assert np.array_equal(results[0], input_data[0])
+
+ request = compiled.create_infer_request()
+ results = request.infer(input_data, shared_memory=shared_flag)
+
+ assert np.array_equal(results[0], input_data[0])
+
+ input_tensor = request.get_input_tensor(0)
+
+ # Not writable inputs should always be copied.
+ assert not np.shares_memory(input_data[0], input_tensor.data)