feat(lite): add get_data_by_share in megenginelite python interface

GitOrigin-RevId: 0ddbb75e823106a61d5802d8e395db99a3e9f1d6

lite/pylite/megenginelite/__init__.py

@@ -8,6 +8,7 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 
 from .base import *
+from .base import version as __version__
 from .global_setting import *
 from .network import *
 from .struct import *

lite/pylite/megenginelite/network.py

@@ -69,7 +69,9 @@ class LiteOptions(Structure):
             "const_shape": bool(self.const_shape),
             "force_dynamic_alloc": bool(self.force_dynamic_alloc),
             "force_output_dynamic_alloc": bool(self.force_output_dynamic_alloc),
-            "force_output_nocopy": bool(self.force_output_nocopy),
+            "force_output_use_user_specified_memory": bool(
+                self.force_output_use_user_specified_memory
+            ),
             "no_profiling_on_shape_change": bool(self.no_profiling_on_shape_change),
             "jit_level": self.jit_level,
             "comp_node_seq_record_level": self.comp_node_seq_record_level,
@@ -99,7 +101,7 @@ class LiteConfig(Structure):
         ("device_id", c_int),
         ("device_type", c_int),
         ("backend", c_int),
-        ("bare_model_cryption_name", c_char_p),
+        ("_bare_model_cryption_name", c_char_p),
         ("options", LiteOptions),
     ]
 
@@ -110,18 +112,30 @@ class LiteConfig(Structure):
         else:
             self.options = LiteOptions()
 
-        self.bare_model_cryption_name = c_char_p(b"")
+        self._bare_model_cryption_name = c_char_p(b"")
         self.use_loader_dynamic_param = 0
         self.has_compression = 0
         self.backend = LiteBackend.LITE_DEFAULT
 
+    @property
+    def bare_model_cryption_name(self):
+        return self._bare_model_cryption_name.decode("utf-8")
+
+    @bare_model_cryption_name.setter
+    def bare_model_cryption_name(self, name):
+        if isinstance(name, str):
+            self._bare_model_cryption_name = name.encode("utf-8")
+        else:
+            assert isinstance(name, bytes), "name should be str or bytes type."
+            self._bare_model_cryption_name = name
+
     def __repr__(self):
         data = {
             "has_compression": bool(self.has_compression),
             "device_id": LiteDeviceType(self.device_id),
             "device_type": LiteDeviceType(self.device_type),
             "backend": LiteBackend(self.backend),
-            "bare_model_cryption_name": self.bare_model_cryption_name.decode("utf-8"),
+            "bare_model_cryption_name": self.bare_model_cryption_name,
             "options": self.options,
         }
         return data.__repr__()
@@ -149,7 +163,7 @@ class LiteIO(Structure):
     """
 
     _fields_ = [
-        ("name", c_char_p),
+        ("_name", c_char_p),
         ("is_host", c_int),
         ("io_type", c_int),
         ("config_layout", LiteLayout),
@@ -159,9 +173,9 @@ class LiteIO(Structure):
         self, name, is_host=True, io_type=LiteIOType.LITE_IO_VALUE, layout=None
     ):
         if type(name) == str:
-            self.name = c_char_p(name.encode("utf-8"))
+            self._name = c_char_p(name.encode("utf-8"))
         else:
-            self.name = c_char_p(name)
+            self._name = c_char_p(name)
 
         if layout:
             self.config_layout = layout
@@ -171,6 +185,18 @@ class LiteIO(Structure):
         self.is_host = is_host
         self.io_type = io_type
 
+    @property
+    def name(self):
+        return self._name.decode("utf-8")
+
+    @name.setter
+    def name(self, name):
+        if isinstance(name, str):
+            self._name = name.encode("utf-8")
+        else:
+            assert isinstance(name, bytes), "name should be str or bytes type."
+            self._name = name
+
     def __repr__(self):
         data = {
             "name": self.name,
@@ -208,17 +234,45 @@ class LiteNetworkIO(object):
     the input and output information for user to construct _LiteNetWorkIO
     """
 
-    def __init__(self):
+    def __init__(self, inputs=None, outputs=None):
         self.inputs = []
         self.outputs = []
+        if inputs:
+            for i in inputs:
+                if isinstance(i, list):
+                    self.inputs.append(LiteIO(*i))
+                else:
+                    assert isinstance(
+                        i, LiteIO
+                    ), "the param to construct LiteNetworkIO must be list of the LiteIO member or the LiteIO."
+                    self.inputs.append(i)
+        if outputs:
+            for i in outputs:
+                if isinstance(i, list):
+                    self.outputs.append(LiteIO(*i))
+                else:
+                    assert isinstance(
+                        i, LiteIO
+                    ), "the param to construct LiteNetworkIO must be list of the LiteIO member or the LiteIO."
+                    self.outputs.append(i)
+
+    def add_input(
+        self, obj, is_host=True, io_type=LiteIOType.LITE_IO_VALUE, layout=None
+    ):
+        if isinstance(obj, LiteIO):
+            self.inputs.append(obj)
+        else:
+            name = obj
+            self.add_input(LiteIO(name, is_host, io_type, layout))
 
-    def add_input(self, input_io):
-        assert isinstance(input_io, LiteIO)
-        self.inputs.append(input_io)
-
-    def add_output(self, output_io):
-        assert isinstance(output_io, LiteIO)
-        self.outputs.append(output_io)
+    def add_output(
+        self, obj, is_host=True, io_type=LiteIOType.LITE_IO_VALUE, layout=None
+    ):
+        if isinstance(obj, LiteIO):
+            self.outputs.append(obj)
+        else:
+            name = obj
+            self.add_output(LiteIO(name, is_host, io_type, layout))
 
     def _create_network_io(self):
         network_io = _LiteNetworkIO()

lite/pylite/megenginelite/tensor.py

@@ -48,6 +48,15 @@ ctype_to_lite_dtypes = {
     c_ushort: LiteDataType.LITE_UINT16,
 }
 
+_lite_dtypes_to_ctype = {
+    LiteDataType.LITE_INT: c_int,
+    LiteDataType.LITE_FLOAT: c_float,
+    LiteDataType.LITE_UINT8: c_ubyte,
+    LiteDataType.LITE_INT8: c_byte,
+    LiteDataType.LITE_INT16: c_short,
+    LiteDataType.LITE_UINT16: c_ushort,
+}
+
 
 class LiteLayout(Structure):
     """
@@ -55,7 +64,7 @@ class LiteLayout(Structure):
     """
 
     _fields_ = [
-        ("shapes", c_size_t * MAX_DIM),
+        ("_shapes", c_size_t * MAX_DIM),
         ("ndim", c_size_t),
         ("data_type", c_int),
     ]
@@ -64,10 +73,10 @@ class LiteLayout(Structure):
         if shape:
             shape = list(shape)
             assert len(shape) <= MAX_DIM, "Layout max dim is 7."
-            self.shapes = (c_size_t * MAX_DIM)(*shape)
+            self._shapes = (c_size_t * MAX_DIM)(*shape)
             self.ndim = len(shape)
         else:
-            self.shapes = (c_size_t * MAX_DIM)()
+            self._shapes = (c_size_t * MAX_DIM)()
             self.ndim = 0
         if not dtype:
             self.data_type = LiteDataType.LITE_FLOAT
@@ -83,9 +92,24 @@ class LiteLayout(Structure):
         else:
             raise RuntimeError("unkonw data type")
 
+    @property
+    def dtype(self):
+        return _lite_type_to_nptypes[LiteDataType(self.data_type)]
+
+    @property
+    def shapes(self):
+        return list(self._shapes)[0 : self.ndim]
+
+    @shapes.setter
+    def shapes(self, shape):
+        shape = list(shape)
+        assert len(shape) <= MAX_DIM, "Layout max dim is 7."
+        self._shapes = (c_size_t * MAX_DIM)(*shape)
+        self.ndim = len(shape)
+
     def __repr__(self):
         data = {
-            "shapes": list(self.shapes)[0 : self.ndim],
+            "shapes": self.shapes,
             "ndim": self.ndim,
             "data_type": _lite_type_to_nptypes[LiteDataType(self.data_type)],
         }
@@ -177,15 +201,20 @@ class LiteTensor(object):
         device_type=LiteDeviceType.LITE_CPU,
         device_id=0,
         is_pinned_host=False,
+        shapes=None,
+        dtype=None,
     ):
         """
-        create a Tensor with layout, device, is_pinned_host param
+        create a Tensor with layout, device, is_pinned_host or shapes, dtype,
+        device_type, device_id, is_pinned_host param
         """
         self._tensor = _Ctensor()
-        if layout:
+        self._layout = LiteLayout()
+        if layout is not None:
             self._layout = layout
-        else:
-            self._layout = LiteLayout()
+        elif shapes is not None:
+            shapes = list(shapes)
+            self._layout = LiteLayout(shapes, dtype)
         self._device_type = device_type
         self._device_id = device_id
         self._is_pinned_host = is_pinned_host
@@ -222,9 +251,12 @@ class LiteTensor(object):
 
     @layout.setter
     def layout(self, layout):
-        assert isinstance(layout, LiteLayout)
-        self._layout = layout
-        self._api.LITE_set_tensor_layout(self._tensor, layout)
+        if isinstance(layout, LiteLayout):
+            self._layout = layout
+        elif isinstance(layout, list):
+            self._layout.shapes = layout
+
+        self._api.LITE_set_tensor_layout(self._tensor, self._layout)
 
     @property
     def is_pinned_host(self):
@@ -270,7 +302,6 @@ class LiteTensor(object):
         """
         get the length of the meomry in byte
         """
-        self.update()
         length = c_size_t()
         self._api.LITE_get_tensor_total_size_in_byte(self._tensor, byref(length))
         return length.value
@@ -336,7 +367,6 @@ class LiteTensor(object):
         """
         get the memory of the tensor, return c_void_p of the tensor memory
         """
-        self.update()
         mem = c_void_p()
         self._api.LITE_get_tensor_memory(self._tensor, byref(mem))
         return mem
@@ -347,7 +377,6 @@ class LiteTensor(object):
         param data: the data will shared to the tensor, it should be a
         numpy.ndarray or ctypes data
         """
-        self.update()
         if isinstance(data, np.ndarray):
             assert (
                 self.is_continue
@@ -356,8 +385,7 @@ class LiteTensor(object):
                 self.is_pinned_host or self.device_type == LiteDeviceType.LITE_CPU
             ), "set_data_by_share can only apply in cpu tensor or pinned tensor."
 
-            np_type = _lite_type_to_nptypes[LiteDataType(self._layout.data_type)]
-            c_type = np.ctypeslib.as_ctypes_type(np_type)
+            c_type = _lite_dtypes_to_ctype[LiteDataType(self._layout.data_type)]
 
             if self.nbytes != data.nbytes:
                 self.layout = LiteLayout(data.shape, ctype_to_lite_dtypes[c_type])
@@ -377,7 +405,6 @@ class LiteTensor(object):
         param data: the data to copy to tensor, it should be list,
         numpy.ndarraya or ctypes with length
         """
-        self.update()
         if layout is not None:
             self.layout = layout
 
@@ -386,8 +413,7 @@ class LiteTensor(object):
             self.is_pinned_host or self.device_type == LiteDeviceType.LITE_CPU
         ), "set_data_by_copy can only apply in cpu tensor or pinned tensor."
 
-        np_type = _lite_type_to_nptypes[LiteDataType(self._layout.data_type)]
-        c_type = np.ctypeslib.as_ctypes_type(np_type)
+        c_type = _lite_dtypes_to_ctype[LiteDataType(self._layout.data_type)]
 
         tensor_memory = c_void_p()
 
@@ -415,6 +441,22 @@ class LiteTensor(object):
             self._api.LITE_get_tensor_memory(self._tensor, byref(tensor_memory))
             memmove(tensor_memory, data, data_length)
 
+    def get_data_by_share(self):
+        """
+        get the data in the tensor, add share the data with a new numpy, and
+        return the numpy arrray, be careful, the data in numpy is valid before
+        the tensor memory is write again, such as LiteNetwok forward next time.
+        """
+        assert self.is_continue, "get_data_by_share can only apply in continue tensor."
+        assert (
+            self.is_pinned_host or self.device_type == LiteDeviceType.LITE_CPU
+        ), "get_data_by_share can only apply in CPU tensor or cpu pinned tensor."
+
+        memory = self.get_ctypes_memory()
+        c_type = _lite_dtypes_to_ctype[LiteDataType(self._layout.data_type)]
+        pnt = cast(memory, POINTER(c_type))
+        return np.ctypeslib.as_array(pnt, self._layout.shapes)
+
     def to_numpy(self):
         """
         get the buffer of the tensor
@@ -475,3 +517,13 @@ def LiteTensorConcat(
     )
     result_tensor.update()
     return result_tensor
+
+
+def lite_dtype_2_numpy(dtype):
+    """
+    convert lite dtype to corresponding numpy dtype
+    """
+    assert isinstance(
+        dtype, LiteDataType
+    ), "input must be LiteDataType when using lite_dtype_2_numpy."
+    return _lite_type_to_nptypes[dtype]

lite/pylite/test/test_network.py

@@ -21,6 +21,12 @@ def test_version():
     print("Lite verson: {}".format(version))
 
 
+def test_config():
+    config = LiteConfig()
+    config.bare_model_cryption_name = "nothing"
+    print(config)
+
+
 def test_network_io():
     input_io1 = LiteIO("data1", is_host=False, io_type=LiteIOType.LITE_IO_VALUE)
     input_io2 = LiteIO(
@@ -32,6 +38,7 @@ def test_network_io():
     io = LiteNetworkIO()
     io.add_input(input_io1)
     io.add_input(input_io2)
+    io.add_input("data3", False)
 
     output_io1 = LiteIO("out1", is_host=False)
     output_io2 = LiteIO("out2", is_host=True, layout=LiteLayout([1, 1000]))
@@ -39,7 +46,7 @@ def test_network_io():
     io.add_output(output_io1)
     io.add_output(output_io2)
 
-    assert len(io.inputs) == 2
+    assert len(io.inputs) == 3
     assert len(io.outputs) == 2
 
     assert io.inputs[0] == input_io1
@@ -47,9 +54,25 @@ def test_network_io():
 
     c_io = io._create_network_io()
 
-    assert c_io.input_size == 2
+    assert c_io.input_size == 3
     assert c_io.output_size == 2
 
+    ins = [["data1", True], ["data2", False, LiteIOType.LITE_IO_SHAPE]]
+    outs = [["out1", True], ["out2", False, LiteIOType.LITE_IO_VALUE]]
+
+    io2 = LiteNetworkIO(ins, outs)
+    assert len(io2.inputs) == 2
+    assert len(io2.outputs) == 2
+
+    io3 = LiteNetworkIO([input_io1, input_io2], [output_io1, output_io2])
+    assert len(io3.inputs) == 2
+    assert len(io3.outputs) == 2
+
+    test_io = LiteIO("test")
+    assert test_io.name == "test"
+    test_io.name = "test2"
+    assert test_io.name == "test2"
+
 
 class TestShuffleNet(unittest.TestCase):
     source_dir = os.getenv("LITE_TEST_RESOURCE")
@@ -319,9 +342,9 @@ class TestNetwork(TestShuffleNet):
                 data = ios[key].to_numpy().flatten()
                 input_data = self.input_data.flatten()
                 assert data.size == input_data.size
-                assert io.name.decode("utf-8") == "data"
+                assert io.name == "data"
                 for i in range(data.size):
-                    assert data[i] == input_data[i]
+                    assert abs(data[i] - input_data[i]) < 1e-5
             return 0
 
         network.set_start_callback(start_callback)
@@ -343,7 +366,7 @@ class TestNetwork(TestShuffleNet):
                 output_data = self.correct_data.flatten()
                 assert data.size == output_data.size
                 for i in range(data.size):
-                    assert data[i] == output_data[i]
+                    assert abs(data[i] - output_data[i]) < 1e-5
             return 0
 
         network.set_finish_callback(finish_callback)
@@ -404,3 +427,27 @@ class TestNetwork(TestShuffleNet):
             binary_equal_between_batch=True,
         )
         self.do_forward(network)
+
+    def test_device_tensor_no_copy(self):
+        # construct LiteOption
+        net_config = LiteConfig()
+        net_config.options.force_output_use_user_specified_memory = True
+
+        network = LiteNetwork(config=net_config)
+        network.load(self.model_path)
+
+        input_tensor = network.get_io_tensor("data")
+        # fill input_data with device data
+        input_tensor.set_data_by_share(self.input_data)
+
+        output_tensor = network.get_io_tensor(network.get_output_name(0))
+        out_array = np.zeros(output_tensor.layout.shapes, output_tensor.layout.dtype)
+
+        output_tensor.set_data_by_share(out_array)
+
+        # inference
+        for i in range(2):
+            network.forward()
+            network.wait()
+
+        self.check_correct(out_array)

lite/pylite/test/test_tensor.py

@@ -54,6 +54,16 @@ def test_tensor_make():
     tensor = LiteTensor(layout, device_id=1)
     assert tensor.device_id == 1
 
+    tensor.layout = [8, 14]
+    assert tensor.layout.shapes[0] == 8
+    assert tensor.layout.shapes[1] == 14
+    assert tensor.layout.data_type == LiteDataType.LITE_FLOAT
+
+    tensor_new = LiteTensor(shapes=[1, 3, 224], dtype=np.int8)
+    assert tensor_new.layout.shapes[1] == 3
+    assert tensor_new.layout.shapes[2] == 224
+    assert tensor_new.layout.data_type == LiteDataType.LITE_INT8
+
 
 def test_tensor_set_data():
     layout = LiteLayout([2, 16], "int8")
@@ -292,3 +302,24 @@ def test_tensor_concat():
         for i in range(128):
             index = j * 128 + i
             assert real_data[index // 32][index % 32] == j
+
+
+def test_tensor_get_memory_by_share():
+    layout = LiteLayout([4, 32], "int16")
+    tensor = LiteTensor(layout)
+    assert tensor.nbytes == 4 * 32 * 2
+
+    arr = np.ones([4, 32], "int16")
+    for i in range(128):
+        arr[i // 32][i % 32] = i
+    tensor.set_data_by_copy(arr)
+    test_data = tensor.get_data_by_share()
+    real_data = tensor.to_numpy()
+    for i in range(128):
+        assert real_data[i // 32][i % 32] == test_data[i // 32][i % 32]
+
+    arr[1][18] = 5
+    arr[3][7] = 345
+    tensor.set_data_by_copy(arr)
+    assert test_data[1][18] == 5
+    assert test_data[3][7] == 345