Use MetaTensor instead of Initializer

mindspore/common/initializer.py

@@ -15,16 +15,13 @@
 """Initializer for cell parameters."""
 import numbers
 import math
-import copy
 
 from functools import reduce
 import numpy as np
 from scipy.stats import truncnorm
-from mindspore import log as logger
 
 from . import dtype as mstype
-from .tensor import Tensor
+from .tensor import Tensor, MetaTensor
-from .seed import get_seed
 from .._c_expression import random_normal
 
 _INITIALIZER_ALIAS = dict()
@@ -52,54 +49,6 @@ class Initializer:
     def __call__(self, arr):
         return self._initialize(arr)
 
-    @property
-    def shape(self):
-        return self._shape
-
-    @shape.setter
-    def shape(self, shape):
-        self._shape = shape
-
-    @property
-    def dtype(self):
-        return self._dtype
-
-    @dtype.setter
-    def dtype(self, dtype):
-        self._dtype = dtype
-
-    def to_tensor(self, slice_index=None, shape=None):
-        """
-        Get the tensor format data of this Initializer.
-
-        Args:
-            slice_index (int): Slice index of a parameter's slices.
-                It is used when initialize a slice of a parameter, it guarantees that devices
-                using the same slice can generate the same tensor.
-            shape (list[int]): Shape of the slice, it is used when initialize a slice of the parameter.
-        """
-        arr = None
-        if shape is None:
-            shape = self.shape
-
-        try:
-            arr = np.ndarray(shape, dtype=mstype.dtype_to_nptype(self.dtype))
-        except ValueError:
-            msg = "Error shape={}".format(shape)
-            logger.error(msg)
-            raise ValueError(msg)
-
-        global_seed = get_seed()
-        need_set_seed = ((slice_index is not None) and (global_seed is None))
-        seed_saved = np.random.get_state()[1][0]
-        if need_set_seed:
-            np.random.seed(slice_index)
-        self.__call__(arr)
-        if need_set_seed:
-            np.random.seed(seed_saved)
-        return Tensor(arr, dtype=self.dtype)
-
-
 def _register(*aliases):
     """Return the alias register."""
     def alias_reg(cls):
@@ -478,27 +427,16 @@ def initializer(init, shape=None, dtype=mstype.float32):
         if not isinstance(value, int) or value <= 0:
             raise ValueError(f"shape is invalid, shape value must be positive integer, shape:{shape}")
 
-    if isinstance(init, Initializer):
-        init_copy = copy.deepcopy(init)
-        init_copy.shape = shape if shape is not None else init.shape
-        init_copy.dtype = init.dtype if init.dtype is not None else dtype
-        return init_copy
-
     if isinstance(init, str):
-        init_obj = _INITIALIZER_ALIAS[init.lower()]()
+        init = _INITIALIZER_ALIAS[init.lower()]()
-        if init_obj is None:
+        if init is None:
             raise ValueError("The class corresponding to '{}' was not found.".format(init))
-        init = init_obj
+    elif isinstance(init, numbers.Number):
-        init.shape = shape
+        init = Constant(init)
-        init.dtype = dtype
+    shape = shape if shape is not None else init.shape
-        return init
+    dtype = init.dtype if init.dtype is not None else dtype
-
+    init_obj = MetaTensor(init, dtype, shape)
-    if isinstance(init, numbers.Number):
+    return init_obj
-        init_obj = Constant(init)
-        init_obj.shape = shape
-        init_obj.dtype = dtype
-        return init_obj
-    raise TypeError("Unsupported init type '{}'.".format(type(init)))
 
 __all__ = [
     'Initializer',

mindspore/common/parameter.py

@@ -16,8 +16,9 @@
 """Parameter for cell."""
 from copy import copy
 from .._c_expression import ParamInfo
+from .._c_expression import MetaTensor as MetaTensor_
 from . import dtype as mstype
-from .initializer import initializer, Initializer
+from .initializer import initializer
 from .tensor import Tensor, MetaTensor
 from .._checkparam import _check_str_by_regular
 from ..parallel._tensor import _get_slice_index
@@ -34,14 +35,14 @@ def _is_in_parallel_mode():
     return auto_parallel_context().get_parallel_mode() in ["semi_auto_parallel", "auto_parallel"]
 
 
-class Parameter(MetaTensor):
+class Parameter(MetaTensor_):
     """
     Parameter types of cell models.
 
     After initialized `Parameter` is a subtype of `Tensor`.
 
     In auto_parallel mode of  "semi_auto_parallel" and "auto_parallel", if init `Parameter` by
-    an `Initializer`, the type of Parameter will be `MetaTensor` not `Tensor`. `MetaTensor`
+    an `MetaTensor`, the type of Parameter will be `MetaTensor` not `Tensor`. `MetaTensor_`
     only saves the shape and type info of a tensor with no memory usage. The shape can be changed while
     compiling for auto-parallel. Call `init_data` will return a Tensor Parameter with initialized data.
 
@@ -52,7 +53,7 @@ class Parameter(MetaTensor):
         then the Parameters as this part of the inputs are not allowed to be cast.
 
     Args:
-        default_input (Union[Tensor, Initializer, Number]): Parameter data, to be set initialized.
+        default_input (Union[Tensor, MetaTensor, Number]): Parameter data, to be set initialized.
         name (str): Name of the child parameter.
         requires_grad (bool): True if the parameter requires gradient. Default: True.
         layerwise_parallel (bool): A kind of model parallel mode. When layerwise_parallel is true in parallel mode,
@@ -94,9 +95,9 @@ class Parameter(MetaTensor):
         input_class.__init__(obj, *class_init_args)
         # it's better to make the Initializer a kind of metatensor.
         obj.init_mode = None
-        obj.is_default_input_initializer = False
+        obj.is_default_input_meta = False
-        if isinstance(default_input, Initializer):
+        if isinstance(default_input, MetaTensor):
-            obj.is_default_input_initializer = True
+            obj.is_default_input_meta = True
         if not isinstance(obj, Tensor):
             obj.init_mode = default_input
         return obj
@@ -142,10 +143,10 @@ class Parameter(MetaTensor):
         """Set `set_data` of current `Parameter`."""
         if isinstance(data, bool):
             raise ValueError('Parameter data can not be `bool`')
-        if isinstance(data, Initializer):
+        if isinstance(data, MetaTensor):
             if _is_in_parallel_mode():
                 # do not init data while in auto parallel.
-                return (MetaTensor, data.dtype, data.shape)
+                return (MetaTensor_, data.dtype, data.shape)
             data = data.to_tensor()
         if isinstance(data, Tensor):
             # make a copy of Tensor to init the parameter
@@ -257,7 +258,7 @@ class Parameter(MetaTensor):
         Args:
             prefix (str): Namespace of parameter. The cloned Parameter name is
                 combined of prefix and current name: `f"{perfix}.{self.name}"`.
-            init (Union[Tensor, str, Initializer, numbers.Number]): Initialize the shape of the parameter.
+            init (Union[Tensor, str, MetaTensor, numbers.Number]): Initialize the shape of the parameter.
                 Default: 'same'.
 
         Returns:
@@ -314,7 +315,7 @@ class Parameter(MetaTensor):
         Set `set_data` of current `Parameter`.
 
         Args:
-            data (Union[Tensor, Initializer, int, float]): new data.
+            data (Union[Tensor, MetaTensor, int, float]): new data.
             slice_shape (bool): If slice the Parameter, will not check if shape is match. Default: False.
 
         Retruns:
@@ -325,9 +326,9 @@ class Parameter(MetaTensor):
                             f"Current dtype is {self.dtype}, and incoming is {incoming}. "
                             f"Use .set_dtype(xxx) to change the dtype.")
 
-        if not isinstance(data, (MetaTensor, Initializer, int, float)):
+        if not isinstance(data, (MetaTensor_, int, float)):
-            raise TypeError(f"Parameter data must be [`Initializer`, `int`, `float`] or a kind of `MetaTensor` "
+            raise TypeError(f"Parameter data must be [`MetaTensor`, `int`, `float`] or a kind of `MetaTensor_` "
-                            f"(like `Tensor` or `MetaTensor`). But with type {type(data)}.")
+                            f"(like `Tensor` or `MetaTensor_`). But with type {type(data)}.")
         if isinstance(data, (int, float)):
             if self.dtype in mstype.int_type and isinstance(data, float):
                 raise_type_error(mstype.float_)
@@ -337,8 +338,8 @@ class Parameter(MetaTensor):
         is_current_tensor = isinstance(self, Tensor)
 
         if is_incoming_tensor and not is_current_tensor:
-            raise TypeError("Parameter is a `MetaTensor` and not initializered, `data` for `set_data`"
+            raise TypeError("Parameter is a `MetaTensor_` and not initializered, `data` for `set_data`"
-                            "should be a Initializer. If you want to update it by Tensor, call method"
+                            "should be a MetaTensor. If you want to update it by Tensor, call method"
                             "`init_parameters_data` of `Cell` to init and replace all the Parameter of"
                             "network, then call this method.")
         if tuple(self.shape) != tuple(data.shape):
@@ -351,7 +352,7 @@ class Parameter(MetaTensor):
                 raise_type_error(data.dtype)
             else:
                 data = Tensor(data, self.dtype)
-        if isinstance(data, Initializer):
+        if isinstance(data, MetaTensor):
             # The parameter has been initializered, directly update by the data
             if is_current_tensor:
                 self._update_tensor_data(data.to_tensor())
@@ -387,10 +388,10 @@ class Parameter(MetaTensor):
             Parameter, the `Parameter` after initializing data. If current `Parameter` was already initialized before,
             returns the same initialized `Parameter`.
         """
-        if self.is_default_input_initializer:
+        if self.is_default_input_meta:
             is_current_in_parallel = _is_in_parallel_mode()
             if self.is_in_parallel != is_current_in_parallel:
-                raise RuntimeError("Must set or change parallel mode before any Initializer created.")
+                raise RuntimeError("Must set or change parallel mode before any MetaTensor created.")
         if self.init_mode is None:
             return self
         if self.inited_param is not None:
@@ -401,12 +402,12 @@ class Parameter(MetaTensor):
             if len(layout) < 3:
                 raise ValueError("The length of layout must be larger than 3! layout is {}.".format(layout))
             slice_index = int(_get_slice_index(layout[0], layout[1]))
-            if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Initializer)):
+            if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, MetaTensor)):
                 data = self.init_mode.to_tensor(0, [1])
             else:
                 data = self.init_mode.to_tensor(slice_index, layout[2])
         else:
-            if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Initializer)):
+            if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, MetaTensor)):
                 data = self.init_mode.to_tensor(0, [1])
             else:
                 data = self.init_mode.to_tensor()

mindspore/common/tensor.py

@@ -15,8 +15,9 @@
 """Tensor implementation."""
 import numpy as np
 
+from mindspore import log as logger
 from .._c_expression import Tensor as Tensor_
-from .._c_expression import MetaTensor
+from .._c_expression import MetaTensor as MetaTensor_
 from .._checkparam import check_type, check_typename
 from . import dtype as mstype
 from ._register_for_tensor import tensor_operator_registry
@@ -395,6 +396,50 @@ class SparseTensor:
         return self.__dense_shape
 
 
+class MetaTensor(MetaTensor_):
+    """
+    The base class of the MetaTensor.
+    Initialization of tensor basic attributes and model weight values.
+
+    Returns:
+        Array, an array after being initialized.
+    """
+    def __init__(self, init, dtype, shape):
+        #check param
+        self.init = init
+        MetaTensor_.__init__(self, dtype, shape)
+
+    def to_tensor(self, slice_index=None, shape=None):
+        """
+        Get the tensor format data of this MetaTensor.
+
+        Args:
+            slice_index (int): Slice index of a parameter's slices.
+                It is used when initialize a slice of a parameter, it guarantees that devices
+                using the same slice can generate the same tensor.
+            shape (list[int]): Shape of the slice, it is used when initialize a slice of the parameter.
+        """
+        if shape is None:
+            shape = self.shape
+
+        try:
+            arr = np.ndarray(shape, dtype=mstype.dtype_to_nptype(self.dtype))
+        except ValueError:
+            msg = "Error shape={}".format(shape)
+            logger.error(msg)
+            raise ValueError(msg)
+        from .seed import get_seed
+        global_seed = get_seed()
+        need_set_seed = ((slice_index is not None) and (global_seed is None))
+        seed_saved = np.random.get_state()[1][0]
+        if need_set_seed:
+            np.random.seed(slice_index)
+        self.init(arr)
+        if need_set_seed:
+            np.random.seed(seed_saved)
+        return Tensor(arr, dtype=self.dtype)
+
+
 def _vm_compare(*args):
     """Implement `vm_compare` for tensor."""
     obj_str = args[-1]

tests/st/pynative/test_pynative_resnet50.py

@@ -32,7 +32,6 @@ from mindspore.nn import Cell
 from mindspore.ops import operations as P
 from mindspore.ops import composite as CP
 from mindspore.nn.optim.momentum import Momentum
-from mindspore.common.initializer import initializer
 from mindspore.nn.wrap.cell_wrapper import WithLossCell
 
 random.seed(1)
@@ -43,14 +42,6 @@ ds.config.set_seed(1)
 grad_by_list = CP.GradOperation(get_by_list=True)
 
 
-def weight_variable(shape):
-    return initializer('XavierUniform', shape=shape, dtype=mstype.float32)
-
-
-def weight_variable_uniform(shape):
-    return initializer('Uniform', shape=shape, dtype=mstype.float32)
-
-
 def weight_variable_0(shape):
     zeros = np.zeros(shape).astype(np.float32)
     return Tensor(zeros)
@@ -63,26 +54,23 @@ def weight_variable_1(shape):
 
 def conv3x3(in_channels, out_channels, stride=1, padding=0):
     """3x3 convolution """
-    weight_shape = (out_channels, in_channels, 3, 3)
-    weight = weight_variable(weight_shape)
     return nn.Conv2d(in_channels, out_channels,
-                     kernel_size=3, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
+                     kernel_size=3, stride=stride, padding=padding, weight_init='XavierUniform',
+                     has_bias=False, pad_mode="same")
 
 
 def conv1x1(in_channels, out_channels, stride=1, padding=0):
     """1x1 convolution"""
-    weight_shape = (out_channels, in_channels, 1, 1)
-    weight = weight_variable(weight_shape)
     return nn.Conv2d(in_channels, out_channels,
-                     kernel_size=1, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
+                     kernel_size=1, stride=stride, padding=padding, weight_init='XavierUniform',
+                     has_bias=False, pad_mode="same")
 
 
 def conv7x7(in_channels, out_channels, stride=1, padding=0):
     """1x1 convolution"""
-    weight_shape = (out_channels, in_channels, 7, 7)
-    weight = weight_variable(weight_shape)
     return nn.Conv2d(in_channels, out_channels,
-                     kernel_size=7, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
+                     kernel_size=7, stride=stride, padding=padding, weight_init='XavierUniform',
+                     has_bias=False, pad_mode="same")
 
 
 def bn_with_initialize(out_channels):
@@ -90,8 +78,7 @@ def bn_with_initialize(out_channels):
     mean = weight_variable_0(shape)
     var = weight_variable_1(shape)
     beta = weight_variable_0(shape)
-    gamma = weight_variable_uniform(shape)
+    bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init='Uniform',
-    bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init=gamma,
                         beta_init=beta, moving_mean_init=mean, moving_var_init=var)
     return bn
 
@@ -101,18 +88,13 @@ def bn_with_initialize_last(out_channels):
     mean = weight_variable_0(shape)
     var = weight_variable_1(shape)
     beta = weight_variable_0(shape)
-    gamma = weight_variable_uniform(shape)
+    bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init='Uniform',
-    bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init=gamma,
                         beta_init=beta, moving_mean_init=mean, moving_var_init=var)
     return bn
 
 
 def fc_with_initialize(input_channels, out_channels):
-    weight_shape = (out_channels, input_channels)
+    return nn.Dense(input_channels, out_channels, weight_init='XavierUniform', bias_init='Uniform')
-    weight = weight_variable(weight_shape)
-    bias_shape = (out_channels)
-    bias = weight_variable_uniform(bias_shape)
-    return nn.Dense(input_channels, out_channels, weight, bias)
 
 
 class ResidualBlock(nn.Cell):

tests/ut/python/pynative_mode/test_staging.py

@@ -20,7 +20,7 @@ import mindspore as ms
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore import context
-from mindspore.common import MetaTensor
+from mindspore._c_expression import MetaTensor as MetaTensor_
 from mindspore.common import dtype
 from mindspore.common.api import ms_function
 from mindspore.ops import functional as F
@@ -70,8 +70,8 @@ def scalar_mul_while(x):
     return rv
 
 
-@ms_function(input_signature=(MetaTensor(dtype.float32, (1, 1, 3, 3)),
+@ms_function(input_signature=(MetaTensor_(dtype.float32, (1, 1, 3, 3)),
-                              MetaTensor(dtype.float32, (1, 1, 3, 3))))
+                              MetaTensor_(dtype.float32, (1, 1, 3, 3))))
 def tensor_add_test(x, y):
     """ tensor_add_test """
     z = F.tensor_add(x, y)

tests/ut/python/utils/test_initializer.py

@@ -24,7 +24,7 @@ import mindspore.common.initializer as init
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.common.parameter import Parameter
-from mindspore.common.tensor import Tensor
+from mindspore.common.tensor import Tensor, MetaTensor
 from mindspore.nn import Conv2d
 from mindspore.ops import operations as P
 from ..ut_filter import non_graph_engine
@@ -58,7 +58,7 @@ def _check_uniform(tensor, boundary_a, boundary_b):
 
 def test_init_Initializer():
     tensor = init.initializer(InitTwo(), [2, 2], ms.int32)
-    assert tensor.shape == (2, 2)
+    assert tensor.shape == [2, 2]
     _check_value(tensor.to_tensor(), 2, 2)
 
 
@@ -119,22 +119,22 @@ def test_init_uniform_alias():
 
 def test_init_normal():
     tensor = init.initializer(init.Normal(), [5, 4], ms.float32)
-    assert isinstance(tensor, init.Normal), 'Normal init failed!'
+    assert isinstance(tensor, MetaTensor), 'Normal init failed!'
 
 
 def test_init_truncated_normal():
     tensor = init.initializer(init.TruncatedNormal(), [5, 4], ms.float32)
-    assert isinstance(tensor, init.TruncatedNormal), 'TruncatedNormal init failed!'
+    assert isinstance(tensor, MetaTensor), 'TruncatedNormal init failed!'
 
 
 def test_init_normal_alias():
     tensor = init.initializer('normal', [5, 4], ms.float32)
-    assert isinstance(tensor, init.Normal), 'Normal init failed!'
+    assert isinstance(tensor, MetaTensor), 'Normal init failed!'
 
 
 def test_init_truncatednormal_alias():
     tensor = init.initializer('truncatednormal', [5, 4], ms.float32)
-    assert isinstance(tensor, init.TruncatedNormal), 'TruncatedNormal init failed!'
+    assert isinstance(tensor, MetaTensor), 'TruncatedNormal init failed!'
 
 
 def test_init_abnormal():
@@ -144,15 +144,7 @@ def test_init_abnormal():
 
 def test_initializer_reinit():
     weights = init.initializer("XavierUniform", shape=(10, 1, 10, 10), dtype=ms.float16)
-    assert weights.dtype == ms.float16
+    assert isinstance(weights, MetaTensor), 'XavierUniform init failed!'
-    assert weights.shape == (10, 1, 10, 10)
-    weights = init.initializer(weights)
-    assert weights.dtype == ms.float16
-    assert weights.shape == (10, 1, 10, 10)
-    weights.shape = None
-    weights = init.initializer(weights, (10, 1))
-    assert weights.dtype == ms.float16
-    assert weights.shape == (10, 1)
 
 
 def test_init_xavier_uniform():