add Dropout2D and rename Dropout3d to Dropout3D

mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_util.h

@@ -52,9 +52,11 @@ constexpr auto kCacheSwapTable = "CacheSwapTable";
 constexpr auto kSubAndFilter = "SubAndFilter";
 constexpr auto kPadAndShift = "PadAndShift";
 constexpr auto kCustRunApi = "RunCpuKernel";
-constexpr auto kDropout3d = "Dropout3d";
+constexpr auto kDropout2D = "Dropout2D";
+constexpr auto kDropout3D = "Dropout3D";
 const std::set<std::string> kCustAiCpuKernelOps{kIdentity};
-const std::set<std::string> kCacheKernelOps{kUpdateCache, kCacheSwapTable, kSubAndFilter, kPadAndShift, kDropout3d};
+const std::set<std::string> kCacheKernelOps{kUpdateCache, kCacheSwapTable, kSubAndFilter,
+                                            kPadAndShift, kDropout3D,      kDropout2D};
 
 struct AicpuParamHead {
   uint32_t length;         // Total length: include cunstom message

mindspore/ops/_grad/grad_nn_ops.py

@@ -1263,6 +1263,46 @@ def get_bprop_dropout(self):
     return bprop
 
 
+@bprop_getters.register(P.Dropout2D)
+def get_bprop_dropout2d(self):
+    """Grad definition for `Dropout2D` operation."""
+    dtype = P.DType()
+    cast = P.Cast()
+    mul = P.Mul()
+    keep_prob = self.keep_prob
+
+    def bprop(x, out, dout):
+        _, mask = dout
+        y = cast(mask, mstype.float32)
+        if keep_prob != 0:
+            y = y * (1 / keep_prob)
+        y = mul(x, y)
+        y = cast(y, dtype(x))
+        return (y,)
+
+    return bprop
+
+
+@bprop_getters.register(P.Dropout3D)
+def get_bprop_dropout3d(self):
+    """Grad definition for `Dropout3D` operation."""
+    dtype = P.DType()
+    cast = P.Cast()
+    mul = P.Mul()
+    keep_prob = self.keep_prob
+
+    def bprop(x, out, dout):
+        _, mask = dout
+        y = cast(mask, mstype.float32)
+        if keep_prob != 0:
+            y = y * (1 / keep_prob)
+        y = mul(x, y)
+        y = cast(y, dtype(x))
+        return (y,)
+
+    return bprop
+
+
 @bprop_getters.register(P.CTCLoss)
 def get_bprop_ctc_loss(self):
     """Grad definition for `CTCLoss` operation"""

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -27,6 +27,7 @@ from .unique_with_pad import _unique_with_pad_aicpu
 from .sub_and_filter import _sub_and_filter_aicpu
 from .pad_and_shift import _pad_and_shift_aicpu
 from .dropout_genmask import _dropout_genmask_aicpu
+from .dropout2d import _dropout2d_aicpu
 from .dropout3d import _dropout3d_aicpu
 from .get_next import _get_next_aicpu
 from .print_tensor import _print_aicpu

mindspore/ops/_op_impl/aicpu/dropout2d.py

@@ -0,0 +1,42 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Dropout2D op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+dropout2d_op_info = AiCPURegOp("Dropout2D") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .output(1, "mask", "required") \
+    .attr("keep_prob", "float") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.BOOL_Default) \
+    .get_op_info()
+
+
+@op_info_register(dropout2d_op_info)
+def _dropout2d_aicpu():
+    """Dropout2D AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/dropout3d.py

@@ -13,30 +13,30 @@
 # limitations under the License.
 # ============================================================================
 
-"""Dropout3d op"""
+"""Dropout3D op"""
 from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
-dropout3d_op_info = AiCPURegOp("Dropout3d") \
+dropout3d_op_info = AiCPURegOp("Dropout3D") \
     .fusion_type("OPAQUE") \
     .input(0, "x", "required") \
     .output(0, "y", "required") \
+    .output(1, "mask", "required") \
     .attr("keep_prob", "float") \
-    .attr("inplace", "bool") \
-    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
-    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
-    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
-    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
-    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
-    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
-    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
-    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
-    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
-    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
-    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
-    .dtype_format(DataType.F64_Default, DataType.F64_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.BOOL_Default) \
     .get_op_info()
 
 
 @op_info_register(dropout3d_op_info)
 def _dropout3d_aicpu():
-    """Dropout3d AiCPU register"""
+    """Dropout3D AiCPU register"""
     return

mindspore/ops/operations/__init__.py

@@ -64,7 +64,7 @@ from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, U
 from .nn_ops import (LSTM, SGD, Adam, FusedSparseAdam, FusedSparseLazyAdam, AdamNoUpdateParam, ApplyMomentum, BatchNorm,
                      BiasAdd, Conv2D,
                      DepthwiseConv2dNative,
-                     DropoutDoMask, Dropout, Dropout3d, DropoutGenMask, Flatten,
+                     DropoutDoMask, Dropout, Dropout2D, Dropout3D, DropoutGenMask, Flatten,
                      FusedBatchNorm, FusedBatchNormEx, InstanceNorm, BNTrainingReduce, BNTrainingUpdate,
                      GeLU, Gelu, FastGeLU, FastGelu, Elu,
 
@@ -243,6 +243,8 @@ __all__ = [
     'DropoutDoMask',
     'DropoutGenMask',
     'Dropout',
+    'Dropout2D',
+    'Dropout3D',
     'Neg',
     'InplaceAdd',
     'InplaceSub',

mindspore/ops/operations/nn_ops.py

@@ -6657,22 +6657,77 @@ class Dropout(PrimitiveWithCheck):
         validator.check_tensor_dtype_valid("x", x_dtype, valid_dtypes, self.name)
 
 
-class Dropout3d(PrimitiveWithInfer):
+class Dropout2D(PrimitiveWithInfer):
     """
     During training, randomly zeroes some of the channels of the input tensor
-    with probability keep_prob from a Bernoulli distribution.
+    with probability 1-`keep_prob` from a Bernoulli distribution.
 
     Args:
         keep_prob (float): The keep probability of a channel, between 0 and 1, e.g. `keep_prob` = 0.8,
-            means dropping out %20 of channels. Default: 0.5.
-        inplace (bool): When `inplace` is True, this operation will be done in-place. Default: False.
+            means dropping out 20% of channels. Default: 0.5.
+
+    Inputs:
+        - **input** (Tensor) - A 4-D tensor with shape :math:`(N, C, H, W)`.
+
+    Outputs:
+        - **output** (Tensor) - with the same shape and data type as the input tensor.
+        - **mask** (Tensor[bool]) - with the same shape as the input tensor.
+
+    Raises:
+        TypeError: If the data type of `keep_prob` is not float.
+        ValueError: If `keep_prob` is out of the range [0.0, 1.0];
+                    or if the dim of input is not 4-D.
+
+    Supported Platforms:
+        ``Ascend``
+
+    Examples:
+        >>> dropout = ops.Dropout2D(keep_prob=0.5)
+        >>> x = Tensor(np.random.randn(2, 1, 2, 3), mindspore.float32)
+        >>> output, mask = dropout(x)
+        >>> print(output)
+        [[[[0. 0. 0.]
+           [0. 0. 0.]]]
+         [[[0.88 -2.98 -0.01]
+           [2.16 -0.34 1.57]]]]
+        >>> print(mask)
+        [[[[False False False]
+           [False False False]]]
+         [[[True True True]
+           [True True True]]]]
+    """
+
+    @prim_attr_register
+    def __init__(self, keep_prob=0.5):
+        self.keep_prob = validator.check_value_type("keep_prob", keep_prob, [float], self.name)
+        self.keep_prob = validator.check_float_range(keep_prob, 0.0, 1.0, Rel.INC_BOTH, "keep_prob", self.name)
+
+    def infer_shape(self, x_shape):
+        validator.check_int(len(x_shape), 4, Rel.EQ, "dim of input", self.name)
+        return x_shape, x_shape
+
+    def infer_dtype(self, x_dtype):
+        valid_dtypes = mstype.int_type + (mstype.float16, mstype.float32)
+        validator.check_tensor_dtype_valid("x", x_dtype, valid_dtypes, self.name)
+        mask_dtype = mstype.tensor_type(mstype.bool_)
+        return x_dtype, mask_dtype
+
+
+class Dropout3D(PrimitiveWithInfer):
+    """
+    During training, randomly zeroes some of the channels of the input tensor
+    with probability 1-`keep_prob` from a Bernoulli distribution.
+
+    Args:
+        keep_prob (float): The keep probability of a channel, between 0 and 1, e.g. `keep_prob` = 0.8,
+            means dropping out 20% of channels. Default: 0.5.
 
     Inputs:
         - **input** (Tensor) - A 5-D tensor with shape :math:`(N, C, D, H, W)`.
-            When `inplace` is True, `input` should be Parameter.
 
     Outputs:
-        - **output** (Tensor) - with the same shape as the input tensor.
+        - **output** (Tensor) - with the same shape and data type as the input tensor.
+        - **mask** (Tensor[bool]) - with the same shape as the input tensor.
 
     Raises:
         TypeError: If the data type of `keep_prob` is not float.
@@ -6683,30 +6738,35 @@ class Dropout3d(PrimitiveWithInfer):
         ``Ascend``
 
     Examples:
-        >>> dropout = ops.Dropout3d(keep_prob=0.5)
+        >>> dropout = ops.Dropout3D(keep_prob=0.5)
         >>> x = Tensor(np.random.randn(2, 1, 2, 1, 2), mindspore.float32)
-        >>> output = dropout(x)
+        >>> output, mask = dropout(x)
         >>> print(output)
         [[[[[0. 0.]]
            [[0. 0.]]]]
          [[[[-2.98 -0.01]]
            [[-0.34 1.57]]]]]
+        >>> print(mask)
+        [[[[[False False]]
+           [[False False]]]]
+         [[[[True True]]
+           [[True True]]]]]
     """
 
     @prim_attr_register
-    def __init__(self, keep_prob=0.5, inplace=False):
-        self.inplace = validator.check_value_type("inplace", inplace, [bool], self.name)
+    def __init__(self, keep_prob=0.5):
         self.keep_prob = validator.check_value_type("keep_prob", keep_prob, [float], self.name)
         self.keep_prob = validator.check_float_range(keep_prob, 0.0, 1.0, Rel.INC_BOTH, "keep_prob", self.name)
 
     def infer_shape(self, x_shape):
-        validator.check_int(len(x_shape), 5, Rel.GE, "dim of input", self.name)
-        return x_shape
+        validator.check_int(len(x_shape), 5, Rel.EQ, "dim of input", self.name)
+        return x_shape, x_shape
 
     def infer_dtype(self, x_dtype):
-        valid_dtypes = mstype.number_type + (mstype.bool_,)
+        valid_dtypes = mstype.int_type + (mstype.float16, mstype.float32)
         validator.check_tensor_dtype_valid("x", x_dtype, valid_dtypes, self.name)
-        return x_dtype
+        mask_dtype = mstype.tensor_type(mstype.bool_)
+        return x_dtype, mask_dtype
 
 
 class CTCLoss(PrimitiveWithInfer):

tests/st/ops/ascend/test_aicpu_ops/test_dropout2d.py

@@ -0,0 +1,69 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops.composite import GradOperation
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+dtype = np.float16
+x0 = Tensor(np.random.randn(3, 4, 3, 3).astype(dtype))
+x1 = Tensor(np.random.randn(3, 4, 3, 3).astype(dtype))
+
+
+class Net(nn.Cell):
+    def __init__(self, keep_prob):
+        super(Net, self).__init__()
+        self.drop = P.Dropout2D(keep_prob=keep_prob)
+
+    def construct(self, x):
+        return self.drop(x)
+
+
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(get_all=True, sens_param=True)
+        self.network = network
+        self.network.set_train()
+
+    def construct(self, x, y):
+        return self.grad(self.network)(x, y)
+
+
+def test_net_float32():
+    net = Net(0.7)
+    output, mask = net(x0)
+    print(x0)
+    print(output)
+
+    y = (output.asnumpy() == (x0.asnumpy()/0.7).astype(dtype)).reshape(3*4, 3*3)
+    output_reshape = output.asnumpy().reshape(3*4, 3*3)
+    for i in range(3*4):
+        if not y[i].all():
+            assert output_reshape[i].sum() == 0
+    return output, mask
+
+
+def test_net_grad():
+    net = Grad(Net(0.7))
+    y = test_net_float32()
+    output = net(x1, y)
+    print("input: ", x1)
+    print("forward output: ", y)
+    print("backward output: ", output)

tests/st/ops/ascend/test_aicpu_ops/test_dropout3d.py

@@ -13,52 +13,57 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
-
-import mindspore
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P
+from mindspore.ops.composite import GradOperation
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+dtype = np.float16
+x0 = Tensor(np.random.randn(3, 4, 3, 3, 3).astype(dtype))
+x1 = Tensor(np.random.randn(3, 4, 3, 3, 3).astype(dtype))
+
 
 class Net(nn.Cell):
-    def __init__(self, keep_prob, inplace):
+    def __init__(self, keep_prob):
         super(Net, self).__init__()
-        self.drop = P.Dropout3d(keep_prob=keep_prob, inplace=inplace)
+        self.drop = P.Dropout3D(keep_prob=keep_prob)
 
     def construct(self, x):
         return self.drop(x)
 
 
-class NetInplace(nn.Cell):
-    def __init__(self, keep_prob, inplace, x):
-        super(NetInplace, self).__init__()
-        self.drop = P.Dropout3d(keep_prob=keep_prob, inplace=inplace)
-        self.x = x
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(get_all=True, sens_param=True)
+        self.network = network
+        self.network.set_train()
 
-    def construct(self):
-        return self.drop(self.x)
+    def construct(self, x, y):
+        return self.grad(self.network)(x, y)
 
 
 def test_net_float32():
-    x = Tensor(np.random.randn(3, 4, 3, 3, 3), mindspore.float32)
-    net = Net(0.7, False)
-    output = net(x)
-    print(x)
+    net = Net(0.7)
+    output, mask = net(x0)
+    print(x0)
     print(output)
 
-    y = (output.asnumpy() == x.asnumpy()/0.7).reshape(3*4, 3*3*3)
+    y = (output.asnumpy() == (x0.asnumpy()/0.7).astype(dtype)).reshape(3*4, 3*3*3)
+    output_reshape = output.asnumpy().reshape(3*4, 3*3*3)
     for i in range(3*4):
         if not y[i].all():
-            assert y[i].sum() == 0
+            assert output_reshape[i].sum() == 0
+    return output, mask
 
 
-def test_net_float32_inplace():
-    x = mindspore.Parameter(Tensor(np.random.randn(3, 4, 3, 3, 3), mindspore.float32))
-    net = NetInplace(0.7, True, x)
-    output = net()
-    print(Tensor(x))
-    print(output)
-    assert np.array_equal(x.asnumpy(), output.asnumpy())
+def test_net_grad():
+    net = Grad(Net(0.7))
+    y = test_net_float32()
+    output = net(x1, y)
+    print("input: ", x1)
+    print("forward output: ", y)
+    print("backward output: ", output)