Add TBE operators SparseApplyFtrlV2\SparseApplyAdagradV2 for VM.

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -70,11 +70,13 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"strided_slice", "strided_slice_d"},
   {"strided_slice_grad", "strided_slice_grad_d"},
   {"sparse_apply_ftrl", "sparse_apply_ftrl_d"},
+  {"sparse_apply_ftrl_v2", "sparse_apply_ftrl_v2_d"},
   {"apply_ada_max", "apply_ada_max_d"},
   {"apply_adadelta", "apply_adadelta_d"},
   {"apply_adagrad", "apply_adagrad_d"},
   {"apply_adagrad_v2", "apply_adagradv2_d"},
   {"sparse_apply_adagrad", "sparse_apply_adagrad_d"},
+  {"sparse_apply_adagrad_v2", "sparse_apply_adagrad_v2_d"},
   {"apply_proximal_adagrad", "apply_proximal_adagrad_d"},
   {"sparse_apply_proximal_adagrad", "sparse_apply_proximal_adagrad_d"},
   {"apply_add_sign", "apply_add_sign_d"},

mindspore/ops/_op_impl/tbe/__init__.py

@@ -38,6 +38,8 @@ from .apply_add_sign import _apply_add_sign_tbe
 from .apply_power_sign import _apply_power_sign_tbe
 from .apply_gradient_descent import _apply_gradient_descent_tbe
 from .apply_proximal_gradient_descent import _apply_proximal_gradient_descent_tbe
+from .sparse_apply_ftrl_v2 import _sparse_apply_ftrl_v2_tbe
+from .sparse_apply_adagrad_v2 import _sparse_apply_adagrad_v2_tbe
 from .approximate_equal import _approximate_equal_tbe
 from .adam_apply_one import _adam_apply_one_tbe
 from .assign import _assign_tbe

mindspore/ops/_op_impl/tbe/sparse_apply_adagrad_v2.py

@@ -0,0 +1,48 @@
+# Copyright 2020 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.
+# ============================================================================
+
+"""SparseApplyAdagradV2D op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+sparse_apply_adagrad_v2_d_op_info = TBERegOp("SparseApplyAdagradV2") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sparse_apply_adagrad_v2_d.so") \
+    .compute_cost(10) \
+    .kernel_name("sparse_apply_adagrad_v2_d") \
+    .partial_flag(True) \
+    .attr("lr", "required", "float", "all") \
+    .attr("epsilon", "required", "float", "all") \
+    .attr("use_locking", "optional", "bool", "all") \
+    .attr("update_slots", "optional", "bool", "all") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "accum", False, "required", "all") \
+    .input(2, "grad", False, "required", "all") \
+    .input(3, "indices", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .output(1, "accum", False, "required", "all") \
+    .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW, DataType.I32_NCHW,
+                  DataType.F32_NCHW, DataType.F32_NCHW) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC, DataType.F32_NHWC, DataType.I32_NHWC,
+                  DataType.F32_NHWC, DataType.F32_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.I32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(sparse_apply_adagrad_v2_d_op_info)
+def _sparse_apply_adagrad_v2_tbe():
+    """SparseApplyAdagradV2D TBE register"""
+    return

mindspore/ops/_op_impl/tbe/sparse_apply_ftrl_v2.py

@@ -0,0 +1,52 @@
+# Copyright 2020 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.
+# ============================================================================
+
+"""SparseApplyFtrlV2D op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+sparse_apply_ftrl_v2_d_op_info = TBERegOp("SparseApplyFtrlV2") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sparse_apply_ftrl_v2_d.so") \
+    .compute_cost(10) \
+    .kernel_name("sparse_apply_ftrl_v2_d") \
+    .partial_flag(True) \
+    .attr("lr", "required", "float", "all") \
+    .attr("l1", "required", "float", "all") \
+    .attr("l2", "required", "float", "all") \
+    .attr("l2_shrinkage", "required", "float", "all") \
+    .attr("lr_power", "required", "float", "all") \
+    .attr("use_locking", "optional", "bool", "true,false", "false") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "accum", False, "required", "all") \
+    .input(2, "linear", False, "required", "all") \
+    .input(3, "grad", False, "required", "all") \
+    .input(4, "indices", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .output(1, "accum", False, "required", "all") \
+    .output(2, "linear", False, "required", "all") \
+    .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW,
+                  DataType.I32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC, DataType.F32_NHWC, DataType.F32_NHWC,
+                  DataType.I32_NHWC, DataType.F32_NHWC, DataType.F32_NHWC, DataType.F32_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(sparse_apply_ftrl_v2_d_op_info)
+def _sparse_apply_ftrl_v2_tbe():
+    """SparseApplyFtrlV2D TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -72,7 +72,7 @@ from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, Appl
                      SoftmaxCrossEntropyWithLogits, ROIAlign,
                      SparseSoftmaxCrossEntropyWithLogits, Tanh,
                      TopK, BinaryCrossEntropy, SparseApplyAdagrad, LARSUpdate, ApplyFtrl, SparseApplyFtrl,
-                     ApplyProximalAdagrad, SparseApplyProximalAdagrad,
+                     ApplyProximalAdagrad, SparseApplyProximalAdagrad, SparseApplyAdagradV2, SparseApplyFtrlV2,
                      ApplyAdaMax, ApplyAdadelta, ApplyAdagrad, ApplyAdagradV2,
                      ApplyAddSign, ApplyPowerSign, ApplyGradientDescent, ApplyProximalGradientDescent,
                      ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK)
@@ -284,6 +284,7 @@ __all__ = [
     "Abs",
     "BinaryCrossEntropy",
     "SparseApplyAdagrad",
+    "SparseApplyAdagradV2",
     "SpaceToDepth",
     "DepthToSpace",
     "Conv2DBackpropInput",
@@ -294,6 +295,7 @@ __all__ = [
     "ApplyFtrl",
     "SpaceToBatch",
     "SparseApplyFtrl",
+    "SparseApplyFtrlV2",
     "ApplyProximalAdagrad",
     "SparseApplyProximalAdagrad",
     "ApplyAdaMax",

mindspore/ops/operations/nn_ops.py

@@ -3600,6 +3600,88 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
         return var_type, accum_type
 
 
+class SparseApplyAdagradV2(PrimitiveWithInfer):
+    r"""
+    Update relevant entries according to the adagrad scheme.
+
+    .. math::
+            accum += grad * grad
+    .. math::
+            var -= lr * grad * \frac{1}{\sqrt{accum} + \epsilon}
+
+    Args:
+        lr (float): Learning rate.
+        epsilon (float): A small value added for numerical stability.
+        use_locking (bool): If `True`, updating of the var and accum tensors will be protected. Default: False.
+        update_slots (bool): If `True`, the computation logic will be different to `False`. Default: True.
+
+    Inputs:
+        - **var** (Parameter) - Variable to be updated. The type must be float32.
+        - **accum** (Parameter) - Accum to be updated. The shape must be the same as `var`'s shape,
+          the type must be float32.
+        - **grad** (Tensor) - Gradient. The shape must be the same as `var`'s shape except first dimension,
+          the type must be float32.
+        - **indices** (Tensor) - A vector of indices into the first dimension of `var` and `accum`.
+          The shape of `indices` must be the same as `grad` in first dimension, the type must be int32.
+
+    Outputs:
+        Tuple of 2 Tensor, the updated parameters.
+
+        - **var** (Tensor) - The same shape and data type as `var`.
+        - **accum** (Tensor) - The same shape and data type as `accum`.
+
+    Examples:
+        >>> import numpy as np
+        >>> import mindspore.nn as nn
+        >>> from mindspore import Tensor, Parameter
+        >>> from mindspore.ops import operations as P
+        >>> import mindspore.common.dtype as mstype
+        >>> class Net(nn.Cell):
+        >>>     def __init__(self):
+        >>>         super(Net, self).__init__()
+        >>>         self.sparse_apply_adagrad_v2 = P.SparseApplyAdagradV2(lr=1e-8, epsilon=1e-6)
+        >>>         self.var = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="var")
+        >>>         self.accum = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="accum")
+        >>>
+        >>>     def construct(self, grad, indices):
+        >>>         out = self.sparse_apply_adagrad_v2(self.var, self.accum, grad, indices)
+        >>>         return out
+        >>> net = Net()
+        >>> grad = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
+        >>> indices = Tensor([0, 1, 2], mstype.int32)
+        >>> result = net(grad, indices)
+    """
+
+    __mindspore_signature__ = (
+        ('var', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
+        ('accum', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
+        ('grad', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
+        ('indices', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T1)
+    )
+
+    @prim_attr_register
+    def __init__(self, lr, epsilon, use_locking=False, update_slots=True):
+        self.lr = validator.check_value_type("lr", lr, [float], self.name)
+        self.epsilon = validator.check_value_type("epsilon", epsilon, [float], self.name)
+        self.use_locking = validator.check_value_type("update_slots", update_slots, [bool], self.name)
+        self.update_slots = validator.check_value_type("use_locking", use_locking, [bool], self.name)
+
+    def infer_shape(self, var_shape, accum_shape, grad_shape, indices_shape):
+        validator.check('var shape', var_shape, 'accum shape', accum_shape, Rel.EQ, self.name)
+        validator.check('len of var shape', len(var_shape), 'len of grad shape', len(grad_shape), Rel.EQ, self.name)
+        if len(var_shape) > 1:
+            validator.check('var_shape[1:]', var_shape[1:], 'grad_shape[1:]', grad_shape[1:], Rel.EQ, self.name)
+        validator.check_integer("indices rank", len(indices_shape), 1, Rel.EQ, self.name)
+        validator.check('grad_shape[0]', grad_shape[0], 'indices_shape[0]', indices_shape[0], Rel.EQ, self.name)
+        return var_shape, accum_shape
+
+    def infer_dtype(self, var_type, accum_type, grad_type, indices_type):
+        args = {'var': var_type, 'accum': accum_type, 'grad': grad_type}
+        validator.check_tensor_type_same(args, [mstype.float32], self.name)
+        validator.check_tensor_type_same({'indices': indices_type}, [mstype.int32], self.name)
+        return var_type, accum_type
+
+
 class ApplyProximalAdagrad(PrimitiveWithInfer):
     r"""
     Update relevant entries according to the proximal adagrad algorithm.
@@ -3664,7 +3746,8 @@ class ApplyProximalAdagrad(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self, use_locking=False):
-        self.init_prim_io_names(inputs=['var', 'accum', 'lr', 'l1', 'l2', 'grad'], outputs=['output'])
+        self.init_prim_io_names(inputs=['var', 'accum', 'lr', 'l1', 'l2', 'grad'],
+                                outputs=['var', 'accum'])
         self.use_locking = validator.check_value_type("use_locking", use_locking, [bool], self.name)
 
     def infer_shape(self, var_shape, accum_shape, lr_shape, l1_shape, l2_shape, grad_shape):
@@ -4371,6 +4454,98 @@ class SparseApplyFtrl(PrimitiveWithInfer):
         return var_dtype, accum_dtype, linear_dtype
 
 
+class SparseApplyFtrlV2(PrimitiveWithInfer):
+    """
+    Update relevant entries according to the FTRL-proximal scheme.
+
+    Args:
+        lr (float): The learning rate value, must be positive.
+        l1 (float): l1 regularization strength, must be greater than or equal to zero.
+        l2 (float): l2 regularization strength, must be greater than or equal to zero.
+        l2_shrinkage (float): L2 shrinkage regularization.
+        lr_power (float): Learning rate power controls how the learning rate decreases during training,
+            must be less than or equal to zero. Use fixed learning rate if `lr_power` is zero.
+        use_locking (bool): If `True`, updating of the var and accum tensors will be protected. Default: False.
+
+    Inputs:
+        - **var** (Parameter) - The variable to be updated. The data type must be float32.
+        - **accum** (Parameter) - The accum to be updated, must be same type and shape as `var`.
+        - **linear** (Parameter) - The linear to be updated, must be same type and shape as `var`.
+        - **grad** (Tensor) - A tensor of the same type as `var`, for the gradient.
+        - **indices** (Tensor) - A vector of indices into the first dimension of `var` and `accum`.
+          The shape of `indices` must be the same as `grad` in first dimension. The type must be int32.
+
+    Outputs:
+        Tuple of 3 Tensor, the updated parameters.
+
+        - **var** (Tensor): Tensor, has the same shape and type as `var`.
+        - **accum** (Tensor): Tensor, has the same shape and type as `accum`.
+        - **linear** (Tensor): Tensor, has the same shape and type as `linear`.
+
+    Examples:
+        >>> import mindspore
+        >>> import mindspore.nn as nn
+        >>> import numpy as np
+        >>> from mindspore import Parameter
+        >>> from mindspore import Tensor
+        >>> from mindspore.ops import operations as P
+        >>> class SparseApplyFtrlV2Net(nn.Cell):
+        >>>     def __init__(self):
+        >>>         super(SparseApplyFtrlV2Net, self).__init__()
+        >>>         self.sparse_apply_ftrl_v2 = P.SparseApplyFtrlV2(lr=0.01, l1=0.0, l2=0.0,
+                                                                    l2_shrinkage=0.0, lr_power=-0.5)
+        >>>         self.var = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="var")
+        >>>         self.accum = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="accum")
+        >>>         self.linear = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="linear")
+        >>>
+        >>>     def construct(self, grad, indices):
+        >>>         out = self.sparse_apply_ftrl_v2(self.var, self.accum, self.linear, grad, indices)
+        >>>         return out
+        >>>
+        >>> net = SparseApplyFtrlV2Net()
+        >>> grad = Tensor(np.random.rand(3, 3).astype(np.float32))
+        >>> indices = Tensor(np.ones([3]), mindspore.int32)
+        >>> output = net(grad, indices)
+    """
+
+    __mindspore_signature__ = (
+        ('var', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
+        ('accum', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
+        ('linear', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
+        ('grad', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
+        ('indices', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T1)
+    )
+
+    @prim_attr_register
+    def __init__(self, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False):
+        validator.check_value_type("lr", lr, [float], self.name)
+        validator.check_value_type("l1", l1, [float], self.name)
+        validator.check_value_type("l2", l2, [float], self.name)
+        validator.check_value_type("lr_power", lr_power, [float], self.name)
+        self.lr = validator.check_number_range("lr", lr, 0.0, float("inf"), Rel.INC_NEITHER, self.name)
+        self.l1 = validator.check_number_range("l1", l1, 0.0, float("inf"), Rel.INC_LEFT, self.name)
+        self.l2 = validator.check_number_range("l2", l2, 0.0, float("inf"), Rel.INC_LEFT, self.name)
+        self.lr_power = validator.check_number("lr_power", lr_power, 0, Rel.LE, self.name)
+        self.l2_shrinkage = validator.check_value_type("l2_shrinkage", l2_shrinkage, [float], self.name)
+        self.use_locking = validator.check_value_type("use_locking", use_locking, [bool], self.name)
+
+    def infer_shape(self, var_shape, accum_shape, linear_shape, grad_shape, indices_shape):
+        validator.check('var shape', var_shape, 'accum shape', accum_shape, Rel.EQ, self.name)
+        validator.check('var shape', var_shape, 'linear shape', linear_shape, Rel.EQ, self.name)
+        if len(var_shape) > 1:
+            validator.check('var_shape[1:]', var_shape[1:], 'grad_shape[1:]', grad_shape[1:], Rel.EQ, self.name)
+        validator.check_integer("indices rank", len(indices_shape), 1, Rel.EQ, self.name)
+        validator.check('grad_shape[0]', grad_shape[0], 'indices_shape[0]', indices_shape[0], Rel.EQ, self.name)
+        return var_shape, accum_shape, linear_shape
+
+    def infer_dtype(self, var_dtype, accum_dtype, linear_dtype, grad_dtype, indices_dtype):
+        args = {"var_dtype": var_dtype, "accum_dtype": accum_dtype,
+                "linear_dtype": linear_dtype, "grad_dtype": grad_dtype}
+        validator.check_tensor_type_same(args, [mstype.float32], self.name)
+        validator.check_tensor_type_same({"indices_dtype": indices_dtype}, [mstype.int32], self.name)
+        return var_dtype, accum_dtype, linear_dtype
+
+
 class ConfusionMulGrad(PrimitiveWithInfer):
     """
     `output0` is the result of which input0 dot multily input1.

tests/ut/python/ops/test_ops.py

@@ -306,6 +306,19 @@ class SparseApplyFtrlNet(nn.Cell):
         return out
 
 
+class SparseApplyFtrlV2Net(nn.Cell):
+    def __init__(self):
+        super(SparseApplyFtrlV2Net, self).__init__()
+        self.sparse_apply_ftrl_v2 = P.SparseApplyFtrlV2(lr=0.001, l1=0.0, l2=0.0, l2_shrinkage=0.0, lr_power=-0.5)
+        self.var = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="var")
+        self.accum = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="accum")
+        self.linear = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="linear")
+
+    def construct(self, grad, indices):
+        out = self.sparse_apply_ftrl_v2(self.var, self.accum, self.linear, grad, indices)
+        return out
+
+
 class SparseApplyProximalAdagradNet(nn.Cell):
     def __init__(self):
         super(SparseApplyProximalAdagradNet, self).__init__()
@@ -467,6 +480,18 @@ class SparseApplyAdagradNet(nn.Cell):
         return out
 
 
+class SparseApplyAdagradV2Net(nn.Cell):
+    def __init__(self):
+        super(SparseApplyAdagradV2Net, self).__init__()
+        self.sparse_apply_adagrad_v2 = P.SparseApplyAdagradV2(lr=0.01, epsilon=0.001)
+        self.var = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="var")
+        self.accum = Parameter(Tensor(np.random.rand(3, 3).astype(np.float32)), name="accum")
+
+    def construct(self, grad, indices):
+        out = self.sparse_apply_adagrad_v2(self.var, self.accum, grad, indices)
+        return out
+
+
 class ApplyRMSNet(nn.Cell):
     def __init__(self):
         super(ApplyRMSNet, self).__init__()
@@ -1376,10 +1401,18 @@ test_case_nn_ops = [
         'desc_inputs': [[3, 3], Tensor(np.ones((3,), np.int32))],
         'desc_bprop': [[3, 3], [3, 3]],
         'skip': ['backward']}),
+    ('SparseApplyAdagradV2', {
+        'block': SparseApplyAdagradV2Net(),
+        'desc_inputs': [[3, 3], Tensor(np.ones((3,), np.int32))],
+        'skip': ['backward']}),
     ('SparseApplyFtrl', {
         'block': SparseApplyFtrlNet(),
         'desc_inputs': [[3, 3], Tensor(np.ones((3,), np.int32))],
         'skip': ['backward']}),
+    ('SparseApplyFtrlV2', {
+        'block': SparseApplyFtrlV2Net(),
+        'desc_inputs': [[3, 3], Tensor(np.ones((3,), np.int32))],
+        'skip': ['backward']}),
     ('ApplyProximalAdagrad', {
         'block': ApplyProximalAdagradNet(),
         'desc_inputs': [[3, 3]],