!1863 add op broadcast_to

Merge pull request !1863 from zhaozhenlong/op/broadcast-to-d-vm

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -105,7 +105,8 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"unsorted_segment_min", "unsorted_segment_min_d"},
   {"reduce_prod", "reduce_prod_d"},
   {"a_cos", "acos"},
-  {"a_cos_grad", "acos_grad"}};
+  {"a_cos_grad", "acos_grad"},
+  {"broadcast_to", "broadcast_to_d"}};
 
 void TbeAdapter::NormalizeFuncName(std::string *func_name) {
   if (func_name == nullptr) {
@@ -139,7 +140,7 @@ void TbeAdapter::NormalizeFuncName(std::string *func_name) {
   *func_name = name_tmp;
   auto iter = tbe_func_adapter_map.find(*func_name);
   if (iter != tbe_func_adapter_map.end()) {
-    MS_LOG(INFO) << "map actual op from me " << func_name << "to tbe op" << iter->second;
+    MS_LOG(INFO) << "map actual op from me " << *func_name << " to tbe op" << iter->second;
     *func_name = iter->second;
   }
 }

mindspore/nn/layer/quant.py

@@ -175,7 +175,7 @@ class FakeQuantWithMinMaxAscend(Cell):
         else:
             quant_fun = P.FakeQuantPerLayer
             ema_fun = P.FakeQuantMinMaxPerLayerUpdate
-        
+
         self.fake_quant = quant_fun(num_bits=self.num_bits,
                                     ema=self.ema,
                                     ema_decay=self.ema_decay,
@@ -272,7 +272,7 @@ class FakeQuantWithMinMaxGPU(Cell):
                 0, self.out_channels)]).astype(np.float32)
         self.minq = Parameter(Tensor(min_array), name='quant_min', requires_grad=False)
         self.maxq = Parameter(Tensor(max_array), name='quant_max', requires_grad=False)
-        
+
         if per_channel:
             quant_fun = partial(P.FakeQuantPerChannel, channel_axis=self.channel_axis)
         else:

mindspore/ops/_grad/grad_array_ops.py

@@ -18,6 +18,7 @@
 from .. import operations as P
 from ..operations import _grad_ops as G
 from ..composite.multitype_ops.zeros_like_impl import zeros_like
+from ..functional import broadcast_gradient_args
 from .. import functional as F
 from .grad_base import bprop_getters
 from ..primitive import constexpr
@@ -580,3 +581,17 @@ def get_bprop_batch_to_space_nd(self):
         dx = batch_to_space_nd_grad(dout)
         return (dx,)
     return bprop
+
+@bprop_getters.register(P.BroadcastTo)
+def get_bprop_broadcast_to(self):
+    """Generate bprop for BroadcastTo"""
+    reduce_keep_dim = P.ReduceSum(keep_dims=True)
+    broadcast_shape = self.shape
+
+    def bprop(x, out, dout):
+        x_shape = shape_op(x)
+        _, reduction_axes = broadcast_gradient_args(broadcast_shape, x_shape)
+        reduced_grad = reduce_keep_dim(dout, reduction_axes)
+        dx = reshape(reduced_grad, x_shape)
+        return (dx,)
+    return bprop

mindspore/ops/_op_impl/tbe/__init__.py

@@ -217,9 +217,9 @@ from .bessel_i0e import _bessel_i0e_tbe
 from .bessel_i1e import _bessel_i1e_tbe
 from .batch_to_space_nd import _batch_to_space_nd_tbe
 from .space_to_batch_nd import _space_to_batch_nd_tbe
-from .bitwise_and import bitwise_and_op_info
-from .bitwise_or import bitwise_or_op_info
-from .bitwise_xor import bitwise_xor_op_info
+from .bitwise_and import _bitwise_and_tbe
+from .bitwise_or import _bitwise_or_tbe
+from .bitwise_xor import _bitwise_xor_tbe
 from .reduce_all import _reduce_all_tbe
 from .sparse_apply_adagrad import _sparse_apply_adagrad_tbe
 from .unsorted_segment_min import _unsorted_segment_min_tbe
@@ -238,3 +238,4 @@ from .basic_lstm_cell_c_state_grad import _basic_lstm_cell_c_state_grad_tbe
 from .basic_lstm_cell_weight_grad import _basic_lstm_cell_weight_grad_tbe
 from .basic_lstm_cell_input_grad import _basic_lstm_cell_input_grad_tbe
 from .confusion_matrix import _confusion_matrix_tbe
+from .broadcast_to import _broadcast_to_tbe

mindspore/ops/_op_impl/tbe/broadcast_to.py

@@ -0,0 +1,40 @@
+# 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.
+# ============================================================================
+
+"""BroadcastTo op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+broadcast_to_op_info = TBERegOp("BroadcastTo") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("broadcast_to_d.so") \
+    .compute_cost(10) \
+    .kernel_name("broadcast_to_d") \
+    .partial_flag(True) \
+    .attr("shape", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U16_Default) \
+    .get_op_info()
+
+
+@op_info_register(broadcast_to_op_info)
+def _broadcast_to_tbe():
+    """BroadcastTo TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -30,7 +30,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Squeeze, StridedSlice, Tile,
                         Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin,
                         UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,
-                        SpaceToBatchND, BatchToSpaceND)
+                        SpaceToBatchND, BatchToSpaceND, BroadcastTo)
 from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast,
                        _MirrorOperator, ReduceOp, _VirtualDataset,
                        _VirtualDiv, _GetTensorSlice,
@@ -289,7 +289,8 @@ __all__ = [
     "Atan",
     "Atanh",
     "BasicLSTMCell",
-    "ConfusionMatrix"
+    "ConfusionMatrix",
+    "BroadcastTo"
 ]
 
 __all__.extend(_quant_ops.__all__)

mindspore/ops/operations/array_ops.py

@@ -2738,3 +2738,40 @@ class BatchToSpaceND(PrimitiveWithInfer):
                              f'block_shape_prod {block_shape_prod}')
         out_shape[0] = out_shape[0] // block_shape_prod
         return out_shape
+
+
+class BroadcastTo(PrimitiveWithInfer):
+    """
+    Broadcasts input tensor to a given shape.
+
+    Args:
+        shape (tuple): The target shape to broadcast.
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor.
+
+    Outputs:
+        Tensor, with the given `shape` and the same data type as `input_x`.
+
+    Examples:
+        >>> shape = (2, 3)
+        >>> input_x = Tensor(np.array([1, 2, 3]).astype(np.float32))
+        >>> broadcast_to = P.BroadcastTo(shape)
+        >>> broadcast_to(input_x)
+        [[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]]
+    """
+
+    @prim_attr_register
+    def __init__(self, shape):
+        """Init BroadcastTo"""
+        validator.check_value_type("shape", shape, (tuple), self.name)
+        for i in shape:
+            validator.check_integer("shape element", i, 0, Rel.GT, self.name)
+        self.shape = shape
+
+    def infer_shape(self, x_shape):
+        return self.shape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_subclass("input_x", x_dtype, mstype.tensor, self.name)
+        return x_dtype

tests/ut/python/ops/test_ops.py

@@ -1396,6 +1396,10 @@ test_case_array_ops = [
         'desc_inputs': [Tensor(np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32)),
                         Tensor(np.array([0, 1, 1]).astype(np.int32))],
         'desc_bprop': [Tensor(np.array([[1, 2, 3], [4, 2, 1]]).astype(np.float32))]}),
+    ('BroadcastTo', {
+        'block': P.BroadcastTo((2,3)),
+        'desc_inputs': [Tensor(np.array([1, 2, 3]).astype(np.float32))],
+        'desc_bprop': [Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.float32))]}),
 ]
 
 test_case_other_ops = [