!244 recover embeddinglookup

Merge pull request !244 from wuxuejian/recover_embeddinglookup

mindspore/ccsrc/parallel/graph_util/generate_graph.cc

@@ -31,13 +31,13 @@ std::string GetOpPythonPath(const OperatorName &op_name) {
   const std::string inner_ops_module = INNER_OP_PATH;
   py::module mod = py::module::import(common::SafeCStr(ops_module));
   py::module inner_mod = py::module::import(common::SafeCStr(inner_ops_module));
-  if (!py::hasattr(mod, common::SafeCStr(op_name))) {
   if (!py::hasattr(inner_mod, common::SafeCStr(op_name))) {
+    if (!py::hasattr(mod, common::SafeCStr(op_name))) {
       MS_LOG(EXCEPTION) << ops_module << " or " << inner_ops_module << " don't have op:" << op_name;
     }
-    return inner_ops_module;
-  }
     return ops_module;
+  }
+  return inner_ops_module;
 }
 
 ValuePtr CreatOpInstance(const OperatorAttrs &attrs, const OperatorName &op_name, const std::string &instance_name) {

mindspore/ops/operations/__init__.py

@@ -27,7 +27,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue,
                         SameTypeShape, ScatterAdd, ScatterMax, ScatterUpdate,
                         ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
-                        Shape, Size, Slice, Split,
+                        Shape, Size, Slice, Split, EmbeddingLookup,
                         Squeeze, StridedSlice, Tile, TensorScatterUpdate,
                         Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin,
                         UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,
@@ -134,6 +134,7 @@ __all__ = [
     'OneHot',
     'GatherV2',
     'SparseGatherV2',
+    'EmbeddingLookup',
     'Concat',
     'Pack',
     'Unpack',

mindspore/ops/operations/array_ops.py

@@ -567,6 +567,51 @@ class SparseGatherV2(GatherV2):
         >>> out = P.GatherV2()(input_params, input_indices, axis)
     """
 
+class EmbeddingLookup(PrimitiveWithInfer):
+    """
+    Returns a slice of input tensor based on the specified indices and axis. This Primitive has the similar
+    functionality as GatherV2, but has one more inputs: `offset`.
+    This primitive runs on the acipu devices.
+
+    Inputs:
+        - **params** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
+          The Tensor slice, instead of the entire Tensor.
+        - **indices** (Tensor) - The shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
+          Specifies the indices of elements of the original Tensor. Values can be out of range of `params`,
+          and the exceeding part will be filled with 0 in the output.
+          The indices to do lookup operation whose data type should be mindspore.int32 or mindspore.int64.
+        - **offset** (int) - Specifies the offset value of this `params` slice. Thus the real indices
+          are equal to `indices` minus `offset`.
+
+
+    Outputs:
+        Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.
+
+    Examples:
+        >>> params = Tensor(np.array([[8, 9], [10, 11], [12, 13], [14, 15]]), mindspore.float32)
+        >>> indices = Tensor(np.array([[5, 2], [8, 5]]), mindspore.int32)
+        >>> offset = 4
+        >>> out = P.EmbeddingLookup()(params, indices, offset)
+        [[[10, 11], [0 ,0]], [[0, 0], [10, 11]]]
+    """
+    @prim_attr_register
+    def __init__(self):
+        """init index_select"""
+        self.init_prim_io_names(inputs=['params', 'indices', 'offset'],
+                                outputs=['output'])
+
+    def __infer__(self, params, indices, offset):
+        validator.check_subclass("params", params['dtype'], mstype.tensor, self.name)
+        valid_types = (mstype.int32, mstype.int64)
+        validator.check_tensor_type_same({"indices": indices['dtype']}, valid_types, self.name)
+        validator.check_subclass("offset", offset['dtype'], mstype.int_, self.name)
+        params_shp = params['shape']
+        out_shape = indices['shape'] + params_shp[1:]
+        out = {'shape': out_shape,
+               'dtype': params['dtype'],
+               'value': None}
+        return out
+
 
 class Split(PrimitiveWithInfer):
     """